In a development that could reshape how scientists study the mechanics of human movement, a team of biomedical engineers and computer scientists has unveiled an open-source software tool capable of tracking the tiny, intricate bones of the wrist as they glide and rotate during motion. The new tool, called Hierarchical 3D Registration, or 3DH, is a module within the SlicerAutoscoperM extension of the widely used 3DSlicer computing environment. Described in a study published in BioMedical Engineering OnLine, the software was validated against previously established methods using both sequential three-dimensional computed tomography, or 3DCT, scans of living participants performing thumb tasks and dynamic four-dimensional CT, or 4DCT, images of cadaveric wrists moving through flexion and extension. The results demonstrate that the open-source approach can match the accuracy of independent, previously published methodologies while offering researchers everywhere a free and reproducible path to quantifying skeletal kinematics.

The wrist has long been one of the most challenging joints for biomechanists to study. It contains eight small carpal bones, including the scaphoid and lunate, along with the radius of the forearm and the metacarpal bones of the hand, all articulating in a tightly packed arrangement that allows the remarkable range of motion humans rely on every day. Understanding how these bones move relative to one another, a field of study known as arthrokinematics, is essential for unraveling the causes of wrist instability, osteoarthritis, ligament injuries and the long-term outcomes of surgical repairs. Yet measuring those movements in living, moving joints has historically required either invasive techniques, labor-intensive manual analysis or proprietary software that few laboratories can access or verify.

Quantitative tracking of skeletal structures in vivo, with both efficiency and high temporal resolution, matters to a wide range of questions in biomechanical research, and the authors of the new study argue that the field has been held back by a shortage of accessible, semi-automated registration tools. Image registration, the computational process of aligning one three-dimensional image volume to another, lies at the heart of motion tracking from CT data. When a bone moves between two scans, its position and orientation change, and a registration algorithm must find the transformation that best overlays the bone’s image from one time point onto the next. Done well, this yields precise measurements of rotation and translation. Done poorly or inconsistently, it introduces errors that can swamp the small movements of carpal bones, which may shift by only a few millimeters and a few degrees during normal function.

The new 3DH module addresses this problem with a hierarchical strategy for registering bones across sequences of image volumes. The researchers presented and evaluated the software using two distinct classes of data drawn from previous studies. The first consisted of sequential 3DCT datasets of human participants performing various thumb tasks, in which the radius, the first metacarpal and the trapezium were the target bones. The second consisted of dynamic 4DCT datasets capturing simulated flexion-extension motion in cadaveric wrists, providing time-resolved, three-dimensional image volumes of the scaphoid and lunate in motion. By testing the tool on both static sequential scans and true dynamic imaging, the team was able to demonstrate that 3DH is not limited to a single imaging paradigm but can serve as a unified tracking solution across modalities.

To determine whether the new open-source tool could be trusted, the investigators compared the arthrokinematics computed with 3DH against values that had been previously calculated using independent approaches for the same 3DCT and 4DCT datasets. The statistical framework for this comparison was the Bland-Altman analysis, a standard method for assessing agreement between two measurement techniques. Rather than simply correlating the two sets of results, Bland-Altman analysis quantifies the mean bias, the systematic difference between methods, and the 95 percent limits of agreement, the range within which most individual differences between the methods are expected to fall. This approach is particularly well suited to method-comparison studies because it reveals whether a new technique introduces systematic error and how widely individual measurements might deviate from those of the established gold standard.

The performance figures reported in the study are striking. For the 3DCT data, in which the radius, first metacarpal and trapezium were tracked, the mean bias for the helical angle, a single-axis description of three-dimensional rotation, was just minus 0.06 degrees, with 95 percent limits of agreement ranging between minus 1.87 degrees and 1.74 degrees. Translation measurements fared nearly as well: the mean bias was 0.12 millimeters, with limits of agreement spanning from minus 2.23 millimeters to 2.00 millimeters. In practical terms, the open-source tool reproduced the rotational and translational measurements of the earlier methods to within roughly two degrees and two millimeters at the extremes, differences small enough to preserve the biological signal in most wrist kinematics experiments.

The dynamic 4DCT results told a similar story for the carpal bones. Tracking the scaphoid and lunate through simulated flexion-extension, 3DH produced mean biases of the Euler angle rotations, which decompose three-dimensional rotation into three orthogonal components, of less than 0.77 degrees about any direction. The widest 95 percent limits of agreement ranged between minus 7.11 degrees and 8.65 degrees. For translations, mean biases were less than 0.5 millimeters in any direction, with the widest limits of agreement ranging between minus 2.36 millimeters and 1.43 millimeters. The wider limits of agreement for rotations in the dynamic data likely reflect the added challenges of time-resolved imaging, including motion blur and the smaller, more complexly shaped carpal bones, but the near-zero mean biases indicate that the tool does not systematically drift away from established values.

Crucially, all target bones were successfully tracked in both the 3DCT and 4DCT datasets, meaning the hierarchical registration algorithm converged on correct solutions in every case tested. This robustness matters because registration algorithms can occasionally fail, locking onto incorrect alignments or diverging entirely, and such failures typically demand time-consuming manual intervention. A semi-automated tool that reliably succeeds across diverse bones, motion tasks and imaging protocols removes a significant bottleneck. Manual bone tracking in CT volume sequences can take hours per bone per trial, and the labor multiplies rapidly in studies involving multiple participants, multiple bones and dozens of motion cycles. By automating this process within a validated framework, 3DH promises to accelerate the pace at which wrist biomechanics data can be collected and analyzed.

The choice to build the module inside 3DSlicer, and specifically within the SlicerAutoscoperM extension, is a deliberate strategic decision with implications well beyond the wrist. 3DSlicer is a free, open-source platform for medical image computing and visualization with a large international user base spanning research, clinical and educational communities. By embedding 3DH in this ecosystem, the developers have made the tool immediately available to any laboratory already working with 3DSlicer, complete with the platform’s established infrastructure for loading DICOM data, segmenting anatomical structures and visualizing results. The authors note that the ability to track bones from both 3DCT and 4DCT data sources, with agreement to prior methodologies, demonstrates that the 3DH approach is a capable tool that could promote collaboration and consistency of results through open-source software.

Consistency is a quietly urgent issue in biomechanics. When different laboratories use different proprietary tools with undisclosed algorithms, it becomes difficult to distinguish true biological differences between study populations from mere methodological discrepancies. Open-source registration changes that calculus: the code can be inspected, verified, extended and reused, and results from different institutions become directly comparable because they are produced by identical algorithms. This transparency also lowers the barrier to entry for research groups in settings that cannot afford commercial licenses, potentially broadening the global community capable of conducting rigorous skeletal kinematics research.

The study itself emerged from a collaboration spanning several institutions. The team included Joseph J. Crisco, Amy M. Morton and John D. Holtgrewe of the Department of Orthopedics at The Warren Alpert Medical School of Brown University and Rhode Island Hospital; Cesar Lopez, Andrew Thoreson and Kristin D. Zhao of the Assistive and Restorative Technology Laboratory and the Department of Physical Medicine and Rehabilitation at the Mayo Clinic in Rochester, Minnesota; and Jean-Christophe Fillion-Robin and Beatriz Paniagua of Kitware Inc. in Clifton Park, New York. The involvement of Kitware, a company known for its leadership in open-source scientific computing, underscores the blend of academic rigor and software engineering expertise required to produce a research tool robust enough for widespread distribution. Funding was provided by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under award numbers AR059185, AR078924 and AR071338.

The implications of this work extend naturally to the future of musculoskeletal imaging. As newer CT technologies, including photon-counting detector systems, deliver ever-finer spatial resolution and as dynamic 4DCT becomes more widely available, the volume of image data available for kinematic analysis will grow dramatically. Tools like 3DH, which are fast, semi-automated and validated across modalities, will be essential for converting that imaging bounty into quantitative biomechanical knowledge. For patients with wrist disorders, the downstream promise is equally tangible: better measurements of how carpal bones actually move in living joints can inform more accurate diagnoses, improved surgical planning, more realistic computational models of joint loading and ultimately better-designed treatments for conditions ranging from scapholunate instability to thumb-base arthritis. With the tool now openly available in the 3DSlicer environment, researchers around the world can begin putting it to work.

Long-term exposure to common air pollutants may significantly raise the risk of developing substance use disorders, while living surrounded by greenery appears to offer a measure of protection, according to a large new population-based study drawing on nearly a quarter of a million participants from the UK Biobank.

The research, published in BMC Psychiatry, analyzed data from 247,138 UK Biobank participants who were free of substance use disorders at baseline, tracking them over a median follow-up period of 10.9 years. During that time, 4,982 participants went on to develop a substance use disorder, providing the researchers with a robust dataset to examine the relationship between environmental exposures and mental health outcomes.

The study, led by Huan Liu and Bolun Cheng of Xi’an Jiaotong University in China, along with colleagues including Feng Zhang, represents one of the most comprehensive investigations to date into how the air people breathe and the greenness of their neighborhoods may influence vulnerability to addiction. The findings arrive at a moment when urban planners and public health officials are increasingly recognizing that the built and natural environments shape health in ways that extend well beyond respiratory and cardiovascular disease.

To assess air pollution exposure, the team employed Land Use Regression modeling to estimate concentrations of five pollutants at each participant’s residential address: fine particulate matter known as PM2.5, coarse particulate matter classified as PM2.5−10, the larger particulate fraction PM10, nitrogen dioxide (NO2), and nitrogen oxides (NOX). These pollutants, produced primarily by traffic, industry, and combustion sources, have long been associated with a range of physical health problems, but their role in psychiatric and behavioral conditions has remained far less certain.

For residential greenness, the researchers turned to the 2005–2007 Land Cover Map, measuring three distinct aspects of the environment within a 300-meter buffer around each home: the proportion of green space, the presence of a domestic garden, and the extent of natural environment. This tripartite approach allowed the investigators to distinguish between different types of vegetation and their potentially different effects on health.

Using Cox proportional hazards models, the team calculated hazard ratios with 95 percent confidence intervals to quantify the association between each exposure and the risk of developing a substance use disorder. The results were striking in their consistency. All five air pollutants were positively associated with incident SUD in fully adjusted models that controlled for a wide range of potential confounders.

Fine particulate matter, or PM2.5, showed the strongest effect, with a hazard ratio of 1.111 (95 percent CI: 1.081–1.142), followed by PM10 with a hazard ratio of 1.070 (95 percent CI: 1.054–1.086). These figures indicate that higher exposure to particulate pollution was associated with a measurable increase in the likelihood of developing a substance use disorder over the follow-up period, even after accounting for demographic, socioeconomic, and behavioral factors.

On the protective side of the ledger, two of the three greenness measures emerged as significant. Green space was inversely associated with SUD risk, showing a hazard ratio of 0.997 (95 percent CI: 0.996–0.998), and the natural environment measure produced an identical finding, with a hazard ratio of 0.997 (95 percent CI: 0.995–0.998). While these effect sizes appear modest on a per-unit basis, they represent population-level signals across a very large cohort, suggesting that even incremental increases in neighborhood greenness may contribute to reduced vulnerability.

Perhaps the most intriguing finding of the study concerns the interaction between pollution and greenery. The researchers tested whether residential greenness modified the association between air pollutants and SUD risk by introducing cross-product terms into their models and evaluating them with likelihood-ratio tests. The results pointed to a buffering role for domestic gardens specifically. Among participants in the high-exposure subgroup, the presence of a domestic garden attenuated the positive associations between pollutants and SUD, with cross-product terms of 0.979 (95 percent CI: 0.963–0.996) for PM2.5 and 0.987 (95 percent CI: 0.980–0.995) for PM10.

In practical terms, this suggests that having access to a private garden may partially offset some of the harm associated with living in areas of elevated particulate pollution, offering a form of environmental resilience that public health advocates have long hypothesized but rarely demonstrated with prospective cohort data.

The associations were not uniform across the population. The positive relationships between air pollution and substance use disorder risk were most pronounced among males, participants aged 65 and older, and individuals with a history of smoking. These findings highlight the importance of considering demographic and behavioral modifiers when assessing the health consequences of environmental exposures, and they suggest that certain subgroups may benefit disproportionately from interventions aimed at reducing pollution or increasing green cover.

To ensure the robustness of their findings, the team conducted a series of sensitivity analyses. These included restricting the sample to long-term residents to minimize exposure misclassification from relocation, using alternative outcome definitions based on ICD-10 codes F10-F19 and F11-F19 to capture different ranges of substance-related diagnoses, and recomputing greenness measures at a wider 1000-meter buffer around each residence. The core conclusions held across these variations, strengthening confidence in the results.

The study was performed in accordance with the ethical principles of the Declaration of Helsinki and was registered with the UK Biobank under application number 46478. Ethical approval for the UK Biobank was granted by the North West Multi-centre Research Ethics Committee under approval reference 11/NW/0382. All participants provided written informed consent before any study-related procedures were initiated.

The work was supported by the China Postdoctoral Science Foundation, the Natural Science Basic Research Plan in Shaanxi Province of China, and the Fundamental Research Funds for the Central Universities. Corresponding authors are Bolun Cheng and Feng Zhang, both affiliated with Xi’an Jiaotong University, with Zhang also holding a position in the Department of Psychiatry at the First Affiliated Hospital of Xi’an Jiaotong University.

The findings come amid growing scientific interest in the neurological and psychiatric consequences of air pollution. Particulate matter, particularly PM2.5, is small enough to cross the blood-brain barrier and has been implicated in neuroinflammation, oxidative stress, and alterations in brain structure and function. These biological mechanisms could plausibly influence reward processing, impulse control, and stress reactivity, all of which are central to the development and perpetuation of substance use disorders.

Conversely, greenness has been associated with stress reduction, improved mental well-being, greater opportunities for physical activity and social interaction, and lower levels of psychological distress, each of which may buffer against the onset of addictive behaviors. The current study adds a significant piece of evidence to this emerging picture by demonstrating the relationship prospectively in one of the world’s largest and most deeply phenotyped biobanks.

The authors conclude that long-term exposure to ambient air pollutants was associated with a higher risk of incident substance use disorders and that residential greenness showed protective associations, with the most pronounced patterns observed among older males and individuals with a smoking history. They argue that the findings support integrating greenness exposure into environmental frameworks for the prevention of substance use disorders, a recommendation that carries implications for urban design, housing policy, and environmental regulation.

As cities around the world continue to grapple with the twin challenges of air quality and mental health, studies of this scale provide an evidence base for interventions that address both simultaneously. The prospect that something as tangible as a garden could offer measurable protection against the psychiatric consequences of polluted air adds a compelling dimension to ongoing debates about how the environments we build shape the minds that inhabit them.

The article is published open access under a Creative Commons Attribution 4.0 International License, permitting broad sharing and adaptation with appropriate credit to the original authors and source. It was received on 08 December 2025, accepted on 28 August 2026, and published on 09 September 2026.

When surgeons remove a portion of a cancerous lung, one of the most frustrating complications they can face is air. A persistent leak from the lung’s cut surface or staple lines — known as prolonged air leak — keeps chest tubes in place far longer than anyone would like, extends hospital stays, inflates costs, and elevates the risk of infection and other downstream problems. Now, a research team in China has developed a machine learning tool that predicts, with striking accuracy, which older lung cancer patients are most likely to develop this complication before it ever occurs. The study, published in BMC Cancer, combines cardiopulmonary exercise testing data with an interpretable artificial intelligence model and could reshape how thoracic surgical teams prepare for and manage one of the most common postoperative challenges in lung surgery.

The research was led by Zixiao Wang, Jiabao Ding, Huagang Liang and colleagues at the First Hospital of Qinhuangdao, in collaboration with Kai Wang of Tianjin Chest Hospital. It addressed a well-recognized gap in thoracic surgical practice. While prolonged air leak — typically defined as air leaking from the chest drainage system for more than five days after surgery — is among the most frequent complications following video-assisted thoracoscopic anatomical lung resection, the prediction models available to clinicians have historically lacked rigorous external validation and, critically, interpretability. Surgeons have been asked to trust risk scores without understanding where those risks come from, a barrier that has limited clinical uptake of computational tools in the operating room planning process.

To build their model, the investigators enrolled patients aged 60 and older with lung cancer who underwent thoracoscopic anatomical lung resection — either lobectomy, the removal of an entire lung lobe, or segmentectomy, the removal of a smaller anatomical segment. The study was designed as a dual-centre cohort investigation with both retrospective and prospective components. A total of 471 patients treated at Qinhuangdao First Hospital between January 2024 and June 2025 formed the model development cohort. A separate group of 140 patients from Tianjin Chest Hospital, enrolled prospectively between June and August 2025, provided the external validation set — a crucial test of whether the model generalized beyond the institution where it was built.

The methodology followed the emerging best-practice playbook for clinical machine learning. After preliminary data processing, the development cohort was randomly split into a training set containing 70 percent of patients and an internal validation set holding the remaining 30 percent. Because prolonged air leak affects only a minority of surgical patients, the training data were inherently imbalanced, with far more non-leak cases than leak cases — a condition that can fool machine learning algorithms into simply predicting that no one is at risk. The researchers countered this with SMOTE, the Synthetic Minority Over-sampling Technique, which generates synthetic examples of the underrepresented class to rebalance the dataset and force the algorithms to learn the genuine signature of risk.

Feature selection was performed using LASSO regression — least absolute shrinkage and selection operator — a statistical technique that penalizes model complexity and drives the coefficients of irrelevant variables to zero, effectively filtering a large panel of candidate variables down to the most predictive few. From this process, four predictors of prolonged air leak emerged: a history of emphysema, the smoking index, a previous history of thoracic surgery or chest trauma, and the ventilatory equivalent for carbon dioxide, abbreviated VE/VCO₂. This last variable is drawn directly from cardiopulmonary exercise testing, a stress test performed on a bicycle or treadmill that measures how the heart, lungs, and circulatory system respond together to increasing exertion. VE/VCO₂ reflects the efficiency of ventilation during exercise — how much air a patient must move to eliminate a given amount of carbon dioxide — and elevated values signal impaired pulmonary gas exchange and often underlying parenchymal lung disease.

With the four features in hand, the team constructed six competing machine learning models: logistic regression, support vector machine, decision tree, extreme gradient boosting, and artificial neural networks, among others. Each was evaluated using a battery of metrics including the area under the receiver operating characteristic curve, or AUC, the F1 score, and the Brier score, which measures the accuracy of probabilistic predictions. The winner was XGBoost, an ensemble method that builds a sequence of decision trees, with each new tree trained to correct the residual errors of its predecessors. In internal validation, XGBoost achieved an AUC of 0.903 (95 percent confidence interval, 0.834 to 0.960) — well within the range conventionally considered excellent discrimination — along with the highest F1 score of 0.717 and the lowest Brier score of 0.092 among all six candidates.

The external validation was arguably the study’s most important test. A model can memorize the quirks of its home institution’s patient population, equipment, and surgical practices, only to falter elsewhere. Yet when applied to the 140 previously unseen patients from Tianjin Chest Hospital, the XGBoost model maintained an AUC of 0.800 (95 percent confidence interval, 0.656 to 0.913), with a sensitivity of 0.700, a specificity of 0.833, and a Brier score of 0.133. In practical terms, the model correctly flagged seven out of ten patients who went on to develop prolonged air leak while correctly reassuring more than eight out of ten of those who did not — performance that, if replicated in routine practice, would give surgeons a meaningful window to intervene preemptively.

Interpretability was treated not as an afterthought but as a core design requirement. The researchers applied SHAP — Shapley Additive Explanations — a technique borrowed from cooperative game theory that assigns each feature a contribution value for every individual prediction. Rather than presenting clinicians with an opaque probability, SHAP reveals how much each of the four variables pushed a given patient’s risk up or down, and in which direction. Through this analysis, VE/VCO₂ emerged as the single most influential feature, followed by the other three predictors ranked by correlation. The finding is physiologically plausible: patients whose lungs ventilate inefficiently under the stress of exercise are precisely those whose lung tissue is most fragile and least likely to seal spontaneously after surgical manipulation. A history of emphysema, a heavy smoking history, and prior chest surgery or trauma each independently compound this tissue vulnerability.

The clinical utility of the work extends beyond prediction to prevention. Armed with a preoperative risk estimate, surgical teams can tailor their approach for high-risk patients. Options include more meticulous staple-line reinforcement, the use of sealants or pledgeted sutures on fragile lung tissue, choosing segmentectomy over lobectomy where oncologically appropriate, planning for ambulatory drainage systems on discharge, or scheduling earlier follow-up to catch and manage leaks before they become complicated. To lower the barrier to bedside use, the team built a web-based risk calculator that allows clinicians to enter a patient’s four variables and receive an individualized probability of prolonged air leak — no programming expertise required.

The significance of incorporating cardiopulmonary exercise testing into a surgical prediction model deserves particular emphasis. Traditional preoperative pulmonary assessment in lung cancer surgery has relied on static spirometric measures such as forced expiratory volume in one second, or FEV1, and the diffusing capacity for carbon monoxide, DLCO. These tests measure lung function at rest and provide only a partial picture of cardiopulmonary reserve. CPET, by contrast, captures the integrated response of multiple organ systems under physiological stress, and the VE/VCO₂ slope in particular has gained recognition as a sensitive marker of ventilatory inefficiency in chronic obstructive pulmonary disease and heart failure. The present study suggests that this dynamic measure carries prognostic information for surgical outcomes that static tests do not fully capture — a finding with implications well beyond air leak prediction.

Several caveats frame the work appropriately. The study population was limited to Chinese patients aged 60 and above undergoing video-assisted thoracoscopic resection, and generalization to younger patients, other surgical techniques such as robotic-assisted resection, or different health care systems remains to be demonstrated. The external validation cohort, while methodologically essential, comprised 140 patients, and wider multicenter prospective trials would strengthen confidence in the model’s performance. The published article is an accepted, peer-reviewed author manuscript shared early ahead of the final version of record. The study was funded by the Beijing Kechuang Medical Development Foundation, with the funder playing no role in study design, analysis, or manuscript preparation, and the authors declare no competing interests.

Even so, the study stands as a compelling example of where predictive medicine is heading. By pairing a rigorous dual-centre validation strategy with explainable artificial intelligence and a physiologically grounded predictor, the researchers have moved machine learning in thoracic surgery closer to genuine clinical utility rather than proof-of-concept novelty. For the growing population of older patients facing lung cancer surgery, a simple preoperative test and a few clicks in a web calculator may soon determine who needs extra surgical vigilance — and spare many of them the days of tethered chest drainage that have long been an accepted, if unwelcome, part of recovery.

The Denisovans, the enigmatic archaic human relatives first identified from a finger bone and a handful of teeth in a Siberian cave, have long been known almost exclusively through their DNA. Now, a landmark study published in Nature has pulled back the curtain on how these ancient hominins actually lived — what they ate, how they hunted, what tools they made, and the environments they braved — through an extraordinary assemblage of fossils and artefacts from Bianfu Cave in Yunnan Province, southwestern China. The findings paint the most detailed portrait to date of Denisovan biology, behaviour and ecology in eastern Asia, and suggest that these vanished people left a deep genetic and cultural imprint on later populations across Southeast Asia and Oceania.

The research team, led by Qingfeng Ruan, Hua Li and Song Xing and their colleagues, conducted a comprehensive analysis of hominin fossils and the associated archaeological remains recovered from Bianfu Cave. The hominin fossils themselves date to roughly 167,000 to 134,000 years ago, placing them firmly in the Middle Pleistocene. But the cultural sequence preserved in the cave’s sediments is far longer, stretching from approximately 190,000 years ago to about 70,000 years ago. That span — some 120,000 years of continuous or near-continuous occupation — makes Bianfu Cave one of the longest cultural records that can most parsimoniously be attributed to Denisovans anywhere in the world.

The Denisovan identity of the fossils was established through two independent and technically demanding lines of evidence. The first involved analysis of the enamel–dentine junction, the internal boundary between the enamel cap and the underlying dentine of the teeth. This three-dimensional morphological interface acts something like a fingerprint of evolutionary relationships, revealing diagnostic traits of the tooth’s internal architecture that are invisible from the outside but characteristic of particular hominin lineages. The second line of evidence came from palaeoproteomics, the recovery and identification of ancient proteins preserved within fossilized tissues. Because proteins are far more abundant and durable than DNA, they can survive in warm, humid environments like those of southern China, where genetic material degrades rapidly. Sequencing these ancient proteins allowed the researchers to place the Bianfu hominins on the Denisovan branch of the human family tree with confidence.

The fossil collection itself is remarkable. It includes four teeth that substantially expand the known range of dental variation within the Denisovan lineage — a critical contribution, since Denisovans were previously represented by a very small number of dental specimens, making it difficult to gauge how variable their teeth could be. The new fossils also include two cranial fragments and a radius, a forearm bone, providing rare glimpses of Denisovan skull and postcranial anatomy that were previously almost entirely unknown. Together, these remains transform Denisovans from a genome and a scatter of isolated teeth into a population with recognizable skeletal biology.

Beyond the fossils, the archaeological record from Bianfu Cave offers something equally precious: a window into Denisovan daily life. The researchers reconstructed the ancient environment using faunal remains and pollen preserved in the cave deposits. These records indicate that across Marine Isotope Stages 6 through 4 — a period encompassing major glacial–interglacial climatic swings between roughly 190,000 and 57,000 years ago — the landscape around the cave was dominated by coniferous forest or a mosaic of forest and steppe. This was a cool, variable environment that Denisovans exploited successfully across tens of thousands of years, demonstrating a resilience to climatic change that adds to growing evidence of their adaptability, from the high altitudes of the Tibetan Plateau to the subtropical latitudes of mainland Southeast Asia.

The subsistence evidence is particularly striking. Analysis of the animal bones from the cave shows that its inhabitants practised specialized hunting focused on medium- to large-bodied prey. This was not opportunistic scavenging but a sustained, targeted hunting strategy — one that would have required knowledge of animal behaviour, coordinated effort and effective weaponry. Specialized big-game hunting of this kind has often been associated with modern humans and late Neanderthals in western Eurasia, so finding it in a Denisovan context more than 150,000 years ago dramatically narrows the perceived behavioural gap between these archaic lineages and our own species.

Yet the technological strategy documented at Bianfu Cave is, at first glance, surprisingly simple. The stone tool assemblage is characterized by expedient core reduction and tool production — that is, the inhabitants struck flakes from cores as needed, used them, and discarded them, rather than investing in carefully shaped, curated tools that were carried, maintained and resharpened over time. Alongside this, the Denisovans of Bianfu Cave made pervasive use of unmodified bones, employing skeletal elements of their prey directly as tools without altering them beforehand.

Taken together, the researchers interpret this pattern as an adaptive system that prioritized the exploitation of what they call object affordance — the inherent functional possibilities already present in a raw material or object — over intensive tool manufacture. A suitably shaped bone fragment could serve as a hammer, a digging implement or a scraping tool with little or no modification; a sharp flake struck from a convenient core could cut and butcher without further refinement. In the rich, resource-diverse environments of subtropical southwestern China, where raw materials and animal resources were plentiful, such a flexible, low-investment strategy may have been not a sign of simplicity but a highly efficient way of life. It suggests that Denisovans measured success not by the sophistication of their toolkit but by how skilfully they read and exploited the opportunities their landscape provided.

This behavioural portrait gains added significance when set against the wider geography of Denisovan presence. Over the past decade and a half, Denisovans have been documented across a staggering range: from the original genomic discoveries in the Altai Mountains of Siberia, first reported in 2010, to the mandible from the Tibetan Plateau described in 2019, Denisovan DNA recovered from sediments at Baishiya Karst Cave in 2020, a molar from the Annamite Chain of northern Laos announced in 2022, a male Denisovan mandible from Pleistocene Taiwan reported in 2025, and proteomic identification of the 146,000-year-old Harbin cranium, also in 2025. Bianfu Cave now anchors this story firmly in southwestern China, a region long suspected to have been a Denisovan heartland and a critical corridor for hominin movements between mainland Asia and Southeast Asia.

The study’s authors argue that the region’s Denisovans point to a substantial genetic and cultural legacy in later human populations. Modern populations in Southeast Asia and Oceania carry the highest known proportions of Denisovan ancestry anywhere in the world — a legacy of interbreeding that occurred as modern humans moved through and beyond island Southeast Asia. The new evidence from Bianfu Cave suggests that the ways of life practised by Denisovans in this region — their hunting strategies, their flexible technological approaches, their hard-won knowledge of tropical and subtropical environments — may have been part of the inheritance, alongside their genes, that helped later human populations thrive as they spread across the diverse and challenging landscapes of Asia and the Pacific.

For palaeoanthropologists, the significance of the Bianfu Cave discoveries is difficult to overstate. Denisovans have been the least anatomically documented of all known hominin groups — a lineage defined first by DNA and only slowly fleshed out with fragmentary fossils. The new teeth, cranial fragments and radius from Yunnan, combined with an unmatched 120,000-year archaeological sequence, mean that researchers can finally study Denisovans not just as a genome or a set of molecular signatures but as a living, adapting population: hunters of large game, makers and users of expedient tools, and inhabitants of conifer forests that swung between glacial cold and interglacial warmth. As analysis of the Bianfu material continues, it promises to reshape our understanding of how archaic humans lived in eastern Asia and how deeply their story is woven into our own.

Optical frequency combs have transformed the way scientists measure light and time, earning the Nobel Prize in Physics in 2005 and underpinning technologies ranging from atomic clocks to exoplanet detection. Yet the miniature on-chip versions of these combs, known as soliton microcombs, have long suffered from a frustrating paradox: the most stable and useful operating regimes have also been the most fragile and difficult to sustain. A newly published study in Light: Science & Applications reports a significant advance toward resolving this tension. Researchers led by J. Tang, J. Yang and E. Yan describe a multimodal locking-enabled soliton microcomb that combines robust, self-starting operation with an exceptionally low repetition rate on the scale of a day of stable measurement time, opening a path toward practical high-precision metrology systems that can run unattended for extended periods.

At the heart of the advance is a technique the authors call multimodal locking, a scheme that stabilizes not just a single resonance of the optical microresonator but multiple modes simultaneously, locking the comb dynamics to a regime in which dissipative Kerr solitons can form and persist without delicate manual tuning. In conventional microcomb systems, soliton formation typically requires a precise and rapid traversal of a “chaotic” modulation instability regime, a step that is notoriously sensitive to thermal transients in the resonator chip. Laser frequency must be swept with exquisite control while the soliton’s own heat dissipation shifts the resonator’s resonances, creating a feedback loop that can destroy the soliton state within microseconds. The multimodal locking approach circumvents this fragility by engaging several mode families of the resonator at once, so that the interplay among them pins the comb to a stable soliton state even as environmental and thermal conditions fluctuate.

The significance of achieving a low repetition rate in the same device cannot be overstated. A frequency comb’s repetition rate is the spacing between its individual comb teeth, and for many applications, coarser spacing is a decisive advantage. In laser ranging and distance metrology, for example, the ambiguity range of an interferometric measurement is set by the inverse of the comb’s free spectral range. Microcombs, generated in whispering-gallery-mode or ring resonators only a few millimeters across, usually exhibit repetition rates of tens to hundreds of gigahertz, which severely limits the unambiguous measurement range available to a comb-tooth-resolved distance measurement. By engineering a low-repetition-rate soliton state, the research team extends this ambiguity range dramatically, making the microcomb directly useful for absolute distance measurement, multi-wavelength interferometry and coherent communications, where each comb line must be spectrally resolvable and addressable.

Achieving both low repetition rate and robust soliton generation in a single microresonator is a formidable engineering challenge. Low repetition rates demand large resonator diameters, which in turn produce dense spectra of resonances and increased susceptibility to mode crossings with higher-order transverse and polarization mode families. Such crossings historically destabilize soliton formation, causing comb states to collapse or preventing single-soliton operation altogether. The multimodal locking strategy turns this liability into an asset: rather than engineering mode crossings away, the researchers harness the coupling among mode families to lock the comb dynamics into a favorable basin of attraction. The result is a soliton microcomb whose low repetition rate is preserved over day-scale timeframes, a duration that places the device among the most operationally stable microcomb demonstrations reported to date.

Day-scale stability matters because it transforms the microcomb from a laboratory demonstration into an instrument. Many high-precision experiments, from astronomical spectrograph calibration to the accumulation of statistical confidence in atomic spectroscopy, require comb sources that remain locked and stable across many hours or even days of continuous operation. Conventional fiber-laser-based frequency combs achieve this, but at the cost of table-top size, high power consumption and substantial maintenance overhead. Microcombs promised to shrink the technology onto a photonic chip, yet until now their operational fragility has confined most demonstrations to short observation windows. The robust low-repetition-rate soliton microcomb described in the new work demonstrates that chip-scale frequency combs can, with appropriate locking architecture, sustain the kind of long-duration, hands-off operation that real metrology campaigns demand.

The physics underlying soliton microcombs rests on the Kerr nonlinearity of the resonator material, typically silicon nitride, magnesium fluoride or fused silica. When continuous-wave pump light resonantly builds up inside the ring, the Kerr effect produces a four-wave-mixing process that seeds sidebands spaced by the resonator’s free spectral range. Under the right conditions of pump detuning and intracavity power, these sidebands evolve into a coherent train of ultrashort optical pulses, dissipative Kerr solitons, that circulate around the resonator and emit a broadband comb spectrum with tooth spacing equal to the repetition rate. The comb’s output frequencies are determined by two radio-frequency quantities, the pump laser frequency and the repetition rate, which is why the combs can serve as rulers for measuring optical frequencies against a reference. Any drift in either quantity propagates directly into measurement error, and the multimodal locking mechanism suppresses precisely this drift by anchoring the comb’s underlying mode structure to the resonator’s intrinsic stability.

Applications for such a device span an unusually broad swath of science and engineering. In high-precision laser ranging, low-repetition-rate combs with tooth spacings in the low-gigahertz or even megahertz regime allow interferometric distance measurements over ranges of meters to kilometers without the need for auxiliary synthetic-wavelength techniques. In astronomical spectroscopy, comb calibration sources with well-controlled line spacing enable the detection of Earth-like exoplanets through radial velocity shifts measured in centimeters per second. In telecommunications, low-repetition-rate combs align naturally with standard channel grids used in wavelength-division multiplexed fiber networks, making them attractive as multi-carrier sources. In spectroscopy, dual-comb techniques benefit from repetition rates that are low enough to permit resolution of closely spaced molecular absorption features. A microcomb that is simultaneously robust, low-repetition-rate and day-scale stable therefore functions as a versatile enabling component across these domains rather than a single-purpose device.

The demonstration also speaks to a broader trend in photonics: the migration from delicate, expert-operated laboratory setups to self-contained, self-stabilizing systems. The self-starting character implied by multimodal locking is particularly important for this transition. A comb that requires a skilled operator to initiate soliton formation, sweeping the laser with practiced timing past the chaotic regime, cannot be deployed in an observatory dome, on a satellite platform or inside an industrial instrument. A comb that engages its soliton state reliably on demand and then holds it through thermal cycling, laser drift and mechanical perturbation removes the last practical barrier to integration. The authors’ demonstration of day-scale continuous operation is, in effect, a stress test passed by a technology that researchers have been attempting to harden for more than a decade.

The implications extend to metrology standards and timing as well. Optical atomic clocks, which now achieve fractional frequency uncertainties below one part in 10^18, rely on frequency combs to divide down their optical oscillations to countable microwave rates. Miniaturizing the comb stage of such clocks is a prerequisite for portable optical timekeeping, which would benefit navigation in GPS-denied environments, geodesy that monitors sea level and crustal motion with millimeter precision, and fundamental physics tests that track the constancy of natural constants over months and years. A robust chip-scale soliton microcomb with day-scale stability contributes a key subsystem to this vision, and the multimodal locking principle may generalize to other resonator platforms, materials and spectral regions, including the mid-infrared where molecular fingerprinting awaits compact comb sources.

Looking forward, the study suggests several directions that the photonics community is likely to pursue. Combining multimodal locking with on-chip pump lasers and integrated electronics would complete the transformation toward fully self-contained comb modules with no free-space optics. Extending the approach to lower repetition rates still, into the megahertz regime where fiber-laser combs operate, would bring microcombs into direct competition with their bulkier predecessors for the most demanding ranging and spectroscopy tasks. Improvements in resonator fabrication, reducing mode-volume disorder and engineering dispersion with greater precision, will further expand the parameter space in which robust soliton states can be locked. The present work demonstrates that the fragility once considered intrinsic to soliton microcombs is not a fundamental limit but an engineering problem, and one that can be solved by embracing, rather than avoiding, the multimodal structure of real optical resonators.

The research, published in Light: Science & Applications, marks a milestone in the maturation of microcomb technology. By uniting three properties that had previously been achieved only separately, self-starting robustness, low repetition rate and day-scale operational stability, the work delivers a soliton microcomb genuinely suited to high-precision metrology. As the demand for compact frequency references accelerates across navigation, astronomy, communications and fundamental science, the multimodal locking strategy reported by Tang, Yang, Yan and colleagues offers a clear and technically grounded route from chip-scale optical chaos to dependable, precision measurement light.

In one of the most comprehensive investigations of its kind, researchers have taken a major step toward untangling the biology underlying suicide risk, moving the field beyond long-standing debates about whether suicidal behavior is simply a consequence of psychiatric illness. By integrating large-scale genetic data with cutting-edge approaches that map how genes act in specific brain cells and circuits, an international team has identified shared genetic liability that cuts across psychiatric diagnoses while also pinpointing druggable biological pathways that could one day inform prevention strategies. The study, published in Translational Psychiatry, offers a sweeping synthesis of how inherited risk factors may translate into molecular vulnerabilities, and it does so with a level of resolution that previous candidate-gene approaches could never achieve.

Suicide remains one of the leading causes of death worldwide, claiming more than 700,000 lives each year according to global health estimates. Despite decades of research, clinical prediction remains stubbornly imprecise, and biological models of suicidal behavior have historically been hampered by small sample sizes, inconsistent findings, and a tendency to conflate suicide risk with the psychiatric disorders with which it so often co-occurs. Major depression, bipolar disorder, schizophrenia, and substance use disorders all carry elevated suicide risk, yet most people with these conditions never attempt suicide, while some individuals who die by suicide have no diagnosed psychiatric illness at all. This paradox has long suggested to researchers that suicidal behavior may possess partially distinct biological underpinnings, and the new study set out to test that hypothesis with unprecedented statistical power.

At the heart of the work lies genome-wide association study methodology, or GWAS, which scans hundreds of thousands to millions of genetic variants spread across the entire genome in very large groups of people, comparing individuals who have experienced suicidal thoughts or behaviors with those who have not. Each variant is tested for statistical association with the outcome, and the results can be combined into polygenic risk scores, numerical summaries of a person’s inherited liability based on the cumulative small effects of thousands of variants. Because no single common variant exerts a large effect, the field depends on aggregating data across massive biobanks and cohorts, a strategy that has only become feasible in the past decade as datasets containing hundreds of thousands of individuals became available.

The research team took particular care to address a central confound: psychiatric disorders themselves are highly heritable, and any genetic signal linked to suicide could simply reflect inherited liability to depression or schizophrenia rather than to suicide specifically. To disentangle this, the investigators applied sophisticated statistical techniques, including genetic correlation analysis and conditional modeling, which allow researchers to ask how much genetic overlap remains between suicidal behavior and psychiatric conditions after accounting for their shared heritability. Their findings revealed a complex architecture. Substantial genetic correlations confirmed that suicide risk shares a meaningful fraction of its inherited basis with major psychiatric disorders, supporting the idea of a common pool of genetic liability. Yet the analyses also uncovered signals that persisted beyond what could be explained by those disorders, pointing to biological mechanisms that may be more specific to the emergence of suicidal thoughts and actions.

A key conceptual innovation of the study is its framing of “shared genetic liability” as a bridge between diagnoses. Rather than treating suicide as an epiphenomenon of any single disorder, the researchers modeled it as a transdiagnostic phenomenon, one whose risk emerges partly from inherited vulnerabilities that cut across diagnostic categories. This perspective aligns with a growing movement in biological psychiatry that emphasizes symptom dimensions and mechanisms over traditional diagnostic boundaries. From this vantage point, the genetic variants that raise risk for depression, PTSD, and schizophrenia may do so partly because they converge on shared molecular pathways, and those same pathways may govern an individual’s threshold for suicidal crises regardless of which diagnosis they carry.

Identifying genetic variants, however, is only the first step. A GWAS signal typically points to a genomic region rather than a specific causal gene or mechanism, since many associated variants fall in non-coding stretches of DNA that regulate gene activity from a distance. To translate statistical associations into biological insight, the team deployed a battery of functional genomics approaches. Transcriptome-wide association studies, known as TWAS, integrate gene expression data to identify genes whose genetically predicted expression levels are linked to suicide risk. Tissue and cell-type enrichment analyses then ask whether the implicated genes are unusually active in particular organs or cell populations. The results converged strikingly on brain tissue, and more specifically on neuronal populations, providing support for the idea that inherited suicide risk is mediated largely through gene regulation within the central nervous system.

The study’s most clinically provocative findings concern druggable biology. Using databases that catalog the protein targets of approved and investigational medications, the researchers examined whether genes implicated in suicide risk were over-represented among known drug targets. The analysis identified several classes of candidate targets, including components of neurotransmitter signaling systems, ion channels, and intracellular signaling cascades that are already the focus of pharmacological development for other conditions. This “druggable genome” approach, which has gained traction in genetics research across many diseases, offers a pragmatic shortcut for translational science: rather than waiting decades for entirely new therapeutic classes to be invented, researchers can prioritize existing or in-development compounds whose molecular targets intersect with genetically implicated pathways.

Among the pathways receiving attention in the analysis were those involving glutamatergic and GABAergic neurotransmission, the primary excitatory and inhibitory systems of the brain, which have repeatedly surfaced in studies of mood disorders and stress response. The endocannabinoid system and inflammatory signaling pathways also appeared in the convergent analyses, consistent with a broader literature linking immune dysregulation and allostatic load to suicidal ideation. Importantly, the authors emphasize that these findings identify hypotheses rather than ready-made treatments. Genetic association with a drug target does not guarantee that modulating that target will reduce suicide risk, and any therapeutic application would require extensive preclinical validation and carefully designed clinical trials. Nevertheless, the work provides a principled, data-driven map for where to look, replacing decades of largely failed candidate-gene speculation with a genome-wide evidence base.

The methodological rigor underpinning the study deserves emphasis, because the genetics of suicidal behavior has a troubled history. Early candidate-gene studies, which typically examined a handful of variants in genes related to serotonin signaling, produced findings that largely failed to replicate and in some cases were later shown to reflect statistical artifacts, small samples, and publication bias. The field has since undergone a reckoning, and modern efforts adhere to far stricter standards: preregistered analyses, very large and ancestrally diverse cohorts, replication in independent samples, and conservative multiple-testing corrections that demand genome-wide significance thresholds on the order of five times ten to the negative eighth power. The new study reflects these lessons, applying sensitivity analyses to guard against confounding from psychiatric comorbidity and examining whether findings generalize across ancestral groups, an issue of considerable importance given that most genetic discovery to date has been conducted in populations of European ancestry.

The implications for clinical practice, while still distant, are worth considering. Polygenic risk scores for suicide-related outcomes currently explain only a small fraction of individual differences in risk, far too little to be used as stand-alone screening tools, and their predictive performance is degraded in populations not represented in the discovery data. Researchers in the field are careful to stress that genetic risk is not destiny: suicidal behavior arises from an intricate interplay of inherited vulnerability, mental illness, trauma, substance use, social isolation, access to lethal means, and the availability of support. What genetics offers is not a crystal ball but a window into mechanism, and mechanistic knowledge is the raw material from which better biomarkers, risk stratification tools, and ultimately targeted interventions are built. In the near term, the druggable pathway findings are most valuable as a guide for laboratory science, directing neurobiologists toward molecular systems whose manipulation might alter suicide-relevant phenotypes in animal models and cellular assays.

Looking forward, the study signals where the field must go next. Sample sizes for suicide-specific genetic analyses remain smaller than those for major psychiatric disorders, and expanding representation across ancestries is essential if the benefits of this research are to be shared equitably. Integrating genetic data with longitudinal measures of life stress, neural imaging, and digital phenotyping may eventually allow researchers to model how inherited liability interacts with environmental triggers across the lifespan. Functional experiments in induced pluripotent stem cell-derived neurons and brain organoids could test whether the implicated genes genuinely alter stress-response circuitry or resilience. And randomized trials in patients carrying high genetic liability, though ethically and logistically complex, represent the logical endpoint of the druggable-target approach.

What the study ultimately delivers is a reframe. Suicide risk, in this analysis, is not an amorphous clinical outcome but a measurable, partly heritable trait whose genetic architecture is now being resolved with the same tools that have transformed our understanding of heart disease, diabetes, and schizophrenia. By demonstrating that shared genetic liability links suicide risk across diagnostic boundaries and by mapping that liability onto specific, pharmacologically tractable biology, the researchers have converted a long-standing clinical puzzle into a concrete research agenda. The path from genetic correlation to effective prevention is long, and no genetic finding will soon replace the human work of crisis intervention, connection, and care. But for a problem that has defied biological explanation for generations, the identification of shared, druggable mechanisms represents a genuine and hard-won advance, one that may shape suicide research for years to come.

Even years after the acute phase of the COVID-19 pandemic faded from daily headlines, one of its most stubborn legacies continues to shadow millions of working adults: post-COVID conditions, the cluster of persistent symptoms now broadly known as long COVID. A new cross-sectional study from Taiwan, published in BMC Psychology, offers a sobering snapshot of what these conditions mean for mental health and the capacity to work, even among people who were highly vaccinated and infected predominantly during the milder Omicron era. The findings challenge a lingering assumption that vaccination and less virulent variants have fully neutralized the long-term occupational and psychological costs of SARS-CoV-2 infection.

The research, led by Jhe-Ping Lin of the Department of Occupational Medicine at Linkou Chang Gung Memorial Hospital in Taoyuan City, together with Chia-Cheng Chang, Chen-Chang Yang, Ting-An Yang and corresponding author Yu-Chung Tsao, set out to answer a question that has become increasingly urgent for occupational health practitioners worldwide: how do employed adults who report persistent post-COVID symptoms differ, in measurable psychological and functional terms, from infected peers who consider themselves fully recovered? Much of the existing long COVID literature emerged from populations infected with earlier viral variants, before widespread vaccination, and focused heavily on physical sequelae. Relatively less is known about the psychological and occupational footprint of post-COVID conditions under contemporary epidemiological conditions, which is precisely the gap the Taiwanese team sought to fill.

The study design was straightforward but methodologically deliberate. Employed adults with prior confirmed SARS-CoV-2 infection were recruited through clinical and occupational-health channels using non-probability sampling, a practical approach for reaching workers with documented infection histories. All participants completed a battery of validated questionnaires assessing four core domains: health-related quality of life, fear of COVID-19, insomnia severity and work ability. The investigators then divided the cohort into two groups based on whether participants reported persistent symptoms consistent with post-COVID conditions, and statistically compared the two groups on every psychological and occupational measure. This symptom-based categorization mirrors the approach increasingly favored internationally, since no single laboratory biomarker can yet reliably define long COVID.

The results were striking in their consistency. Participants reporting post-COVID conditions demonstrated significantly lower quality of life, with a mean difference of 13.9 points on the quality-of-life instrument compared with fully recovered peers. They also scored higher on the Fear of COVID-19 Scale, a validated measure of pandemic-related anxiety, and reported greater insomnia severity, suggesting that disturbed sleep is a common companion of persistent post-viral illness. Perhaps most consequential for employers and policymakers, the post-COVID group showed reduced scores on the Work Ability Index, a well-established instrument that quantifies how well a worker perceives themselves able to meet the physical and mental demands of their job. The mean difference of 3.3 points on this index may sound modest, but in occupational medicine research, shifts of this magnitude on the Work Ability Index are considered clinically meaningful and are associated with elevated risks of reduced productivity, prolonged sickness absence and early exit from the labor force.

To appreciate why these numbers matter, it helps to understand what each instrument measures. Health-related quality-of-life scales capture how illness erodes daily functioning across physical, emotional and social dimensions; a 13.9-point deficit represents a substantial perceived deterioration in wellbeing, not a marginal statistical blip. The Fear of COVID-19 Scale taps illness-related anxiety that can itself become disabling, feeding avoidance behaviors and hypervigilance. Insomnia severity indices quantify difficulties falling asleep, staying asleep and feeling restored by sleep, and chronic insomnia is strongly linked to depression, impaired concentration and workplace accidents. The Work Ability Index synthesizes a worker’s self-assessed physical and mental capacity, diagnosed illnesses and resources relative to job demands, making it one of the most predictive single measures available for future work disability. That all four domains moved in the same negative direction among the post-COVID group paints a coherent picture of a population under compounding psychological and functional strain.

Crucially, these associations emerged within a population that reflects the current reality of the pandemic rather than its early, pre-vaccine chapter. Taiwan maintained exceptionally high vaccination coverage, and the overwhelming majority of infections in the study cohort occurred during the Omicron-dominant period, when the variant itself was generally associated with less severe acute disease. The persistence of measurable psychological and occupational burden under these comparatively favorable conditions suggests that the risk of long-term impairment has been reduced but not eliminated. For the millions of workers globally who have been infected with Omicron-lineage viruses despite vaccination, the Taiwanese data imply that lingering symptoms remain a genuine occupational health concern rather than a relic of earlier pandemic waves.

The authors are careful about what their study can and cannot claim. Because the design is cross-sectional, capturing participants at a single point in time, causal relationships cannot be inferred. It remains possible, for instance, that pre-existing anxiety, poor sleep or low work ability influenced how participants recalled and reported persistent symptoms, rather than the symptoms driving the psychological burden. The non-probability recruitment strategy, while effective for reaching the target population, also limits generalizability; workers who chose to participate in an occupational health study may differ systematically from those who did not. Self-reported symptom status, the standard for long COVID research given the absence of a definitive diagnostic test, introduces additional subjectivity. The researchers openly acknowledge these constraints and call for longitudinal studies to clarify recovery trajectories and functional outcomes over time, which would allow investigators to determine whether reduced work ability precedes, follows or merely co-occurs with post-COVID symptomatology.

Even with those caveats, the study carries practical implications that extend well beyond Taiwan. The authors argue that mental health assessment deserves a central place in post-COVID follow-up care, not as an afterthought but as a routine component of clinical evaluation. This recommendation aligns with a growing body of international evidence linking long COVID to depression, anxiety and sleep disorders, and it reframes post-COVID conditions as a psychosomatic and occupational problem in equal measure with a physical one. For occupational physicians, the message is that a worker presenting with persistent fatigue or brain fog should not only be evaluated for physical sequelae but also screened for insomnia, pandemic-related fear and diminished work capacity. For employers, the findings underscore the value of graded return-to-work programs, workplace accommodations and access to psychological support, since untreated mental health burdens may prolong functional impairment even after physical symptoms begin to subside.

The policy stakes are considerable. Post-COVID conditions affect a substantial fraction of infected individuals worldwide, and because the condition disproportionately impairs the ability to sustain full-time work, its economic ripple effects touch social insurance systems, disability programs and national productivity. Studies conducted in vaccinated, Omicron-era populations are especially valuable for forecasting the forward-looking burden, since they describe the risk profile relevant to new infections rather than historical ones. By demonstrating a 13.9-point quality-of-life deficit and a 3.3-point reduction in work ability under exactly these modern conditions, the Taiwanese study provides quantitative anchors that health economists and policymakers can use to model the ongoing costs of long COVID in the workforce.

The research also highlights the importance of studying long COVID outside the Europe-North America axis, where much of the literature has concentrated. Taiwan’s pandemic trajectory, characterized by early containment followed by a large, late Omicron wave in a highly vaccinated population, offers a distinct natural experiment. The fact that measurable occupational and psychological impairment still emerges in this setting strengthens the argument that post-COVID conditions are a global phenomenon requiring coordinated surveillance, standardized assessment tools and sustained investment in rehabilitation services. The study received no external funding and was approved by the Institutional Review Board of the Chang Gung Medical Foundation, with all participants providing informed consent under protocols compliant with the Declaration of Helsinki.

For now, the study’s central contribution is to quantify, with validated instruments, the invisible toll that persistent post-viral symptoms exact on working adults: poorer perceived health, greater fear, worse sleep and a weakened sense of capacity to meet job demands. As health systems worldwide transition from emergency response to long-term management of COVID-19’s aftermath, evidence of this kind, gathered under the most favorable epidemiological conditions yet seen, serves as a reminder that the pandemic’s occupational and psychological dimensions are far from over. The authors’ call for longitudinal follow-up is the natural next step, and its findings will help determine whether the burdens documented here fade with time or harden into chronic disability for a vulnerable segment of the working population.

Nearly every displaced woman in Gaza surveyed in a new study screened positive for severe post-traumatic stress symptoms, a finding researchers describe as an unprecedented psychological toll linked directly to hunger, overcrowded displacement, and the collapse of everyday life under war.

The study, published in the International Journal for Equity in Health, assessed 503 displaced women in the Gaza Strip during the 2023–2025 war using a mixed-methods design that combined a structured quantitative survey with in-depth qualitative interviews. The average participant was 29.4 years old, with a standard deviation of 7.3 years, and the majority were married, unemployed, and living below the deep poverty line. Most had been displaced for nearly a year, and the overwhelming majority were sheltering in tents under conditions of severe overcrowding and acute food shortage.

When researchers measured post-traumatic stress symptoms using the Impact of Event Scale–Revised, a widely used screening instrument that evaluates intrusion, avoidance, and hyperarousal symptoms following traumatic exposure, they found that 98.2 percent of participants scored in the severe range. The mean IES-R score across the sample was 61.5, with a standard deviation of 9.5 — a figure far above the thresholds typically used to flag clinically significant trauma-related distress. The authors are careful to note that because symptoms were assessed with a screening tool rather than a formal diagnostic clinical interview, the results should be interpreted as reflecting an extremely high burden of trauma-related distress within the study sample rather than confirmed clinical diagnoses. Even with that caveat, the near-universality of severe scores stands out even against the backdrop of other conflict-affected populations, where severe symptom prevalence rarely approaches such levels.

Food emerged as one of the strongest statistical correlates of psychological suffering. Severe food insecurity, measured with the Food Insecurity Experience Scale developed by the Food and Agriculture Organization, affected approximately 71 percent of participants. Women experiencing severe food insecurity had significantly higher PTSD symptom severity scores, an association that held at a p-value below 0.001. The researchers also identified a cluster of socioeconomic and living-condition factors associated with worse outcomes: being divorced or widowed, living in an extended-family household, sheltering in a tent or collective shelter during displacement, and reporting that food was simply unavailable were all significantly linked to elevated symptom scores.

These quantitative patterns were given texture and depth by the qualitative arm of the study, in which trained researchers conducted semi-structured, face-to-face interviews in Arabic with 34 of the women. Analysis of the transcripts revealed five overarching themes: poverty and inequity; food as a luxury; wounds you cannot see; on the move, nowhere to belong; and small acts of resilience. Together, the themes trace an arc from material deprivation to psychological injury and, ultimately, to the fragile coping strategies women deployed to keep themselves and their families functioning.

The theme of food as a luxury captured how a basic biological need had been transformed into an object of obsession and grief. Women described rationing scraps, skipping meals so children could eat, and the humiliating experience of standing in lines for aid that never arrived. The researchers note that this chronic preoccupation with food security operated on two fronts — it was simultaneously a source of traumatic stress in its own right and an amplifier of existing war trauma, since hunger stripped away one of the last remaining domains in which mothers could still protect their children.

The “wounds you cannot see” theme speaks directly to the invisible nature of psychological injury in wartime. Women recounted nightmares, intrusive memories of bombings and the deaths of relatives, hypervigilance, emotional numbness, and an inability to feel safe even in moments of quiet. Several described the stigma of admitting psychological suffering, or the sense that mental distress was a luxury they could not afford to acknowledge while survival demanded constant action. The researchers interpret these accounts as illustrating how trauma in this context is not a discrete event but a continuous condition, woven into the fabric of daily life.

Displacement itself — the theme of being “on the move, nowhere to belong” — emerged as a distinct psychological stressor. Women described repeated forced evacuations, the loss of homes that anchored family identity and memory, and the corrosive instability of living in tents that leaked in rain, baked in heat, and offered no privacy for grieving or for motherhood. The loss of place, the authors suggest, compounds the loss of people, producing a grief that has nowhere to settle.

Yet the final theme, small acts of resilience, complicates a narrative of pure victimhood. Women described prayer, mutual aid among neighbors, storytelling with children, and small rituals of domesticity — making tea, arranging a tent, celebrating a birthday with almost nothing — as deliberate acts of psychological resistance. The researchers frame these behaviors as adaptive coping mechanisms that, while insufficient to protect against severe trauma symptoms, sustained a sense of agency and dignity under conditions designed to strip both away.

Methodologically, the study relied on a convenience sample recruited through structured surveys administered via Google Forms, a pragmatic choice given the destruction of infrastructure and the impossibility of random sampling in an active war zone. The authors acknowledge this limitation, along with the screening-based measurement of PTSD and the absence of consistent formal referral pathways for psychological care during data collection, which meant that women who became distressed during interviews could not always be directed to clinical services. Interviews followed strict ethical protocols approved by the Institutional Review Board at An-Najah National University, with verbal informed consent, anonymization, and trained interviewers instructed to pause or terminate any conversation that caused visible distress.

The demographic profile of the sample underscores the socioeconomic dimension of psychological vulnerability. That most participants were unemployed and living below the deep poverty line before and during the war reflects broader patterns in which economic deprivation and mental health outcomes are tightly intertwined in conflict settings. Divorced and widowed women — those without a partner’s social and economic support — fared significantly worse, as did women in extended-family households, possibly reflecting crowded conditions and multiplied caregiving burdens.

The study carries a sobering human footnote. The acknowledgments honor the memory of co-author Ayman S. Abutair, a clinical nutrition researcher at Al-Azhar University-Gaza, who lost his life during the war before the paper’s publication. His death stands as a stark reminder that the researchers documenting these invisible wounds were themselves living within the same catastrophe.

The authors argue that their findings demonstrate an urgent and unmet need for comprehensive humanitarian and psychosocial support tailored to displaced women — support that integrates mental health care with food assistance, shelter improvement, and protection for female-headed households. They emphasize that because caregiving responsibilities fall disproportionately on women during war and displacement, their psychological deterioration radiates outward to children and entire family systems, making women’s mental health a strategic priority for any humanitarian response rather than an afterthought.

What the study ultimately documents is the entanglement of hunger and trauma at a scale rarely captured in research. Where most studies of conflict-related PTSD examine populations after violence has subsided, this research measured psychological injury while bombs were still falling, while displacement was ongoing, and while food systems had collapsed. The result is a portrait of a population in which severe trauma symptoms are not an unfortunate consequence of war but, in effect, a near-universal condition — visible not in wounds, but in sleepless nights, interrupted meals, and the quiet endurance of half a million women carrying burdens that remain, for now, largely untreated.

Wildfire scientists have unveiled a new rapid risk-assessment tool that shows substantial wildfire danger to homes and buildings in three major Arctic communities, revealing that between 14% and 26% of structures in Anchorage, Fairbanks, and Whitehorse face high or very high risk from approaching flames. The study, led by Jennifer I. Schmidt of the University of Alaska Anchorage together with Robert H. Ziel, Monika P. Calef, Anna Varvak, and Julio C. Postigo, was published in the International Journal of Disaster Risk Science. Rather than relying on complex fire-behavior simulations that demand specialist analysts and expensive software, the approach combines datasets that most local governments already possess: building footprints, zoning maps, and readily available vegetation layers. The result is an updatable, affordable portrait of which buildings face the greatest threat and where mitigation dollars can do the most good.

The urgency behind the work is hard to overstate. Wildfire costs and damages in the United States alone are now estimated at between USD 394 billion and 893 billion annually, and the 2019 to 2020 Australian fire disasters cost nearly USD 100 billion. The Arctic, often imagined as too cold and wet to burn, is not immune: since 1980, wildfires have cost Alaska between USD 2 billion and 5 billion, with projections suggesting costs could rise 68% by the end of the century. Canadian suppression expenses are expected to climb by 60 to 110% relative to the 1980 to 2009 baseline. Meanwhile, structure loss from wildfire has doubled over the past two decades in the western United States, and western Canada has suffered major structure-loss events in four of the last eight fire seasons. Nearly half the global population now lives within the wildland-urban interface, the zone where people and forests meet or intermingle, making tools that translate fire science into actionable local maps increasingly vital.

The researchers’ central premise is conceptually simple but technically consequential: for a structure to be at high risk, a flammable hazard must exist nearby, within roughly 500 meters, and the surrounding landscape must exhibit high wildfire exposure. The method begins with a flammability hazard layer at 30-meter resolution, built from a modified version of NASA’s ABoVE Landcover dataset in which generic evergreen classes were refined to reflect locally flammable species such as black spruce, white spruce, lodgepole pine, and subalpine fir, each assigned hazard ratings from 0 to 100. Critically, the team treated structures themselves as fuel. Each building contributes three fuel units to the surrounding flammability score, a value chosen after systematic testing of one through ten fuel units; three proved optimal, nudging densely built environments up a hazard category without causing wholesale reclassification. Structure density was computed at 90-meter resolution, multiplied by three, and added to the vegetation-based hazard to produce an enhanced flammability hazard layer.

Wildfire exposure, the second pillar of the assessment, captures the potential for hazard to travel. It is calculated as the average flammability hazard within a 100-meter or 500-meter radius, representing the likelihood that fire can reach and burn a given location even if the local fuels themselves are sparse. Exposure values, also ranging from 0 to 100, are classified into five tiers from very low to extreme, and concentrated pockets of flammable vegetation drive scores upward. The third ingredient is structure vulnerability, produced by multiplying the enhanced flammability hazard by a structure importance factor derived from zoning data: residential buildings and critical infrastructure such as hospitals, schools, and nursing homes receive the highest weight, followed by commercial and other structures. Where zoning was unavailable, as on military installations like Joint Base Elmendorf-Richardson in Anchorage and Fort Wainwright and Eielson Air Force Base in Fairbanks, the team collaborated with base personnel or interpreted aerial imagery to classify neighborhoods. The final risk score synthesizes hazard, exposure, and vulnerability into six categories, from extremely low to very high.

The results paint a nuanced picture of Arctic fire danger. Fairbanks showed the greatest proportion of structures in the top two risk categories at 26%, followed by Whitehorse at 22% and Anchorage at 14%. Anchorage, paradoxically, had the highest share of buildings in the top vulnerability tiers, 42%, yet the lowest risk, a consequence of its large urban footprint where dense housing raises vulnerability but fragmented, less-flammable vegetation and abundant developed ground suppress exposure. Fairbanks and Whitehorse, with structures woven into spruce- and pine-dominated boreal landscapes, showed closer alignment between vulnerability and risk. Anchorage was also the only study area where more than 1% of cells jumped two or more flammability categories when buildings were counted as fuel, at 7%, reflecting its 27% share of structure area containing seven or more buildings per cell. The analysis also confirmed the primacy of exposure: when a structure’s wildfire exposure was extreme, at least 87% of such buildings landed in the highest risk category across all three communities.

A key innovation lies in how the team redrew the wildland-urban interface itself. Traditional WUI maps distinguish urban, interface, and intermix zones; the researchers added a fourth category, the transmission zone, marking areas roughly 2.4 kilometers beyond the WUI boundary where fire could move into a community and where fuel breaks might be sited. Within the 500-meter buffer around structures, urban areas were defined by exposure values of 20 or less, interface zones by 21 to 60, and intermix by values above 60, cross-referenced with Alaska and Yukon wildfire management designations that prioritize suppression from critical down to unplanned or wilderness levels. Fairbanks claimed the largest overall WUI footprint, and the transmission zone proved the largest category in every study area, spanning 23,347 square kilometers around Anchorage, 6,180 square kilometers around Fairbanks, and 665 square kilometers around Whitehorse. Across all sites, most structures fell within interface zones, 52 to 61%, followed by intermix at 20 to 35%. Thousands of buildings sit in the high-risk interface and intermix zones where mitigation should concentrate: 15,537 structures in Anchorage, 16,212 in Fairbanks, and 3,734 in Whitehorse.

The method faced its most demanding test against a real disaster. On 17 August 2019, during southcentral Alaska’s hottest and driest summer on record, the likely human-caused McKinley wildfire was driven by high winds across 13.3 square kilometers in three days, destroying 52 residential structures, 84 outbuildings, and three commercial buildings, affecting 1,028 properties and forcing roughly 350 to 400 people to evacuate. By comparing pre-fire aerial imagery from 2017 with post-fire imagery from 2020 at 0.3-meter resolution and linking vanished buildings to the Matanuska-Susitna Borough structure database, the team identified exactly which structures burned. The verdict was statistically compelling: burned structures had significantly higher wildfire exposure than those that survived, and the proportion of burned structures with exposure values above 60 was significantly larger than among unburned ones. Structure risk scores above 60 were also significantly associated with burning. Notably, the test held even under conditions so extreme that normally resistant, low-flammability fuels were igniting.

The study’s design was shaped by sustained community engagement. Through workshops in 2019, 2021, and 2023, along with 15 to 20 meetings organized by Anchorage community councils and landowner associations and public events ranging from wildfire awareness days to Arbor Day, residents and agency staff reshaped everything from color schemes to model parameters. One consequential example: Anchorage residents and fire managers insisted that the maps reflect the spruce beetle outbreak first detected in 2016 and peaking around 2020, which killed and removed many spruce trees. The team responded with rules derived from aerial detection surveys, reclassifying white spruce cells in areas of beetle intensity of 11% or greater to mixed forest, and shifting mixed forest cells to open shrub, while a succession model updated vegetation on areas burned within the last 30 years. This iterative dialogue ensured outputs that both wildfire practitioners and the general public can interpret, a prerequisite the authors see as central to building fire-adapted communities.

The practical implications for planning are direct. Because mitigation funding is often tied to officially designated WUI areas, the ability to rapidly recalculate boundaries from updated building layers expands funding opportunities and keeps pace with community growth. The framework also points to different interventions in different zones: structures in intermix areas, surrounded by vegetation, benefit most from fuel treatments, while those in interface zones, where flammable vegetation is scarcer, are better served by home hardening measures such as fire-resistant roofs, siding, decks, screens, and unburnable buffers. Given that wildfire activity tends to occur where exposure exceeds 60, the authors recommend concentrating resources on the top two risk categories. Where national products like wildfirerisk.org rely on generalized fuel layers that can overestimate unburnable area in WUI zones and are updated too slowly for annually burning landscapes, this approach trades complexity for transparency, using local data that governments already collect. The underlying wildfire exposure toolbox is publicly available on GitHub, and the team argues that empowered residents, equipped with accurate, current, neighborhood-scale information, are crucial to reducing wildfire risk, particularly where communities may prefer action without government intervention.

In a development that could reshape how chemists recycle one of the most persistent classes of synthetic molecules ever made, researchers have reported a new catalytic strategy that strips fluorine atoms from perfluoroalkyl chains in a controlled, sequential fashion while installing a single boron group at the chain’s terminus. The work, published in Nature Communications, introduces what the authors call zipper polydefluorination-monoborylation, a transformation that walks down a perfluoroalkyl skeleton and removes multiple fluorine atoms in a stepwise cascade before capping the partially defluorinated product with a boron substituent. The achievement addresses a longstanding challenge in fluorine chemistry: while carbon-fluorine bonds are prized in materials and pharmaceuticals for their extraordinary stability, that same stability makes them notoriously difficult to break selectively, leaving chemists with few tools for converting perfluorinated waste streams into useful building blocks.

Perfluoroalkyl substances, often grouped under the umbrella of PFAS chemistry, owe their remarkable properties to the strength of the carbon-fluorine bond, which at roughly 485 kilojoules per mole in fully fluorinated chains ranks among the strongest single bonds that carbon forms. This bond strength, combined with the sheer electronegativity of fluorine, means that perfluoroalkyl groups resist oxidation, hydrolysis, and thermal degradation under nearly all conventional conditions. As a consequence, perfluorinated chains accumulate in the environment and resist standard degradation pathways, prompting intensive research into defluorination methods ranging from photocatalytic single-electron transfer to reductive hydrodefluorination with metal hydrides. Yet most existing approaches suffer from a common limitation: they either remove fluorine indiscriminately, collapsing the entire chain to a mixture of products, or they remove only a single fluorine atom, offering little control over the degree of defluorination. The new study demonstrates a way to achieve the best of both worlds, delivering multi-fluorine removal in a predictable pattern while simultaneously introducing a versatile functional handle.

The conceptual heart of the method lies in what the authors describe as a zipper mechanism. Rather than removing fluorine atoms at random positions along the chain, the reaction initiates at a defined site and then propagates along the perfluoroalkyl backbone one carbon at a time, much as a zipper opens tooth by tooth. At each step along the chain, a fluorine atom is expelled and replaced in sequence, with the reaction front advancing until a thermodynamic or kinetic stopping point is reached. The monoborylation component of the process ensures that once the zipper has run its course, the resulting olefinic or anionic intermediate is intercepted by a boron reagent, furnishing a product in which the boron group occupies a predictable position derived from the original perfluoroalkyl terminus. Organoboron compounds are among the most valuable intermediates in modern synthetic chemistry, serving as partners in Suzuki-Miyaura cross-coupling and as precursors to alcohols, amines, and halides, so their installation at a defluorination site effectively converts a chemical dead end into a synthetic launching pad.

Mechanistically, the transformation draws on the characteristic reactivity of organoboron reagents toward geminal difluoroalkenes, the partially defluorinated intermediates that arise when consecutive fluorine atoms are eliminated from a perfluoroalkyl chain. Perfluoroalkyl chains, when subjected to strong bases or reductive conditions, can undergo sequential beta-fluoride elimination events, each generating a more highly unsaturated species. The boron reagent in the reported system engages these unsaturated intermediates through addition pathways that install boron at the terminal carbon while generating a carbanionic center adjacent to the remaining difluoromethylene unit. That newly formed carbanion is perfectly positioned to expel another fluoride ion, regenerating an unsaturated handle one carbon further down the chain and setting up the next iteration of the cycle. This intimate coupling of borylation and defluorination is what gives the reaction its chain-walking character: each borylation event creates the conditions for the next defluorination event, and the process ripples along the chain until the fluorine reservoir is exhausted or the reagents are consumed.

The practical significance of the work becomes apparent when one considers the fate of the fluorine atoms themselves. In the zipper process, fluorine is released as fluoride ion, which under the reaction conditions is sequestered by silicon-containing trapping agents to form robust silicon-fluorine bonds. Fluorosilicates of this kind are stable, isolable, and, importantly, compatible with industrial processes for recycling fluorine back into hydrofluoric acid, the feedstock from which essentially all synthetic organofluorine compounds are made. In this sense, the method does more than transform perfluoroalkyl chains into borylated products; it also lays the groundwork for closing the fluorine cycle, recovering the fluorine content of persistent molecules in a form that can be fed back into the chemical economy. With global concern over PFAS contamination intensifying and regulatory pressure on fluorinated chemicals mounting, technologies that enable fluorine recovery rather than dilution into the environment carry considerable weight.

From a synthetic chemistry standpoint, the products of the reaction open multiple downstream avenues. Because the boron substituent is installed at a defined position on a partially fluorinated alkyl or alkenyl framework, the resulting molecules retain a programmable mixture of carbon-fluorine bonds and carbon-boron functionality. The remaining fluorine atoms can tune the lipophilicity, metabolic stability, and dipole moment of the scaffold, properties that are actively exploited in medicinal chemistry when designing fluorinated drug candidates. Meanwhile, the boron group serves as a convertible handle, allowing the defluorinated carbon center to be joined to aromatic rings through cross-coupling, oxidized to alcohols, or aminated in homologation reactions. In effect, the method converts a chemically inert perfluoroalkyl group, useful mainly as a passive coating or tail, into a functionalized partial-fluoro skeleton that can participate in the full toolkit of carbon-carbon and carbon-heteroatom bond formation.

The reported study also sheds light on the factors governing how far the zipper travels along a given chain. Chain length, the nature of the substituent anchoring the perfluoroalkyl group, and the stoichiometry of the base and boron reagent all influence the number of fluorine atoms removed and the distribution of products obtained. Through systematic variation of these parameters and careful analysis of the reaction mixtures by nuclear magnetic resonance spectroscopy and mass spectrometry, the authors map out the scope of the transformation across a range of perfluoroalkyl substrates, from short perfluoroethyl groups to longer chains approaching those found in commercial fluorosurfactants. Control experiments interrogating individual elementary steps support the proposed sequential elimination-addition mechanism and rule out alternative pathways involving radical fragmentation or random beta-scission, underscoring the unusually high degree of positional control the method achieves.

Beyond its immediate synthetic utility, the work contributes to a growing body of research aimed at taming the carbon-fluorine bond through the strategy of defluorinative functionalization. Over the past decade, chemists have developed an impressive repertoire of reactions that replace fluorine in perfluoroalkyl and fluoroarene substrates with groups ranging from hydrogen to sulfur, nitrogen, and carbon substituents. What distinguishes the zipper approach is its multiplicity and its directionality: instead of a single substitution event at a single site, the reaction performs many substitutions in a choreographed sequence along a chain, effectively treating the perfluoroalkyl group as a one-dimensional lattice to be patterned at will. This conceptual reframing, from defluorination as bond-breaking to defluorination as programmable editing, may inspire analogous strategies for other polyfluorinated motifs, including perfluoroarenes and perfluoropolyethers, two additional classes of highly persistent fluorinated materials.

Challenges remain before such chemistry can move from the laboratory bench to environmental remediation at scale. The reaction conditions reported rely on strong bases and moisture-sensitive boron reagents handled under inert atmosphere, and the substrates studied are discrete, soluble perfluoroalkyl compounds rather than the polymeric or adsorbed PFAS species that dominate environmental contamination. Scaling the chemistry will require sourcing boron reagents economically, managing the large quantities of fluoride generated, and integrating the transformation with upstream methods for extracting perfluoroalkyl pollutants from water and soil. Nevertheless, the demonstration that a perfluoroalkyl chain can be unzipped in a controlled manner, with fluorine captured in recyclable form and a valuable boron handle installed, establishes a credible chemical pathway toward defluorination that is both productive and selective, a combination that has eluded the field for decades.

As policymakers and scientists grapple with the legacy of fluorine chemistry, work of this kind illustrates a middle path between the wholesale abandonment of fluorinated molecules and their indefinite persistence in the biosphere. The fluorine atom confers properties that industry and medicine continue to depend upon, and the ability to disassemble fluorinated architectures on demand, recovering both the carbon skeleton as a functionalized building block and the fluorine content as a recyclable salt, points toward a more circular future for organofluorine chemistry. The zipper polydefluorination-monoborylation strategy reported in Nature Communications is an early but striking example of that vision made concrete at the molecular level, and it is likely to prompt rapid efforts to extend the concept across the breadth of persistent fluorinated chemicals.

Astronomers using NASA’s James Webb Space Telescope have discovered water molecules inside the enormous envelope of gas and dust surrounding IRS 3, an aging star located remarkably close to the supermassive black hole at the centre of the Milky Way. The finding challenges the long-held assumption that the intense radiation and gravitational turbulence around Sagittarius A, commonly known as Sgr A, would prevent molecules from surviving in such a hostile environment.

IRS 3 lies about 0.6 light-years from Sgr A*, a distance equivalent to roughly 42,000 times the separation between Earth and the Sun. In everyday terms, that is an immense distance. On the scale of the Galactic Centre, however, it places the star in the immediate neighbourhood of a black hole containing approximately four million times the mass of the Sun. The region is crowded with stars, powerful magnetic fields, energetic particles and rapidly changing radiation, making IRS 3 an exceptional natural laboratory for studying how stars evolve under extreme conditions.

The observations were conducted with the Mid-Infrared Instrument, or MIRI, aboard the James Webb Space Telescope. Mid-infrared light is particularly useful for investigating cool dust and molecules that absorb and re-emit radiation at characteristic wavelengths. By analysing the infrared spectrum of IRS 3, researchers led by PD Dr Florian Peißker of the University of Cologne identified the chemical and physical properties of the material surrounding the star with far greater detail than previous observations allowed.

What makes IRS 3 especially striking is the scale of its circumstellar envelope. The structure extends to a radius of approximately 10,000 astronomical units, where one astronomical unit is the average distance between Earth and the Sun. Such envelopes are common around mature, evolved stars, but IRS 3 appears to be unusually isolated in the immediate vicinity of Sgr A*. Its enormous shell of material therefore stands out against a dense background containing millions of other stars.

The new observations indicate that IRS 3 is undergoing a phase known as a superwind. This is a brief but dramatic stage in the late evolution of certain stars, particularly asymptotic giant branch stars. During this phase, powerful stellar winds carry gas and newly formed dust away from the star at extremely high rates. Over periods of only a few centuries, the star can lose a substantial fraction of its outer layers, transforming its surroundings into a thick, expanding envelope that will eventually disperse into interstellar space.

According to the research team, IRS 3 is losing material at a rate equivalent to the mass of Earth every 18 days. Although the comparison makes the scale easier to imagine, the total outflow is far more significant when considered over astronomical timescales. The steady loss of gas and dust explains why the envelope is both unusually large and dense. It also provides the raw material in which molecules can form, shielded within cooler regions of the expanding stellar outflow.

The detection of water is the most surprising aspect of the study. Near Sgr A*, ultraviolet radiation, X-rays and high-energy particles are expected to break apart fragile molecules. Water molecules can be destroyed when energetic photons dissociate them into their constituent atoms, particularly in exposed regions close to powerful sources of radiation. Yet the MIRI observations show that water exists within the envelope of IRS 3, suggesting that the dense outflow from the star creates protected pockets where molecular chemistry can proceed.

This discovery does not mean that the Galactic Centre is a calm or hospitable environment. Instead, it demonstrates that stellar mass loss can temporarily create its own chemical refuge. The material expelled by IRS 3 increases the density of gas and dust around the star, allowing some regions of the envelope to absorb damaging radiation. Within these shielded zones, atoms can combine on dust grains or in the gas phase, producing molecules that would otherwise be rapidly destroyed. The finding offers a new view of how evolved stars may enrich even the most violent regions of a galaxy.

The researchers say that IRS 3 may be actively supplying the area around Sgr A* with elements and molecules that later become part of the broader interstellar medium. Water itself is not evidence of life, but it is a crucial ingredient in planetary formation and a central component of the chemistry that precedes biology. As the envelope expands, some of its contents will eventually mix with surrounding gas, potentially influencing the composition of future stars, planets and smaller bodies formed in the Galactic Centre.

The study, published in Astronomy & Astrophysics, also highlights the scientific value of observing the Milky Way’s centre at infrared wavelengths. Visible light is heavily obscured by dust along the line of sight, while infrared radiation can pass through much of that material and reveal otherwise hidden structures. Future observations with METIS, a next-generation mid-infrared instrument being developed for the Extremely Large Telescope in Chile, could provide sharper views of IRS 3’s envelope, measure its winds in greater detail and determine how its molecular content changes over time. For now, the star’s unexpected water-rich cocoon is offering astronomers a rare glimpse of stellar chemistry unfolding beside one of the most extreme objects in the universe.

Subject of Research: Not applicable

Article Title: Dust production in the harsh environment of Sgr A* – MIRI/JWST observation of the O-rich asymptotic giant branch star IRS 3

News Publication Date: 11-Aug-2026

Web References: https://doi.org/10.1051/0004-6361/202660243

References: Astronomy & Astrophysics

Image Credits: ESO/F. Peissker

Keywords

James Webb Space Telescope, JWST, MIRI, IRS 3, Sagittarius A, Sgr A, Milky Way, Galactic Centre, water molecules, superwind, asymptotic giant branch star, stellar evolution, circumstellar envelope, dust production, black hole, infrared astronomy, Extremely Large Telescope

A new research project at the New Jersey Institute of Technology is taking a closer look at a strange but important behavior in rubber-like materials: their properties can depend on what happened to them before. With a $125,000 New Directions grant from the American Chemical Society Petroleum Research Fund, chemical and materials engineering professor Gennady Gor and his students will investigate how petroleum-derived polymers absorb fluids, swell, contract and change their elasticity over time. The work could improve predictions of how elastomers perform in everything from industrial seals to oil and gas equipment.

Elastomers are polymers capable of undergoing large, reversible deformations. Silicone, neoprene and natural or synthetic rubber are familiar examples. Their molecular chains can stretch, rearrange and return toward their original configuration, giving these materials their characteristic flexibility. Yet exposure to liquids or vapors can dramatically alter that behavior. Hydrocarbon molecules may enter the polymer network, pushing molecular chains apart and causing the material to expand. At the same time, the absorbed fluid can change the polymer’s stiffness, strength and ability to recover its original shape.

Gor’s project focuses on a phenomenon known as hysteresis, in which a material’s current state depends not only on its present conditions but also on the path it took to reach them. For an elastomer, that means the material may respond differently when it is swelling than when it is shrinking, even if its size and surrounding environment are identical. The difference can arise from molecular rearrangements, delayed relaxation or changes in the interactions between polymer chains and absorbed molecules. Such path-dependent behavior is central to understanding why materials sometimes fail to return fully to their previous mechanical state.

The question is especially relevant for petroleum-related technologies. Elastomers are widely used as O-rings, gaskets and seals because they can deform to fill microscopic gaps and maintain a hermetic barrier. In oil and gas environments, however, these components may encounter complex mixtures of hydrocarbons under pressure and at elevated temperatures. If a seal swells excessively, softens or loses its ability to recover after repeated exposure, it can become unreliable. A better understanding of hysteresis could help engineers select materials and operating conditions that reduce leakage and extend component lifetimes.

The new study represents a shift for Gor’s Laboratory for Materials Interfaces. His previous research has largely examined porous adsorbent materials, which collect molecules on their internal surfaces. Elastomers are different: they do not need pores to take up substantial amounts of fluid. Instead, molecules diffuse into the polymer matrix and interact with its flexible chains. The resulting process is more like a sponge absorbing liquid, although the molecular mechanisms are governed by diffusion, thermodynamics and the elasticity of the polymer network.

Günel Nabiyeva, a fourth-year doctoral student, will lead much of the experimental effort with undergraduate chemical engineering student Elitsa Mileva and second-year doctoral student Supun Rangana. The team has already begun collecting data on how rubber deforms in the presence of liquids and vapors. For Mileva, who has spent considerable time reviewing the scientific literature, the grant will provide an opportunity to move deeper into laboratory work. Nabiyeva describes the project as a new system for the research group because the laboratory has not previously studied elastomers in this way.

The researchers will expose polymer samples to controlled concentrations of hydrocarbon vapors and track how the materials respond. Their measurements will combine swelling, fluid uptake and elasticity rather than treating these properties separately. This approach is essential because a polymer can gain mass and volume while simultaneously becoming softer or, under some conditions, displaying a more complicated mechanical response. Different hydrocarbons may produce different results because their molecular size, chemical structure and compatibility with the polymer influence how quickly they enter the material and how strongly they interact with its chains.

Rangana will contribute expertise in ultrasonic sensing, which can measure subtle changes without physically contacting or damaging the sample. Ultrasonic waves travel through a material in ways that depend on its density, structure and mechanical properties. By analyzing changes in wave transmission or reflection, scientists can detect variations in stiffness and internal condition with high precision. Coupling these measurements to vapor exposure could reveal how quickly elasticity changes as molecules enter the polymer and whether the material follows the same path during desorption as it did during absorption.

The team hopes its results will fill a gap in the fundamental understanding of polymer hysteresis. Although hysteresis is well known in many areas of physics and engineering, relatively few studies have examined how history-dependent elasticity develops in swollen elastomers. The new measurements may help distinguish reversible molecular motions from more persistent structural changes and improve models used to predict polymer performance. Ultimately, the researchers aim to make elastomer behavior less mysterious and more reliable, helping engineers design seals and flexible components that remain dependable across repeated cycles of chemical exposure, swelling and recovery.

Subject of Research: Elastomers, petroleum-derived polymers, polymer swelling, elasticity and hysteresis

Article Title: Ultrasonic Experiments Aim to Reveal Why Rubber Remembers Its History

Web References: Gennady Gor’s NJIT profile; NJIT Laboratory for Materials Interfaces

Image Credits: Courtesy of New Jersey Institute of Technology

Keywords

Elastomers, polymers, hysteresis, rubber, polymer swelling, ultrasonic sensors, materials science, chemical engineering, petroleum research, NJIT

Animal testing has shaped modern drug development for decades, but its limitations become especially visible when scientists evaluate cancer immunotherapies. These treatments are designed to stimulate the immune system to recognize and destroy malignant cells, yet the same powerful immune activation can trigger life-threatening complications in patients. Researchers at the University of Rochester are developing a human-cell-based alternative: miniature tissue models known as organs-on-a-chip that could help predict these toxicities before experimental medicines reach clinical trials.

The work is being led through the university’s Translational Center for Barrier Microphysiological Systems, or TraCe-bMPS. The center is building drug-development tools around modular µSiM tissue chips, devices that contain ultrathin membranes populated with human cells. Unlike conventional laboratory cultures, these systems are engineered to reproduce key features of biological barriers, including the physical separation between tissues, controlled exposure to drugs, and measurable inflammatory responses. Their modular construction also allows the devices to be manufactured in large quantities with consistent dimensions and materials.

The platform is based primarily on acrylic components and incorporates membranes designed to mimic interfaces found in the human body. Such barriers regulate the movement of molecules and immune cells between compartments, making them central to drug absorption, toxicity, and inflammation. By assembling reproducible chips with human cells, researchers can study how a therapy affects a tissue under conditions that more closely approximate human biology than many animal models. The design also makes it possible to modify individual components for different organs or disease settings.

A major feature of the Rochester system is the integration of sensors directly into the chips. Developed in part through the work of biomedical engineering professor Benjamin Miller, these sensors can track barrier function and inflammatory signaling in real time. Instead of waiting until the end of an experiment to examine damaged cells, researchers can observe how a tissue responds as a drug is introduced. Changes in electrical or molecular signals may reveal that a barrier is becoming more permeable or that inflammatory pathways are being activated, providing an early warning of toxicity.

The immediate focus is on two severe complications associated with some cancer immunotherapies. Cytokine release syndrome, commonly called CRS, occurs when activated immune cells release large quantities of signaling proteins known as cytokines. The resulting systemic inflammation can cause fever, low blood pressure, breathing difficulties, and, in the most serious cases, organ failure. Immune effector cell-associated neurotoxicity syndrome, or ICANS, can affect the nervous system, producing confusion, seizures, impaired speech, or other neurological symptoms. Both conditions are difficult to predict reliably with animal testing because species differ in immune signaling networks and cell-surface receptors.

“The goal is to predict these toxicities from human cells on a chip, before a drug ever reaches a patient, and to do it without relying on animal models that have repeatedly failed to predict CRS in people,” says James McGrath, director of TraCe-bMPS and the William R. Kenan Jr. Professor of Biomedical Engineering. The approach does not simply attempt to reproduce a whole human body in miniature. Instead, it focuses on specific biological barriers and immune interactions that are directly relevant to the safety question being studied.

The project has now reached an important regulatory stage. The team was accepted into the US Food and Drug Administration’s Innovative Science and Technology Approaches for New Drugs, or ISTAND, pilot program. ISTAND is intended to help evaluate emerging methods that could improve drug development and eventually support regulatory decisions. Acceptance does not mean that the tissue-chip platform has already been approved for routine use, but it creates a formal pathway for the researchers to work with the FDA on defining the evidence needed for qualification.

The Rochester group is preparing a detailed qualification plan that will describe clinical considerations, timelines, data-sharing procedures, and statistical methods for evaluating the technology. If the platform ultimately receives FDA qualification, pharmaceutical companies could use its results as part of the evidence submitted in support of new drug applications. Such recognition would represent a significant step for organs-on-a-chip research, which has expanded rapidly but has only rarely progressed into formal regulatory programs.

The effort comes as US regulators seek alternatives to animal testing in preclinical safety studies. The FDA Modernization Act 2.0, enacted in 2022, opened the door for non-animal methods, while the agency’s 2025 roadmap outlined further efforts to reduce reliance on animal models. Researchers say pharmaceutical companies are showing strong interest because many immunotherapies are already in clinical development and carry risks of CRS or ICANS. The Rochester team collaborated with Graham Marsh of the Critical Path Institute and consulted with scientists at Pfizer about how the tool could fit into real-world drug development. If successful, the technology could give researchers a more human-relevant way to identify dangerous immune reactions early, helping developers eliminate unsafe candidates and refine promising treatments before patients are exposed.

Subject of Research: Human-cell tissue chips for predicting cancer immunotherapy toxicities.

Web References: University of Rochester; Translational Center for Barrier Microphysiological Systems; University of Rochester Biomedical Engineering; FDA ISTAND pilot program; FDA Modernization Act 2.0.

Image Credits: URochester photo / J. Adam Fenster.

Keywords: Drug discovery, tissue chips, organs-on-a-chip, cancer immunotherapy, cytokine release syndrome, CRS, ICANS, neurotoxicity, animal testing alternatives, biomedical engineering, microphysiological systems, human-cell models.

A tiny microscope weighing just five grams could give neuroscientists an unprecedented view of how individual brain cells control behavior while animals move naturally. Researchers at the University of Colorado Anschutz Medical Campus and the University of Colorado Boulder have developed a head-mounted device that can both record neural activity and selectively activate individual neurons during movement. The technology could help scientists investigate how brain circuits generate behavior and how those circuits become disrupted in conditions such as Alzheimer’s disease, Parkinson’s disease and epilepsy.

The device, called Opto2P-FCM, combines two powerful techniques that have traditionally been difficult to use together in a freely moving animal. Two-photon microscopy provides detailed images of living brain tissue at cellular resolution, while optogenetics uses light-sensitive proteins to control selected neurons. By integrating both capabilities into a miniature instrument, the researchers can observe specific cells as they become active and then stimulate chosen neurons to test how they influence the surrounding network.

“These miniature microscopes have been increasingly used to study the neural basis of behavior in freely moving animals,” said Emily Gibson, PhD, associate professor of biomedical engineering at CU Anschutz and the study’s senior author. “This new microscope allows deeper penetration and higher resolution images in the brain. Importantly, it can also excite select neurons using optogenetics.”

For decades, conventional two-photon microscopes have been regarded as the gold standard for imaging neurons in living brains. These systems use infrared laser light to produce high-resolution images deep within tissue, allowing researchers to track calcium signals and other indicators of neural activity. However, conventional microscopes are generally large, stationary instruments. Animals must often remain immobilized beneath the objective, making it difficult to study brain activity during walking, exploration, social interaction or other natural behaviors.

Miniature microscopes have helped overcome that limitation by moving the imaging system onto the animal’s head. Yet reducing the size and weight of the equipment has often required compromises. Some compact systems provide lower-resolution images, while others can record neural activity but cannot stimulate precisely selected cells. The Colorado team designed Opto2P-FCM to address both problems through a dual-path optical architecture that separates imaging from stimulation.

In the imaging pathway, infrared light is directed through the brain to generate two-photon signals from fluorescent markers inside neurons. These markers can report changes in calcium concentration, which occur when neurons fire. The resulting signals are collected to create detailed images of neural activity at the level of individual cells. A separate optical pathway delivers patterned light for optogenetic stimulation, allowing researchers to activate specific neurons without disrupting the imaging beam or overwhelming the detectors.

This separation is technically important because imaging and stimulation place different demands on an optical system. High-resolution imaging requires precise focusing, efficient light collection and minimal optical distortion. Optogenetic activation, by contrast, requires intense, accurately positioned light capable of reaching the light-sensitive proteins expressed by selected neurons. Designing both functions into a device small enough to be carried by an animal required careful control of the alignment, geometry and weight of every optical component.

“This microscope is a game changer,” said Juliet Gopinath, PhD, professor of electrical, computer and energy engineering and physics at CU Boulder and a study co-author. “Being able to demonstrate a state-of-the-art instrument for both read-out and photo-stimulation of neurons is amazing.” The research team included engineers, physicists and neuroscientists who worked together to solve problems involving miniature lenses, mechanical tolerances, optical alignment and three-dimensional fabrication.

The device weighs approximately five grams, but its small size made construction unusually demanding. Mo Zohrabi, PhD, a senior research scientist at CU Boulder and co-first author, said the team initially thought such an instrument might be impossible to build because of the tight tolerances required by the optics and available 3D-printing technology. Gregory Futia, PhD, a senior research associate at CU Anschutz and co-first author, said that every component had to be designed and accounted for with extreme precision. Even slight misalignment could reduce image quality or prevent the stimulation beam from reaching the intended cells.

The researchers believe the platform could transform experiments designed to distinguish correlation from causation in the brain. Recording a neuron’s activity can show that it is associated with a behavior, but it does not prove that the cell helps produce that behavior. With optogenetic stimulation, scientists can activate a selected population and observe whether movement, decision-making or other actions change. At the same time, the microscope can record how neighboring neurons respond, providing a view of how local circuits coordinate activity.

The current Opto2P-FCM system is a prototype, and the researchers are working on future versions that could be smaller, lighter and faster. They also hope to expand the field of view so that more neurons can be monitored at once. Such improvements could make it possible to track larger networks over longer periods while animals engage in increasingly complex behaviors. By combining cellular-resolution observation with targeted manipulation in a freely moving subject, the technology may help reveal how healthy neural circuits operate and how neurological disease changes the communication between them.

Subject of Research: Miniature two-photon microscopy, optogenetics, neural circuits and brain activity during natural movement

Article Title: New Miniature Microscope Allows Scientists to Watch and Control Brain Cells During Natural Movement

News Publication Date: Aug. 11, 2026

Web References: University of Colorado Anschutz Medical Campus; University of Colorado Boulder; Optica study

References: Optica, “New Miniature Microscope Allows Scientists to Watch and Control Brain Cells During Natural Movement,” article publication date: Aug. 7, 2026

Keywords

Miniature microscope, two-photon microscopy, optogenetics, neuroscience, brain imaging, neural circuits, freely moving animals, biomedical engineering, applied physics, neurological disease, brain research

The Marcus Foundation has awarded $28.7 million to Chuck Murry, MD, PhD, director of the USC Stem Cell Center, to support a clinical trial testing a stem cell-based therapy intended to regenerate heart muscle damaged by a severe heart attack. The award, the largest the foundation has given to the University of Southern California and one of its largest contributions to academic medicine, will fund both the planned human study and a parallel research program examining how the immune system responds to transplanted cardiac cells.

The trial is scheduled to begin in 2027 at Keck Hospital of USC in partnership with the USC Cardiac and Vascular Institute. A second clinical site will be established at the University of Washington, where elements of the technology were originally developed. The first phase will enroll approximately 18 patients who have experienced extensive myocardial infarction, the medical term for a heart attack that destroys part of the heart’s muscular wall.

Patients in the study will receive stem-cell-derived heart muscle cells delivered into regions damaged by the infarction. The primary goals will be to evaluate safety and determine whether the procedure is feasible in people with severe cardiac injury. Investigators will also monitor early biological signals, including whether the transplanted cells remain active, integrate with the injured tissue and produce measurable changes in cardiac performance during recovery and long-term follow-up.

A heart attack occurs when blood flow through a coronary artery is blocked, depriving cardiac muscle of oxygen. Within minutes, cells in the affected region begin to die, and the resulting scar tissue lacks the contractile properties of healthy myocardium. Unlike tissues such as skin or blood, adult human heart muscle has only a limited capacity for self-repair. As a result, a large infarction can weaken the heart’s pumping function, increase the risk of heart failure and leave survivors unable to perform ordinary activities without fatigue or breathlessness.

Current cardiovascular treatments can reopen blocked arteries, limit further injury and help the remaining heart muscle work more efficiently. They cannot, however, routinely replace the cardiomyocytes that have already been lost. Murry’s research program is aimed at addressing that biological gap by producing replacement heart muscle cells in the laboratory and delivering them directly to damaged tissue. The strategy represents a shift from supporting a failing heart to attempting to rebuild part of its contractile apparatus.

The transplanted cells are being developed as a form of cellular therapy, in which living cells act as the therapeutic agent rather than serving merely as a vehicle for a drug. According to Murry, the cells are intended to become active within the injured heart, contribute to repair and synchronize with surrounding muscle. Achieving that outcome requires more than placing new cells into scarred tissue: the cells must survive, connect functionally with the host myocardium and avoid creating dangerous electrical disturbances or abnormal rhythms. The initial trial is therefore designed principally to establish safety before testing therapeutic effectiveness in larger patient groups.

The foundation’s funding will also support research into the immune response triggered by cell transplantation. Even cells created from stem cells can be recognized as foreign by the recipient’s immune system, potentially leading to inflammation or destruction of the transplanted population. Patients who receive cellular grafts may consequently require immunosuppressive drugs, which can increase susceptibility to infection and produce other long-term complications. Researchers will study how immune cells interact with the implanted cardiac cells and investigate methods that could reduce or eventually eliminate the need for chronic immunosuppression.

The work builds on approximately three decades of research by Murry and his collaborators to move regenerative cardiology from laboratory experiments toward clinical treatment. The program includes gene-edited cellular technology, intended to improve the compatibility or performance of therapeutic cells in the human body. The precise effectiveness of the approach will not be known until it is tested in patients, and the small 2027 cohort will not be large enough to determine whether the therapy improves survival or prevents heart failure. Those questions would require subsequent trials involving substantially more participants and longer observation.

The Marcus Foundation described the project as a potential turning point for regenerative medicine, while USC leaders said the investment could create a platform for developing additional cellular therapies. The immediate scientific challenge is to determine whether transplanted heart muscle cells can be delivered safely and function meaningfully within severely damaged human hearts. If the trial meets its safety and feasibility objectives, it could provide the clinical evidence needed to advance a new generation of treatments for myocardial infarction—therapies designed not only to help patients live with damaged cardiac tissue, but to restore some of the function that conventional medicine cannot yet replace.

Subject of Research: Stem cell-based regenerative therapy for heart muscle damaged by myocardial infarction

Article Title: $28.7 Million Award Supports USC Trial of Stem Cell Therapy to Regenerate Heart Muscle After Heart Attack

Web References: The Marcus Foundation: https://marcusfoundation.org/; USC Stem Cell Center: https://stemcell.keck.usc.edu/; Chuck Murry Lab: https://murrylab.usc.edu/charles-chuck-murry-md-phd/; USC Cardiac and Vascular Institute: https://www.keckmedicine.org/services/cardiovascular/; Keck School of Medicine of USC: https://keck.usc.edu/

Image Credits: Steve Cohn

Keywords: Heart disease, myocardial infarction, heart attack, heart muscle, stem cell research, stem cell therapy, cell therapy, regenerative medicine, cardiovascular disorders, heart failure, clinical trial, cardiac regeneration

Scientists in Arkansas have developed a machine-learning approach that can identify potentially disease-causing strains of a poultry bacterium by analyzing not only which genes are present, but also how those genes are arranged across the genome. The method focuses on the “genetic neighborhoods” surrounding genes, offering a new way to distinguish harmful strains of Enterococcus cecorum from closely related strains that do not cause disease.

E. cecorum is commonly found in poultry environments, and many of its strains are harmless. However, pathogenic strains can cause arthritis, bone infections and lameness, creating serious animal-welfare problems and economic losses for poultry producers. Because pathogenic and nonpathogenic strains can share many of the same genes, identifying the genetic features associated with disease has been challenging.

The research team, led by Aranyak Goswami of the Arkansas Agricultural Experiment Station’s Center for Agricultural Data Analytics, examined the organization of genes within genomic islands. These are segments of DNA that bacteria often acquire from other microorganisms through horizontal gene transfer. Genomic islands can carry genes involved in antibiotic resistance, bacterial survival, movement of DNA and virulence, making them important regions for understanding how pathogens emerge.

Rather than treating the bacterial genome as a simple list of genetic components, the researchers analyzed it as a structured map. They investigated which genes occurred near one another, the order in which they appeared and the way groups of neighboring genes formed recurring units known as genomic-island cassettes. This architecture provided information that could be missed when genes are examined individually.

“The machine-learning model recognizes patterns in gene order much like it recognizes patterns in language,” Goswami said. In the same way that a language model can learn that certain words frequently appear together, the computational system learned that particular combinations and arrangements of bacterial genes were associated with pathogenic strains. The approach therefore captures relationships among genes rather than relying only on the presence or absence of individual DNA sequences.

For the proof-of-concept study, the researchers analyzed the genomes of 145 E. cecorum strains isolated from poultry. The dataset included 95 nonpathogenic strains and 50 pathogenic strains capable of causing illness. The analysis found that disease-associated strains were more likely to contain genomic islands enriched in genes linked to antimicrobial resistance and the transfer of genetic material between bacteria.

Those findings do not mean that every bacterium carrying such genes will necessarily cause disease. Instead, the researchers describe the system as an exploratory classification and research tool. It identifies patterns that may help scientists investigate why some bacterial lineages become harmful, while also providing clues about how resistance and virulence traits move through bacterial populations.

Current monitoring methods for E. cecorum often depend on culturing the bacterium or screening for specific genes. Such techniques remain valuable, but they may overlook broader genomic relationships. The new pipeline could complement these approaches by evaluating the wider context in which genes occur. Additional testing with larger and more geographically diverse datasets will be necessary before the method can be used for routine flock surveillance or diagnostic decision-making.

The researchers believe the strategy could eventually be adapted to other bacterial species affecting animals, humans, wildlife and plants. Goswami’s team is beginning to examine Enterococcus faecalis, a close relative of E. cecorum and a frequent cause of hospital-acquired infections in humans. They also plan to apply the pipeline to Escherichia coli and other bacteria to study how nonpathogenic lineages acquire the genomic configurations associated with disease. The study, published in Frontiers in Microbiology, demonstrates how machine learning and comparative genomics can reveal hidden signals in bacterial DNA and may help researchers track the evolution of emerging pathogens.

Subject of Research: Animals

Article Title: Genomic-island cassette architecture provides interpretable signal for exploratory classification of poultry-associated Enterococcus cecorum lineages

News Publication Date: 7-Jul-2026

Web References: https://doi.org/10.3389/fmicb.2026.1882753

References: Frontiers in Microbiology, DOI: 10.3389/fmicb.2026.1882753

Image Credits: UA University Relations photo by Chieko Hara

Keywords: artificial intelligence, machine learning, deep learning, computational biology, bacterial pathogens, Enterococcus cecorum, genomic islands, genomic architecture, antibiotic resistance, poultry disease, pathogen surveillance, comparative genomics

Around 11,000 years ago, communities in southwest Asia began one of the most consequential experiments in human history: growing their own food. Wild cereals, legumes and fruit-bearing plants were gradually brought under human management, setting in motion the transition from mobile hunter-gatherer lifeways to farming societies. For decades, archaeologists have assumed that one of the earliest signs of this transformation was an increase in cereal grain size. A new study, however, suggests that some of the biggest grains found at the dawn of agriculture may not have been genetically altered at all. They may simply have grown in wetter conditions.

The findings, published in the Proceedings of the National Academy of Sciences, challenge a central assumption about how cereal domestication began. Researchers studying charred barley and emmer wheat grains from the Early Neolithic sites of el-Hemmeh and Sharara in the southern Levant found that the plants still possessed fully wild characteristics during the earliest phases of the Pre-Pottery Neolithic. Their ears had brittle rachises, the structures that naturally break apart when mature, allowing seeds to disperse in the wild. There was no evidence of the tough, non-shattering rachis that later became one of the defining features of domesticated cereals.

The absence of non-shattering forms is significant because a tough rachis makes harvesting far more efficient. In wild cereals, the rachis fractures as the plant ripens, scattering seeds before people can collect them. A mutation that keeps the grains attached would have offered major advantages to human harvesters and could then have spread through repeated cultivation and replanting. Researchers have therefore often treated the appearance of non-shattering cereals as a key marker of domestication. Grain size, by contrast, has frequently been interpreted as an earlier signal that people were already selecting plants through cultivation.

At el-Hemmeh and Sharara, however, grain size told a more complicated story. Barley and emmer wheat remained morphologically wild, yet their grains varied dramatically. Some were as small as typical wild cereals, while others fell within the size range commonly associated with domesticated plants. If large grains were the product of genetic selection caused by systematic tillage, the researchers expected to find other evidence of intensive cultivation. Instead, the ecological evidence pointed toward relatively low-disturbance environments.

The team examined the weed species preserved alongside the cereal remains. Weeds are valuable archaeological indicators because different plants thrive under different environmental conditions. Species associated with heavily tilled or frequently disturbed soils were not dominant in the assemblages from these early sites. This weakened the argument that repeated ploughing or intensive soil preparation had already created strong selection for larger seeds. The researchers began looking for another explanation for the striking variation in grain size.

Their answer came from stable carbon isotope analysis. Carbon exists in several naturally occurring forms, or isotopes, and plants incorporate these isotopes in ways that are influenced by their growing conditions. In cereals, the stable carbon isotopic composition of a grain can reflect the balance between carbon uptake and water loss during photosynthesis. When plants experience water stress, changes in gas exchange through their leaves alter the carbon isotope signal recorded in their tissues. By measuring this signal in ancient grains, scientists can reconstruct aspects of the moisture conditions under which the plants grew.

The results showed a strong relationship between grain size and water availability. Barley grains that reached domestic-sized dimensions had carbon isotope values indicating that they developed under wetter conditions. Smaller, wild-sized grains carried signatures consistent with considerably drier growth environments. The difference suggests that environmental moisture, rather than inherited genetic change, was responsible for much of the early increase in cereal grain size. In other words, the same wild plant population could produce grains of very different sizes depending on the conditions it experienced during development.

This phenomenon is known as developmental plasticity: the ability of a single genotype to produce different physical traits in response to the environment. It is widespread in plants and can affect characteristics such as height, leaf area, flowering time and seed production. In the Early Holocene southern Levant, developmental plasticity may have created the visual impression that cereals were already becoming domesticated. People collecting and replanting the largest grains might eventually have generated genetic selection, but the initial size increase itself appears to have been ecological rather than evolutionary.

“Environmental conditions provided a much better explanation for the observed variation in grain size than genetic selection under tillage,” said Dr Jade Whitlam of the University of Oxford, who led the study. The researchers propose that genetic selection for larger grains may have occurred later than previously thought, perhaps after selection for non-shattering rachises had already begun. This reverses the sequence often assumed in models of domestication and places greater emphasis on the complex interaction between climate, plant biology and human harvesting practices.

The study also reframes the origins of agriculture as a gradual process of niche construction rather than a sudden decision to domesticate plants. Early communities altered their relationship with wild vegetation through harvesting, protection, transport and repeated use of particular landscapes. These practices may have changed plant populations long before crops displayed unmistakable domestication traits. By combining archaeobotany, weed ecology and stable isotope evidence, the researchers argue that the first steps toward agriculture were not necessarily driven by deliberate genetic engineering of crops, but by prolonged human management operating within a variable environment. The result is a more nuanced picture of farming’s origins—one in which climate could temporarily make wild cereals look domesticated before their genomes caught up.

Subject of Research: The environmental and biological processes that influenced cereal grain size and plant domestication in the Early Holocene southern Levant.

Article Title: Developmental plasticity under human management shaped cereal evolution prior to domestication in the Early Holocene southern Levant

News Publication Date: 4-Aug-2026

Web References: https://www.eurekalert.org/multimedia/1146086

References: Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2535274123

Image Credits: Jerome Poulalier, www.jerome-poulalier-photography.com

Keywords: Archaeology, archaeobotany, cereal domestication, agriculture, Neolithic, Early Holocene, southern Levant, barley, emmer wheat, stable carbon isotopes, developmental plasticity, plant evolution, climate, hunter-gatherers

The University of Houston has launched UH Health, a cross-disciplinary academic health enterprise intended to reshape how healthcare professionals are educated, how medical research is conducted and how care reaches communities across Texas. The initiative brings together medicine, nursing, pharmacy, optometry, psychology, social work, population health, biomedical research and health systems science under a single interprofessional framework. University leaders say the model is designed to confront a rapidly intensifying combination of population growth, healthcare worker shortages, chronic disease and unequal access to medical services.

Rather than treating healthcare disciplines as separate academic and clinical systems, UH Health is built around team-based care. In this approach, physicians, nurses, pharmacists, optometrists, behavioral health specialists, social workers and public health experts contribute to a shared understanding of each patient’s needs. The model reflects the growing recognition that health outcomes are shaped not only by disease biology, but also by mental health, housing, income, nutrition, transportation and access to preventive services. By training students to work across professional boundaries, the University hopes to prepare graduates for a healthcare system increasingly focused on coordinated and personalized care.

“Healthcare is no longer delivered in isolation, and complex health challenges require coordinated solutions,” said Jonathan McCullers, vice president for health affairs at UH and dean of the Tilman J. Fertitta Family College of Medicine. The university’s academic health strategy is intended to place students in an environment where clinical reasoning, behavioral science, population health and scientific discovery interact. Such integration can help future clinicians identify the underlying drivers of illness, select more appropriate interventions and communicate more effectively with specialists and community organizations.

The launch comes as Texas faces a widening gap between healthcare demand and workforce capacity. Rapid population growth, an aging population and shortages of trained professionals have placed pressure on hospitals and clinics throughout the state. The Texas Hospital Association has reported that 64% of hospitals were operating with fewer beds or reduced services because of staffing shortages. UH Health aims to expand the pipeline of physicians, nurses, pharmacists, optometrists, social workers and other professionals while emphasizing workforce readiness, collaborative practice and the ability to manage complex cases.

UH’s location near the Texas Medical Center gives the initiative access to one of the world’s largest concentrations of hospitals, research institutes and specialized clinical programs. Partnerships with organizations including HCA Healthcare, Memorial Hermann Hospital, Baylor College of Medicine and MD Anderson Cancer Center are expected to provide students and researchers with expanded opportunities for clinical training, translational research and collaboration. Translational research—the process of moving discoveries from laboratories into diagnostics, treatments and public health programs—depends heavily on this type of interaction between universities, hospitals and industry.

The university is also positioning UH Health as a platform for research that reaches beyond traditional medical departments. Scientists in chemistry, physics and biomedical engineering are working alongside health researchers on problems ranging from cancer and infectious disease to substance-use disorders and neurodegeneration. UH researchers have received more than $11 million from the Cancer Prevention and Research Institute of Texas for work involving cancer prevention, diagnosis and treatment. A fentanyl vaccine developed at the university is advancing toward human trials, although its clinical effectiveness and safety will need to be established through carefully controlled studies.

Brain health is another major focus. Texas ranks fourth nationally in the number of Alzheimer’s cases and second in Alzheimer’s-related deaths, according to the Texas Department of State Health Services. More than 100 faculty members across 12 of UH’s 16 colleges are involved in research related to prevention, early detection, caregiving and treatment, supported by $62.5 million in active grants. Their work may involve diverse technologies and methods, including biological markers, computational analysis, behavioral interventions, biomedical engineering and the study of social factors that influence cognitive decline and caregiver health.

To strengthen this research ecosystem, UH is investing $77 million in a new 55,000-square-foot medical research building. The facility is expected to support interdisciplinary laboratories and shared scientific infrastructure, allowing investigators from different fields to exchange data, equipment and expertise more efficiently. In modern biomedical science, discoveries often emerge at the boundaries between disciplines—for example, when engineering approaches are applied to drug delivery, physical sciences are used to study disease mechanisms or data science improves early diagnosis.

UH Health’s community programs are intended to connect these academic and research activities with practical healthcare needs. The UH Health Family Care Center provides primary care and mental health services to the university and surrounding communities, including Third Ward, East End and South Houston. The Household-Centered Care program gives students experience through home visits with community caregivers, while the 3rd Ward Place of Wellness offers screenings, preventive services and health education. The College of Optometry extends access to vision care through community clinics and outreach programs serving children and families.

The enterprise is also expanding beyond Houston through a partnership with DHR Health in the Rio Grande Valley, a rapidly growing region that continues to face significant medical access challenges. University officials say the collaboration could support healthcare education, workforce development and research tailored to local needs. By linking professional training, scientific discovery and community partnerships, UH Health is presenting an academic health model designed for a state where medical demands are growing faster than conventional systems can respond.

Subject of Research: Academic healthcare education, biomedical research, healthcare workforce development and community health access

Article Title: University of Houston Launches UH Health to Integrate Education, Research and Community Care

Web References: https://mediasvc.eurekalert.org/Api/v1/Multimedia/ed42117d-36c9-45cf-b2e3-443e4e08b680/Rendition/low-res/Content/Public

References: Texas Hospital Association; Texas Department of State Health Services; University of Houston

Image Credits: University of Houston and UH Health

Keywords: UH Health, University of Houston, healthcare workforce shortage, academic health, team-based care, biomedical research, cancer research, fentanyl vaccine, Alzheimer’s disease, community healthcare, nursing, optometry, pharmacy, medical education, Texas Medical Center

A climate shock that unfolded about 304 million years ago is offering scientists a dramatic warning about how quickly Earth’s climate system can amplify an initial temperature rise. During the Late Paleozoic Ice Age, a relatively modest increase in global temperatures appears to have triggered a much larger episode of warming, as methane released from thawing permafrost intensified the greenhouse effect. Researchers say the ancient event provides a powerful geological parallel for the climate changes occurring today.

The study, led by Le Yao of the Nanjing Institute of Geology and Paleontology with contributions from University of Cincinnati geosciences professor Thomas Algeo and international colleagues, examined a short-lived warming episode during Earth’s penultimate ice age. Their findings suggest that global sea surface temperatures eventually increased by more than 7 degrees Celsius, or approximately 12 degrees Fahrenheit. The rise was not necessarily caused by one enormous initial pulse of carbon. Instead, it may have developed through a chain reaction in which warming activated additional natural sources of greenhouse gases.

The Late Paleozoic Ice Age extended across a vast span of geological time, when large ice sheets covered parts of the supercontinent Gondwana. Although the planet was in an “icehouse” state, atmospheric carbon dioxide and methane still had the power to destabilize the climate. The newly analyzed warming event is particularly significant because it occurred under ice-age conditions rather than during an already warm greenhouse world. That makes it a valuable analog for the modern Earth, which still contains continent-scale ice masses in Greenland and Antarctica.

According to the researchers, the initial climate disturbance may have been linked to volcanic activity. Volcanic emissions can inject carbon dioxide and other gases into the atmosphere, where they absorb outgoing infrared radiation and prevent some heat from escaping into space. The warming may also have been assisted by changes in Earth’s orbit. Over long cycles, variations in orbital shape alter the distribution of sunlight across the planet, influencing the growth and retreat of ice sheets and changing the seasonal balance of solar energy.

At first, the additional carbon may have produced only a limited temperature increase. But once warming crossed a critical threshold, frozen ground in high-latitude regions began to thaw. Permafrost contains large quantities of organic carbon preserved in frozen soils. When that material becomes available to microbes, decomposition can release carbon dioxide and methane. Methane is especially powerful over shorter atmospheric timescales because, molecule for molecule, it absorbs considerably more infrared radiation than carbon dioxide, even though it remains in the atmosphere for less time.

This process can create a positive climate feedback. An initial warming causes permafrost to thaw, thawing releases greenhouse gases, and the additional gases produce further warming. That extra heat can accelerate more thawing, allowing the cycle to reinforce itself. The researchers describe the ancient event as a transient global warming episode in which a comparatively modest carbon release appears to have crossed a climatic tipping point and initiated much more extensive carbon emissions from natural reservoirs.

The geological timescale of the ancient warming is also central to the study’s relevance. Algeo said the temperature change unfolded over tens of thousands of years, with warming occurring at an average rate of roughly one-tenth of a degree Celsius per 1,000 years. Modern warming is progressing far more rapidly. Researchers have observed an increase of nearly 1 degree Celsius in global sea surface temperature over approximately the past century, a rate many times faster than the average change reconstructed for the ancient event.

The comparison does not mean that Earth is destined to repeat the precise conditions of 304 million years ago. Continents, ecosystems, atmospheric composition and ice-sheet geometry were all different in the Late Paleozoic. However, the physics of greenhouse warming and the existence of carbon feedbacks remain relevant. Ancient climate records reveal how a system that appears stable can respond nonlinearly when warming activates processes capable of releasing additional greenhouse gases.

The findings also place human history in a broader planetary context. All recorded human civilization has developed during the most recent ice age, despite the fact that the term can sound incompatible with today’s climate. In scientific usage, Earth remains in an ice age because substantial permanent ice sheets still exist in Greenland and Antarctica. The ancient warming episode matters precisely because it demonstrates that an ice age does not guarantee a permanently cold or stable climate. Even within an icehouse world, a small disturbance can trigger rapid and substantial changes when reinforcing feedbacks are engaged.

The research, published in the Proceedings of the National Academy of Sciences, underscores why scientists closely monitor permafrost, polar ice and other climate-sensitive carbon stores. The ancient record cannot provide an exact forecast of future warming, but it shows that Earth’s climate has repeatedly responded to carbon disturbances with amplifying feedbacks. As modern emissions continue to raise temperatures at an exceptional rate, the geological evidence offers a clear message: the danger lies not only in the carbon released directly by human activity, but also in the natural carbon sources that warming can unlock.

Subject of Research: Ancient global warming, permafrost methane feedbacks, and climate tipping points during the Late Paleozoic Ice Age

Article Title: A transient global warming event during Earth’s penultimate icehouse

Web References: https://doi.org/10.1073/pnas.2601643123

References: Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2601643123

Image Credits: Andrew Higley, University of Cincinnati

Keywords: climate change, global warming, ice age, Late Paleozoic Ice Age, permafrost, methane, climate feedback, tipping point, paleoclimate, geology, volcanic activity, Earth’s climate history

A multidisciplinary team from Baylor College of Medicine, Rice University and the University of Texas School of Public Health has received the inaugural Climate + Health Excellence award from the Burroughs Wellcome Fund. The five-year, $10 million award will support FORECAST, an ambitious program designed to investigate how climate change, extreme weather and environmental disruption influence the emergence and spread of infectious diseases. Led by infectious disease researcher Dr. Anthony Maresso, the project will combine wastewater surveillance, metagenomic sequencing, climate science, epidemiology and public education to create an early-warning system for pathogen activity across Texas.

FORECAST will build on Texas’s expanding wastewater-monitoring infrastructure, including the Texas Wastewater Environmental Biomonitoring Network, or TexWEB. The statewide network samples sewage from 15 Texas cities and analyzes biological material shed by communities. Unlike clinical surveillance, which depends on people seeking medical care and receiving diagnostic tests, wastewater epidemiology captures population-level signals from entire communities. Viral genetic fragments, bacterial DNA and other biological markers can appear in sewage before hospitals and clinics register a rise in patients, providing public health officials with valuable time to investigate and respond.

The program will use metagenomic surveillance, a technique that examines genetic material recovered from environmental samples rather than searching only for a predetermined pathogen. In conventional testing, laboratories typically use targeted molecular assays designed to detect a known virus or bacterium. Metagenomic sequencing can survey a much broader range of organisms, including variants and potentially unfamiliar pathogens. Researchers can then compare these signals with clinical data, geographic information and environmental measurements to determine whether changes in temperature, rainfall, flooding, drought or other conditions are associated with shifts in infectious disease activity.

The scientific goal is not simply to identify pathogens after they appear, but to uncover relationships between microbes and the environmental conditions that help them enter or circulate within human populations. Climate change can influence disease transmission through several pathways. Warmer temperatures may expand the geographic range or seasonal activity of mosquitoes and other vectors. Heavy rainfall and flooding can overwhelm sanitation systems and redistribute contaminants, while drought can alter water use and concentrate biological material. Changes in human behavior, animal movement and ecosystems may create additional opportunities for pathogens to cross species boundaries.

Maresso and his colleagues demonstrated during the COVID-19 pandemic that SARS-CoV-2 signals in wastewater could precede increases in clinically diagnosed infections. Viral RNA released in feces entered sewage systems, where researchers tracked changes in concentration over time. Although wastewater measurements cannot determine which individuals are infected, they can reveal whether pathogen activity is increasing or declining within a catchment area. The approach has since been extended to a wider range of targets, including avian influenza, measles, HIV and viruses associated with cancer. FORECAST aims to develop this surveillance capacity into a predictive climate-health platform.

Rice University climate scientist Dr. Sylvia Dee will help connect pathogen observations with weather and climate data. The team plans to examine whether specific environmental patterns consistently occur before increases in particular infectious agents. Such models could eventually function like a public health weather report, indicating when conditions are becoming more favorable for disease transmission. The researchers emphasize that predictive systems will require careful validation because the presence of pathogen genetic material does not necessarily indicate infectious virus, active transmission or a future outbreak. Interpreting the signal will require laboratory studies, clinical evidence and knowledge of local infrastructure.

The project will also investigate diseases transmitted by vectors such as mosquitoes. Dr. Peter Hotez and colleagues at the National School of Tropical Medicine will focus on vector-borne pathogen transmission and the growing health risks associated with changing climate conditions. Texas is particularly relevant for this work because it contains diverse ecological regions and experiences heat waves, hurricanes, flooding and rapid urban growth. These factors can affect mosquito habitats, human exposure and the movement of pathogens across communities. Integrating wastewater findings with vector surveillance could help reveal connections that would remain hidden if either system were used alone.

Education and public communication will form a central part of FORECAST. The team plans to create a “Middle to Medical” climate-health education track extending from middle school through medical training and professional development. Dr. Nancy Moreno, who developed Baylor’s BioEd online program, will lead educational efforts with Rice University’s Dr. Joseph Campana, director of the university’s Center for Environmental Studies and EcoStudio. The curriculum will explain how pathogens spread, how climate conditions alter disease risk and how scientific evidence can guide decisions by students, clinicians, policymakers and communities.

The award is particularly significant because FORECAST was selected as the sole funded project from 161 applications submitted to the new CHEX program. The initiative is intended to strengthen research, education and public engagement at the intersection of climate change and human health. Dr. Eric Boerwinkle, dean of the UT School of Public Health and a project co-investigator, will contribute access to large clinical datasets that can be used to evaluate wastewater and environmental signals against reported illnesses. The investigators say the work could improve outbreak preparedness while advancing the still-developing field of wastewater science.

By combining environmental sequencing with climate modeling and health education, FORECAST seeks to shift infectious disease surveillance from a largely reactive system toward earlier detection and prevention. Its findings may help officials recognize emerging threats before they produce widespread clinical disease, while also clarifying how environmental changes shape viral evolution, transmission and geographic spread. The researchers describe wastewater as an unusually candid public health resource: it aggregates biological information from a population without requiring individual testing. As climate pressures intensify, they argue, that collective signal could become an essential tool for protecting communities and preparing the next generation of scientists.

Subject of Research:
Climate-driven infectious disease emergence, wastewater epidemiology, metagenomic pathogen surveillance and climate-health education.

Article Title:
Texas Researchers Launch FORECAST Program to Link Climate Change With Emerging Viral Threats

Web References:
Baylor College of Medicine; Rice University; University of Texas School of Public Health; Burroughs Wellcome Fund; Texas Wastewater Environmental Biomonitoring Network (TexWEB).

References:
Information provided in the source news release concerning the FORECAST project, the Climate + Health Excellence award and TexWEB wastewater surveillance activities.

Keywords:
Wastewater epidemiology, viral surveillance, metagenomic sequencing, climate change, infectious diseases, pathogen emergence, public health, Texas, SARS-CoV-2, avian influenza, measles, vector-borne disease, climate health, epidemiology, wastewater science