Human T cell leukemia virus type 1, the first human retrovirus ever identified, infects an estimated 5 to 10 million people worldwide, a figure extrapolated from surveys covering only about one-seventh of the global population. The virus inserts a DNA copy of its RNA genome into the chromosomes of host T cells, establishing a lifelong infection that can drive a spectrum of diseases, including adult T cell leukemia/lymphoma, HTLV-1-associated myelopathy, and HTLV-1-associated uveitis, a chronic inflammation inside the eye that is particularly common in endemic regions such as Japan. Despite the scale of the problem, coordinated global action has been slow; the World Health Organization only published technical reports aimed at reducing HTLV-1-related morbidity and mortality in 2021, and no broadly effective intervention has emerged in decades of research.

Current treatment for the ocular disease relies heavily on corticosteroids, delivered as topical drops, injections into the eye, or oral prednisone. This approach has serious shortcomings. Studies indicate that 30 to 60 percent of patients with HTLV-1-associated uveitis experience recurrent episodes, and long-term steroid exposure can trigger cataracts, glaucoma, macular edema, epiretinal membranes, retinal degeneration, optic nerve atrophy, and bullous keratopathy. Most importantly, corticosteroids suppress inflammation without reducing the number of virus-infected cells or halting disease progression. Newer molecular targeted agents, including the anti-CCR4 antibody mogamulizumab and the immunomodulatory drug lenalidomide, have been explored, but treatment-related inflammation and adverse effects remain significant obstacles. This therapeutic gap motivated a team led by Yaru Zou and Koju Kamoi of Tokyo Medical and Dental University to investigate whether an epigenetic drug already approved for blood cancer could address the root of the ocular inflammation.

The drug in question is valemetostat, marketed as EZHARMIA, a dual inhibitor of the enzymes EZH1 and EZH2 that was approved in 2022 for adult T cell leukemia/lymphoma. Both enzymes are the catalytic components of polycomb repressive complex 2, a molecular machine that silences genes by adding three methyl groups to lysine 27 on histone H3, producing the repressive chromatin mark known as H3K27me3. Previous work had shown that EZH1 and EZH2 are overexpressed, and H3K27me3 deposition increased, in ATL cells and in cells expressing the viral oncoprotein Tax. Dual EZH1/2 inhibitors have also been reported to curb the spontaneous proliferation and exaggerated immune responses of HTLV-1-infected cells in patients with HTLV-1-associated myelopathy. What remained unknown was whether such inhibition could benefit the inflamed eye.

To find out, the researchers built a series of laboratory models centered on ARPE-19 cells, a human retinal pigment epithelial cell line chosen because the retinal pigment epithelium forms the outer blood-retinal barrier and governs retinal immune homeostasis. They exposed these ocular cells to three kinds of T cells: MT-2 cells, which carry transcriptionally active HTLV-1 and express the viral protein Tax; TL-Om1 cells, an ATL-derived line in which the virus is largely silent and Tax is low or absent; and Jurkat cells, which are uninfected and served as controls. In parallel, they treated the T cell lines themselves. Western blotting and quantitative PCR revealed that HTLV-1 infection drove significant upregulation of both EZH1 and EZH2, along with elevated H3K27me3, in infected retinal pigment epithelial cells and in MT-2 cells, with EZH1 protein rising roughly 2.5-fold in infected ocular cells and six-fold in MT-2 cells. This was the first demonstration that HTLV-1 infection induces polycomb-mediated epigenetic changes in retinal cells.

The team then treated the models with valemetostat at 0.01, 0.1, and 1 micromolar for 96 hours, concentrations selected in preliminary experiments because they harmed infected T cells while leaving normal ocular cells, including primary human trabecular meshwork cells, unaffected below 10 micromolar. The results were strikingly dependent on the model. In co-cultures of retinal cells with Tax-positive MT-2 cells, and in retinal cells directly infected with HTLV-1, the drug produced modest but statistically significant reductions in cell viability at the highest dose, accompanied by small increases in late-stage apoptosis. No such effects appeared in co-cultures with TL-Om1 or Jurkat cells. Crucially, the absolute magnitude of apoptosis remained limited, typically only 1 to 3 percent above baseline, indicating that valemetostat is not broadly toxic to retinal cells, an important safety consideration given their role in maintaining vision.

The virological findings were equally nuanced. Valemetostat did not significantly reduce the HTLV-1 proviral load in co-culture systems or in directly infected retinal pigment epithelial cells. However, in MT-2 cells themselves, 1 micromolar valemetostat significantly lowered proviral load and suppressed the messenger RNA levels of both Tax and HBZ, the two hallmark transcripts of the virus. Because MT-2 cells represent the transcriptionally active, highly infectious state of HTLV-1, while TL-Om1 represents latency, the results suggest that the drug’s antiviral effect is gated by the transcriptional status of the infected cell. Within the four-day treatment window, the authors conclude, valemetostat’s benefit to the ocular environment is likely indirect, acting primarily by quieting the most virally active cells rather than by eradicating provirus everywhere.

The most clinically relevant effects emerged in the inflammatory readouts. HTLV-1-infected T cells recovered from the eyes of patients with uveitis are known to secrete copious interleukin-6, interleukin-8, and interleukin-10, all implicated in disease. In the new study, valemetostat produced a dose-dependent increase in the anti-inflammatory cytokine IL-10 in MT-2-containing conditions, significant at 1 micromolar in the co-culture system and across all doses in MT-2 cells. This fits a mechanistic expectation: blocking EZH1/2 removes H3K27me3 marks that repress the IL-10 gene, and prior genetic studies showed that loss of EZH2 directly activates IL-10 transcription in several CD4-positive T cell subsets. Meanwhile, pro-inflammatory signals fell in a context-dependent pattern. IL-12p70 and the chemokine CXCL-10 dropped in co-cultures, IL-6 and IL-8 declined mainly in MT-2 cells, and CCL-2 fell at low doses in the MT-2 co-culture, whereas the chemokine CCL-5 was unchanged in every model.

The drug’s influence on NF-kappaB, the central inflammatory pathway that Tax hijacks to drive both viral replication and cytokine production, was more subdued. Valemetostat modestly reduced NF-kappaB p65 phosphorylation in the MT-2 co-culture at the lowest dose and slightly decreased the nuclear-to-cytoplasmic ratio of p65 in retinal cells stimulated with TNF-alpha, but it did not abolish nuclear translocation. The authors attribute this to the overwhelming strength of Tax-driven NF-kappaB activation, which a 96-hour course of EZH1/2 inhibition may be insufficient to counteract, and to the fact that NF-kappaB and cytokine networks are regulated by many factors beyond polycomb complexes. Immunofluorescence confirmed that valemetostat significantly lowered H3K27me3 in retinal cells exposed to MT-2 cells or directly infected, but not in cells co-cultured with TL-Om1 or Jurkat cells, again underscoring the model dependence of the epigenetic response.

The study also carries a cautionary note about IL-10. Although the cytokine is anti-inflammatory, it can promote the survival of HTLV-1-infected T cells and facilitate immune evasion by impairing dendritic cell antigen presentation and downregulating MHC class I molecules, and patients with aggressive ATL often show abnormally elevated IL-10. The authors therefore suggest that combining EZH1/2 inhibition with IL-10 pathway blockade could prove more effective, noting evidence that IL-10 neutralization sensitizes ATL cells to standard therapies. In the TL-Om1 model of ATL-related ocular infiltration, valemetostat failed to reduce H3K27me3, proviral load, or NF-kappaB activation within the malignant cells, yet it suppressed TL-Om1-induced CXCL10 secretion and induced dose-dependent apoptosis, pointing to a benefit mediated through the inflammatory microenvironment rather than direct tumor killing.

The authors are candid about the limitations. The work relied entirely on in vitro co-cultures and established cell lines, which cannot reproduce the blood-retinal barrier, retinal architecture, or local immune regulation of a living eye, and the intraocular pharmacokinetics of valemetostat after systemic administration remain undefined. Sex as a biological variable could not be assessed because donor-defined primary cells were not used. Even so, the findings establish a proof of concept that an approved epigenetic therapy can counteract HTLV-1-induced chromatin changes in retinal cells, dampen viral transcription in the most active infected cells, and reshape the cytokine milieu that sustains uveitis. Longer treatment studies, in vivo models, and patient-derived systems will be needed to determine whether these modest, context-dependent effects can be translated into a genuine steroid-sparing therapy for one of the most burdensome manifestations of a virus that medicine has long neglected.

Subject of Research: EZH1/2-targeted epigenetic therapy with valemetostat for HTLV-1-associated uveitis

Article Title: Dual impact of EZH1/2-targeted therapy in HTLV-1-associated inflammation: Direct and indirect mechanisms

Article References: Zou, Y., Kamoi, K., Yang, M., Zong, Y., Zhang, J., & Ohno-Matsui, K. (2026). Dual impact of EZH1/2-targeted therapy in HTLV-1-associated inflammation: Direct and indirect mechanisms. iScience, 29(11), Article 117775. https://doi.org/10.1016/j.isci.2026.117775

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117775

Keywords: HTLV-1, valemetostat, EZH1, EZH2, H3K27me3, uveitis, epigenetics, PRC2, Tax, IL-10, NF-kappaB, retinal pigment epithelium

Palliative care has long been framed as a specialist discipline reserved for hospices and oncology wards, but a growing body of evidence insists it belongs everywhere people approach the end of life, including nursing homes, where a large share of deaths in ageing societies now occur. A new qualitative study from Sweden, published in BMC Nursing, offers an unusually candid look at what happens inside care teams after they receive structured palliative care education, and the central finding is striking: training changes how staff think, but thinking alone does not change care. The researchers, Camilla Ekeblom, Carina Lundh Hagelin and Jane Österlind of Marie Cederschiöld University in Stockholm, found that the value of an educational intervention depends on an intricate alignment between individual emotions, relational dynamics and organisational realities, and that when any one of these dimensions falls out of step, the benefits of education can quietly dissolve.

The study set out to explore how nursing home staff reason about providing palliative care after participating in an interprofessional educational intervention, abbreviated in the literature as IPEI. Rather than measuring outcomes with questionnaires, the team chose a descriptive qualitative design, interviewing eighteen staff members drawn from five Swedish nursing homes. The participants represented the full spectrum of people who actually deliver and manage care: registered nurses, assistant nurses, a physician, first-line managers and designated palliative care champions. This breadth matters, because palliative care in nursing homes is not delivered by any single profession; it emerges, or fails to emerge, from the daily interplay of people with different training levels, responsibilities and emotional exposures to dying residents.

Data were collected through semi-structured individual interviews conducted between March and April 2024, and analysed using inductive qualitative content analysis, a method that allows themes to rise from the material itself rather than being forced into pre-existing theoretical boxes. From this analysis, three main categories emerged, and together they map the terrain on which palliative care is actually practised. The first concerns managing one’s own emotions, thoughts and attitudes. The second addresses the significance of relationships, communication and interactions in care. The third captures how staff navigate norms, assumptions and organisational structures in everyday palliative care. The categories correspond, respectively, to the individual, relational and organisational dimensions of the work, and the authors argue that it is the interplay among all three, not any single factor, that determines whether good palliative care becomes possible.

The individual dimension is perhaps the most human part of the findings. Staff described the ongoing internal work of confronting their own feelings about death, ageing and suffering, and of examining assumptions they carried about what palliative care even means. A recurring tension in the literature, echoed here, is the tendency to equate palliative care narrowly with end-of-life care, the final days and hours, rather than understanding it as an approach that can and should be integrated into everyday care over a longer trajectory. The interviews suggested that the educational intervention gave participants opportunities for personal as well as professional development, creating space to reflect on attitudes that had previously gone unexamined. In this sense, education functioned less like a knowledge transfer and more like a mirror, prompting staff to notice how their own emotional responses shaped their clinical decisions.

The relational dimension proved equally consequential. Palliative care in a nursing home is saturated with communication: conversations with residents about what matters to them, exchanges with distressed relatives, coordination between assistant nurses who know the residents intimately and nurses and physicians who carry clinical responsibility. The participants’ reasoning placed relationships and interactions at the heart of care provision, indicating that no amount of theoretical knowledge substitutes for the ability to build trust, listen and communicate across professional boundaries. This is where the interprofessional design of the intervention appears to have paid off. By bringing different professions into a shared educational space, the programme created a common language and, implicitly, a shared understanding of what the team was trying to achieve together, something that fragmented, profession-specific training often fails to produce.

The third dimension, the organisational, is where the study’s message becomes most uncomfortable for policymakers and care home leadership. Staff described navigating norms and assumptions embedded in their workplaces, along with structural conditions that could either support or obstruct a palliative approach. Staffing levels, routines, leadership priorities and workplace culture all shape what is practically possible on a shift where residents are dying, families are anxious and time is scarce. The authors’ conclusion is explicit on this point: providing palliative care was not solely a matter of enhancing staff knowledge. Knowledge and the conditions for care are shaped through the interplay between staff attitudes, their reasoning about care, relational dynamics and organisational conditions. When these dimensions align and mutually reinforce one another, favourable conditions for palliative care may be supported; when they do not, even well-trained staff may find their intentions frustrated.

Why does this matter on a societal scale? Nursing homes accommodate older people with complex needs and account for a large proportion of deaths, which makes palliative care a central component of care in these settings rather than an occasional emergency. Yet access to palliative care remains unequal, particularly for older people, and staff in long-term care often lack sufficient training. The demographic arithmetic is unforgiving: as populations age, the number of people who will die in nursing homes rises, and the quality of their final months depends heavily on the competence and confidence of the workforce that cares for them. Studies like this one move the conversation beyond the simplistic assumption that a training course fixes the problem, and toward a systems view in which education is necessary but insufficient on its own.

The study also carries a methodological lesson for how we evaluate educational interventions in care settings. Much of the existing research measures knowledge gains or self-reported confidence before and after training. Those metrics are useful, but they cannot reveal how staff actually reason about applying what they have learned in the messy reality of daily care. By using inductive content analysis of interviews, the Swedish team surfaced the texture of that reasoning: the hesitations, the emotional labour, the negotiations with colleagues and managers, the quiet adjustments between ideal practice and what the shift allows. The findings suggest that interprofessional educational initiatives work best when they combine theoretical knowledge with opportunities for reflection and practical application, allowing staff to translate concepts into their own working context rather than leaving translation to chance.

There are limits worth noting. The study involved eighteen participants in five Swedish nursing homes, interviewed at a single point in time after the intervention, and qualitative findings are not designed to be statistically generalised. The researchers themselves submitted the work for ethical review, receiving approval from the Swedish Ethical Review Authority, and obtained written informed consent from all participants, but the transferability of the results to other health systems will depend on how similar those systems’ nursing homes and educational programmes are to the Swedish context. Still, the three-dimensional framework that emerged, individual, relational and organisational, offers a portable lens that other teams can use to examine their own settings, and it aligns with a broader international consensus that palliative care competence must be built at every level of an organisation simultaneously.

The most durable insight from this research may be its refusal to locate the solution in any single place. Staff who completed the intervention perceived it as an opportunity for growth, and their reasoning showed genuine movement in how they understood palliative care as an ongoing approach rather than a final act. But their accounts also made clear that attitudes, relationships and structures must pull in the same direction for that understanding to reach residents’ bedsides. For nursing homes everywhere, the implication is a practical checklist of a different kind: train your staff together, give them room to reflect, examine the norms your workplace quietly enforces, and make sure the organisation’s daily conditions match what the education teaches. Where those elements align, palliative care stops being an aspiration and becomes, simply, the way care is done.

Subject of Research: Nursing home staff's reasoning about providing palliative care after an interprofessional educational intervention

Article Title: “Nursing home staff’s reasoning about providing palliative care following an educational intervention – a qualitative interview study”

Article References: Ekeblom, C., Lundh Hagelin, C., & Österlind, J. (2026). “Nursing home staff’s reasoning about providing palliative care following an educational intervention – a qualitative interview study”. BMC Nursing, 25(1), Article 956. https://doi.org/10.1186/s12912-026-05480-4

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05480-4

Keywords: palliative care, nursing homes, older people, interprofessional education, qualitative research, nursing education, end-of-life care, care staff attitudes, organisational conditions, Sweden, BMC Nursing, healthcare workforce

Inside every stressed cell, a quiet emergency response unfolds. When oxygen runs low, toxins accumulate, or nutrients grow scarce, mammalian cells assemble temporary structures known as stress granules, non-membranous aggregates of proteins and RNA that condense in the cytoplasm. In healthy tissue, these assemblies act as a protective pause button, halting protein production so the cell can weather the storm and resume normal function once conditions improve. But a new study suggests that in oral squamous cell carcinoma, one of the most common and disfiguring malignancies of the head and neck, this ancient survival machinery may be hijacked to serve a darker purpose: helping tumors grow and spread.

The research, conducted by a team at Kangwon National University in South Korea and published in Cell Death Discovery, examined how stress granule formation influences tumor progression and metastasis in oral squamous cell carcinoma, or OSCC. The findings point to a specific molecular switch, the phosphorylation of a protein called eIF2α, as a central driver of the process. When that switch is suppressed, the study reports, stress granule formation drops, and with it the growth and metastatic burden of tumors in mouse models. The work adds oral cancer to a growing list of malignancies in which the integrated stress response appears to fuel, rather than restrain, disease.

To understand why this matters, it helps to consider what stress granules actually do. Under stress, cells activate kinases that phosphorylate eIF2α, the alpha subunit of a translation initiation factor. Phosphorylated eIF2α dampens global protein synthesis, and the messenger RNAs that would otherwise be translated are instead sequestered, along with RNA-binding proteins, into dense cytoplasmic droplets. Key markers of these structures include G3BP1, TIAR, and PABP, proteins that researchers use to identify stress granules under the microscope. In normal cells, this translational arrest conserves energy and prevents the accumulation of damaged or misfolded proteins. The system is meant to be reversible: when the stress passes, the granules dissolve and translation restarts.

Cancer cells, however, live in a state of chronic stress. Tumors outgrow their blood supply, creating regions of hypoxia, low oxygen tension that would kill most normal cells. They also face nutrient deprivation, acidic microenvironments, and assault from chemotherapy and radiation. In this context, the stress response can become a liability for the patient rather than a safeguard for the cell. Previous work in other cancers has implicated stress granules in tumor progression and metastasis, partly through their interactions with key signaling pathways, but their role in oral squamous cell carcinoma had remained poorly understood, despite growing scientific interest in the topic.

The Korean team began by comparing human tumor tissue with adjacent normal tissue. They found that markers of stress granules, including G3BP1, eIF2α, TIAR, and PABP, were significantly overexpressed in OSCC tissues compared with their normal counterparts. This observation established that the stress granule machinery is not merely present in oral cancer but is amplified, suggesting that tumor cells may rely on it more heavily than the healthy cells around them. Overexpression of these markers alone does not prove causation, but it set the stage for the functional experiments that followed.

Those experiments turned to mouse models built from OSCC cells that had been preconditioned with hypoxia, deliberately exposing the cells to low oxygen before implantation. The logic was to mimic the stressful conditions tumor cells encounter in a real growing tumor and to ask whether cells that had already mounted a stress response behaved differently afterward. In parallel, the researchers used a compound called ISRIB, an inhibitor of the integrated stress response that acts downstream of eIF2α phosphorylation, in mice subjected to sodium arsenite-induced stress. Sodium arsenite is a classic laboratory trigger of oxidative stress and a reliable inducer of stress granule formation, making it a useful tool for probing the pathway.

The results were striking. In both experimental settings, suppression of eIF2α phosphorylation was associated with reduced stress granule formation, reduced tumor growth, and a reduced metastatic burden. In other words, when the researchers blocked the molecular event that nucleates stress granules, the tumors not only grew more slowly but also spread less aggressively. Conversely, the study found that stress granule formation was elevated during hypoxia-induced stress, underscoring a stress-responsive regulatory mechanism in which the harsh conditions of the tumor microenvironment actively promote the assembly of these granules. Together, the data sketch a coherent chain of causation: hypoxia and other stresses trigger eIF2α phosphorylation, phosphorylation drives stress granule assembly, and the granules support the cellular programs that allow OSCC cells to proliferate and colonize distant sites.

Why would a structure designed to protect cells become an engine of metastasis? The authors and other researchers in the field point to the selective nature of stress granules. While global translation is arrested, certain messenger RNAs are preferentially stored within the granules and protected from degradation. Among the transcripts sheltered this way are often those encoding proteins that promote survival, invasion, and epithelial-to-mesenchymal transition, the process by which stationary epithelial cancer cells acquire the motility needed to invade surrounding tissue and enter the bloodstream. By acting as temporary warehouses for pro-metastatic messages, stress granules may allow tumor cells to stockpile the tools of aggression, releasing them for translation once the acute stress subsides. In this view, the granule is not merely a damage-control structure but a staging ground for the next phase of tumor progression.

The therapeutic implications are considerable. Oral squamous cell carcinoma is frequently diagnosed at advanced stages, and metastasis to cervical lymph nodes is one of the strongest predictors of poor outcome. Existing treatments, surgery, radiation, and chemotherapy, remain limited in their ability to control disseminated disease. If stress granule formation genuinely drives metastatic behavior, then the pathway offers a new target. ISRIB, the compound used in the mouse experiments, has attracted attention in recent years for its ability to blunt the integrated stress response, and the new findings suggest that agents with a similar mechanism could be explored as anti-metastatic therapies in oral cancer. The authors conclude that stress granules promote OSCC progression via eIF2α phosphorylation and might serve as a novel therapeutic target, a formulation that is deliberately cautious but clinically suggestive.

As with any preclinical study, important caveats apply. The evidence comes from human tissue comparisons and mouse models, and translating these findings into patients will require further work to confirm that the same mechanism operates in human tumors in real time, to identify which patient subsets have the highest stress granule activity, and to develop inhibitors with acceptable safety profiles. Suppressing a fundamental cellular stress pathway carries risks, since normal tissues also rely on the integrated stress response during injury and inflammation. Still, the study fills a genuine gap in the literature by connecting a well-characterized cellular stress mechanism to one of the most clinically feared behaviors of oral cancer. It also adds to a broader shift in cancer biology: the recognition that tumors are not just collections of mutated genes but ecosystems shaped by physical and chemical stress, and that the survival programs cells use to endure hardship can be turned against the organism that hosts them. For patients with oral squamous cell carcinoma, whose disease too often spreads before it can be stopped, the humble stress granule may prove to be an unexpected and consequential adversary.

Subject of Research: The role of stress-induced eIF2α phosphorylation and stress granule formation in promoting metastasis of oral squamous cell carcinoma

Article Title: Stress-induced eIF2α phosphorylation promotes tumor metastasis in oral squamous cell carcinoma

Article References: Kim, S., Park, S., Lee, Y., Park, G., & Choi, J. (2026). Stress-induced eIF2α phosphorylation promotes tumor metastasis in oral squamous cell carcinoma. Cell Death Discovery. https://doi.org/10.1038/s41420-026-03412-4

Image Credits: AI Generated

DOI: 10.1038/s41420-026-03412-4

Keywords: stress granules, eIF2α phosphorylation, oral squamous cell carcinoma, metastasis, integrated stress response, ISRIB, hypoxia, G3BP1, tumor progression, translational arrest, cancer biology, therapeutic target

Neutral mesons, the fleeting particles that endlessly oscillate between matter and antimatter forms, have just acquired a strikingly new mathematical lens. In a theoretical study published in The European Physical Journal C, physicist Swarup Sangiri of the Indian Institute of Technology Kharagpur has constructed a fresh class of geometric objects, called connected sequential Bargmann invariants, that encode how two different decay channels of an entangled meson pair relate to one another inside the projective geometry of quantum states. The work extends an earlier framework and shows that when decay-projected states are wired directly into a cyclic chain of quantum overlaps, the resulting quantities behave in a fundamentally different way near the boundary where CP violation switches off.

To appreciate why this matters, it helps to recall what makes neutral meson systems so special. Particles such as the neutral kaon, the D meson, and the two neutral B mesons are produced as flavor eigenstates but propagate as mixtures of heavy and light eigenstates, conventionally written as combinations of the meson and its antiparticle with coefficients p and q. When the magnitudes of p and q differ, the two propagation eigenstates are no longer orthogonal, and this nonorthogonality is the hallmark of indirect CP violation, the subtle asymmetry between matter and antimatter that was famously discovered in kaons in 1964. Because any physical observable must be independent of arbitrary phase conventions attached to quantum states, the natural language for describing interference in these systems is one of rephasing-invariant quantities.

Bargmann invariants provide exactly that language. For a closed sequence of quantum states, the nth-order Bargmann invariant is the product of overlaps taken around the cycle, and it remains unchanged under independent phase redefinitions of each state. Its phase is a geometric phase, the kind of holonomy familiar from the Berry phase, accumulated along a closed loop in the space of rays. Earlier work had applied this machinery to correlated neutral meson decays, constructing third-order invariants that probe single-channel interference and fourth-order invariants that correlate two decay channels through the heavy and light propagation eigenstates. Those constructions linked the geometric picture to conventional flavor observables, including Jarlskog-type structures, without introducing any new phenomenological parameters.

The new study asks a deceptively simple question: what happens if the two decay-projected states themselves are connected directly inside the cyclic overlap chain? When one meson of an entangled pair, produced for example in the decay of a phi or Upsilon resonance, decays into a channel f, its partner is projected into a conditional state determined by the decay amplitudes. A second channel g prepares a different conditional state. The connected sequential fourth-order Bargmann invariant follows the loop from the heavy eigenstate to the f-conditioned state, then directly to the g-conditioned state through their mutual overlap, then to the light eigenstate, and finally back to the heavy one. The crucial new ingredient is the overlap between the two conditional states, a quantity built purely from the decay amplitudes that measures how similar the two post-selected meson states are in projective Hilbert space.

This change in connectivity has dramatic consequences for how the geometric quantities scale as CP violation becomes small. The previously studied disconnected fourth-order invariant, when normalized by the product of two third-order invariants, produced a ratio that blows up quadratically, scaling as the inverse square of the mixing asymmetry, the difference between the squared magnitudes of p and q. The connected sequential ratio, by contrast, carries only a single factor of that asymmetry in its denominator, so its enhancement near the CP-conserving limit is merely linear. Physically, replacing one propagation-mediated link with a direct overlap between the projected states removes one power of the heavy-light suppression, placing the two classes of geometric correlation in distinct scaling classes.

A second new ratio compares the sequential invariant directly with the disconnected one. This comparison ratio behaves in the opposite manner: it is proportional to the mixing asymmetry itself, vanishing in the exact CP-symmetric limit. Its behavior is governed by a competition between two effects, the shrinking of the heavy-light overlap on one side and the possible alignment of interference phases between the two decay channels on the other. When the conditional states prepared by the two channels become geometrically aligned, the suppression can be weakened, revealing that the connected structure is sensitive not only to mixing asymmetry but also to the relative coherence of the decay projections.

To make these statements precise, the analysis is carried out in terms of the standard rephasing-invariant interference parameters lambda, the products of the mixing ratio q over p and the ratios of decay amplitudes to their CP-conjugates. A systematic small-asymmetry expansion, with the squared magnitudes of p and q written as one half plus or minus a small parameter, shows that the sequential ratio depends explicitly on the phase difference between the two channels through the numerator structure. This dependence has no analogue in the disconnected construction, where the channels communicate only through the propagation eigenstates. It means the connected sequential geometry probes the relative orientation of the decay-conditioned rays themselves, a piece of interference information that conventional observables do not isolate on their own.

The geometric interpretation is perhaps the most elegant part of the story. Both the disconnected and the sequential fourth-order invariants involve the same four rays, the heavy and light propagation eigenstates and the two decay-projected states, yet they define different closed paths through projective Hilbert space. A Bargmann invariant depends not just on which states participate but on how they are stitched together, so rewiring the cycle produces a genuinely different geometric correlation. In the CP-conserving limit the heavy and light eigenstates become orthogonal, one link of every cycle collapses, and the entire cyclic structure degenerates, which is why the invariants vanish there. The formalism thus ties the very existence of these CP-sensitive geometric objects to the nonorthogonality of the propagation eigenstates.

Crucially for experiment, none of this requires new physics inputs. The sequential invariant and both ratios can be written entirely in terms of the mixing parameters and decay amplitudes already extracted from time-dependent flavor analyses at flavor factories such as the KLOE experiment with entangled kaons and the BABAR and Belle measurements with entangled B mesons. The framework simply reorganizes the same accessible information into cyclic geometric quantities. The neutral kaon system, with its small indirect CP violation and long-lived interference phenomena, is flagged as a particularly natural arena where the linear enhancement of the sequential ratio could be pronounced, with candidate channels including two-pion, semileptonic, and multi-pion final states. The B meson systems, including channels like B decays to J/psi kaons and to pion pairs, offer rich phase-dependent interference structures, while the D system, where both mixing and CP violation remain tiny in the Standard Model, could serve as a sensitive testing ground for small deviations from exact symmetry.

The study stops short of claiming a new dynamical observable. The author is careful to note that the sequential construction does not add an extra propagation stage in the manner of conventional cascade CP analyses; it is a reorganization of overlap geometry within a single meson system. Even so, the result establishes a hierarchy of Bargmann invariant structures, from single-channel triangles through disconnected quadrilaterals to directly connected sequential cycles, each capturing a different layer of CP-sensitive interference. As precision flavor physics continues to squeeze ever tighter constraints on the origins of matter-antimatter asymmetry, geometric tools of this kind may reveal correlation patterns hiding in plain sight within data that experiments already collect.

Subject of Research: Geometric Bargmann invariant structures and CP-sensitive interference in correlated neutral meson systems

Article Title: Connected sequential Bargmann invariants and (\mathcal CP~)-sensitive geometric correlation structures in neutral meson systems

Article References: Sangiri, S. (2026). Connected sequential Bargmann invariants and $$\mathcal CP~$$-sensitive geometric correlation structures in neutral meson systems. The European Physical Journal C, 86(9), Article 1056. https://doi.org/10.1140/epjc/s10052-026-16308-5

Image Credits: AI Generated

DOI: 10.1140/epjc/s10052-026-16308-5

Keywords: Bargmann invariants, neutral mesons, CP violation, geometric phase, quantum interference, meson mixing, projective Hilbert space, kaons, B mesons, rephasing invariance, entangled meson pairs, flavor physics

Every time a power grid operator decides how much electricity each generator should produce, a quiet balancing act takes place. Burning less fuel saves money, but it often means allowing more pollution, and cutting emissions usually drives costs up. Engineers have long searched for a way to satisfy both goals at once, a challenge known as combined economic emission dispatch, or CEED. Now, a team of researchers has turned to one of the ocean’s most graceful feeders, the manta ray, for inspiration, and the result is an optimization algorithm that outperforms a crowded field of competitors on both cost and cleanliness.

The study, published in the journal Results in Engineering by Yu-Feng Sun, Jie-Sheng Wang and colleagues, describes an enhanced version of the Manta Ray Foraging Optimization algorithm, or MRFO. The original algorithm, first introduced a few years ago, mimics the cooperative hunting strategies that manta rays use to harvest plankton. Because plankton drifts unpredictably with seasons and weather, manta rays have evolved three distinct foraging behaviors: chain foraging, in which animals line up in arcs so that each one catches food missed by the one ahead; spiral foraging, where the group coils around a dense patch of food; and somersaulting, in which individuals flip back and forth around a promising spot. Translated into mathematics, these behaviors become rules that move a population of candidate solutions around a search space, gradually converging on the best answer.

What makes the CEED problem so stubborn is its mathematical character. The relationship between a generator’s output and its fuel consumption follows a cubic curve, and the emissions of carbon dioxide, nitrogen oxides and sulfur dioxide each follow their own cubic functions. The combined objective is nonlinear, nonconvex and discontinuous, riddled with local minima that can trap classical techniques such as lambda iteration or gradient-based methods. To make matters harder, the problem is genuinely multi-objective, so the researchers converted it into a single objective by applying price penalty factors that fold the cost of each pollutant into one total figure in dollars per hour.

Constraint handling is where many algorithms stumble. Traditional approaches add a fixed penalty whenever a solution violates the power balance requirement, the equation demanding that total generation minus transmission losses equals demand. But a fixed penalty is a blunt instrument. Set it too low and the algorithm happily returns infeasible schedules; set it too high and the search becomes so restricted that it cannot explore properly. Finding the right value through trial and simulation is time-consuming and inefficient. The team’s answer was to make the penalty itself dynamic, drawing on five different mathematical functions: sinusoidal, hyperbolic tangent, arctangent, linear and hyperbolic secant. Each function is raised to an order that changes during the run, so the penalty strength adapts to both the size of the violation and the stage of the optimization. Early on, mild penalties preserve exploration; later, penalties tighten to push solutions toward feasibility.

The second major innovation targets the heart of the MRFO algorithm. In the original design, a parameter called C governs the switch between exploration, casting the net wide across the search space, and exploitation, homing in on the best solution found so far. This parameter rises monotonically from zero to one, a predictable march that can leave the algorithm poorly balanced at certain stages. The researchers replaced it with a chaotic switching factor, drawn from nine well-known chaotic maps including Chebyshev, logistic, tent, sine, Gauss and singer maps, combined with an exponential adjustment function. The result is a parameter that still trends upward but oscillates along the way, periodically reopening the search space and preventing the population from settling prematurely into a local optimum.

To find out which chaotic map worked best, the team ran all nine enhanced variants, named C1MRFO through C9MRFO, through the CEC-2022 benchmark suite of twelve test functions, using a population of thirty and five hundred iterations averaged over thirty independent runs. The Chebyshev-based variant, C1MRFO, emerged as the overall winner, ranking first among the nine and beating the original MRFO, which finished last. Wilcoxon rank-sum tests confirmed that most of the improvements were statistically significant, with p-values below the conventional 0.05 threshold. When C1MRFO was pitted against a roster of state-of-the-art metaheuristics, including the dhole optimization algorithm, evolutionary mating algorithm, dung beetle optimizer, snow geese algorithm, particle swarm optimization, grey wolf optimizer, sparrow search algorithm and marine predators algorithm, it achieved the best final rank, taking first place on many of the test functions while keeping computational times competitive.

The real test, however, came when the algorithm was applied to actual dispatch problems. The researchers simulated a six-unit power system under four load demands of 150, 175, 200 and 225 megawatts. Across all four scenarios, C1MRFO produced the lowest comprehensive total cost, which combines fuel cost with the weighted penalties for carbon dioxide, nitrogen oxide and sulfur dioxide emissions. At 150 megawatts, for example, it reached a total cost of about 10,137 dollars per hour, edging out the grey wolf optimizer and clearly beating algorithms such as the arithmetic optimization algorithm, Harris hawks optimization, whale optimization algorithm, sine cosine algorithm, particle swarm optimization and the lambda iteration method. The pattern held at every load level, with the margin over the weakest classical method growing to thousands of dollars per hour at 225 megawatts.

The dynamic penalty functions delivered a further, striking gain. When the sinusoidal penalty function was applied with the parameter order set to dy5, the total cost at 150 megawatts dropped from roughly 10,137 to 9,065.71 dollars per hour, and similar reductions appeared at the higher loads, reaching 14,621.45 dollars per hour at 225 megawatts. Comparing the five penalty shapes, the sinusoidal function consistently produced the lowest minimum costs, beating the hyperbolic tangent, arctangent, linear and hyperbolic secant alternatives by margins ranging from hundreds to nearly two thousand dollars per hour depending on the load. Notably, when the penalty order was fixed rather than dynamic, performance was worst across the board, confirming that the adaptivity of the penalty, not merely its presence, is what matters.

To verify that each improvement contributes on its own, the team conducted an ablation study on a much larger twenty-unit system with a total demand of 2,500 megawatts. The results formed a clear hierarchy: the original MRFO achieved 61,835.16 dollars per hour, the chaos-enhanced C1MRFO reached 61,826.11, and the fully improved version combining chaos with the optimal sinusoidal dynamic penalty, sin(dy5)MRFO, achieved the best figure of 61,807.36 dollars per hour. All three outperformed the phasor particle swarm optimization benchmark, which came in at 61,895.57 dollars per hour. The stepwise improvement demonstrates that the two enhancements are not redundant but superimpose their benefits, and that the framework scales to systems far larger than the six-unit case.

The authors are careful to note where the work goes next. The current approach converts the multi-objective problem into a single objective through price penalty factors, but future versions could adopt Pareto-based multi-objective optimization to generate full sets of non-dominated trade-off solutions. The test cases also assume deterministic demand, so extending the algorithm to stochastic dispatch with wind and solar integration is another goal, as is combining the dynamic penalty with feasibility-first constraint-handling strategies. For now, the study offers a compelling demonstration that borrowing the foraging logic of a filter-feeding fish, perturbing it with chaos, and teaching it to calibrate its own punishments can squeeze meaningful savings and emission reductions out of one of power engineering’s hardest scheduling problems.

Subject of Research: An improved manta ray foraging optimization algorithm with chaotic switching factors and dynamic penalty functions for solving the combined economic emission dispatch problem in power systems.

Article Title: Manta ray foraging optimization algorithm with dynamic penalty function and chaos switching factor to solve combined economic emission dispatch problem

Article References: Sun, Y.-F., Wang, J.-S., Zhang, X.-Y., Li, Y.-X., Guan, X.-Y., & Liu, X. (2026). Manta ray foraging optimization algorithm with dynamic penalty function and chaos switching factor to solve combined economic emission dispatch problem. Results in Engineering, 32, Article 113383. https://doi.org/10.1016/j.rineng.2026.113383

Image Credits: AI Generated

DOI: Not provided

Keywords: manta ray foraging optimization, combined economic emission dispatch, metaheuristic algorithms, chaotic maps, dynamic penalty function, power system optimization, emission reduction, fuel cost minimization, CEC-2022 benchmarks, swarm intelligence, constraint handling, Results in Engineering

Frost is one of the most punishing adversaries of tea cultivation. A single cold snap can scorch tender leaves, derail an entire season’s harvest, and degrade the delicate chemistry that gives tea its value. For growers and breeders, however, judging how badly a field has been damaged has long been a stubbornly subjective exercise: visual ratings lump continuous injury into crude categories, and laboratory assays destroy the very tissue they measure. A new study published in Plant Methods by Zhengtong He of Shihezi University, Zhiwei Chen of the Tea Research Institute of Shandong Academy of Agricultural Sciences, and colleagues offers a way out, pairing hyperspectral imaging with a purpose-built deep learning model to read frost damage directly from the way leaves reflect light.

The core problem the team confronted is that frost injury is not a binary event but a continuum. Leaves exposed to sub-zero temperatures suffer a cascade of overlapping stresses: photosynthetic pigments degrade, the machinery of photosynthesis falters, and cell membranes and walls break down. Any single measurement captures only a sliver of that cascade. To capture the whole picture, the researchers collected hyperspectral data together with eight key physicochemical indicators from tea plants growing across natural frost damage gradients in the field, rather than in the artificial conditions of a growth chamber. From these multidimensional physiological responses they constructed a composite measure, the tea cold stress response index, or TCSRI, which expresses frost damage severity as a continuous value instead of a coarse visual class.

Building TCSRI is a meaningful advance in itself. Traditional frost assessment often relies on ordinal scales, where an observer assigns a score of mild, moderate, or severe. Such scales throw away information and make it difficult to track recovery or compare cultivars with fine precision. By integrating several physiological dimensions into one index, the team created a target variable that reflects the plant’s actual internal state. The challenge then became predicting that index, and the individual chemical indicators behind it, from spectral data alone, without touching or destroying a single leaf.

Hyperspectral sensing is well suited to this task in principle. Across hundreds of narrow wavelength bands, plant leaves encode a wealth of information: chlorophyll absorption in the visible range, water and structural features in the near infrared. But the data are high-dimensional, with far more spectral channels than samples, a regime that invites overfitting, especially in field studies where collecting ground-truth measurements is laborious. Conventional chemometric approaches, from partial least squares regression to standard machine learning pipelines, often struggle to separate genuine physiological signals from noise, sensor drift, and the confounding effects of leaf surface properties and illumination variation.

The researchers’ answer is a model they call STAMPformer, short for spectral-prior-guided multi-view attention pooling. Its architecture reflects a deliberate design philosophy: rather than letting a neural network rediscover everything from scratch, the model is steered by what spectroscopists already know about how plants respond to stress. At its front end, a parameter-free attention convolution extraction module jointly models both the raw reflectance spectra and their first-order derivatives. Derivative spectra are a classic spectral prior: they accentuate subtle local features such as absorption peaks and shoulders that are easily drowned out in raw reflectance, while suppressing broad baseline drift and noise. By processing both views together, the module sharpens the local spectral signatures that carry physiological meaning.

On top of this extraction stage sits the second innovation, a multi-view attention pooling module. Deep networks processing spectral sequences produce long streams of features, but not all wavelengths matter equally for a given prediction. The pooling module adaptively aggregates sequence features drawn from different feature subspaces, learning to amplify the responses that are most diagnostic of cold stress and to down-weight the rest. This attention mechanism acts like a learned spotlight, and because it operates across multiple subspaces, it can capture complementary patterns that a single attention head might miss. The result is a compact representation tailored to the prediction task at hand, whether that is estimating a specific chemical indicator or the composite TCSRI.

The performance numbers are striking. Across all nine prediction tasks, spanning the eight physicochemical indicators and the TCSRI itself, STAMPformer achieved coefficients of determination above 0.87 and ratios of performance to deviation above 2.80, thresholds generally regarded as indicating reliable quantitative prediction. In other words, from a leaf’s reflectance fingerprint alone, the model could reconstruct the plant’s internal frost-stress state with an accuracy that approaches laboratory measurement, while remaining entirely non-destructive. The team did not stop at headline metrics. Ablation experiments systematically removed each proposed module to confirm that both the attention convolution extraction and the multi-view pooling contributed measurably to the gains, and paired statistical tests across multiple random seeds ensured the improvements were not artifacts of a lucky initialization.

Interpretability was a further priority, and here the study delivers something rarer than raw accuracy. Applying SHAP analysis, a technique that attributes each prediction to the contributions of individual input features, the researchers identified the wavelengths the model relied on most heavily. These key spectral regions correspond to physiologically meaningful processes: pigment degradation, photosynthetic impairment, and the disruption of cellular structure. That alignment between machine attention and plant biology matters on two fronts. It gives spectroscopists confidence that the model has learned genuine stress signatures rather than spurious correlations, and it hands plant physiologists a data-driven map of which parts of the spectrum track which facets of cold injury, potentially guiding future sensor design and breeding assays.

Crucially, the model also proved itself on data it had never seen. Independent external validation, using samples not involved in training, demonstrated that STAMPformer’s predictive ability generalizes beyond its original dataset, addressing one of the most common failure modes of machine learning in agricultural sensing. For a field where models are often tuned and tested on the same plots, this external check is an important credibility marker, suggesting the framework could be deployed across different farms, seasons, and frost events rather than remaining a laboratory curiosity.

The practical implications reach well beyond tea. An accurate, non-destructive, continuous measure of cold damage opens the door to rapid post-frost field surveys, letting growers quantify losses within hours instead of waiting for symptoms to develop or sending samples to a lab. For breeders screening for cold-tolerant germplasm, a spectral readout of TCSRI could accelerate the evaluation of hundreds of candidate lines without destructive sampling. And in the emerging vision of the smart tea garden, where drones and handheld spectrometers roam the rows, a model like STAMPformer could feed real-time frost maps into irrigation, shading, and recovery-management decisions. The authors frame their work as supporting frost monitoring, cold-tolerant germplasm evaluation, and intelligent garden management, and the architecture’s reliance on general spectral priors rather than tea-specific tricks suggests the approach could be adapted to other crops facing abiotic stress. As climate variability makes late-season frosts increasingly erratic, tools that turn light reflected from a leaf into a precise physiological diagnosis may become as essential to the tea garden as the thermometer.

Subject of Research: Hyperspectral deep learning prediction of continuous frost damage severity in tea plants

Article Title: STAMPformer: spectral-prior-guided multi-view attention learning for hyperspectral prediction of continuous frost damage severity in field-grown tea plants

Article References: He, Z., Chen, Z., Ding, Z., Wang, Z., Zhang, F., Zhao, Y., & Dong, C. (2026). STAMPformer: spectral-prior-guided multi-view attention learning for hyperspectral prediction of continuous frost damage severity in field-grown tea plants. Plant Methods. https://doi.org/10.1186/s13007-026-01597-4

Image Credits: AI Generated

DOI: 10.1186/s13007-026-01597-4

Keywords: tea plant, frost damage, hyperspectral imaging, deep learning, attention mechanism, cold stress index, non-destructive prediction, SHAP analysis, precision agriculture, spectral priors, plant physiology, smart farming

The viruses that gave rise to SARS-CoV-2 have been circulating in horseshoe bats and related mammals across Asia for millennia, and most of them have never spilled over into people. Yet a small number of these bat sarbecoviruses clearly can infect humans, as the COVID-19 pandemic demonstrated. A central question for pandemic preparedness has therefore been whether scientists can look at the properties of a newly discovered bat coronavirus and make an evidence-based judgement about how dangerous it might be. A new study published in PLOS Biology suggests that one measurable property, the breadth of species whose ACE2 receptor a virus can use, may be a surprisingly informative signal, predicting not only which animals a virus can infect but also how closely related it is to SARS-CoV-2 in the eyes of the human immune system.

Sarbecoviruses, the subgenus of coronaviruses that includes SARS-CoV and SARS-CoV-2, attach to their host cells through the receptor-binding domain, or RBD, of the Spike protein. This domain grips the angiotensin-converting enzyme 2 receptor, ACE2, on the surface of host cells, and it is also the principal target of neutralising antibodies. That dual role creates an intimate link between host range and antigenicity: the same molecular surface that determines which species’ receptors a virus can engage also determines how well antibodies raised against one virus recognise another. The research team, led by scientists at The Pirbright Institute together with collaborators including researchers at King’s College London, set out to map that relationship systematically across a diverse collection of bat sarbecoviruses.

The experimental strategy combined three complementary approaches. First, the researchers used binding assays to test whether the Spike proteins of different sarbecoviruses could attach to ACE2 proteins from a wide panel of mammalian species. Second, they used pseudotype-entry assays, in which harmless lentiviral particles are decorated with each coronavirus Spike protein, to test whether receptor binding actually translates into cell entry. Third, they measured how sera from SARS-CoV-2 convalescent individuals and monoclonal antibodies from vaccinated people who had experienced breakthrough infections neutralised the different viruses. By running the same viruses through all three assays, the team could directly correlate receptor usage with antigenic behaviour.

The results revealed a striking pattern. Sarbecoviruses in clade 1, the group that contains SARS-CoV-2 itself and the BANAL viruses isolated from bats in Laos, tended to be generalists. Their Spike proteins could productively engage ACE2 receptors from many different bat species, indicating that they are not finely tuned to a single host. In contrast, viruses from clade 3, exemplified by RhGB07, and clade 5, exemplified by Rc-o319, behaved as specialists, using a much narrower range of ACE2 receptors. Rc-o319, which was previously known to be unable to use human ACE2 efficiently, fits this specialist profile, whereas the BANAL viruses, which can use human ACE2, sit firmly in the generalist camp alongside SARS-CoV-2.

To understand the structural basis of this specialism, the team solved a novel structure of the RhGB07 Spike protein. The structure allowed them to identify specific residues within the RBD that are associated with the restricted receptor usage of this clade 3 virus. These residues presumably shape the receptor-binding interface in ways that favour particular bat ACE2 variants while excluding others. Such structural insights are valuable because they point to the molecular features that distinguish viruses capable of flexible receptor engagement from those locked into narrow host ranges, and they may help predict the properties of newly discovered sarbecoviruses from sequence data alone.

When the researchers extended their receptor panel beyond bats to include more diverse mammalian ACE2 proteins, the generalist phenotype largely held. Clade 1 viruses could bind and enter cells expressing ACE2 from humans, non-human primates, livestock, and rodents, as well as from species considered potential intermediate reservoir hosts, such as civets, raccoon dogs, and pangolins. This is significant because intermediate hosts are thought to have played a role in previous coronavirus spillovers, including the emergence of SARS-CoV in 2002. A virus that can already use the receptors of many species faces fewer barriers on the path from bat to human. The specialist viruses, by contrast, showed wider phenotypic diversity across this expanded panel, with RhGB07 exhibiting a more idiosyncratic pattern of receptor usage that did not simply mirror its bat-host profile.

The study also examined how SARS-CoV-2’s ongoing evolution in humans has changed its receptor preferences. Comparing variants that have emerged during the pandemic, the researchers found an expanding and, in some cases, shifting pattern of generalism, particularly for Omicron and its many sub-lineages. Omicron’s Spike has accumulated numerous mutations in the RBD, and these changes appear to have broadened or reconfigured the range of ACE2 molecules the virus can engage. This observation underscores that receptor usage is not a fixed property of a virus but a dynamic trait that can evolve under immune pressure and changing transmission landscapes, with potential consequences for which animal species might serve as new reservoirs for human-derived variants.

The antigenic side of the analysis produced equally important findings. When convalescent sera from people who had recovered from COVID-19 were tested against the panel of bat sarbecoviruses, clade 1 viruses, being phylogenetically closest to SARS-CoV-2, were antigenically the most similar. There was robust evidence for cross-neutralisation of these viruses, meaning that immunity generated against SARS-CoV-2 offers meaningful protection against its closest bat relatives. Importantly, however, the team also found evidence for limited cross-neutralisation across the entire subgenus, suggesting that even the more distantly related specialist viruses are not completely invisible to immunity primed by SARS-CoV-2 infection. This graded pattern of cross-reactivity maps closely onto the phylogenetic and receptor-usage relationships the researchers had established.

Using monoclonal antibodies derived from COVID-19 vaccinees who subsequently experienced breakthrough infections, the researchers were able to pinpoint which classes of antibody epitopes are responsible for the broader neutralisation observed across the sarbecovirus panel. Because antibodies recognise specific structural features of the RBD, identifying the epitope classes that mediate cross-neutralisation provides a rational basis for designing vaccines and therapeutics with deliberately broad coverage. Antibodies directed against more conserved features of the receptor-binding interface, rather than those specific to SARS-CoV-2’s particular configuration, appear to underpin the wider neutralising activity.

Taken together, the study delivers a unifying message: generalist ACE2 usage, phylogenetic proximity to SARS-CoV-2, and antigenic similarity travel together. A bat sarbecovirus that can flexibly use receptors from many mammalian species is likely to be a close relative of SARS-CoV-2 and likely to be partially neutralised by existing human immunity. This constellation of traits offers surveillance teams a practical framework for triage. When a new sarbecovirus is discovered, characterising its receptor-binding breadth through binding and pseudotype assays could provide an early, laboratory-based assessment of both its zoonotic potential and the degree to which current vaccines and antibodies might protect against it. As bat habitats are disturbed and human-wildlife interfaces expand, such tools for distinguishing the generalists from the specialists among the thousands of circulating bat coronaviruses could prove essential for anticipating, and perhaps preventing, the next spillover.

Subject of Research: ACE2 receptor usage breadth as a predictor of host range and antigenic relatedness across bat sarbecoviruses

Article Title: Breadth of ACE2 receptor usage predicts host range and antigenic relatedness across bat sarbecoviruses

Article References: Thakur, N., Newman, J., Ni, D., Seow, J., Hay, A. L., Lee, Y., Upadhyay, A., Ekundayo, B. E., Hammond, J. A., Peacock, T. P., Lau, K., Doores, K. J., & Bailey, D. (2026). Breadth of ACE2 receptor usage predicts host range and antigenic relatedness across bat sarbecoviruses. PLOS Biology, 24(10), e3003944. https://doi.org/10.1371/journal.pbio.3003944

Image Credits: AI Generated

DOI: 10.1371/journal.pbio.3003944

Keywords: sarbecoviruses, ACE2, SARS-CoV-2, host range, receptor-binding domain, antigenicity, cross-neutralisation, bat coronaviruses, zoonotic spillover, Omicron, monoclonal antibodies, pseudotype assays

Where is the line between firm discipline and child abuse? For millions of families in the United States, the answer depends on where they grew up, what their parents did, and how their culture defines good parenting. A new study published in the Journal of Child and Family Studies tackles this thorny question with an ambitious goal: building a scientific instrument that can measure how people from different backgrounds judge parenting practices that are accepted in some communities but condemned in others. The Parenting Practices and Discipline Scale, or PPDS, developed by Kevin Ortiz Diaz, Maureen C. Kenny, and Helia Baez at Florida International University, is one of the first tools designed specifically to capture culturally situated discipline practices that existing measures simply ignore.

The stakes are enormous. Child abuse is recognized as a global health issue, and in the United States it occurs at what researchers describe as epidemic proportions. Federal data show 546,159 victims of child abuse and neglect in 2023 alone, and the vast majority of these cases, roughly 89 percent, are committed by one or both parents. Children from minority groups are often the most affected, with Black or African American children among the most likely to experience abuse, second only to American Indian or Alaska Native children. Yet the definitions of what constitutes abuse vary dramatically across countries and cultures, creating a legal and ethical minefield for immigrant families who may not realize that practices considered normal at home are illegal in their new country.

The problem is particularly acute for Hispanic families, who make up 19 percent of the American population and represent the largest share of recent immigrants. Hispanic culture is far from monolithic, encompassing communities from South and Central America and the Caribbean with distinct traditions, but certain common threads run through the research. The concept of familismo, a cultural orientation and sense of obligation to family, has been linked to greater parental warmth in some studies. At the same time, physical violence as a disciplinary method remains common in many Hispanic cultures, and the chances that such behaviors are carried over by families immigrating to the United States are high. The result is a potential collision between generations of cultural practice and a legal system, anchored by the Child Abuse Prevention and Treatment Act of 1974, that defines abuse in terms of serious physical or emotional harm.

What makes the new study distinctive is its grounding in intersectionality theory, a framework rooted in feminist scholarship that examines how multiple categories of identity, such as race, ethnicity, gender, and nativity, interact to shape experience. Rather than treating demographic characteristics as independent variables, the researchers designed their analyses to test whether these dimensions jointly predict how individuals classify discipline practices as culturally acceptable or abusive. The framework has practical urgency: racial and ethnic disparities exist in both the reporting and substantiation of child maltreatment, and current child protection policies can disproportionately affect women of color who use social services and face heightened surveillance and risk of reports to child protective services.

The scale itself emerged from a careful two-stage piloting process. First, four experienced mental health clinicians specializing in child abuse, with 10 to 20 years of experience each, reviewed a draft pool of 54 items describing parenting behaviors and case vignettes. Then ten first-generation Hispanic immigrants from a range of nationalities, including Puerto Rican, Cuban, Colombian, Mexican, Dominican, and Nicaraguan participants, examined the refined items and identified which behaviors they would associate with parents in their own cultures and whether they would label those behaviors abusive or acceptable. Consensus required agreement from at least 70 percent of the pilot group. Eighteen items survived, ten identified as culturally acceptable behaviors such as yelling, using the chancla, the slipper famously wielded in many Latino households, or letting a child sleep with parents, and eight identified as abusive behaviors such as spanking, leaving a child alone in a car, or forcing a child to bathe with an adult.

The main study recruited 1,477 undergraduates at a large Hispanic-Serving Institution in Miami, ultimately yielding an analytic sample of 1,089 participants after exclusions for failed attention checks, missing data, and groups too small for meaningful analysis. The final sample was 72.2 percent Hispanic, 16.3 percent non-Hispanic Black, and 11.6 percent non-Hispanic White, nearly 70 percent U.S.-born, and predominantly female at 88.8 percent. Participants rated each of the 18 behaviors on a three-point scale: not abusive, inappropriate but not abusive, or abusive. The choice of a college population was deliberate, since emerging adults are on the cusp of parenthood, attitudes at this life stage predict later parenting intentions, and parenting practices tend to be transmitted intergenerationally.

Psychometrically, the study unfolded in two parts. Study 1 established the scale’s initial properties using exploratory factor analysis with principal axis factoring and oblique rotation on a polychoric correlation matrix, an approach suited to the ordinal, non-normally distributed responses. The analysis supported a two-factor structure: Factor 1 captured discipline and psychological aggression, including spanking, yelling, the chancla, cursing, ignoring, comparing, fighting, and religion, while Factor 2 reflected neglect and physical endangerment, such as using drugs in front of a child, refusing treatment for illness, and allowing smoking. The two factors accounted for 29.5 percent of the variance and were moderately correlated. Internal consistency for the full 18-item measure was acceptable at a Cronbach’s alpha of 0.71, though the abusive subscale showed weaker reliability, a limitation the authors acknowledge candidly.

Study 2 then deployed regression models to test how demographic identities shaped perceptions, and the results were revealing. Gender and parental status emerged as the strongest and most consistent predictors: women and parents were significantly more likely to classify culturally acceptable practices as abusive. Among Hispanic participants, those not born in the United States were more likely than U.S.-born Hispanics to view behaviors like spanking as abusive, a finding that ran counter to the researchers’ hypothesis and suggests that acculturation may shift parenting beliefs toward American child protection norms. Across racial and ethnic groups, Hispanic participants were more likely than Black participants to classify culturally acceptable practices as abusive. Notably, for the more overtly harmful behaviors in Factor 2, parental status was the only significant predictor, suggesting broad agreement that neglecting a sick child or encouraging substance use transcends cultural and gendered expectations.

The effect sizes were small, with the models explaining roughly 1 to 6 percent of the variance, a reminder that unmeasured cultural and contextual factors account for most of the variation in how people judge discipline. Still, the patterns carry real implications for prevention. The authors suggest that father-focused education programs may be valuable, since men were less likely to identify physical discipline as harmful and may therefore be at greater risk of normalizing such practices. Culturally adapted interventions have already shown promise, from brief summer parent training programs benefiting Hispanic children to the multinational Triple P Positive Parenting Program, and the PPDS could help practitioners identify which disciplinary practices are perceived as acceptable within specific communities, allowing messaging to be tailored rather than uniform.

The researchers are careful to frame their findings as preliminary. The sample was heavily female and drawn from a single Miami institution in a minority-majority city, socioeconomic status was not assessed, and social desirability bias may have influenced responses to questions about parenting. The abusive subscale needs refinement, and future work should apply confirmatory factor analysis and measurement invariance testing across cultural groups. Yet the core contribution stands: a novel measure that captures culturally situated discipline practices invisible to existing tools like the Alabama Parenting Questionnaire or the Child Abuse Potential Inventory. As parenting practices pass from generation to generation and families cross borders into legal systems with different rules, understanding exactly how culture shapes perceptions of abuse may be the first step toward protecting children without criminalizing the traditions their parents brought with them.

Subject of Research: Development and validation of a scale measuring cultural perceptions of parenting and discipline practices

Article Title: Development and Validation of the Parenting Practices and Discipline Scale in an Ethnically Diverse Sample

Article References: Ortiz Diaz, K., Kenny, M. C., & Baez, H. (2026). Development and Validation of the Parenting Practices and Discipline Scale in an Ethnically Diverse Sample. Journal of Child and Family Studies. https://doi.org/10.1007/s10826-026-03370-6

Image Credits: AI Generated

DOI: 10.1007/s10826-026-03370-6

Keywords: child abuse, parenting practices, discipline, Hispanic families, intersectionality, psychometrics, scale development, cultural perceptions, child welfare, corporal punishment, acculturation, exploratory factor analysis

Metal ions are everywhere in biology and the environment, and telling them apart quickly and cheaply has long been a challenge for chemists. A new open-access review published in Discover Chemistry by M. S. Ajay Kumar, Pradeep Bhaskar, and B. Anindita of Presidency University in Bengaluru takes a fresh look at one of the most versatile tools in the detection toolbox: organic fluorescent probes. Rather than simply cataloguing probes and their detection limits, the review argues that the real key to building better sensors lies in understanding why different metal ions produce fundamentally different fluorescence responses, from brilliant turn-on emission to complete quenching.

The stakes are high. Essential ions such as copper, zinc, and iron participate in enzymatic catalysis, cellular signaling, redox regulation, and gene expression, but when their balance is disrupted they can contribute to oxidative stress, metabolic disorders, and neurodegenerative diseases. Toxic heavy metals such as mercury and lead offer no biological benefit at all and are hazardous even at trace levels because of their persistence, bioaccumulation, and long biological half-lives. Conventional analytical techniques like atomic absorption spectroscopy and inductively coupled plasma mass spectrometry deliver excellent accuracy, but they demand expensive instruments, trained personnel, and complex sample preparation, making them poorly suited for real-time or on-site detection. Fluorescent probes, which convert metal-ligand interactions into measurable optical signals, promise something different: fast, simple, and biologically compatible sensing.

The central insight of the review is that probe performance depends not only on the fluorophore but on the compatibility between the target ion and the sensing mechanism. Paramagnetic ions such as Cu2+ and Fe3+ usually promote fluorescence quenching through non-radiative decay, intersystem crossing, and charge-transfer pathways. Diamagnetic Zn2+, by contrast, more readily supports turn-on emission through suppression of photoinduced electron transfer, known as PET, and chelation-induced rigidification of the molecular framework. Soft heavy metals such as Hg2+ and Pb2+ occupy a middle ground: they bind strongly to carefully selected donor atoms, yet their heavy-atom character can trigger spin-orbit coupling that quenches emission, so designers must balance strong binding against signal reliability.

The authors walk through the major photophysical mechanisms one by one. PET-based probes contain an electron-rich donor unit that quenches the excited fluorophore in the free state; when a metal ion coordinates to the donor, the electron-transfer pathway is blocked and fluorescence returns. This OFF-ON switching works beautifully for closed-shell d10 ions like Zn2+ but often fails for paramagnetic targets, where suppressing PET merely opens the door to stronger quenching routes such as ligand-to-metal charge transfer. Intramolecular charge transfer, or ICT, systems integrate the binding site directly into the conjugated framework, producing wavelength shifts that enable ratiometric sensing, a more reliable readout than simple intensity changes because it reduces dependence on probe concentration and excitation conditions. Twisted intramolecular charge transfer adds a conformational dimension: metal coordination can restrict torsional motion, suppressing non-emissive twisted states and boosting emission.

Chelation-enhanced fluorescence and its counterpart, chelation-enhanced quenching, are presented not as separate mechanisms but as opposite outcomes of the same coordination event. When metal binding locks a flexible ligand into a rigid, planar geometry, non-radiative decay is suppressed and brightness increases; when the bound ion instead introduces intersystem crossing or electron transfer, the same binding event extinguishes the signal. Excited-state intramolecular proton transfer, or ESIPT, offers another elegant route: photoexcitation drives a proton from a donor group to a nearby acceptor, generating a keto tautomer with a much larger Stokes shift. Metal ions can either disrupt the hydrogen bond required for this process or stabilize it, giving designers a tunable switch between enol and keto emission bands.

The review also highlights through-bond energy transfer, an emerging mechanism in which excitation energy moves along a covalently linked donor-acceptor backbone rather than through space as in FRET. Because TBET does not require strict spectral overlap or precise donor-acceptor distances, it can generate large pseudo-Stokes shifts and support ratiometric, multi-channel detection. A representative Hg2+-responsive cassette shows weak emission in the OFF state but strong acceptor fluorescence once metal binding enhances electronic communication between the chromophores. The authors caution, however, that TBET systems demand precise molecular architecture and often struggle with aggregation-induced quenching in water.

Computational chemistry receives a notably critical treatment. Density functional theory and time-dependent DFT are now standard in probe studies, but the authors argue their value is frequently squandered on decorative HOMO-LUMO diagrams. Used properly, ground-state DFT can confirm binding sites, coordination geometry, and structural rearrangements such as rigidification or ring opening, while TD-DFT can assign whether an observed transition is locally excited, charge-transfer, or ligand-to-metal in character. The review stresses that a smaller HOMO-LUMO gap alone proves nothing; orbital localization, charge-transfer direction, and experimental fluorescence output must all be correlated. Method choice matters too: hybrid functionals like B3LYP suit organic fluorophores, while effective core potential basis sets such as LANL2DZ are needed for heavy metals, and long-range corrected functionals like CAM-B3LYP or omega-B97XD better describe charge-transfer states.

Metal-by-metal, the review distills design lessons from dozens of representative probes. For Cu2+, quenching dominates because of its paramagnetic d9 configuration, with peptide-based probes reaching detection limits as low as 1.5 nanomolar, while rhodamine ring-opening and ratiometric platforms offer turn-on and quantitative alternatives. Iron sensing is even harder because Fe3+ is a hard, highly charged, paramagnetic d5 ion that promotes ligand-to-metal charge transfer, so most probes are turn-off or colorimetric. Zn2+ is the friendliest target: ratiometric and turn-on probes routinely achieve low-nanomolar detection in seconds, though discriminating Zn2+ from its fellow d10 ion Cd2+ remains the field’s stubborn challenge. For Hg2+ and Pb2+, the standout performers combine aggregation-induced emission, near-infrared output, and ratiometric readout, with some systems reporting detection limits down to the picomolar range in water, food, and living cells.

The application landscape is shifting rapidly from bench-top demonstrations to real-world deployment. Recent probes have been validated in tap water, groundwater, river water, milk, soil, fruit juice, and plant tissues, with recovery values typically between 90 and 102 percent. Smartphone-assisted detection, 3D-printed sensing devices, and paper test strips are turning molecular recognition into field-ready tools. In bioimaging, ratiometric probes have tracked intracellular Cu2+ in HeLa cells, an AIE probe with a 130-nanometer Stokes shift visualized Hg2+ in A549 cells and in the roots of rice plants, and a peptide-based self-assembling system detected Pb2+ in groundwater and living cells below 10 nanomolar. Nitrogen and sulfur co-doped carbon dots have even combined metal sensing with logic-gate behavior in a single platform.

Significant hurdles remain before fluorescent probes fulfill their promise. Absolute selectivity in complex matrices is still elusive, because borderline and soft ions such as Pb2+, Hg2+, and Cd2+ exhibit overlapping coordination behavior. Quantitative accuracy inside cells is compromised by probe distribution, intracellular heterogeneity, and interference from endogenous biomolecules like glutathione. Many probes lack reversibility, subcellular targeting, or the long-term stability needed for continuous monitoring. The review’s closing message is clear and likely to shape the next generation of research: successful probes must be mechanism-guided rather than structure-driven, matching the electronic personality of each metal ion with the right signal-transduction pathway, and validated not under ideal laboratory conditions but in the messy, competitive environments where metal ions actually matter.

Subject of Research: Organic fluorescent probes for detecting metal ions through mechanism-guided photophysical design and bioimaging

Article Title: Organic fluorescent metal ion probes for understanding design rules photophysical mechanisms and bioimaging applications

Article References: Ajay Kumar, M. S., Bhaskar, P., & Anindita, B. (2026). Organic fluorescent metal ion probes for understanding design rules photophysical mechanisms and bioimaging applications. Discover Chemistry, 3(1), Article 472. https://doi.org/10.1007/s44371-026-00857-5

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00857-5

Keywords: fluorescent probes, metal ion sensing, photoinduced electron transfer, intramolecular charge transfer, ESIPT, chelation-enhanced fluorescence, aggregation-induced emission, DFT calculations, zinc detection, mercury detection, lead detection, bioimaging

Buildings are responsible for an enormous share of global electricity demand, and the way their air conditioners, lights, and heating systems are scheduled can make the difference between a grid under strain and one running smoothly. For years, engineers have tried to optimize these schedules with evolutionary algorithms and reinforcement learning, but the problem becomes brutally hard when dozens of buildings share a single electrical network and must be coordinated simultaneously. A new study published in Discover Artificial Intelligence proposes a strikingly different approach: instead of searching step by step for the best schedule, a generative artificial intelligence model learns to paint one directly out of pure noise, guided by a map of how the buildings are physically wired together.

The framework, called DMCBO, short for Diffusion-driven Multi-objective Collaborative Optimization for Building Clusters, was developed by Yalan Zheng of Zhengzhou Technical College and Taixiang Yin of North China University of Water Resources and Electric Power. Their central insight is that the joint scheduling of equipment settings and load distribution across an entire cluster of buildings can be treated as a high-dimensional data sample, much like an image. Diffusion models, the same family of generative techniques behind modern text-to-image systems, work by progressively corrupting data with Gaussian noise and then learning to reverse that corruption. The researchers reformulated multi-objective building optimization as exactly this kind of conditional denoising process: starting from random noise, the model strips away noise step by step until a complete, Pareto-optimal collaborative schedule for every piece of equipment in every building emerges.

Technically, the forward diffusion process is a fixed Markov chain that adds tiny amounts of Gaussian noise to real scheduling data over one hundred steps, with the noise coefficient increasing linearly from 0.0001 to 0.02 until the original data becomes indistinguishable from standard Gaussian noise. A neural network is then trained to predict the noise that was added at each step, given the noisy schedule and a conditional encoding vector describing the current situation. Once trained, the model can generate fresh solutions at inference time by sampling pure noise and running the reverse process, refining it over one hundred denoising steps into a schedule tailored to the prevailing weather, occupancy, and grid conditions. Because this generation is a gradient-based learning process rather than an iterative search, it avoids the local optima traps that plague traditional multi-objective evolutionary algorithms and the reward oscillations that destabilize deep reinforcement learning.

What makes the system genuinely cluster-aware is its use of graph neural networks. The researchers model the building cluster as a graph mapped onto a radial distribution microgrid topology, where nodes represent buildings and edges encode physical constraints such as maximum line thermal capacities and transformer limits. A two-layer graph convolutional network passes messages between neighboring buildings, fusing spatial-topological dependencies and network constraints into a single graph embedding. This embedding is concatenated with an adjustable preference weight vector to form the condition that steers the denoising trajectory. In effect, when the model generates a control strategy for one building, it simultaneously perceives the state of its neighbors and the limits of the shared electrical infrastructure, suppressing systemic coordination errors from 0.18 down to 0.06 in the team’s experiments.

The multi-objective aspect is handled through an elegant scalarization trick. Each objective, such as energy consumption, operating cost, or thermal comfort, is normalized to a dimensionless value between zero and one using Min-Max normalization based on historical operational bounds. The normalized objectives are then blended into a single scalar condition via a weight vector whose components sum to one. During training, these weights are sampled from a uniform Dirichlet distribution, exposing the model to the entire space of possible preferences so it learns the distribution of the full Pareto front. At deployment, a decision maker can slide the weight vector like an adjustment knob, instantly generating schedules that emphasize energy saving, comfort, or a balanced compromise, all within the same trained model.

The denoising backbone itself is a U-Net adapted to process one- and two-dimensional building scheduling tensors. Diffusion time steps are embedded through sinusoidal positional encodings, while the graph-derived condition is injected into every residual block via Adaptive Layer Normalization. A bottleneck layer with multi-head self-attention captures long-range dependencies across the entire daily scheduling horizon, and symmetric skip connections preserve high-resolution information during reconstruction. The authors trained the model for 300 epochs on dual NVIDIA Tesla V100 GPUs, and the loss curves dropped from 0.75 to below 0.03 with training and validation curves closely coinciding, indicating no overfitting. As the reverse diffusion step count decreased from 90 to 10 during analysis, the noise-prediction error distribution became sharply concentrated at zero, demonstrating extremely high denoising precision at the end of generation.

Validation was carried out on the open-access Building Data Genome Project 2 dataset, which supplied real hourly weather records, operational schedules, and annual electricity, cooling, and heating load profiles across diverse climate zones. Because that dataset provides metered energy data rather than physical building models, the team used United States Department of Energy Commercial Reference Buildings as templates and calibrated them in EnergyPlus, a high-fidelity thermodynamic simulation platform, to create a digital twin consistent with the real data. All results were benchmarked against a rule-based control strategy with static thermostat setpoints of 25 degrees Celsius in summer and 22 degrees in winter, and every experiment was repeated ten independent times to confirm statistical significance.

The headline numbers are impressive. In high-demand summer conditions, DMCBO achieved an average energy-saving rate of 28.6 percent and cut peak load by 38.5 percent, shaving the afternoon peak from roughly 560 kilowatts down to about 440 kilowatts and saving approximately 142.80 US dollars per day. Cumulative daily power savings exceeded 1,000 kilowatt-hours, compared with 400 kilowatt-hours for a traditional collaborative strategy and just 120 kilowatt-hours without coordination. In winter, peak shaving and valley filling reached about 39 and 42 percent respectively, far above the 22 and 25 percent of conventional methods. When the solution sets were projected into objective space, DMCBO dominated the baselines: it reached a hypervolume indicator of 0.95 within 12,000 function evaluations, versus 0.85 for NSGA-II and 0.70 for MOPSO, while keeping operating costs between 140 and 160 dollars against more than 210 dollars for deep reinforcement learning and 260 to 300 dollars for a GAN-based generative approach.

Robustness and scalability proved equally compelling. Under injected stochastic disturbances mimicking sudden weather changes and occupancy swings, the framework confined power fluctuations to a narrow band of plus or minus 5 kilowatts, whereas the deep reinforcement learning baseline swung between minus 15 and plus 20 kilowatts. Scaling from 5 to 50 buildings, the global energy-saving rate actually rose from 28.5 to 31.2 percent, because larger clusters unlock more load-complementary opportunities that the graph encoder can safely exploit within encoded grid boundaries. Computation time grew to 9.8 seconds at the 50-building scale, but that remains comfortably within the 15-minute control horizon used by real building management systems, and the hypervolume index slipped only slightly to 0.88. Ablation experiments confirmed that both the graph neural network module and the choice of 100 diffusion steps, which yields a 50-millisecond inference latency, were essential to this balance of speed, accuracy, and stability.

The authors are candid about limitations. The framework has so far been validated only in simulation, and EnergyPlus simplifies sub-minute mechanical actuation latencies that real HVAC hardware would introduce; they propose cascading PID loops and predictive safety margins to absorb those delays in practice. Data quality, generalization to residential buildings and extreme climates, the computational cost of scaling to city-sized networks, and the black-box nature of the generative process all remain open challenges, with explainable AI and transfer learning flagged as future work. Still, the architecture is designed to slot into standard building management systems as a supervisory layer, transmitting setpoints over protocols such as BACnet or Modbus. If the simulation gains survive contact with physical buildings, the idea of generating optimal energy schedules from noise, guided by a graph of the grid itself, could become a powerful new tool in the push toward decarbonized cities.

Subject of Research: Conditional diffusion models with graph neural networks for multi-objective energy optimization in building clusters

Article Title: Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks

Article References: Zheng, Y., & Yin, T. (2026). Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks. Discover Artificial Intelligence, 6(1), Article 1383. https://doi.org/10.1007/s44163-026-02140-z

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02140-z

Keywords: diffusion models, graph neural networks, building energy management, multi-objective optimization, EnergyPlus, smart buildings, Pareto optimization, deep learning, energy efficiency, HVAC control, microgrids, generative AI

Major TV networks were notified on Friday that the department was examining whether they violated antitrust laws when they suspended pool coverage of President Trump.

The Philadelphia Police Department said the agents had also sent in a false homicide tip. The incidents led the White House to call for better disclosure of rogue A.I. behavior.

As part of the agency’s Inspiration Tour flyover, NASA Administrator Jared Isaacman will pilot one of three F-5 fighter jets to kick off the New York Jets vs. Cleveland Browns game in East Rutherford, New Jersey, on Sunday, Oct. 11.

NASA team members will engage with fans at the agency’s Experience Zone, located at the Jets Tailgate Zone outside the stadium, from 10 a.m. to 1 p.m. EDT before the game. Fans can learn more about NASA’s return to the Moon under the agency’s Artemis dome, enjoy interactive games, and capture photos with a large, inflatable SLS (Space Launch System) rocket.

Isaacman will be available for limited media interviews during the game. To arrange interviews, please contact Camille Gallo at: camille.m.gallo@nasa.gov or 202-531-8057.

With stops across the nation, NASA’s Inspiration Tour convenes academic, industry, and public sector stakeholders to connect the agency with the people, technologies, and organizations that drive American leadership in space.

The tour will culminate in MAX POWER, a public exposition of American air and space innovation, Saturday, Nov. 7, and Sunday, Nov. 8, on and near the agency’s Kennedy Space Center in Florida. Held in honor of America’s historic 250th anniversary, the multi-day, family-friendly event will showcase the next-generation aircraft, spacecraft, autonomous vehicles, and technologies that will help define the future of transportation in air and space.

For more information about MAX POWER and the agency’s missions, visit:

https://www.nasa.gov/maxpower

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Camille Gallo / Jessica Taveau
Headquarters, Washington
202-358-1600
camille.m.gallo@nasa.gov / jessica.c.taveau@nasa.gov

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Coeliac disease is an autoimmune condition in which the immune system reacts adversely to gluten. Eating gluten causes inflammation in the gut, which can damage the intestine and impair its ability to absorb nutrients.

Neuroblastoma is the most common solid tumor found outside of the brain in children, yet why some children respond well to treatment while others do not has remained poorly understood.

Two major international studies are providing new insight into how human milk is regulated, showing that some components respond to maternal nutrition, while others remain stable or reflect biological processes involved in lactation and infant growth.