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Pincer movement: fossil pushes origins of chelicerate arthropods back to the Cambrian period

Multidimensional profiling of heterogeneity in supratentorial ependymomas

Outplaying elite table tennis players with an autonomous robot

A chelicera-bearing arthropod reveals the Cambrian origin of chelicerates

The misunderstood sex chromosome: how X affects your health

Non-equilibrium condensation of the first Solar System solids

Abstract Primitive meteorites (chondrites) consist of an out-of-equilibrium assemblage of minerals formed during the assembly of our solar nebula 1 . The conditions under which their precursors condensed remain unclear as a result of subsequent reprocessing in the protoplanetary disk or in asteroidal parent bodies. Chondrites are classified into three main classes—enstatite, ordinary and carbonaceous—and these are distinguished by different bulk composition and oxidation state 2 . Although equilibrium condensation models explain the composition of some of their refractory components 3,4 , they do not explain the emergence of three mineralogical classes. Moreover, the low pressures, steep temperature gradients and short dynamical transport timescales in forming protoplanetary discs probably hindered equilibrium. Here we test the hypothesis that chondrite precursors formed via kinetic non-equilibrium condensation. Using a new time-dependent condensation model, we show that varying the cooling rate and pressure produce only three types of mineralogies. Departure from equilibrium yields increasingly oxidized and hydrous mineralogies. When projected into a Urey–Craig diagram, the predicted mineralogical types fall close to the redox states of enstatite, ordinary and carbonaceous chondrites. These results suggest that the mineralogical diversity of chondrites may reflect, in part, local condensation kinetics, offering an alternative to large-scale variations of oxidation conditions.

How to secure philanthropic funding in a competitive climate

Using tree-based ensemble methods to produce a population-based mortality risk score in Ontario, Canada

Introduction Risk adjustment is critical in observational epidemiology to control for confounding of the exposure-outcome relationship. Accurate prediction of outcomes, such as mortality, can improve risk adjustment. In the present study, we compared logistic regression with a range of tree-based ensemble methods to predict 1-year mortality in the general population of Ontario, Canada. Methods Ontario adults (age 18 years and older) who were alive as of January 1, 2022 were included. Using a window of up to 3 years, various measures of health and healthcare utilization were captured from administrative databases. To predict 1-year mortality, we applied logistic regression, random forests, extremely randomized trees, adaptive boosting, gradient boosting, extreme gradient boosting, Newton boosting, and CatBoost. All models also included age and sex. Performance was evaluated using the area under the ROC curve (AUROC), the area under the precision-recall curve (PR-AUC), the Brier score, and a quantile-based version of the Integrated Calibration Index (ICI), reported in the 30% test set. Feature importance was assessed using CatBoost’s internal model structure, supplemented with permutation feature importance, explainable boosted machines, and marginal effects. Results A total of 12,080,801 Ontarians were included and 121,951 (1.0%) died within 1 year. Logistic regression showed excellent discrimination (AUROC 0.926; PR-AUC 0.256) and acceptable calibration (ICI 0.0022). The best model was CatBoost, which had the best discrimination (AUROC 0.933, PR-AUC 0.280) and calibration (ICI 0.0003). In sensitivity analyses of the CatBoost model, including more detailed definitions of cancer (to include its subtype) and chronic kidney disease (defined using serum creatinine instead of diagnostic codes) produced modest improvements in PR-AUC (0.290), along with substantially improved calibration amongst the highest-risk (70–100%) individuals. The most influential model-building feature was age. Residence in long-term care and receipt of palliative care was associated with the largest marginal effects. Conclusion The machine learning model CatBoost yielded the most accurate predictive model for 1-year mortality using individual comorbidities and additional measures of healthcare utilization for the general population. These findings demonstrate that machine learning methods can enhance risk adjustment efforts in observational studies, leading to more accurate confounder control and better support for health policy and epidemiologic research.

Robot can beat elite players at table tennis

Neural network architectures and normalization techniques for automated sleep stage classification using rodent EEG and EMG signals

Accurate sleep stage classification in animal models is crucial for translational sleep research, enabling the study of mechanistic pathways and therapeutic interventions. Because manual scoring is labor-intensive and variable, artificial neural networks are increasingly used for automation. However, few models are tailored for animal sleep staging, and direct cross-model comparisons under consistent conditions remain limited. We presents a systematic evaluation of three representative neural architectures for automated sleep stage classification using rodent electroencephalogram and electromyogram: a conventional 1-dimensional convolutional neural network (1D-CNN), a 2-dimensional convolutional neural network (AccuSleep), and a convolutional neural network combined with bidirectional long short-term memory (DeepSleepNet). Performance was assessed under within-subject and cross-subject validation frameworks, comparing raw input, z-scoring, and mixture z-scoring. Both 1D-CNN and DeepSleepNet consistently outperformed AccuSleep, particularly for Rapid Eye Movement (REM), where AccuSleep exhibited marked deficits plausibly attributable to class imbalance. Class-wise analysis confirmed stable Non-Rapid Eye Movement (NREM) classification across models, while AccuSleep showed reduced robustness in REM and Wake. Normalization effects were model-dependent: raw data yielded superior outcomes for 1D-CNN and DeepSleepNet, whereas AccuSleep showed modest improvement in Wake detection under mixture z-scoring. Comparison with human electroencephalogram literature indicated that DeepSleepNet’s advantage over 1D-CNN is more pronounced in human datasets (especially NREM 1), likely reflecting differences in sleep architecture. These findings highlight the suitability of simpler CNNs for rodent sleep stage classification and underscore the importance of aligning preprocessing strategies with model architecture and data characteristics.

The microscopic mechanism of water immersion and collapsibility in Malan loess with different particle size

Collapsibility of loess is a widespread, highly destructive geological hazard on the Chinese Loess Plateau. Malan loess exhibits distinct regional particle size variations, but the collapsible deformation characteristics and underlying microscopic mechanisms of loess with different particle sizes remain insufficiently understood. This study selected sandy (Jingbian), silty (Yan’an), and clayey (Jingyang) Malan loess in Shaanxi as representative samples to investigate collapsible deformation and clarify intrinsic mechanisms. Results show particle size and clay content significantly affect loess’ physical-mechanical properties: particle shape transitions from angular to sub-rounded/rounded, with clay distributing as adhesion (sandy), bridging (silty), or filling (clayey). Collapse is dominated by clay softening, skeleton destruction, and void filling. Post-collapse, macropores (>50 μm) convert to mesopores (2–50 μm), porosity drops ~10%, and pore orientation homogenizes. Generalized collapse mechanism models for different particle size Malan loess are proposed, providing a theoretical basis for hazard mitigation.

Quantification of pulmonary arterial pressure with 4D flow cardiac MRI velocity mapping in patients with suspected pulmonary hypertension: Comparison with right heart catheterization

Objectives 4D flow MRI is becoming a promising tool to assess pulmonary hypertension which remains a progressive fatal disease. The aim of this study was to compare the quantification of pulmonary arterial pressure derived from 4D flow MRI with right heart catheterization in patients with pulmonary hypertension. Methods Thirty-two patients (22 men, 10 women, mean age 62.6 years old) with known or suspected pulmonary hypertension were enrolled in this prospective study. Subjects were split into two consecutive groups, with the first 22 subjects dedicated to analysis and the last 10 subjects dedicated to validation. All patients underwent right heart catheterization and cardiac MRI examinations. Pulmonary arterial pressures were measured by catheterization. An accelerated kat-arc 4D flow MRI sequence allowed the analysis of cardiac blood and pulmonary artery (PA) flows. Multivariate linear regression models were obtained using stepwise, bottom-up and top-down covariate selection procedures. Results Using right heart catheterization as reference, the multivariate estimates of mean (mPAP) and systolic (sPAP) pulmonary arterial pressures only included 4D flow MRI parameters: mean helicity in right ventricle (RV), mean vorticity in right atrium (RA) and maximum cross-sectional PA area (A max _PA). The models yielded mPAP = 0.04.A max _PA + 0.061.mean_helicity_RV – 2.42 (R² = 0.69) and sPAP = 0.066.A max _PA + 0.134.mean_helicity_RV – 0.613.mean_vorticity_RA + 23.98 (R² = 0.80). Bland-Altman bias were 0.42 and 0.38 mmHg, respectively. Conclusion This study suggests that kat-arc accelerated 4D flow MRI is a potential non-invasive technique for pulmonary arterial pressure estimation. Therefore, this short-duration sequence could become a useful diagnostic and follow-up exam for patients with pulmonary hypertension.

Relationships between pre-pandemic mental health, sociodemographic factors and health behaviours in older adults during the acute onset of COVID-19 in Australia: A descriptive analysis

Objective To gain a comprehensive understanding of associations between mental health symptoms and sociodemographic and health factors assessed during COVID-19 restrictions in existing, longitudinal community-based cohorts. Methods Participants of The North West Adelaide Health Study (NWAHS, n = 982) and the Florey Adelaide Male Ageing Study (FAMAS, n = 338) in South Australia, undertook a COVID-19 impacts survey during October 2020-May 2021. The Centre for Epidemiologic Studies Depression Scale (score≥16;NWAHS) and the Beck Depression Inventory 1A (score≥13;FAMAS) were used to characterise mild-severe depressive symptoms. The Generalised Anxiety Disorder questionnaire was used to identify moderate-severe anxiety (score 10–21). Results Of 1,320 participants (male n = 797), 62.4% (n = 824) were aged ≥65years (range 36−100 years), and 37.8% reported workforce participation at the time of the COVID-19 survey. Depressive and anxiety symptoms were observed for participants aged 35−54years (OR=1.92,95%CI = 1.01–3.67), financial stress (1.81,1.02–3.21), change in overall food intake (increase and decrease), social support none/sometimes(2.74,1.48–5.07), low control/mastery since COVID-19 (6.00,3.37–10.6) and poor sleep during restrictions (7.94,4.25–14.8), independent of previous depressive symptoms (8.30,1.9–13.2). Change in mental health status from pre-COVID to COVID-19 restriction was associated with sex (p = 0.013) and age (p < 0.001), such that females and younger participants (35−54yr) reported depressive symptoms at both times. Younger adults (35-54 yr) showed a higher prevalence of depressive symptoms only during COVID-19. Conclusions Depressive and anxiety symptoms were consistent during COVID-19 relative to pre-COVID-19. Those with a history of depression, were more likely to report depressive and anxiety symptoms during COVID-19. Government-funded initiatives employed during future pandemics should consider tailored mental health and social support for vulnerable groups.

Editorial Note: Risk factors for mild depression in older women with overactive bladder syndrome—A cross sectional study

Nucleotide signals coordinate activation and inhibition of bacterial immunity

A load forecasting method based on edge graph attention network

Given the increasing demand for high-accuracy power load forecasting, traditional load forecasting methods can capture long-term dependencies in time series, but cannot fully capture the complex relationships between multi-dimensional features. This paper proposes an innovative method to convert time series data into graph features. By constructing a graph structure based on time nodes, the time series forecasting problem is transformed into a graph-based load forecasting problem. On this basis, the Edge Graph Attention Network (EGAT) is used to combine the feature information of nodes and edges to further enhance the ability to represent feature interactions and improve the accuracy of load forecasting. This paper compares the EGAT model with common load forecasting methods, including gated recurrent units (GRU), multi-layer perceptron networks (MLP) and long short-term memory (LSTM). The results show that EGAT is effective at finding important features and understanding complex time patterns, which means it shows strong potential in predicting energy demand. A limitation of the proposed approach is its increased computational cost introduced by graph construction and attention-based aggregation, which may raise training time and memory usage for large-scale graphs. In addition, the forecasting performance can be influenced by the design of the time-series graph (e.g., connectivity patterns) and the availability/quality of edge features.

Effects of peppermint (Mentha x piperita L.) oil on cardiometabolic outcomes in patients with pre- and stage 1 hypertension: A placebo randomized controlled trial

Hypertension represents the predominant risk factor for cardiovascular disease morbidity and mortality; with significant healthcare utilization and expenditure. Pharmaceutical management is habitually adopted; although its long-term effectiveness remains ambiguous, and accompanying adverse effects are disquieting. Peppermint, which is rich in menthol and flavonoids, may exert potential benefits relevant to hypertension. This trial aimed to explore the effects of twice-daily peppermint oil supplementation in individuals with pre- and stage 1 hypertension. A 20 day, parallel randomized, placebo-controlled trial was adopted (NCT05561543). 40 individuals with pre- and stage 1 hypertension were randomly assigned to receive 100 μL per day of either peppermint oil or peppermint-flavoured placebo. The primary trial outcome was the between-group difference in systolic blood pressure from baseline to 20 days. Secondary outcome measurements were the between-group differences in anthropometric, haematological, diastolic blood pressure/resting heart rate, psychological wellbeing, and sleep efficacy indices. Statistical analysis was conducted on an intention-to-treat basis using baseline-adjusted linear regression models comparing post intervention values between trial arms with the corresponding baseline value entered as a covariate; adjusted mean differences ( b ), 95% confidence intervals, and effect sizes ( d ) were calculated. In relation to the primary outcome, adjusted systolic blood pressure at 20 days was significantly lower ( b  = −8.48 mmHg, 95% CI = −14.24 to −2.73, d  = −0.94) in the peppermint trial arm (baseline = 130.05 mmHg, 20 days = 121.97 mmHg) than in placebo (baseline = 130.93 mmHg, 20 days = 131.05 mmHg). Loss to follow-up (N = 1) and adverse events (N = 1) were low, both occurring in the peppermint arm, and compliance was very high in the peppermint (93.3%) trial arm. Given the substantial health and economic burden associated with hypertension worldwide, these findings suggest that twice-daily peppermint supplementation may represent a simple, low-cost, and well-tolerated strategy to support blood pressure reduction in this population. Trial registration ClinicalTrials.gov NCT05561543

A rapid evaluation of quality of sedation and ventilation care processes for critically ill patients in Vietnam

Background Sedation assessment, spontaneous awakening and breathing trials are evidence-based practices which can minimise harm from ventilation and sedation of critically ill patients. There are known difficulties in implementing these processes which are likely to be exacerbated in low-resource settings. This study aimed to describe current delivery of these care processes in three intensive care units in Vietnam; identify barriers and facilitators to their delivery; and describe local capacity for improvement. Methods We conducted a prospective rapid evaluation between 01/11/2021 and 31/12/2023 comprising registry-enabled measurement of daily care processes, process mapping, observations, focus group discussions, semi-structured interviews and a structured assessment of local capacity for improvement. Contextual determinants of care quality were analysed using the Consolidated Framework for Implementation Research. Organisational capacity for improvement was analysed using the Model for Understanding Success in Quality. Results Sedation was assessed qualitatively rather than using systematic tools. Spontaneous Awakening and Breathing Trials were both performed according to individual doctors’ clinical judgement in a non-protocolised manner. Barriers to delivering these processes included the lack of locally-adapted protocols, perceived safety concerns exacerbated by staffing shortages and lack of familiarity due to confusing terminology. Facilitators to improvement included quality improvement champions, registry-enabled audit and feedback, training, and partnerships within and between hospitals. Conclusion We identified opportunities to improve sedation and ventilation in the three study settings in Vietnam. The barriers to delivering the care processes we studied echoed those reported in high-income countries, but were exacerbated by local contextual factors such as staffing shortages and differences in professional roles. We developed recommendations for future improvement projects: implementing setting-adapted protocols, standardising terminology to improve documentation, engaging clinical staff with feedback, identifying champions, educate staff regarding the clinical processes and quality improvement and leverage existing internal expertise. These recommendations may have applicability to other care processes and/or settings.

Rapid cooling shaped the formation of the first meteorites in the Solar System

Plasma-activated water: Mechanism and treatment duration for postharvest disease control and shelf-life enhancement of mango under ambient storage

In Bangladesh, a large quantity of mango is lost every year due to post-harvest diseases, particularly anthracnose and stem-end rot. Therefore, sustainable post-harvest management is crucial for reducing the losses. In this study, we investigated the effects of plasma treatments on mango to mitigate post-harvest losses. The mangoes were submerged in distilled water (DW), and then DW-submerged mangos were treated for 10 minutes employing multi-capillary bubble discharged plasma jet system using air and oxygen gases separately. Plasma treatments significantly influenced disease incidence, severity, and physio-chemical properties of mangoes. On the 10 th day, the Khirsapat mango, treated with a 10-minute air-discharge plasma, exhibited a significant (≤0.05) reduction in anthracnose incidence (20%) and severity (2.33%) compared with control (incidence 80% and severity 61.67%). Similarly, the 10-minute air-discharge plasma treatment consistently reduced the incidence (20%) and severity (15%) of stem-end rot. For the Fazlee variety, the incidence (22%) and severity (3.67%) of anthracnose were reduced compared with the control (89% and 56.67%), while stem-end rot was completely inhibited for up to 10 days under the 10-minute air-plasma treatment. In addition, both mango varieties showed increased total soluble solid (18% and 19%), retained good moisture content (77.82% and 85.34%), but reduced physiological weight loss (3.12% and 8.36%), and extended shelf life (6 days). Firmness degradation was lowest in air plasma treatment (4.20% in Khirsapat and 4% in Fazlee) compared to control (5.76% and 5.79%). It is interesting to note that the plasma treatment of both varieties showed higher mineral contents (K, Ca, Mg, and P), while Vit C declined modestly (16.0 and 25.10 mg/100g) compared to control. Therefore, a 10-minute air-discharge plasma treatment effectively reduced disease incidence and severity through enhancing TSS, mineral contents, physiological properties, and overall storage life that highlighting its potentiality as an eco-friendly postharvest technology.