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Liquid–Solid Interface Reactions Drive Enhanced Thermoelectric Performance in Ag<sub>2</sub>Se
Mixed-Solvent Crystallization Expands the Chemical Space of 2D Perovskites and Enables Phonon-Limited Exciton Transport
Pre-hatching social interactions mediated by acoustic signals. Dynamics of click emission and hatching synchronization in birds
The present paper analyzes the sounds emitted by pre-hatching chicks, focusing on those named as “clicks,” which are thought to mediate pre-hatching social interactions and hatching synchronization. Representative acoustic signals were analyzed under three incubation conditions: (1) isolated pre-hatching chicks (n = 13), (2) pre-hatching chicks in contact with others of the same age (n = 14), and (3) pre-hatching chicks in contact with other of different age (n = 10 for each group: leader and follower). Customized MATLAB software was developed to (a) identify and isolate clicks from other recorded sounds, (b) represent them as temporal series of stochastic point processes, and (c) determine whether click emission dynamics resembled white noise or exhibited characteristics of informative signals. Mathematical methods were applied to analyze (a) temporal dynamics, (b) clustering patterns (via hierarchical clustering and log–log scaling), and (c) scaling properties (via power spectral density analysis) of clicks under each condition. The results reveal developmental-dependent changes in click temporal patterns. As hatching approaches, clicks evolve from isolated events to highly organized hierarchical clusters. Contacting chicks displayed greater temporal organization than isolated ones. Significantly, contact with more advanced chicks accelerated click dynamics in less developed embryos, while older embryos showed a slight delay, suggesting reciprocal social interactions. Spectral analysis revealed long-range correlations consistent with fractional Gaussian noise. These findings confirm that click sequences (a) exhibit physical characteristics of informative signals, (b) function as communication signals, and (c) align developmental processes among pre-hatching chicks. The study underscores the value of fractal analysis in describing physiological signals and expands our understanding of prenatal social interactions. The results suggest that acoustic signals may influence both hatching coordination and central nervous system development. This work provides insight into the evolutionary advantage of embryo communication and highlights the importance of studying how environmental disruptions may affect these critical prenatal processes.
Quantifying the Localization of Charges Generated upon Molecular Doping of Conjugated Polymers
Sex differences in performance and pacing in the greatest Quintuple Iron ultra-triathlon race in history: The IUTA World Championship 2024 in France
Background Pacing in ultra-triathlon has been investigated by analyzing lap times from Double to Deca Iron ultra-triathlon for World Cup races but not for a World Championship. The present study aimed to investigate pacing in ultra-triathletes competing in the fastest and largest World Championship in Quintuple Iron ultra-triathlon ever held in history. Methods A total of 11 female and 24 male finishers who completed the 2024 Quintuple Ultra Triathlon World Championship in Colmar, France, were analyzed. Independent t-tests assessed sex-based performance variations with effect sizes (Cohen’s d). A two-way ANOVA evaluated the effects of sex and performance quartiles on cycling and running, with eta squared (η²) used to measure effect sizes. Results Overall, men were slower in swimming and cycling and faster in running and overall race time. The variability in lap times was similar in cycling for both women and men but higher in running for women. There was a significant interaction between sex and performance quartiles in cycling but not running. For cycling, the variability in performance was higher in men compared to women; for running, it was similar for both women and men. Conclusions The finding that women outperformed men in swimming and cycling, likely due to the elite nature of the World Championship, which featured a highly selected and committed female cohort with a high completion rate. While both sexes showed consistent pacing in cycling, women exhibited greater variability in running, possibly due to more frequent breaks.
Ultrafast Tyrosinase-Mediated Biotinylation of Living Cell Surface Analysis Reveals Novel Cell Surface Proteins Responsible for Influenza A Virus Entry
Recovery strategy of fault distribution network considering collaborative optimization of recovery and repair
For the fault recovery and emergency repair after multiple faults in the distribution network, this paper proposes a fault distribution network recovery strategy considering the collaborative optimization of recovery and emergency repair. Initially, due to the difference and uncertainty between the system load demand and the distributed generation (DG) output, a bilayer dynamic fault recovery with phase type in time scale was constructed. The upper layer considers the recovery of the distribution network during faults, optimizing network reconfiguration schemes using DG outputs predicted by deep stochastic configuration network in conjunction with time-varying load demands. The lower considers the economic effect of the loss during repair and determines the optimal fault repair sequence. Furthermore, an enhanced Nutcracker Optimization algorithm to solve the bilayer model was proposed, determining the dynamic combination of the fault reconfiguration scheme and the repair sequence. Finally, to validate this strategy, this paper conducted simulations using the IEEE 33-node system. The experimental results under multiple strategies show the feasibility and effectiveness of this paper strategy, which ensures the effective recovery of fault nodes after power failure.
Structure and Nitrite Reductase Activity of the Di-iron Protein ScdA in <i>Staphylococcus aureus</i>
Genomic regions and candidate genes associated with seed nitrogen, phosphorus, and sulfur accumulation identified in the soybean ‘Forrest’ by ‘Williams 82’ RIL population
Nitrogen (N), phosphorus (P), and sulfur (S) are essential nutrients for plant health. Deficiencies in N, P, or S in plants lead to lower seed production and seed quality in grain crops, including soybean seed. Soybean seed is a source of protein, oil, essential amino acids, and minerals. These nutrients are essential for plant health, and maintaining N, P, and S levels in soybean seed is crucial for higher seed nutritional value and amino acids quality. There is limited information on genomic regions, candidate genes, and molecular markers associated with soybean seed N, P, and S. Two field experiments were carried out in two locations using a ‘Forrest’ × ‘Williams 82’ recombinant inbred lines (RIL) population. A 306 RIL population and 2075 SNP markers were used to create the genetic map. The results showed a wide range of N, P, and S concentrations in both locations among RIL population lines. Based on the broad-sense heritability (H2), 91.7% of seed N concentration variation was due to genetic effects, followed by 48.2% for S seed concentration, and a heritability of close to zero for seed P concentration. Eleven QTL were identified for seed N, seven QTL for seed P, and nine QTL for seed S in two locations. All these QTL had a significant linkage to the trait as their LOD ranged from 2.5 to 6.48 in 2018 and from 2.75 to 128.72 in 2020. Two QTL for seed N (qN-02-[IL-2020] on Chr 4, and qN-03-[IL-2020] on Chr 4 were identified at the marker Gm04_4687302-Gm04_7672403 and Gm04_7672403, and their LOD were 45.06 and 96.98, and their contribution to the phenotypic variation were 45.85% and 48.37%, respectively. The low heritability of P indicated a major interactions between the trait (P) and environment. Except for the seed N, P, and S QTL, identified on Chr 16, 11 QTL reported here were not previously identified and therefore are novel. Several functional genes encoding N-, P-, and S-proteins, enzymes, and transporters were identified and located within the QTL interval. To our knowledge, the QTL identified here on Chr 2 and 6 are novel and were not previously identified. Therefore, QTL, genes, and molecular markers discovered in this research will provide breeders with new knowledge and tools for soybean selection for optimum seed mineral nutritional qualities. Also, this new findings advance our knowledge of physiology and genetics of seed N, S, and P candidate genes for genetic engineering application.
Practical Ligand-Enabled C–H Halogenation of (Hetero)Benzoic and (Hetero)Aryl Acetic Acids
Retraction: The ING4 Binding with p53 and Induced p53 Acetylation were Attenuated by Human Papillomavirus 16 E6
Ultralong Room-Temperature Qubit Lifetimes of Covalent Organic Frameworks
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.