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Heightened risk of fatal police violence in and around reservations for American Indian/Alaska Native peoples in the United States

Fatal police violence is a public health problem in the United States, with large racial and spatial inequities that remain understudied. Indigenous people, for example, experience some of the highest rates of fatal police violence of any racial/ethnic group. Residents of American Indian/Alaska Native (AIAN) reservations may be at particular risk, given geographically specific police surveillance, jurisdictional mazes, and structural disinvestment. Yet little quantitative work has examined fatal police violence against Indigenous peoples in the United States, including by reservation geography. We examined whether AIAN people experience higher rates of fatal police violence in and around reservations at the population level. We analyzed data on all AIAN people killed by police (n = 203, 2013–2024) from the Mapping Police Violence database. We first summarized their geographic distribution and characteristics using descriptive statistics. We then estimated rate ratios using quasi-Poisson regression models with population offsets, comparing AIAN peoples’ fatal police violence rates in and around reservations versus farther away. We find that fatal police violence against AIAN people is strongly concentrated in and around reservations: 73% of AIAN deaths occurred on or within 10 miles of reservations, despite only 39 to 51% of the AIAN population living there. Those areas’ residents were 1.60 to 5.84 times more likely to be killed by police than those living farther away, depending on location and choice of population offsets. Sensitivity analyses indicated these elevated risks could not plausibly be explained away by differential racial/ethnic misclassification. A coordinated public health response to police violence is urgently needed in Indian Country.

Cost-effectiveness of percutaneous patent foramen ovale closure versus medical therapy for cryptogenic stroke prevention: A Chinese healthcare perspective

Background Patent foramen ovale occurs in approximately 25% of cryptogenic stroke patients, with paradoxical embolization representing a key pathophysiological mechanism. While recent trials have established the clinical efficacy of percutaneous PFO closure in reducing recurrent stroke risk, comprehensive economic evaluation within the Chinese healthcare framework remains limited. This study assessed the cost-effectiveness of PFO closure compared with medical therapy alone in Chinese patients with cryptogenic stroke. Methods A Markov cohort model was constructed with four health states (stable, post-minor recurrent stroke, post-moderate-to-severe recurrent stroke, death) using 3-month cycles over a 30-year horizon. The analysis adopted a Chinese healthcare payer perspective, incorporating transition probabilities derived primarily from the RESPECT trial extended follow-up data and Chinese-specific cost data from multi-center hospital surveys. Primary outcome was the incremental cost-effectiveness ratio (ICER) evaluated against China’s willingness-to-pay threshold of $37,654 per quality-adjusted life year (QALY). Extensive sensitivity analyses examined parameter uncertainty and model robustness. Results PFO closure demonstrated economic dominance over medical therapy, yielding 1.41 additional QALYs (13.42 vs 12.01) while reducing lifetime costs by $4,045 per patient ($8,847 vs $12,892). The resulting negative ICER of -$2,868 per QALY indicated superior outcomes at lower cost. Probabilistic sensitivity analysis revealed 94.2% probability of cost-effectiveness at the Chinese threshold. One-way sensitivity analyses consistently showed negative ICERs across all parameter ranges, with time horizon and psychological comorbidity costs exerting the greatest influence on results. Conclusions In the Chinese healthcare context, PFO closure represents a dominant economic strategy for cryptogenic stroke patients, providing both clinical benefits and cost savings. These findings support broader implementation of PFO closure programs and inform evidence-based resource allocation decisions for stroke prevention services.

Deubiquitinase OTULIN dampens RIG-I-dependent antiviral signaling by removing linear ubiquitination from TRAF6

Linear ubiquitination and deubiquitination represent critical regulatory mechanisms in inflammation and cell death, yet their roles in RIG-I-like receptor (RLR)-dependent signaling remain unclear. Here, we identified OTU deubiquitinase with linear linkage specificity (OTULIN) as a negative regulator of RIG-I-dependent type I IFN (IFN-I) signaling. Overexpression of OTULIN markedly attenuated NF-κB and IFNβ reporter activation triggered by RNA viruses or synthetic analogs, suppressed downstream gene expression, impaired IFN-I production, and diminished phosphorylation of IκBα, TBK1, and IRF3, thereby facilitating viral replication. Conversely, knockout of OTULIN in HeLa and iBMDM cells enhanced these antiviral signaling events and restricted viral replication. Critically, reintroducing OTULIN into OTULIN -KO cells via lentiviral transduction reversed this enhanced phenotype, restoring the suppression of IFN-I signaling. Mechanistically, RNA virus infection induced linear ubiquitination of TRAF6 at K104, K142, and K371. LUBAC promoted antiviral innate immune signaling by enhancing the linear ubiquitination of TRAF6, which was antagonized by OTULIN. Notably, the linear ubiquitination of TRAF6 facilitated its K63-linked ubiquitination and strengthened its association with MAVS, amplifying the antiviral response. Furthermore, Otulin +/− mice exhibited enhanced antiviral immunity and more efficient viral clearance than wild-type littermates. Collectively, these findings unveil a regulatory role of OTULIN in attenuating RIG-I-dependent IFN-I signaling through removal of linear ubiquitination from TRAF6, highlighting the essential equilibrium between linear ubiquitination and deubiquitination in antiviral innate immunity and immune homeostasis.

Experienced teammates increase productivity in remote work: Evidence from a full remote work company in Japan

This study examines peer effects among employees working fully remotely. We use panel data from a company that has operated with an entirely remote workforce since its inception and leverage as-if random assignment of new hires to teams as a quasi-natural experiment. We find no evidence that the average productivity of a worker’s teammates affects that worker’s own productivity. However, when team members are highly experienced, the productivity of employees on those teams increases by about 12.2%. In particular, employees with the shortest tenure see an increase in productivity of approximately 26.2%. Furthermore, this effect appears unrelated to the volume of communication within the team, suggesting that experienced teammates can have a positive influence even with minimal interaction, possibly through more efficient, targeted communication.

Extent of alveolar collapse in expiratory CT as a prognostic marker in idiopathic pulmonary fibrosis

To evaluate whether distribution measures of CT-based attenuation histograms in inspiration and expiration can indicate alveolar collapse and serve as a predictive marker in patients with idiopathic pulmonary fibrosis (IPF). This single-center retrospective longitudinal study analyzed CT scans of IPF patients in inspiration and expiration. The patient population was divided into two subgroups based on their status 3 years after baseline CT (death or transplantation versus clinical surveillance). Attenuation histograms in inspiration and expiration were created and analyzed. A Mann-Whitney U test was conducted to assess the difference of CT-derived histogram measures (including skewness) between the two subgroups. Logistic regression was applied to model the ability to distinguish between subgroups using baseline forced vital capacity (FVC%) and CT-derived histogram measures. The study included 66 patients (mean age 69.5 ± 10.9 years, 58 males). After the individual three-year observation period, 37 patients were still alive while 29 had either died or received a transplantation. The two patient subgroups were significantly different in terms of all CT-derived histogram measures and the baseline FVC%. A logistic regression model that only included the CT-derived histogram measure skewness had a better predictive performance (AUC = 0.793, 95% CI = 0.685–0.900) compared to the FVC% model alone (0.708, 0.581–0.836). Whereas further evaluation is needed, paired inspiratory/expiratory attenuation histogram analysis offers a promising approach as a prognostic imaging marker to improve outcome prediction and assess alveolar collapse in IPF.

Insights into US life expectancy stagnation from birth cohort mortality dynamics

US life expectancy remained essentially flat in the 2010s. This trend has occurred in the presence of advancing innovations in medical care, especially for chronic disease, and a growing economy. While research has identified selected contributing factors, including “deaths of despair” and cardiovascular disease mortality stagnation, a comprehensive understanding of the underlying mortality dynamics has not been provided. Notably missing is a systematic evaluation of birth cohort dynamics of mortality, which can reveal whether specific generations are driving adverse trends. Using Lexis diagrams, a powerful tool for visualizing the demographic landscape, we analyzed changes in mortality from 1979–2023 for all-cause mortality and three major cause groups (cardiovascular disease, cancer, external causes). Data included cohorts born between the 1890s and 1980s. Results reveal that both cohort- and period-based processes have produced stalling life expectancy improvement. The 1950–1959 birth cohort represents a transition cohort, wherein there were general improvements in mortality across cohorts born before and general deterioration in mortality across cohorts born after. Alarmingly, cohorts born after 1970 exhibited deteriorating patterns in all major cause groups at young and middle-adult ages. Layered on these cohort dynamics was a broad mortality deterioration that began around 2010 and was experienced by nearly all living adult cohorts at the time, driven primarily by cardiovascular disease mortality. These patterns reflect the complex, multifaceted nature of stalled life expectancy improvements that cannot be attributed to any single cause or temporal mechanism. It also portends an unprecedented longer-run stagnation, or even sustained decline, in US life expectancy.

Impact of commodity terms-of-trade shocks at disaggregate level

This paper provides new evidence on the impact of country-specific commodity terms-of-trade shocks on economic growth for developing and emerging countries, not only at aggregate level but also at disaggregate level (agricultural raw materials, food and beverages, energy, and metals). Results suggest: (i) at the country group level, we find evidence supporting the so-called “terms-of-trade disconnect puzzle”; (ii) at the specific country level, the evidence is mixed (i.e., “blessing effect”, “curse effect” or “negligible effect”); (iii) at the commodity category level, it seems that output is mainly affected by shocks to the terms-of-trade for metals, followed to a lesser extent by those for energy; and (iv) statistically significant shocks occur mainly in the short run.

Haplodiploidy and the evolution of eusociality: A long-standing question is finally resolved

Correction: The WEAR-BOT checklist: A risk of bias tool for evaluating validity and reliability research in wearable technology

The path to room-temperature superconductivity: A programmatic approach

Room-temperature superconductivity is arguably the greatest challenge in condensed matter physics, with significant practical and commercial implications if it can be solved. There are no physical laws preventing this from occurring; indeed, superconductivity has been observed in so many different materials under so many different conditions that it is almost a “generic” property of nonmagnetic metals. This guides our viewpoint that high-temperature superconductivity is possible, if difficult to realize. Here, we lay out two grand challenges facing the field, titled the Prediction Challenge and the Engineering Challenge, and put forward a programmatic approach for overcoming them. The Prediction Challenge addresses the fact that our ability to predict new conventional superconductors has dramatically advanced in recent years, but most predicted materials are not experimentally synthesizable. To address this challenge, we propose a shift from modeling the superconducting critical temperature and dynamic stability toward high-throughput ab initio and predictive thermodynamics/synthesis modeling. The Engineering Challenge describes how we can control superconductivity with various “knobs,” including pressure, nanostructuring, and light. However, our ability to predict how a specific knob will modify a given superconductor is limited, making it difficult to fully exploit them. We describe the current status and identify areas where additional work is needed to fully exploit six of the most common knobs. Progress in both of these grand challenges, while closely integrating theory and experiment into a continuous feedback loop and incorporating insights from fields beyond physics and materials science, could unlock the underlying keys to room-temperature superconductivity.

Enhanced ganoderic acids production by using thermotolerant Ganoderma tsugae at high-temperature liquid cultivation

Ganoderic acids (GAs) are bioactive triterpenoids produced by Ganoderma species with demonstrated anticancer properties. While the yield of GAs in Ganoderma tsugae is typically low, heat stress has been shown to enhance its production. This study employed atmospheric and room temperature plasma (ARTP) mutagenesis to develop thermotolerant G. tsugae mutants for high-temperature cultivation at 35°C. From 59 mutants generated, strain Ganoderma tsugae 9 (GT9) demonstrated superior thermotolerance, showing 51.48% increased mycelial growth rate and 76.03% higher biomass compared to wild-type (WT) at 35°C (one-way ANOVA with Dunnett’s test, p < 0.05). Physiological characterization revealed GT9 possessed enhanced membrane fluidity, elevated intracellular levels of lanosterol (92.49% increase), squalene (1.36-fold increase), trehalose, and ergosterol (66.98% increase) (two-way ANOVA with Tukey’s test, p < 0.05). Transcriptional analysis revealed significant upregulation of key GAs biosynthetic genes ( hmgr , sqs , se , ls ) and heat shock protein genes ( hsp17.4 , hsp22 , hsp70 , hsp90 ). After 10-day cultivation at 35°C, GT9 produced 1.01-fold more GAs than the WT at 35°C and 22.64% more than the WT at 25°C (two-way ANOVA with Tukey’s test, p < 0.05). However, the difference in GAs production between the WT strain cultured at 25°C and the GT9 strain cultured at 35°C was not significant. ARTP-generated thermotolerant G. tsugae mutants enable efficient high-temperature fermentation for enhanced GAs production. This strategy provides significant advantages for industrial-scale application while elucidating the physiological and molecular mechanisms underlying improved GAs biosynthesis under heat stress.

Reassessing evidence for symmetric histone inheritance in <i>Drosophila</i> stem cells

Enhancing cancer drug discovery: QSAR modeling with machine learning and chemical representations

Accurately predicting the bioactivity of small molecules against cancer therapeutic targets remains a significant challenge at the intersection of cheminformatics and drug discovery. This study comprehensively evaluates chemical representations, including AtomPair Counts (APC),Avalon (AVN), Extended-Connectivity Fingerprint diameter 4 (ECFP4), Extended-Connectivity Fingerprint diameter 6 (ECFP6), Feature-based Morgan 2 (FM2), Feature-based Morgan 3 (FM3), Mol2Vec (M2V), Molecular ACCess System (MACCS), Mordred 2D Chi Kappa (MK2), RDKFingerprint (RDF), Rdkit PhysChem (RDC), Torsion (TSN) combined with machine learning algorithms (Bayesian Ridge (BRG), Elastic Net (ENT), Extra Trees (ETT), Hist Gradient Boosting (HGT), K-Nearest Neighbors ( k NN), Lasso (LSS), Multi-layer Perceptron (MLP), Partial least squares (PLS), Random Forest (RFT), Ridge (RDG), Support Vector Regressor (SVR), and XGBoost (XGB)) for predicting cancer bioactivities. The results show that while AVN chemical representation, in conjunction with SVR algorithm, achieved the highest predictive accuracy, with R 2 of 0.735 in FGFR1 dataset; The mTOR dataset demonstrated the highest average performance across all models and chemical representations, with an R 2 of 0.592 across various cancer datasets. These findings demonstrate how cheminformatics tools like molecular fingerprints and quantitative structure-activity relationship (QSAR) modeling can significantly enhance bioactivity prediction, ultimately contributing to more efficient and targeted cancer drug discovery.

Breaking barriers: A study protocol on unveiling gender, racial and other intersectional dynamics in post-secondary institutions and identifying solutions for advancing primary care and public health research

Background and objectives This study protocol employs critical race and intersectionality theories to investigate barriers faced by racialized women at various academic career stages within Canadian primary care (PC) and public health (PH). The objectives are to identify faculty characteristics, examine intersectional barriers, and recommend equity-focused, inclusive strategies and policies. Research design and methods The study adopts a sequential mixed-methods approach. A quantitative survey and/or existing datasets will be used to collect demographic data on PC and PH academic position holders in Ontario and British Columbia, Canada. Data will also examine experiences of workplace discrimination; equity, diversity and inclusion (EDI) resource use; and departmental satisfaction. Subsequently, we will conduct interviews with researchers and leaders who are responsible for hiring and involved in or addressing matters related to EDI. Inductive and deductive approaches will be used to analyze the data in accordance with theoretical frameworks to deepen insights into equity and inclusion in academia. Results The quantitative phase will profile PC and PH academic position holders, highlighting disparities in positions and leadership roles. The qualitative study will explore intersectional challenges faced by racialized women academic position holders during career progression. Preliminary findings will inform effective equity-promoting strategies. Discussion and implications This study aims to contribute to the existing body of knowledge on gender and racial inequities in academia by uncovering systemic identity-based disparities in the careers of PC and PH researchers in Canada. The findings will inform the development of targeted interventions to promote equitable hiring, faculty support, and leadership advancement, enhancing diversity and productivity through an inclusive and equitable academic environment. Findings will be shared via publications, policy briefs, workshops, and online platforms to engage academics, advocacy groups, funders and policymakers in promoting equity and driving institutional change in PC and PH research.

Evaluation of a commercial AI-assisted cell counting software for dopaminergic neurons across species

Quantification of dopaminergic neurons in the substantia nigra pars compacta (SNc) of animal models is important for understanding the pathogenesis of Parkinson’s disease (PD). However, conventional manual cell counting method requires the time and effort, and has limited reproducibility due to inter- and intra-examiner variability. Here, we demonstrate that a commercially available convolutional neural network–based artificial intelligence (AI) counting method (TruAI, OLYMPUS, Tokyo, Japan) enables robust and reproducible quantification of TH-positive dopaminergic neurons in mouse, marmoset, and human SNc samples when compared with conventional manual counting. AI-based counting showed a strong correlation with manual counting across mouse, marmoset, and human samples. Good agreement between AI-based and manual counting was observed in mouse and marmoset samples, supporting the applicability of this approach for cross-species quantification of dopaminergic neurons. In the mouse model treated with α-syn preformed fibrils (PFFs), AI-based counting detected a significant reduction in TH-positive neurons consistent with expert manual counting. Non-experts exhibited greater intra-examiner variability than an expert, indicating that the reliability of manual counting depends on experience. Overall, AI-based quantification provides a robust and objective approach for TH-positive cell counting and may improve reproducibility in dopaminergic neuron analysis, particularly for non-expert users and cross-species studies of PD.

Assessing factors that influence perceived burnout in postdoctoral fellows and identifying recommendations to support their well-being

Background While current evidence suggests rates of stress and burnout among healthcare professionals and graduate trainees are up to 50%, higher than the general population, there is a critical gap in the literature concerning factors which influence well-being among postdoctoral fellows in health sciences programs. This exploratory study aims to identify factors influencing well-being and burnout among these postdoctoral fellows and identify recommendations to improve their well-being. Methods A two-stage sampling approach was used: (1) Purposive sample of postdoctoral fellows employed at a public university were recruited to participate in semi-structured focus groups to assess workplace factors which influence their perceived burnout and well-being and to solicit recommendations to improve well-being; (2) Stratified sampling was used to assign participants into focus groups by industry-sponsored or academic (non-industry-sponsored) positions to explore experiences that may be unique to these groups. Inductive coding and thematic analysis of Zoom transcripts were used. Results Seven postdoctoral fellows participated in three focus group sessions, with two industry-sponsored fellows in one group, three academic fellows in one group, and two academic fellows in one group. Participants identified insufficient resources, difficult transition, and workload and program structure as factors contributing to their perceived burnout. Factors contributing to their well-being included reasonable supervisor expectations, personal and professional support, and resource support. Participant recommendations to improve well-being included institutional initiatives and resources, additional non-supervisor support, and workload strategies. Conclusions This study expands upon the sparse literature on postdoctoral fellows by exploring factors that contribute to their perceived well-being and burnout, as well as provide suggestions to support their well-being. Findings contribute to the broader conversation of postdoctoral fellow well-being and burnout and informs the academy of focused strategies to improve their well-being and reduce burnout.

Multiple molecular mimics in Epstein Barr Nuclear Antigen-1, and the pathogenesis of multiple sclerosis

The Epstein–Barr virus (EBV) infects greater than 95% of humans and is associated with initiating and perpetuating multiple sclerosis (MS). Antibody to Epstein–Barr Nuclear Antigen-1 (EBNA1) is present in nearly 100% of patients with MS before the development of clinical symptoms. Infection with EBV is necessary, but not sufficient, for causation of disease. Within the EBNA1 transcription factor is a stretch of 47 amino acids containing three regions with shared linear sequences of portions of three molecules, Glialcell adhesion molecule (CAM), alpha Crystallin-B, and Anoctamin-2. These cross-reactive linear sequences between EBNA1 and each of these three molecules are termed “molecular mimics.” Cross-reactive adaptive immunity to these three molecules mimicking regions of EBNA1 each play distinct roles in the pathogenesis of MS. Antibodies to each of these molecules greatly increase the chance of developing MS. Analysis of the cellular landscape of MS lesions reveals EBNA1 in B cells, glial cells, and neurons. Here, we provide commentary on recent publications on the molecular and cellular landscape of EBV infection in studies on the blood, cerebrospinal fluid, and brain specimens of individuals with MS. The published studies reveal perspectives on the pathology of MS in detail ranging from the atomic level using crystallography to multiplexed anatomical imaging of lesions in the brain.

Genetic and genomic analyses of tree architectural traits in Hevea brasiliensis revealed genes underlying QTLs linked to key developmental processes

The architectural characteristics of rubber trees are increasingly important in the context of climate change. Branching, canopy shape and growth pattern have to be adapted to monocropping and intercropping systems to foster latex and wood production, wind-tolerance, light availability and microclimate for intercrops, and soil stability and fertility. This study aimed to identify key architectural traits that could be used in breeding programs as well as chromosomal regions underlying QTLs that could be targeted for marker-assisted selection. Five quantitative (height of bole, trunk girth, estimated bole volume, number of terminal branches and apical shoots) and five qualitative (tree straightness, axillary shoot score, type of crown and axillary shoot, diameter of axillary shoot) architectural traits were phenotyped in a segregating population derived from clones PB 260 and SP 217. The frequencies of categories for each qualitative architectural variable were analysed as quantitative variables. A principal component analysis performed with these traits showed that trunk girth, estimated bole volume, round crown and medium diameter of axillary branches are negatively correlated with small diameter of axillary branches and conical type of crown. Seven architectural variables have heritability greater than 0.50. Twenty quantitative trait loci and their underlying genes and functions were pinpointed using the high-density genetic map previously constructed and an improved high-quality genome of the parent clone PB 260. Of the 680 genes found in chromosomal regions under QTLs, 19 genes have a function directly involved in plant development such as transcription factors related to the regulation of shoot apical meristem (SAM) and vascular cambium activity (WUSCHEL). A literature review was also conducted to provide additional insights into tree architecture and its impacts on agricultural systems. This first genetic analysis of architectural traits in rubber revealed that seven traits (trunk girth, bole height, estimated bole volume, number of apical branches, diameter of axillary branches, number of terminal branches and type of crown) could play a major role in rubber breeding both for monoculture and agroforestry single and double row systems. Chromosomal regions harbouring developmental genes could be used to develop specific strategy of marker-assisted selection.

Cryo-EM maps of human DNA polymerase ε should be reevaluated in light of its unexpected behavior in vitro

Classification of customer retention using hybrid SVC-SDNN to enhance customer relationship management

Many banking and corporate sector organization problems are resolved by clever, creative solutions based on artificial intelligence (AI). Any financial institution has to use AI-enabled churn detection solutions to improve customer relationship management (CRM). In order to effectively predict churn in a publicly accessible datasets, we suggest a novel hybrid deep method. It functions efficiently on any private, hidden banking dataset in the specified format. Other hybrid algorithms’ performances are contrasted with this one. The predictive analytics of SDNN and SVM coupled is excellent using accuracy, precision, recall, and F1-score matrices, according to our thorough search for the best intelligent solution. It is essential to correctly identify the important variables or churn-causing elements. SVC-SDNN’s strength is in its ability to anticipate which customers are most likely to leave and identify the critical elements affecting customer retention. The suggested approach has an AUC-ROC of 0.881696 and an accuracy of 0.95.