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AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors

Background Diabetes remains a major public health concern in the United States, with a complex interplay of behavioral, demographic, and clinical risk factors. This study aims to identify the three best-performing machine learning models for diabetes risk prediction and to visualize the most influential predictors affecting diabetes likelihood. By leveraging a large, representative dataset, the study contributes to evidence-based strategies for targeted prevention. Methods Data were obtained from the 2015 Behavioral Risk Factor Surveillance System (BRFSS), a nationally representative, population-based survey collecting information on health behaviors, chronic conditions, and preventive care. The analytical sample included 253,680 adult respondents and over twenty features encompassing sociodemographic variables (e.g., age, sex, race, income, education), health behaviors (e.g., smoking, physical activity, diet), and outcomes (e.g., BMI, hypertension, diabetes status). Eighteen machine learning models were trained and evaluated, including AdaBoost, Extra Trees Classifier, C5.0 Decision Tree, and CatBoost. Models were assessed using predictive accuracy and AUC scores. SHAP (SHapley Additive exPlanations) analysis was used to interpret the top model and examine how changes in key features influence diabetes risk. Results Among the evaluated models, the Extra Trees Classifier achieved the highest predictive accuracy (>90%) and an AUC of 0.99. AdaBoost and CatBoost also demonstrated strong performance. Feature importance analysis identified BMI, age, general health status, income, physical health days, and education as the top predictors. A nonlinear association between income and diabetes risk was observed, with the highest prevalence in individuals earning $20,000–$25,000. Risk was also elevated in individuals aged 65–69 and those reporting poor general health. Hypertension showed a strong positive correlation with diabetes risk. Conclusions Machine learning models, particularly tree-based ensemble methods, offer robust tools for diabetes risk prediction. These findings support their integration into public health analytics for personalized risk assessment and data-driven prevention strategies.

Diastereomeric Fullerene Composite Engineering for Enhanced Perovskite Solar Cells

Ongoing circulation of emerging tick-borne viruses in Poland, Eastern Europe

In order to investigate previously reported expansion of tick-borne pathogenic viruses in Eastern Europe, we conducted this study using pooled ticks collected from various locations in Poland, utilizing Sequence Independent Single Primer Amplification (SISPA) and metagenomic sequencing. We processed 575 Dermacentor reticulatus and Ixodes ricinus ticks and generated 280 virus assemblies in 20 pools. Viruses representing 28 species or strains classified in 12 families or higher taxonomic ranks were observed. We identified four tick-borne human pathogens including Alongshan virus (ALSV), Tacheng tick virus 1 (TcTV-1), Tacheng tick virus 2 (TcTV-2) and Nuomin virus (NUMV), in 55% of the pools, comprising 19.2% of the assemblies. We detected ALSV in I. ricinus ticks, with virus genome segments in complete or near-complete forms, comprising the initial reporting of ALSV from Poland. Further analyses revealed phylogenomic clustering with ALSV strains from Europe and lack of recombination signals among virus genomes. TcTV-1 was detected in 35% of the pools comprising D. reticulatus and I. ricinus ticks, implicating I. ricinus in TcTV-1 transmission for the first time. Maximum likelihood analyses on TcTV-1 and TcTV-2 genome segments indicated separate clustering patterns suggesting geographically-segregated clades. Evidence for NUMV or a closely-related chuvirus in I. ricinus ticks was further noted. In conclusion, we identified persistence of previously-documented tick-borne pathogens in Poland as well as additional viruses such as ALSV. Assessment of temporal and spatial patterns for virus circulation and diagnostic assays for these agents is needed. The distribution and public health impact of these pathogens throughout Europe require further investigation.

Probing Dopant Size Effects on Defect Clustering and Vacancy Ordering in Lanthanide-doped Ceria

Start learning coding without computers? A case study on children’s unplugged gamified coding education tool with explanatory sequential mixed method

Instruction in coding for children has emerged as a significant means of fostering computational thinking, with gamification serving a crucial reinforcing function in this educational process. This experimental study integrates four principal gamification elements—role-playing, rewards, challenges, and cooperation—into unplugged children’s coding education tools to examine their impacts on children’s flow experience and learning engagement. Under the theoretical framework of Stimulus-Organism-Response (S-O-R), researchers developed an unplugged coding education prototype named “Coding Adventure,” employing an explanatory sequential mixed-methods approach with 295 Chinese elementary students (aged 8–10 years). Subsequent qualitative interviews were conducted with 12 stratified participants. Empirical findings demonstrate that role-playing and cooperation effectively enhance children’s flow experience when engaging in coding education tools. Children role-play heroes and engage in “real fights,” engage in teamwork and communicate with others, which can bring them into a flow experience. The flow experience effectively enhances children’s learning engagement. The main reasons are the immersion, upgrade experience, practicality, goal-orientedness, teamwork and partner’s suggestions brought by gamification elements. Moreover, rewards and cooperation also directly positively influence children’s learning engagement. Intrinsic and extrinsic rewards, engagement in teamwork and receiving encouragement and suggestions from interactions with classmates are thought to increase children’s motivation to learn. This study examined the effects of diverse gamification elements on children’s flow experience and learning engagement in an unplugged gamified coding education tool under the framework of the S-O-R theory. Additionally, this study demonstrated important practical implications by providing developers of coding education tools with a clear path to enhancing participants’ sense of immersion and achievement.

New insights into the molecular biology of Alzheimer’s-like cerebral amyloidosis achieved through multi‐omics approaches

Background One of the neuropathologic hallmarks of Alzheimer’s disease (AD) is amyloid plaques composed of fibrillar amyloid beta (Aβ) that accumulate in the hippocampus and cerebral cortex. The identification of molecular changes and interactions associated with Aβ-dependent cerebral amyloidosis is a need in the field. We hypothesize that structured datasets linking proteins to differentially abundant metabolites may provide an indirect but effective means of elucidating the processes and functions in which these metabolites are involved. The goal of this study was to identify core network modules related to AD-like cerebral amyloidosis to provide new insights into the molecular underpinnings of this brain disorder potentially associated with diet and microbiota modulation. Methods We performed fecal bacterial genotyping and untargeted metabolomic analysis of plasma and feces from wild-type and McGill-R-Thy1-APP transgenic (Tg) rats, a model of AD-like cerebral amyloidosis, that were exposed to a high-fat diet protocol. To identify relevant proteins associated with the discriminant metabolites, we used several structured databases. Protein-metabolite associations (both physical and functional) were retrieved, and a collection of AD-associated protein-protein interaction (PPI) networks were built using a near-neighborhood approach. Results A total of 44 bacterial genera and 636 plasma and 576 fecal metabolites were analyzed. From the discriminating metabolites of the Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) models, 657 networks were collected and a subset of the top 20 exploratory networks was defined. The first ranked network in terms of seed protein enrichment and number of participating metabolites showed strong biological signals of innate and adaptive immunity processes, with CD36 emerging as a central hub, orchestrating immunity, metabolic pathways, and fatty acid trafficking. Conclusions The network biology approach enabled a precise definition of the metabolic pathways underlying the disease biology highlighting the role of immune system in the complex interaction of the brain-gut axis.

Making Sense of Heteroatom Effects in π–π Interactions

Correction: Allochthonous marsh subsidies enhances food web productivity in an estuary and its surrounding ecosystem mosaic

A Biomimetic Twisting Strategy Enables Efficient Electrocatalytic Oxidation of Energy-Dense Hydrazine Hydrate on FeN₂₊₂C₄₊₄ Sites

Star Power: Early life stages of an endangered sea star are robust to current and near-future warming

The sunflower star, Pycnopodia helianthoides , was a top benthic predator throughout its former range from Alaska to northern Mexico, until its populations were devastated starting in 2013 by a disease known as seastar wasting. The subsequent absence of sunflower stars from northern California waters was coincident with a dramatic ecological phase shift from healthy bull kelp forests ( Nereocystis luetkeana ) to barrens formed by purple sea urchins ( Strongylocentrotus purpuratus ), a prey of sunflower stars. Modeling suggests that restoration and resilience of kelp forests can be enhanced by the return of sunflower stars. Towards this end we run a conservation breeding program for sunflower stars in the Salish Sea of Washington, where sunflower stars have persisted in much reduced numbers. We here report on a variety of investigations into the temperature tolerance of sunflower stars, focusing on their poorly studied early life stages from their planktonic embryos and larvae, through metamorphosis and settlement as they transition to the benthos, and then for eight months of juvenile growth. Our results indicate that the optimum temperature for early life stage sunflower stars is more than 4°C higher than ambient temperatures in the Salish Sea, and that the juveniles demonstrate enhanced performance to a simulated marine heat wave. These results suggest that Salish Sea-derived sunflower stars would be robust to current and even near-future predicted temperatures in the south of their former range.

Red Light-Driven, Oxygen-Tolerant RAFT Polymerization Enabled by Methylene Blue

Transcriptome analysis reveals rootstock-driven effects on growth and photosynthesis in Camellia chekiangoleosa: A phenotypic and biochemical perspective

Camellia chekiangoleosa is a significant oil-bearing tree species, known for its high oleic acid content and shorter reproductive cycle compared to traditional oil-tea plants. However, there are few studies on the molecular mechanism and compatibility of the interaction between oil-Camellia scion and rootstock, which poses certain challenges to the cultivation and promotion of oil-Camellia. This study systematically evaluates the effects of hetero-grafting Camellia chekiangoleosa scions onto divergent rootstocks (Camellia chekiangoleosa, Camellia oleifera, and Camellia yuhsienensis). Then the research investigates how rootstock selection alters scion growth and development through phenotypic, biochemical, and transcriptomic analyses. Our findings reveal that the combination of C. oleifera scion grafted onto C. yuhsienensis suppresses auxin (IAA) and cytokinin (ZR) levels while elevating abscisic acid (ABA). Transcriptomic analysis identified that the PYL1, AMY, and INV1 screened by transcriptome data were mainly enriched in starch and sucrose metabolic pathways and plant hormone signal transduction, which collectively prioritize carbon allocation toward growth over storage. Meanwhile, hetero-grafting improved photosynthetic capacity by upregulating light-harvesting complex (LHC) genes and carotenoid biosynthesis enzymes (ZEP), optimizing light energy conversion and photoprotection. These findings provide novel insights into the molecular mechanisms underlying rootstock-scion interactions in oil-Camellia, bridging a critical knowledge gap in this economically important genus.

Computationally Designed Nanobinders as Affinity Ligands in Diagnostic and Therapeutic Applications

Introducing CACIE: Development of the first Conceptual Assessment of Children’s Ideas about Evolution

The theory of evolution is the core theory of the life sciences. However, due to its counterintuitive nature, learners of all ages have difficulties building coherent knowledge about evolution. Researchers propose to facilitate learning about evolution in school by introducing the topic to children at a younger age to foster learners’ pre-scientific ideas and prevent the establishment of inaccurate beliefs. However, assessment tools that could be used with young children are still lacking. This article presents the development and psychometric evaluation of the interview-based Conceptual Assessment of Children’s Ideas about Evolution (CACIE). The CACIE comprises 20 items about 10 concepts of the evolutionary principles variation, inheritance, and selection. They can be used with six different animal and plant species. The CACIE was tested with 85 children (1) in cross-sectional interviews and (2) in a test-retest design (n = 14). The instrument was developed using an empirically validated theoretical framework, informed by published instruments and interviews, and refined through pilot studies and observations. The assessment showed good agreement between raters and moderate test-retest reliability. The validity evidence for the responses generated by the CACIE is discussed, and guidelines for its use to measure children’s ideas about evolution are provided.

Universal Formation, Dynamics, and Reactivities of *CO_bridge Accompanying Spontaneous Reconstruction of Cu during Electrochemical CO₂ or CO Reduction

Radiotherapy continuity for cancer treatment: Lessons learned from natural disasters

Background The contemporary world is challenged by natural disasters accelerated by climate change, affecting a growing world population. Simultaneously, cancer remains a persistent threat as a leading cause of death, killing 10 million people annually. The efficacy of radiotherapy, a cornerstone in cancer treatment worldwide, depends on an uninterrupted course of therapy. However, natural disasters cause significant disruptions to the continuity of radiotherapy services, posing a critical challenge to cancer treatment. This paper explores how natural disasters impact radiotherapy practice, compares them to man-made disasters, and outlines strategies to mitigate adverse effects of natural disasters. Through this analysis, the study seeks to contribute to developing resilient healthcare frameworks capable of sustaining essential cancer treatment amidst the challenges posed by natural disasters. Method We conducted a Structured Literature Review to investigate this matter comprehensively, gathering and evaluating relevant academic publications. We explored how natural disasters affected radiotherapy practice and examined the experience of radiotherapy centres worldwide in resuming operations after such events. Subsequently, we validated and extended our research findings through a global online survey involving radiotherapy professionals. Results The Structured Literature Review identified twelve academic publications describing hurricanes, floods, and earthquakes as the primary disruptors of radiotherapy practice. The analysis confirms and complements risk mitigation themes identified in our previous research, which focused on the continuity of radiotherapy practice during the COVID-19 pandemic. Our work describes nine overarching themes, forming the basis for a taxonomy of 36 distinct groups. The subsequent confirmative online survey supported and solidified our findings and served as a basis for developing a conceptual framework for natural disaster-resilient radiotherapy as well as a checklist for practitioners. Discussion The growing threat posed by natural disasters underscores the need to develop business continuity programs and define risk mitigation measures to ensure the uninterrupted provision of radiotherapy services. By drawing lessons from past disasters, we can better prepare for future hazards, supporting disaster management and planning efforts, particularly enhancing the resilience of radiotherapy practice. Additionally, our study can serve as a resource for shaping policy initiatives aimed at mitigating the impact of natural hazards.

How the brain’s amygdala reacts when making decisions to avoid losses

Photo-Triggerable Polymerization and Depolymerization of Stiff-Stilbene Lactones

A Later Stone Age quartz knapping workshop and fireplace dated to the Early Holocene in Senegal: The Ravin Blanc X site (RBX)

Well-dated and well-preserved Later Stone Age sites are unfortunately scarce in West Africa. The few known ones exhibit significant typo-technical variability, reflecting diverse socio-cultural behaviors that remain poorly understood. The Ravin Blanc X (RBX) site in eastern Senegal provides new insights into this period. Excavations at one of the sectors of the site (RBX-1) have revealed a well-preserved Early Holocene occupation, featuring a quartz knapping workshop associated with a fireplace. This site is the latest known LSA occupation in the Falémé valley and bridges a critical gap in the region’s prehistoric sequence. The lithic industry at RBX-1 is dominated by a very homogeneous quartz, which was specifically selected for its high-quality knapping properties. Two main categories of sought blanks were produced: broad, thick, and rectilinear blanks, and elongated, thin and narrow blanks with an oblique distal termination forming a natural asymmetric point. The strong investment in blank standardization from the extraction stage significantly reduced the need for subsequent retouching, which was rarely observed in the RBX-1 lithic assemblage. Comparisons with other LSA sites in West Africa suggest that RBX-1 shares technological similarities with the sites of Fatandi V (Falémé valley, Senegal) and Damatoumou 1 (Ounjougou, Mali), possibly indicating a West African Late LSA Sahelo-Sudanian facies. In contrast, sites located in Guineo-Congolian forest contexts exhibit different knapping strategies and typological choices. The discovery of RBX-1 enhances our understanding of the LSA in West Africa by providing a rare, well-dated stratigraphic context (around 9100 calBP/7100 calBCE) which highlights the complexity of regional lithic traditions and raises new hypotheses about cultural transitions during the Pleistocene-Holocene shift.

Earth’s capacity to store carbon could max out surprisingly soon