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Tree ring segmentation performance in highly disturbed trees using deep learning

Dendrogeomorphology provides valuable insights into the dating of geomorphic events but requires complex analyses of tree-ring records from highly disturbed trees. While deep learning algorithms have been successfully applied to detect boundaries in normally developed growth rings, their performance under severely disturbed growth conditions remains largely unexplored. This study evaluates whether deep learning can effectively segment tree rings exhibiting abnormal growth patterns commonly observed in dendrogeomorphological contexts. Increment cores were collected from a debris-flow-affected area. High-resolution images were subsequently acquired and manually annotated to identify tree rings boundaries and growth disturbances. A series of experiments was conducted using different neural network architectures, image resolutions, and filtering techniques to examine the relationship between convolutional neural network (CNN)–based models and the level of cellular detail represented in the images. Our results indicate that segmentation performance declines in growth disturbances characterised by pronounced changes in colour and texture relative to normal growth patterns. Nevertheless, the proposed framework successfully identified sets of narrow ring boundaries spaced more than 200 μm apart when colour remained consistent, correctly segmenting most rings associated with the most severe growth suppressions in our dataset. Notably, models relying primarily on simple features such as colour variation performed comparably to those incorporating finer cellular details. We also found that, within a patch-based processing framework, performance decreased when growth direction was not specified in advance. Overall, this study provides a systematic evaluation of CNN-based methods under highly disturbed growth conditions, highlighting both their potential and current limitations in dendrogeomorphological applications.

Prehospital Whole Blood in Traumatic Hemorrhage — A Randomized Controlled Trial

Naturalistic driving data extraction and processing for studying driver head scanning behavior at intersections

A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory

Predicting the epidemic threshold τ in contact networks is a central challenge in computational epidemiology. Classical structural approaches based on spectral graph theory—most notably Quenched Mean-Field (QMF) and the recently proposed KSEL (K Spectral Energy of Laplacian) method—deliver fast but approximate predictions. We propose a hybrid AI-mathematical framework that integrates spectral graph features, epidemiological parameters, and epidemiologically motivated soft constraints derived from compartmental theory into a physics-guided neural network (PGNN). Rather than claiming state-of-the-art predictive performance, this work makes three complementary contributions: (i) a rigorous stochastic ground-truth estimation procedure with Monte Carlo uncertainty quantification (median σ τ * = 0.0072 , n  = 775 networks); (ii) a systematic comparative evaluation of seven methods—including tree-based models (Random Forest, Gradient Boosting) trained on the same feature set—revealing the conditions under which deep learning surpasses and falls short of simpler baselines; and (iii) a full ablation study and SHAP interpretability analysis identifying the role of individual spectral features and physical constraints as structured regularisers. Evaluated on 775 synthetic networks spanning Erdős–Rényi, Barabási–Albert, Watts–Strogatz, and regular topologies, Gradient Boosting achieves the best predictive accuracy ( R 2  = 0.908, RMSE = 0.0731), while the PGNN ( R 2  = 0.093) offers complementary value through physical consistency and interpretability. These results are established on synthetic benchmarks; application to empirical contact networks (hospital, school, workplace settings) is a natural next step but requires dedicated validation beyond the scope of the present study. Ablation results show that the boundedness constraint is a beneficial regulariser while the stability constraint over-regularises in the low-data regime. Automatic gradient-based calibration of the KSEL coefficient yields topology-dependent optimal values ( k * ∈ [ 0.803 , 1.458 ] ), substantially departing from the universal constant k  = 0.3 of prior work.

Stemness as the Engine of Checkpoint Durability

Exploring the panel and country wise effect of circular economy on environmental sustainability across six leading circular economies

Abstract Investigating the circular economy’s impact on addressing escalating climate change concerns is crucial. The objective of this research is to examine the possible implications of a circular economy on production-based carbon emission, this domain has yet to be thoroughly investigated. This investigation fills the research gap through presenting an under examined proxy for assessing the circular economy through municipal waste generation and treatment. This research investigated data spanning 1991 to 2020 from six advanced circular economies namely, Austria, Belgium, South Korea, Spain, Sweden, and USA, applying numerous estimation techniques to ensure robust and reliable results namely Autoregressive Distributed Lag-Pooled Mean Group (ARDL-PMG), Autoregressive Distributed Lag-Mean Group (ARDL-MG), Autoregressive Distributed Lag-Dynamic Fixed Effects (ARDL-DFE), Fully Modified Ordinary Least Squares (FMOLS), Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM), stability analysis and Pairwise Dumitrescu-Hurlin (D-H) causality. The study determined the influence of the circular economy on production-based emissions using both country-specific and panel data approaches. The findings demonstrate that implementing a circular economy leads to a long-term reduction in production-based carbon emissions in the panel and country wise estimation except Spain. These findings develop understanding of sustainable development difficulties and encourage practitioners to build successful strategies.

A study protocol for mixed-methods evaluation of the structure, design, and availability of medical student wellbeing programs

Introduction In recent years, there has been growing concern over the wellbeing and mental health of medical students in the United States, driven by the academic, personal, and professional challenges inherent in medical school. Recent data indicates that medical students experience higher rates of psychological stress, anxiety, and depression compared to the general population, with the COVID-19 pandemic exacerbating these challenges. Medical student suicide, linked to burnout and depression, highlights the urgent need for effective wellbeing support. Despite the documented barriers to mental wellbeing, such as self-imposed pressures, imposter syndrome, stigma around help-seeking, and financial difficulties, medical student wellbeing programs remain understudied at the structural and design level. Methods This is a multi‑phase qualitative study (sequential-exploratory) that combines a web-based environmental scan and content analysis with key informant interviews and focus groups, using methodological triangulation to develop a framework for evaluating wellbeing programs. First, we will conduct a web-based content analysis of publicly available resources across medical school websites. We will identify key characteristics of wellbeing programs, such as mental health resources, structural well-being components, and culturally integrated approaches. Then, we will conduct key informant interviews with medical school administrative staff to discuss wellbeing programs in detail and hold focus group interviews with medical students to gather their perspectives on how to improve their health and wellbeing. Based on the findings from these three components, we will develop a comprehensive and standardized framework for evaluating medical school wellbeing programs that can be used across institutions. Ethics and dissemination Human Research Ethics Approval was obtained from the NYU Langone Health Institutional Review Board (IRB ID: i25-00965). The content analysis results and qualitative themes extracted from key informant and focus group interviews will be made available to all study participants. They will also be disseminated in a peer-reviewed journal.

Mavacamten in Nonobstructive Hypertrophic Cardiomyopathy

Short-term load forecasting using a two-stage CPO-PSO hyperparameter optimization of LSSVM

Earth’s east–west albedo symmetry

Abstract Earth’s albedo is fundamental to the planetary energy budget 1 . The Northern Hemisphere (NH) and Southern Hemisphere (SH) contribute essentially equally to the planetary albedo—a remarkable yet puzzling phenomenon known as hemispheric albedo symmetry 1–6 . Although such symmetry is rare, it is not unique 7 . Nevertheless, other symmetry pairs have remained unexplored, despite their potential to illuminate possible causes of albedo symmetries and implications for the planetary energy budget. Using a 25-year satellite record, here we show that Earth also exhibits a unique and persistent east–west (E–W) albedo symmetry: the 27° E meridian divides the planet into an Eastern Hemisphere (EH) and a Western Hemisphere (WH) that reflect nearly identical amounts of sunlight. In contrast to the NH–SH symmetry, the EH–WH symmetry encapsulates a distinctive ‘triple symmetry’ in which clear-sky albedo, cloud radiative effect and open-ocean fraction all exhibit hemispheric symmetry around this meridian. This EH–WH symmetry arises from greater high-cloud reflection in the EH balancing greater low-cloud reflection in the WH. Furthermore, interannual variability in the EH–WH symmetry tracks the phase of the El Niño–Southern Oscillation (ENSO), indicating a potential connection to general circulation. This discovery of the EH–WH albedo symmetry and its emergence as a triple symmetry provides a reduced degree-of-freedom constraint for Earth system models (ESMs) and stresses the critical nature of continued Earth radiation budget observations under a rapidly changing climate.

Biodiversity drives the choice; linguistic diversity fine-tunes the direction: Ethnofloral megadiversity in the Mexican ethnobotany

People around the world have developed distinctive sets of useful plants, known as ethnofloras, which comprise a significant portion of Earth’s biodiversity. However, little is known about the factors that determine the composition of these collections and how different groups use plants. These differences may increase with geographic distance and linguistic separation due to barriers to communication and a lack of similarity in locally-available species. Using published ethnobotanical information, we analyzed the divergence in the composition of wild-species and the ways plants are used among 22 Mexican ethnic groups that use 2,855 species. We standardized plant use into ten categories and recorded them for each plant species and ethnic group. Each ethnic group uses a very large number of species (α-diversity), but few species are shared with other groups. Consequently, species turnover (β-diversity) between ethnic groups is very high. As expected, geographic distance fostered high differentiation in species composition of ethnofloras, probably reflecting differences in wild floras. Linguistic proximity promoted ethnoflora composition similarity, suggesting that communication plays a role in shaping the set of plants that are used. Geographic proximity also promotes similarity in how plants are used, though it is unclear whether language also plays a role. This suggests that language barriers are quite permeable. Social interactions and the use of Spanish as a lingua franca may favor the convergence of uses. This study provides a novel analysis of ethnofloras. It emphasizes the value of cultural and biological diversity and their importance in shaping ethnobotanical heritage.

Is Change Possible?

Versatile, marker-free platform for life cycle-wide imaging of Plasmodium falciparum by integrating an exogenous gene cassette into a conserved intergenic locus

Abstract The creation of transgenic Plasmodium falciparum lines with robust fluorescence across the entire life cycle is essential for advancing our understanding of parasite biology, which in turn informs the development of new drugs and vaccines. In this study, we utilized Plasmodium -optimized genome editing to integrate an mCherry expression cassette into a selected intergenic locus without gene disruption. The resulting marker-free line, NF54-mCh, exhibited intense fluorescence throughout all developmental stages, including asexual and sexual blood stages, as well as mosquito (ookinete, oocyst, and sporozoite) and liver stages. NF54-mCh showed normal proliferation, gametocytogenesis, and efficient transmission to mosquitoes. The ultra-high brightness in salivary gland sporozoites allowed for the non-invasive identification of infected mosquitoes. Sporozoites remained highly infectious to humanized mouse livers, thus enabling the completion of the full life cycle. NF54-mCh serves as a parental line for performing additional genetic modifications, because the CRISPR/Cas9-based genome editing method is free of introduced drug resistance markers. The broader applicability of this strategy was validated by generating similar reporter lines in Plasmodium species utilized in rodent malaria models. In summary, NF54-mCh represents a unique, versatile platform that will accelerate fundamental research and support the future development of malaria control strategies, including new vaccines and drugs.

Comparative evaluation of deep learning models for plant disease classification with edge-aware performance analysis

Agricultural disease monitoring remains a critical challenge in precision farming, particularly when deploying computer vision systems on resource-constrained platforms. This study presents a rigorous comparative evaluation of four deep learning architectures—ResNet50, DenseNet121, a Binarized Neural Network (BNN), and YOLOv8-cls—for multi-class plant disease classification using the PlantVillage dataset (15 classes). Unlike prior benchmarking studies, we incorporate statistical validation through repeated stratified experiments (5 runs) and report mean ± standard deviation for accuracy, precision, recall, and F1-score. Results show that while DenseNet121 achieves high classification accuracy (99.48)% ± 0.12), it exhibits significantly higher inference latency. The BNN achieves minimal latency but suffers substantial performance degradation (88.31% ± 0.45). YOLOv8-cls provides the best trade-off, achieving 99.64% ± 0.09 accuracy with low latency (3.3 ms ± 0.2). Statistical comparison using paired t-tests confirms that YOLOv8 significantly outperforms ResNet50 p  < 0.05 while maintaining substantially lower inference time.We further discuss generalization limitations due to the controlled nature of PlantVillage and moderate claims regarding edge deployment feasibility based on model size and computational profiling. The study provides a statistically grounded and edge-aware benchmarking framework for plant disease classification models.

Psychedelic Therapies in the United States — Balancing State and Federal Oversight

Parameter-free hybrid RAO algorithm for reactive power optimization

Abstract The Rao algorithms are a relatively new parameter-free optimisation technique that has grown in importance as an intelligent optimisation tool. Rao algorithms are effective algorithms that embody the idea of attracting toward a global optimum, illustrating the algorithm’s swarm intelligence and the survival of the fittest principle, like that of an evolutionary algorithm. However, it has found applications in other areas related to optimization, primarily in engineering practice, where it is analyzed and summarized based on the specifics of each situation. The absence of algorithm-specific parameters explains the wide applicability of Rao optimization. Although several studies have addressed these methods, limited effort has been made to provide a comprehensive summary of Rao algorithms. This work aims to provide up-to-date information on Rao algorithms, which will be useful for researchers in this field. Additionally, a hybrid Rao algorithm is developed to overcome the limitations of traditional Rao algorithms. The proposed algorithm was implemented in the IEEE 30-bus and IEEE 57 bus systems, to resolve the Optimal VAR Dispatch Problem, and to assess its effectiveness. Results show that hybrid Rao algorithm deliver higher quality solutions within a reasonable timeframe, outperforming other approaches stated in the recent literature. The paper ends with recommendations for future developments to improve the Rao algorithm’s performance.

Oral health in professional Slovak soccer players: Assessment of dental risks, subgingival microbiota and genetic influences

Background Recent studies reveal high rates of dental issues among professional soccer players, worsened by poor hygiene, frequent sport drinks consumption and limited preventive care. Busy schedules, frequent relocations and changing clubs further disrupt dental routines, impacting performance and well-being. Therefore, we decided to assess the oral health status of professional Slovak soccer players and address these critical concerns. Methods We assessed the oral health of 51 male soccer players from two elite Slovak soccer clubs during 2023/2024 season. Data collection included two paper-based questionnaires and a clinical oral examination by dentists. Additionally, clinical laboratory data were collected from saliva to test for presence of periopathogenic bacteria and DNA polymorphisms. Results Although 92.2% had valid health insurance, 36% did not attend dental check-ups in the past year, indicating underutilization of preventive care. While 86.2% brushed their teeth more than twice daily, only 48% practiced interdental cleaning and 35.3% used mouthwash. A significant 83.7% consumed sports drinks high in sugar and acid, influencing oral health risks. Clinical examinations revealed that 86.3% had a moderate to high DMF index (mean decayed teeth: 3.8) and 54.9% exhibited gingivitis. Bacterial analyses showed 25.5–74.5% carried highly periopathogenic bacteria, indicating a high risk for periodontitis. Additionally, 15.7% of players exhibited presence of DNA polymorphisms associated with risk of periodontitis onset. Conclusion This study reveals a gap in preventive dental care among professional soccer players, emphasizing the urgent need for integrated oral health strategies within sports programs.

Prehospital Resuscitation with Type O Whole Blood for Trauma and Hemorrhage

Integrated mineralogical, geochemical, and log derived TOC evaluation of source rock potential in the Khatatba Formation (Western Desert, Egypt)

Abstract The Middle Jurassic Khatatba Formation represents one of the principal petroleum system elements in the Western Desert of Egypt and contains important organic-rich shale intervals within the Upper Safa Member. This study integrates mineralogical, geochemical, and calibrated log-derived TOC analyses to evaluate the source rock potential of the Upper Safa Member in the Obaiyed Field. A total of 100 ditch-cutting samples from four wells were examined using XRD, XRF, TOC, TS, and Rock-Eval pyrolysis analyses, combined with continuous log-derived TOC prediction. Mineralogical results indicate a kaolinite-rich siliciclastic assemblage with quartz and minor calcite, while geochemical data suggest mixed siliciclastic and marine influence. TOC values range from 0.50 to 3.90 wt% (average ~ 1.8 wt%), indicating moderate to very good organic richness. Rock-Eval data reveal mixed Type II/III kerogen with HI values of 99–186 mg HC/g TOC and Tmax values between 441 °C and 457 °C, confirming thermal maturity within the hydrocarbon generation window. Log-derived TOC calibrated against laboratory measurements shows strong agreement (R² = 0.93), demonstrating the reliability of the integrated workflow for continuous source rock characterization. TOC–TS relationships suggest deposition under oxygen-restricted conditions interpreted conservatively as dysoxic to suboxic, favorable for organic matter preservation. The Upper Safa Member is interpreted as a mature, gas-prone source rock with fair to good hydrocarbon generation potential and strong source–reservoir coupling with adjacent Safa sandstone reservoirs. The integrated workflow presented in this study provides a reliable framework for continuous source rock evaluation and improved exploration targeting in the northwestern Western Desert of Egypt.

Nutritional availability and carbon footprints of omnivorous, vegetarian and vegan diets: A cross-sectional analysis of dietary data for UK children aged 2–12

As plant-based (PB) diets become more common among UK children, understanding their nutritional adequacy and environmental impact is vital. This study addresses that lack of understanding through assessment of the nutrient content and greenhouse gas emissions for omnivorous, vegetarian, and vegan diets. A cross-sectional analysis was conducted using three-day weighed food diaries from 39 UK children aged 2–12 years (omnivore n = 15; and PB: vegetarian n = 11; vegan n = 13). Nutrients were analysed with and without supplementation using Nutritics software. GHG emissions were calculated at the ingredient level (kgCO₂e/day) and grouped by Eatwell Guide food categories. No dietary group met all nutrient reference values. Omnivores exceeded recommended intakes for saturated fat and free sugars while failing to meet the recommended intake for fibre, whereas PB children had intakes of these nutrients in the healthy range. PB diets were adequate in protein and vitamin B12 even in the absence of supplementation. Vegan children also met iron requirements from diet alone, whereas omnivore and vegetarian children did not meet iron targets without supplementation. Vitamin D intake was insufficient across all groups when supplements were excluded, with only vegan children achieving recommended levels through supplementation. Zinc requirements were met only by vegetarian children with the aid of supplements and were not met by vegan or omnivore children with or without supplementation. Iodine intake remained inadequate in vegan children even with supplementation. Mean daily greenhouse gas (GHG) emissions differed significantly between diet groups, with omnivores having the highest emissions, while vegans had the lowest emissions: 46% lower than omnivores, and 20% lower than vegetarians. Well-planned PB diets can meet most nutrient needs in UK children when supported by fortified foods and supplements, while substantially reducing dietary GHG emissions compared with omnivorous diets. Shifting away from animal protein and dairy provides an opportunity for improving both nutritional quality and environmental sustainability.