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Demographic responses of North Atlantic seabirds to seasonal ocean warming
Climate-driven ocean warming is profoundly reshaping marine ecosystems, with cascading effects on biodiversity and trophic interactions. For migratory marine predators such as seabirds, demographic responses to warming depend on when and where populations are exposed across the annual cycle. Therefore, integrating demographic monitoring and tracking data, across broad geographic and temporal scales, is essential, given the spatial and seasonal variability in ocean warming. Here, we integrated long-term demographic data, seasonal distributions, and sea surface temperatures (SSTs) for 26 populations of five seabird species breeding in the North–East Atlantic to assess the effects of SSTs on reproduction, survival, and population growth trajectories. Demographic responses varied widely among populations and seasons, but negative effects were most consistently associated with warming during the autumn period postbreeding, particularly in the Barents and East Greenland Seas. Winter warming also corresponded to reduced survival, while breeding-season SSTs showed fewer significant effects on reproductive rates. Populations with dual responses to warming in both the breeding and nonbreeding seasons had the lowest projected population growth rates under future SSTs given a high emissions scenario. These results demonstrate that population vulnerability reflects the interaction between seabirds’ year-round distributions and regional ocean warming. This underlines the need to integrate year-round tracking and long-term monitoring to inform conservation strategies and marine spatial planning to ensure climate-resilient marine ecosystems.
Polygenic prediction of cardiorespiratory fitness in the Trøndelag health study (HUNT)
Abstract Cardiorespiratory fitness (CRF) has a strong genetic component and low CRF is a major risk factor for cardiovascular morbidity and mortality. The purpose of this study was to develop and validate a polygenic score (PGS) for CRF (CRF PGS ) and assess its associations with cardiovascular disease (CVD) and all-cause mortality. We hypothesized that the CRF PGS would demonstrate similar cardioprotective benefits as the CRF phenotype. Effect estimates from a genome-wide association study on directly measured CRF in the Trøndelag Health Study (HUNT; n = 4525) were used in a Bayesian regression framework to develop multiple PGSs in an independent cohort from the UK Biobank ( n = 65,165). The top performing score was applied in the HUNT target cohort, excluding the discovery sample ( n = 82,109). The PGS-CRF association varied considerably as a function of model fit and phenotypic accuracy. There was a difference of 1.55 [95% confidence interval: 1.26, 1.84] mL·kg −1 ·min −1 between the bottom and top decile of the CRF PGS . Moreover, a high CRF PGS demonstrated cardioprotective effects, with reduced risk for CVD, myocardial infarction, hypertension, and all-cause mortality. Additionally, in women, we observed that the CRF PGS predisposed to lower risk of heart failure and hypertrophic cardiomyopathy. We developed the first PGS for CRF using gold standard phenotypes and multiple independent cohorts. Genetic susceptibility to high CRF may have a clinically meaningful impact on the phenotype. The CRF PGS was better to identify individuals with slightly higher lifelong levels of CRF, which appears to protect against cardiovascular morbidity and mortality.
Chemoautotrophic carbon fixation in thermokarst lakes on the Tibetan Plateau
Actin network heterogeneity tunes activator–inhibitor dynamics at the cell cortex
Biological systems can display diverse patterns of self-organization, even when built on conserved networks of interaction between molecular species. In these cases, reaction–diffusion equations provide a valuable tool to learn how new dynamics could emerge from quantitative tuning of parameters. Bringing these models into quantitative correspondence with biological data remains an outstanding challenge, especially when the data manifest heterogeneities that are difficult to account for mathematically. One particular example occurs in cell biology, where the membrane-bound regulatory protein RhoA interacts with the filamentous actin cortex in an activator–inhibitor loop. Though this core biochemical circuit is conserved across multiple cell types in different organisms, it produces different patterns of RhoA activity in different contexts, from traveling waves in starfish to transient pulses in Caenorhabditis elegans . To understand how this variation emerges, we develop an activator–inhibitor model that accounts explicitly for actin assembly and heterogeneity. By fitting the model to summary statistics of experimental data, subject to known parameter constraints, we show that F-actin assembly dynamics tune the spatiotemporal patterns of RhoA activity. A minimal representation of these dynamics reveals how directional transport (via polymerization) combines with stochasticity in F-actin number and orientation to produce the observed patterns. This work sheds light on how phenotypic diversity arises from heterogeneity and anisotropy, with important implications for the next generation of activator–inhibitor models.
An enhanced dual inception-attention-BiGRU-attention model integrating wavelet transform for wearable sensor-based human activity recognition
A general framework for nitrogen deposition effects on soil respiration in global forests
Abstract Since the Industrial Revolution, human activities have altered atmospheric nitrogen (N) deposition to global forests, affecting carbon dioxide emissions from soils (soil respiration or SR) – one of the largest land-atmosphere carbon fluxes. However, experimental studies have demonstrated both positive and negative effects of N deposition on SR in global forests, leading to debates on how N deposition increases or decreases SR. We developed a framework for generalizing SR responses to N deposition using synthesized data from 168 N addition experiments worldwide and observed SR across the global natural N deposition gradient. The findings indicate that N deposition decreased SR in 2.9% of global forested areas, particularly in eastern China, western Europe, and the eastern USA. However, the net effect of N deposition increased the global forest SR by ~5% (1.7 ± 0.1 PgC yr –1 ). If N pollution could be effectively controlled, global forest SR would decrease, potentially contributing to a reduction in the terrestrial carbon emissions.
Amazon forest faces severe decline under the dual pressures of anthropogenic climate change and land-use change
The Amazon is a key climate system component, hotspot of biodiversity and many other ecosystem functions. However, progressive rainforest degradation, driven by anthropogenic climate change and land-use change, is increasing the risk of a large-scale critical ecosystem transition. Previous studies highlight forest vulnerability to isolated or combined climate change and land-use pressures, but have not disentangled individual driver contributions. This crucial knowledge gap needs to be addressed for a holistic understanding of the risks that the rainforest is facing. Combining Earth System Model data with a robust detection and attribution framework, we assess forest decline under individual and combined pressures of climate change and land-use change. We assess abrupt shifts and nonlinearities in local and basin-wide forest decline to reveal signs of resilience loss and potentially imminent forest transitions. We identify land-use change as the dominant driver of past degradation, accounting for 80% of the historical (1950 to 2014) forest decline. Future projections reveal that up to 38% of the mid-20th century forest area could be lost by 2100, with 25% caused by continued deforestation and 13% caused by unmitigated global warming. Importantly, the risk of abrupt rather than gradual forest decline increases as global warming progresses, with a strong nonlinear trend beyond a threshold of 2.3°. These findings highlight a substantial risk of a large-scale transition, with potentially devastating consequences for the global climate system, regional water and carbon cycles, human livelihoods, and biodiversity. Limiting this risk requires rigorous forest protection and climate mitigation in line with the Paris Agreement.
Real-time retail planogram compliance application using computer vision and virtual shelves
Dramatic expansion of bimodal redox window of indigo by two-electron redox processes
Mapping epileptogenic brain using a unified spatial–temporal–spectral source imaging framework
Noninvasive electrophysiological source imaging (ESI) is a valuable tool for localizing and imaging brain activity, with significant potential to aid presurgical planning in focal drug-resistant epilepsy (fDRE) patients. Scalp electroencephalography (EEG) biomarkers, including interictal spikes, high-frequency oscillations (HFOs), and seizures, each offer unique capabilities in estimating the epileptogenic zone (EZ). However, there is a limited quantitative understanding of how these biomarkers differ in source-imaging precision, requiring distinct processing pipelines. Here, we developed a spatial–temporal–spectral imaging (STSI) framework for precision source imaging, and quantitatively evaluated various epilepsy biomarkers for source imaging in 2,081 individual events (spikes, HFOs, and seizures) from a cohort of 42 fDRE patients, comparing results to clinical ground truth such as surgical resection outcomes and intracranial EEG-defined seizure onset zones. The STSI enabled quantitative comparisons across key EEG epilepsy-related biomarkers, with averaged localization errors of 6.67 mm for seizures, 8.73 mm for HFOs overlapping with spikes (pHFO), 10.28 mm for HFO-riding spikes (pSpike), 19.59 mm for general spikes (aSpike), and 36.53 mm for general HFOs (aHFO), respectively, for seizure-free patients. These findings indicate that HFOs overlapping with spikes is the most spatially accurate interictal biomarker for mapping the EZ. The proposed STSI framework not only establishes a unified analysis approach for epileptic biomarkers to enhance presurgical planning in focal drug-resistant epilepsy, but could also generalize as a versatile tool for mapping event-related potentials, neural oscillations, and dynamic brain states, within a single framework to advance cognitive neuroscience research and clinical management of neurological and psychiatric disorders.
Unveiling potent xanthine oxidase inhibitors in two Balanophora spp. using machine learning-based virtual screening and molecular docking approach
Abstract Pharmacological studies revealed that the Balanophora species contains diverse phytochemicals which enable interesting biological activities and emphasize their pharmaceutical relevance. Previously, we identified significant xanthine oxidase (XO) inhibitory activity from extracts of the two Balanophora spp. ( Balanophora subcupularis P.C. Tam and Balanophora tobiracola Makino). However, the specific compounds responsible for this activity remain unidentified so far. Thus, in the present study, we focused on elucidating the compounds inducing the XO inhibitory effect of extracts from Balanophora species. Therefore, a combination of advanced liquid chromatography and mass spectrometry (LC-QToF-HRMS), virtual screening using machine learning (ML) models, and molecular docking simulation was applied. Using LC-QToF-HRMS, 23 and 21 compounds were identified in the ethyl acetate fractions of B. subcupularis and B. tobiracola , respectively. Next, a curated dataset of natural and synthetic compounds with known XO inhibitory activity was employed to train several ML models. Adducing five selected ML models, the virtual screening process identified the potentially active compounds 1-(3,4-dihydroxyphenyl)-6,7-dihydroxy-1,2-dihydro-2,3-naphthalenedicarboxylic acid, taxifolin, and 1- O -caffeoyl-6- O -(S)-brevifolincarboxyl- β -D-glucopyranose. All the compounds found in the two Balanophora spp. underwent docking simulations, in which MTE, FES, and AFH were retained in the active site of XO, ensuring reliable re-docking results. Finally, taxifolin emerged as the most promising novel XO inhibitor, demonstrating greater potential than the established drug allopurinol, as supported by both the virtual screening nomination and docking simuation. These findings contribute to the development of natural XO inhibitors and may open new opportunities for gout treatment and uric acid level control.
Accelerated land surface greening caused by earlier permafrost thawing
PFAS-contaminated drinking water harms infants
There is evidence of widespread human exposure to per- and polyfluoroalkyl substances (PFAS) but limited evidence of the human health impacts of this exposure. Using data on New Hampshire births from 2010–2019, we show that mothers receiving water that had flowed beneath a PFAS-contaminated site, as opposed to comparable mothers receiving water that had flowed toward a PFAS-contaminated site, had 191% [95% CI: 83–298%] higher first-year infant mortality (611 [268–955] additional first-year deaths per 100k births); 168% [42–294%] more births before 28 wk of gestational age (466 [116–817] additional such births per 100k births); and 180% [57–302%] more births with weight below 1,000 g (607 [192–1022] additional such births per 100k births). Extrapolating to the contiguous U.S., PFAS contamination imposes annual social costs of approximately $8 billion. These health costs are substantially larger than current outside estimates of the cost of removing PFAS from the public water supply.
Automated computer vision and dose–response modeling improve throughput and accuracy of an ex vivo functional precision medicine platform
Cancer stage at diagnosis by duration of pre-existing chronic analgesic use and anxiety or depression
Abstract Pre-existing chronic diseases may delay or expedite cancer diagnosis. Here, we examine variations in cancer stage at diagnosis based on duration and type of common chronic conditions. We identify lung and colon cancers diagnosed 2012-2018 from national cancer registration, and pre-existing physical and mental-health conditions from linked primary care records. Using multivariable logistic regression, we explore associations between the most prevalent conditions (Anxiety/Depression and Chronic Analgesic Medication use), classified as “Recent-onset” (first recorded <12months pre-cancer) or “Persistent/Historic” (12-72 months pre-cancer), and cancer stage at diagnosis. We show that recent-onset Analgesic Medication use can be associated with increased odds of advanced stage lung or colon cancer diagnosis. Conversely, persistent/historic Chronic Analgesic Medication use can be associated with reduced odds of advanced stage lung cancer and persistent/historic Anxiety/Depression with reduced odds of advanced stage lung or colon cancer. Persistent or historic conditions may increase healthcare utilisation, offering opportunities for early cancer diagnosis. Recent-onset conditions may lead to delays through the alternative explanations or competing demands mechanisms.
Multiple weak brakes act in concert to control STIM1 and store-operated calcium entry
Store-operated Ca 2+ entry is a key signaling pathway controlled by the interaction of the ER Ca 2+ sensor STIM1 with the Orai1 Ca 2+ channel following ER Ca 2+ depletion. To avoid generating pathological effects, STIM1 must remain mostly inactive under resting, ER-replete conditions yet respond rapidly and reversibly to changes in ER Ca 2+ content. It is not well understood how these conflicting requirements are met. Here we combine single-molecule FRET measurements of full-length dimeric STIM1 in lipid membranes with an AlphaFold2 structural model to describe the structure and regulation of the resting state. We show that STIM1 activity is controlled by the combined operation of four relatively weak restraints, or brakes. The Ca 2+ -bound EF-SAM luminal domain acts as a steric restraint to inhibit spontaneous activity. In the cytosolic region, the domain-swapped hydrophobic interaction and alignment of CC1α1 with CC3 of the CRAC activation domain (CAD) positions the apex of CAD next to the ER membrane, where electrostatic lipid–protein interactions further stabilize the inactive conformation. A fourth brake is created by hydrophobic and electrostatic interactions of the two CC1α2/3 domains attached to the base of CAD. Disruption of any one of these brakes triggers spontaneous STIM1 activation, showing that the concerted action of these relatively weak restraints serves to minimize spontaneous activity in resting cells with full ER Ca 2+ stores, while allowing rapid activation in response to changes in store content.
Computational drug repositioning approach to predict multi-target therapeutics for epilepsy
Abstract Epilepsy affects millions of people globally, with approximately one-third of patients experiencing drug-resistant seizures. Developing new anti-epileptic drugs is time-intensive and costly, prompting interest in computational drug repositioning strategies. Here, we report on a comprehensive drug repositioning approach to identify the multi-targeted therapeutic option(s) for epileptic seizures. All approved drugs from the DrugBank database were screened for their anti-epileptic properties, which involved predicting their blood-brain permeability and clustering them based on structural similarity with marketed anti-epilepsy drugs. The screened drugs were subjected to molecular docking against previously identified therapeutic target proteins (Voltage-Gated Sodium Channel α2; GABA receptor α1-β1; and Voltage-Gated Calcium Channel α1G), A total of 46 drugs showed better binding affinity than the respective standard drugs - Carbamazepine, Clonazepam and Pregabalin for the selected target proteins - Voltage-Gated Sodium Channel α2; GABA receptor α1-β1; and Voltage-Gated Calcium Channel α1G, respectively. The binding pocket and literature data mining revealed three drugs, Oxaprozin, Pizotifen, and Cyproheptadine, that bind within the precise binding pocket and have no reported severe side effects related to seizure onset. The molecular dynamics simulation studies revealed that all three compounds exhibited more stable and better binding interactions with their corresponding drug targets. Oxaprozin, among the identified three drugs, showed a very stable binding and can be considered a potential repurposed drug for epilepsy, warranting further preclinical trials.