A new Bayesian system called ALADYNOULLI is designed to forecast how multiple diseases unfold over time from longitudinal electronic health record (EHR) histories. The work targets a central challenge in longitudinal modelling: evaluating prediction without “information leakage,” so that future diagnoses never influence training for past timepoints. By explicitly simulating real clinical follow-up, the researchers aim to make risk estimates more trustworthy for decision-making.

In the primary assessment, ALADYNOULLI produced dynamic 1-year forecasts at 10 fixed moments during follow-up, starting at enrollment and extending to years 1 through 9. At each timepoint, models were retrained using only data available up to that moment, then tested on 1-year outcomes. The key metric was the median area under the receiver operating characteristic curve (AUC) across these dynamic evaluations, summarizing how predictive accuracy changes as new diagnoses accumulate.

The study spans 28 diseases and reports robust discrimination for several clinically important endpoints. Performance was strong for atherosclerotic cardiovascular disease (ASCVD), breast cancer, atrial fibrillation, heart failure, and Parkinson’s disease, with AUC values of 0.879, 0.867, 0.801, 0.811, and 0.796, respectively. Importantly, the evaluation excluded people with prevalent disease at prediction time, aligning the comparisons with prospective risk.

ALADYNOULLI was also benchmarked against established clinical risk tools where feasible. At enrollment, static models trained only on baseline information were compared for both 1-year and 10-year horizons. While longer-term predictions naturally degraded, ALADYNOULLI retained meaningful power—for example, for ASCVD at 10 years—highlighting the advantage of learning from longitudinal EHR trajectories rather than relying solely on cross-sectional inputs.

Calibration analyses further supported reliability: predicted and observed event rates matched closely across millions of at-risk patient-time observations. The researchers report a mean squared error on calibration of 4.67 × 10⁻⁷, with mean predicted rates (5.55 × 10⁻⁴) closely tracking mean observed rates (5.45 × 10⁻⁴). This suggests not only ranking accuracy, but also quantitative risk realism.

To test whether results were driven by subtle temporal artifacts, the team ran sensitivity analyses that excluded events occurring shortly before prediction and varied washout windows. They also examined alternative horizons and a dynamic 10-year rolling interpolation strategy, which updates risk estimates as annual information becomes available. Together, these checks help confirm that temporal patterns are being learned in a causally aligned manner.

Overall, the findings position ALADYNOULLI as a clinically relevant framework for multi-disease risk forecasting, combining longitudinal EHR modelling with predictive discrimination and calibration. By rigorously preventing leakage and validating dynamic follow-up scenarios, the approach offers a pathway toward more actionable, time-aware risk prediction in real-world healthcare settings.

Subject of Research: Longitudinal EHR-based multi-disease risk prediction using Bayesian modelling and genetic/EHR integration
Article Title: A Bayesian framework for longitudinal EHR and genetic discovery.
Article References: Urbut, S.M., Ding, Y., Nakao, T. et al. A Bayesian framework for longitudinal EHR and genetic discovery. Nature (2026). https://doi.org/10.1038/s41586-026-10780-5
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41586-026-10780-5
Keywords: longitudinal EHR, Bayesian framework, disease risk prediction, dynamic forecasting, temporal validation, calibration, AUC, ASCVD

Climate change is poised to reshuffle the reliability problem facing decarbonized power systems, according to a new study that links weather extremes, long-term climate projections, and real-world infrastructure constraints. As grids rely more heavily on wind and solar, they become sensitive not just to the amount of renewable capacity installed, but to where that capacity is sited relative to transmission lines, load centers, and the local meteorological patterns that govern output.

Using high-resolution climate projections at 12-km scale, the researchers examined how future conditions could alter both renewable generation and electricity demand across two distinct US regions: New England and Texas. They then coupled these climate inputs with an optimization model operating at the county level, allowing the team to test how system design choices—particularly spatial siting of wind and solar and the presence of transmission bottlenecks—affect long-run adequacy.

The results suggest a sharp increase in resource inadequacy risk by mid-century. In both regions, climate change can drive prolonged periods of low renewable generation, which occur when meteorological conditions reduce wind and solar output while demand simultaneously rises. For Texas, the study finds the frequency of inadequacy events could increase dramatically—up to fivefold—if planning proceeds using non-climate-informed assumptions.

Crucially, the work shows that these risks are not determined by aggregate capacity targets alone. Fine-scale siting decisions can lock the system into unfavorable “weather-resource–grid” pairings, amplifying periods when local supply falls short and transmission limits prevent imports from compensating.

Yet the study also offers a pathway to resilience. Climate-informed planning can mitigate the emerging adequacy threat at relatively modest cost. Texas, for example, can maintain near-zero marginal investment cost by shifting wind capacity westward, improving alignment with future wind patterns and reducing reliance on constrained transfers.

New England requires a different strategy. The region faces a higher investment need—about a 2.34% increase—achieved through targeted solar and transmission expansion closer to load centers. This approach helps buffer localized shortfalls during future, climate-driven demand and renewable output mismatches.

Overall, the study argues that achieving climate-resilient decarbonization means moving beyond simple build-out schedules. Instead, it calls for high-resolution, climate-aware spatial planning that reveals siting opportunities invisible to coarse models—turning reliability from a static design metric into a dynamic, region-specific forecast problem.

Subject of Research: Climate change and resource adequacy risks in wind/solar grids, including optimal renewable siting
Article Title: Climate change reshapes resource adequacy risks and optimal renewable energy siting in wind and solar energy systems
Article References: Qiu, L., Khorramfar, R., Wang, S. et al. Climate change reshapes resource adequacy risks and optimal renewable energy siting in wind and solar energy systems. Nat Energy (2026). https://doi.org/10.1038/s41560-026-02109-3
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41560-026-02109-3
Keywords:

Naked mole-rats have long puzzled biologists with their striking eusocial structure, where most adults remain reproductively suppressed while a queen controls the colony’s future. Now, a new study identifies a chemical “queen odour” that links social status to endocrine control, revealing a mammalian analogue to insect queen pheromones.

The molecule, termed IPM, is enriched by the queen and acts as the key mediator of reproductive suppression in non-breeding females. When IPM is present, circulating prolactin rises while progesterone is suppressed, locking the colony into a stable reproductive hierarchy characterized by reduced conflict and maintained group cohesion.

Crucially, the team connects this behavioural outcome to the brain. Using multiple approaches—including non-invasive functional ultrasound imaging (fUSI)—they show that IPM provokes strong activation of olfactory neurons and central neural circuits. This provides a neural substrate for how a chemical cue can trigger colony-wide physiological change, rather than relying on social learning or passive association.

The experiments also clarify sensory routing. Ablating olfaction abolishes aggression initiation and lowers prolactin levels, directly tying IPM detection to reproductive suppression. While the findings are consistent with sensing through the main olfactory epithelium, the authors note that the vomeronasal system may still contribute.

Queen removal normally destabilizes colonies, leading to aggression, reproductive competition, prolactin decline, and, in extreme cases, mortality. Yet when IPM is added to queenless bedding, the colony remains stable for months. During this period, fecal progesterone metabolite levels stay low, indicating sustained ovarian quiescence alongside elevated prolactin.

The study also addresses a longstanding paradox: transferring soiled bedding and urine from parent colonies fails to maintain suppression in isolated individuals. The authors propose that IPM’s chemistry helps explain this discrepancy. As a long-chain fatty acid ester with low volatility, IPM may persist within the colony environment but fail to travel effectively over distance or survive transfer pathways that preferentially carry other chemicals.

Comparative work extends beyond naked mole-rats. IPM is detected in breeding females of four additional Fukomys species. Although reproductive suppression mechanisms may differ across species, the presence of a similar queen-linked odorant suggests convergent chemical strategies for managing reproductive skew in eusocial mammals.

Overall, the research reframes reproduction control as a chemosensory-to-endocrine pathway: IPM activates olfactory–hypothalamic signalling, elevating prolactin and inhibiting GnRH release. This “social hormone” model offers a new principle for social evolution—chemical cues can synchronize behaviour and physiology at the scale of an entire mammal colony.

Subject of Research: Reproductive suppression and social hierarchy in eusocial mammals (naked mole-rats and related Fukomys species)

Article Title: A queen odour mediates reproductive suppression in a eusocial mammal

Article References:

Khallaf, M.A., Hart, D.W., Luo, W. et al. A queen odour mediates reproductive suppression in a eusocial mammal.
Nature (2026). https://doi.org/10.1038/s41586-026-10772-5

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-026-10772-5

Keywords: IPM; queen odour; reproductive suppression; prolactin; GnRH; olfaction; eusociality; functional ultrasound imaging; Fukomys

Bacterial cellulose—nature’s own polymer scaffold—is gaining attention as a renewable feedstock for supercapacitor electrodes. With modern devices demanding fast charge, high power bursts, and long cycle life, researchers are searching for electrode materials that combine performance with sustainability.

A new systematic literature review led by Prof. Dahlang Tahir at Hasanuddin University, Indonesia, maps how bacterial cellulose-derived carbon (BCC) is turned into energy-storage electrodes—and why some fabrication paths outperform others. The study synthesizes evidence from the scientific record to clarify which processing choices control electrochemical behavior.

The review focuses on BCC as a precursor to porous carbon. Bacterial cellulose forms a naturally pure, interconnected network of nanoscale fibers, and heat treatment can convert that architecture into carbon structures with tunable porosity—critical for charge storage. The authors emphasize that electrical performance is not just a matter of “making carbon,” but of preserving and engineering the fiber network before carbonization.

Across 49 Scopus-indexed journal articles, the team compares major strategies including direct carbonization, chemical activation to enlarge pore systems, heteroatom doping to modify surface chemistry, and composite fabrication with materials that can add rapid redox (pseudocapacitive) contributions.

A recurring message is the importance of pre-carbonization drying. Freeze-drying appears as the most commonly used approach because it limits collapse of the wet nanofiber structure during water removal. Since pore architecture governs ion access and charge transport, maintaining nanoscale structure can translate into higher effective capacitance.

The review also distinguishes test formats. Three-electrode measurements are frequently reported, but two-electrode devices better represent real supercapacitor operation, where electrode–electrode interactions shape performance.

When processing is optimized, the results point toward a pathway for BCC-based electrodes to rival or surpass commercial activated carbon under comparable conditions. Activation and heteroatom doping generally increase accessible surface area and create additional active sites, while composites often achieve the strongest capacitance by combining electrical double-layer effects with fast surface reactions.

Yet the authors warn that progress is constrained by inconsistent experimental reporting, uneven protocols, and limited mechanistic understanding. To move beyond laboratory demonstrations, they call for predictive design, data-driven structure–performance models, scalable carbonization methods, and robust flexible devices resistant to deformation and humidity.

Subject of Research: Supercapacitor electrodes using bacterial cellulose-derived carbon
Article Title: Bacterial cellulose-derived carbon electrodes for supercapacitors: Fabrication strategies, electrochemical performance, and mechanical properties — A review
News Publication Date: 9 June 2026
Web References: https://doi.org/10.1016/j.est.2026.123044
References: 10.1016/j.est.2026.123044
Image Credits: Lightenoughtotravel from Wikimedia Commons

Keywords

bacterial cellulose; supercapacitors; porous carbon; freeze-drying; chemical activation; heteroatom doping; electrode materials; two-electrode testing; pseudocapacitance; sustainable energy storage