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Chiral laser gyroscopes breaking the lock-in limit

Efficient epileptic seizure prediction using single channel EEG signal and knowledge distillation on deep neural networks

Predicting band gap from chemical composition: a comparative study of machine learning and deep learning models

Large-scale analysis in LoS-NLoS indoor corridor at 18 GHz

Alteration in maternal behaviors of mice exposed to alpha-naphthoflavone

Family history of diabetes and glycemic progression: A propensity score-based analysis using health checkup data

Background Family history of diabetes mellitus (FH_ DM) is a well-established risk factor for diabetes, but most studies have focused on disease incidence rather than glycemic changes over time. Understanding how FH_ DM affects the magnitude of change in blood glucose biomarkers before clinical diagnosis can inform earlier intervention strategies and optimize screening intervals. Methods We analyzed standardized health checkup data from Korean adults aged 20 years or older with repeated measurements over approximately 2-year (n = 25,647) and 4-year (n = 12,831) intervals. Using propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and doubly robust (DR) estimation, we estimated the average treatment effect of FH_ DM on changes in fasting blood glucose (FBG) and hemoglobin A1c (HbA1c). The analytic cohort included individuals with normoglycemia, prediabetes, and diabetes. Sensitivity analyses using robustness values assessed potential unmeasured confounding. Findings After adjusting for 49 measured confounders, individuals with FH_ DM showed consistently greater glycemic progression than those without FH_ DM across all methods. Over 2 years, HbA1c showed a 0.02% greater increase (95% CI: 0.01–0.03) and FBG showed a 0.3 mg/dL greater increase (95% CI: 0.1–0.6) in the FH_ DM group. Over 4 years, the difference in FBG change was 1.3–1.9 mg/dL. Results remained robust when excluding individuals on diabetes medication and across sensitivity analyses. Conclusions Family history of diabetes is independently associated with greater glycemic progression across the normoglycemia-to-diabetes spectrum, even after rigorous adjustment for confounding. These findings support the use of FH_ DM as a practical marker for risk-stratified screening strategies. Individuals with FH_ DM may benefit from more frequent glucose monitoring and earlier preventive interventions to delay or prevent diabetes onset.

Revitalizing contaminated soils: The combined power of modified biochar and intrinsic bacteria for heavy metal and petroleum hydrocarbon removal and plants performance

Soil contamination with heavy metals and petroleum hydrocarbons poses a critical environmental challenge, threatening food security, human health, and ecosystem sustainability in various regions by reducing crop yields and introducing toxic pollutants into the food chain. Therefore, sustainable remediation strategies are essential to protect agricultural productivity. This study aimed to isolate native bacteria capable of degrading these contaminants from Kerman’s polluted soils and assess their synergistic effects with microbially modified biochar (MB) on soil bioremediation, quality enhancement, and maize performance. A total of 30 soil samples were collected from industrially contaminated sites in Kerman, Iran, and analyzed to isolate native bacterial species using biochemical and molecular tests. Biochars, prepared from rice husk and inoculated with the bacterial consortium, and were evaluated in a factorial greenhouse experiment. The experiment utilized a completely randomized design with 90 pots containing 4 kg of artificially contaminated soil (Cr, Pb, Cd, Cu; petroleum hydrocarbons) and four treatments: control, pristine biochar (PB), bacterial inoculants, and MB. Maize was grown for 90 days under controlled conditions, and soil and plant parameters, including physicochemical properties, contaminant levels, growth characteristics, transfer factor (TF), and bioaccumulation factor (BAF), were assessed. Five bacterial species ( P. fluorescens , R. qingshengii , B. metallica , B. cereus , S. pactum ) were isolated from 30 soil samples and tested with MB in a greenhouse experiment. Results showed MB reduced heavy metal bioavailability by 45–55% and hydrocarbons by 70% (p < 0.001), enhanced soil organic carbon, lowered metal uptake (e.g., TF for Cd: 0.27 vs. 0.95), and increased maize biomass (shoot: 30 g, root: 18 g vs. 15 g, 8 g, p < 0.001), offering a sustainable remediation strategy. These findings demonstrate the potential of integrating novel bacterial strains with modified biochar for effective, sustainable soil remediation for arid regions like Kerman, Iran.

Addition of a single bacterial isolate to conventional larval rearing water can impact wing size and longevity in adult male Aedes aegypti

Male mosquitoes are mass-reared around the globe for use in mosquito control programs like Sterile Insect Technique and Incompatible Insect Technique. During larval development, mosquitoes co-exist with complex microbial communities that serve as food and also form the internal microbiota of the organism. The microbiota can impact multiple larval and adult life history traits including development rate and male body size. In the present study, we investigated how larval development, male wing length and adult male longevity is impacted by the addition of a single bacterial isolate to otherwise conventional larval rearing water. Of three isolates tested, we found that larval exposure to one, Cedecea sp. , resulted in slowed pupation and adult males with significantly reduced wing length and longevity. Our findings suggest that minor modifications of the microbial community during larval development can have life-long effects on mosquitoes which, like many organisms, acquire their microbiota from the environment. Moreover, they suggest that changes to the larval water microbial community could impact the efficacy and efficiency of mosquito mass rearing for vector control.

The influence of academic values on academic engagement among Chinese college students: A serial mediation model

This study investigated how academic values shape academic engagement among Chinese college students by examining the mediating roles of academic self-efficacy, autonomous motivation, and controlled motivation. Using a quantitative design with random sampling, valid data were collected from 1,110 students who completed four validated instruments: the College Students’ Academic Values Questionnaire, the Academic Self-Efficacy Scale, the Academic Motivation Questionnaire, and the Academic Engagement Scale. Structural equation modeling was used to test a serial mediation model. The findings revealed that academic self-efficacy, autonomous motivation, and controlled motivation mediated the relationship between academic values and academic engagement. Moreover, academic self-efficacy and both motivational types formed significant serial mediation pathways that explain how values influence engagement. Notably, the mediation pathways involving controlled motivation produced negative indirect effects, suggesting that controlled motivation may weaken students’ academic engagement.

Optimal control of Typhoid fever transmission under environmental and public health interventions

Background This study investigates the transmission dynamics of typhoid fever and assesses the impact of environmental factors and public health interventions on disease spread. Typhoid fever, caused by Salmonella Typhi, remains a major public health concern in regions with poor sanitation, high population density, and limited access to clean water. Although environmental contamination plays a critical role in sustaining transmission, its contribution is often under explored in mathematical modeling studies. Methods We developed a deterministic compartmental model incorporating environmental transmission pathways to better understand the role of contaminated water sources and human-environment interactions in the spread of typhoid fever. The model is formulated as a system of nonlinear ordinary differential equations. The basic reproduction number, R 0 was derived using the next-generation matrix approach to determine the threshold conditions for disease persistence. We analyzed the existence and stability of the disease-free and endemic equilibrium points, establishing local and global stability results for R 0 ≤ 1 and R 0  > 1, respectively. Sensitivity analysis on the reproduction number and the endemic equilibrium was conducted to identify parameters with the greatest influence on disease transmission. Furthermore, the model was extended to an optimal control framework incorporating two intervention strategies: public health education campaigns and treatment of contaminated water bodies. Pontryagin’s Maximum Principle was applied to characterize the optimal controls and derive the associated optimality system. Model parameters were estimated using reported typhoid fever data from Ethiopia obtained through the World Health Organization. Numerical simulations were performed to evaluate the impact of individual and combined intervention strategies. Results Simulation results indicate that the combined implementation of environmental sanitation measures and educational interventions significantly reduces disease burden, particularly during outbreak periods. Conclusion These findings highlight the importance of integrating environmental management and community-based public health strategies in typhoid control programs.

Zero-shot design of drug-binding proteins via neural iterative selection−expansion

Abstract The design of proteins that bind to small molecules has been challenging because it requires simultaneous optimization of the protein sequence, protein structure and ligand conformation 1–7 . Current deep-learning algorithms have struggled to navigate this landscape, precluding the zero-shot design of binders. Here we show that by combining two neural networks in an iterative design algorithm, small-molecule binding proteins can be created from scratch with high accuracy. We trained a graph neural network—ligand-aware sequence engineering message-passing neural network (LASErMPNN)—to design compatible protein sequences for an input protein backbone and docked ligand. We paired  LASErMPNN with a structure predictor that models a three-dimensional protein–ligand complex for an input protein sequence and ligand identity. The closed-loop iteration of these reciprocal networks optimized sequence–structure–ligand compatibility, and outperformed a comparable design loop using a physics-based energy function. We used our strategy, termed neural iterative selection–expansion (NISE), to design proteins that, using different folds, specifically bind to two chemically distinct small-molecule drugs, exatecan and apixaban, with success rates of 100% and 83%, respectively. The tightest NISE binders had nanomolar-to-picomolar affinities, surpassing those of the next-leading method by 70-fold for exatecan and nearly 10,000-fold for apixaban. LASErMPNN then suggested two amino-acid substitutions that improved the affinity of the tightest exatecan binder by 100-fold without any experimental input. The optimized binder protected the labile lactone ring of exatecan from hydrolysis for days. Our work describes a general recipe for using neural networks to automate the design of small-molecule binding proteins for applications in drug delivery, sensing and catalysis.

Suitability of stability assessment methods for topical formulations enriched with apple pomace extract

The cosmetic industry has shown a growing interest in incorporating natural ingredients derived from food waste due to their perceived benefits and consumer preference for eco-friendly and sustainable products. Apple pomace, a by-product of apple juice and cider production, is rich in bioactive compounds such as polyphenols, mainly flavonoids and triterpenoids, offering antioxidant, anti-aging and anti-inflammatory benefits. The study aimed to develop and compare the stability of topical formulations containing apple pomace extract, including a cleansing gel, a serum, and a face cream (1%, 3% and 2% w/w extract, respectively), formulated in stable (A) and unstable (B) variants and to assess the effectiveness and efficiency of advanced stability testing methods for cosmetics. The key distinction in cleansing gels was the use of either Guar Hydroxypropyltrimonium Chloride or Cyamopsis Gum Tetragonoloba; in serums, stability depended on xanthan gum or Cyamopsis Gum Tetragonoloba; and for face creams, the critical factor was the incorporation of Cetearyl Olivate and Sorbitan Olivate. Formulations were stored for 30 days at 4°C, 25°C, and 45°C and analyzed using multiple light scattering (MLS), laser diffraction (LD) and optical microscopy. MLS confirmed high stability of all A variants, with negligible fluctuations in their transmission/backscattering profiles, whereas B variants exhibited pronounced instability reflected by marked changes in transmission signals (up to 58.6%). Optical microscopy confirmed these findings, with homogeneous droplet distribution in Cream A and heterogeneous coalescence in Cream B. These results demonstrate that MLS and LD were suitable for detecting changes in both emulsions and hydrogels, whereas optical microscopy was effective only for emulsions. The application of these advanced stability measurement techniques can enhance cosmetic formulation design and support sustainable cosmetic development.

The value of looking ahead: Comparing conventional and strategic Mountain Pine Beetle (Dendroctonus ponderosae) management policies in North America

Forest management in North American montane and boreal biomes is facing multiple challenges, including climate change, wildfires and pest outbreaks. Mountain pine beetle (MPB, Dendroctonus ponderosae ) is a notable example of a major threat to forest health in western North America whose impacts are compounded by warmer winters, which allowed it to establish further north and east of its historical distributional range. In western Canada, delimiting surveys and control of range-expanding MPB populations are managed through collaborative intergovernmental initiatives. While this approach has been shown to effectively slow MPB spread, these management efforts were planned without assessments of the long-term impacts of survey and control decisions on future rates of MPB spread. Here, we compared the current approach to managing MPB populations driven by recent detections and short-term forecasts against an optimisation-assisted approach that finds a MPB management strategy by factoring in the long-term impacts of survey and management decisions on future spread. We also evaluated a reduced-complexity approach where MPB spread forecasts are factored into decisions without tracking the long-term impacts from future management actions. We found that strategic long-term management could help reduce the future MPB-infested area by 65%−112% compared to the current short-term management strategy. Also, the reduced-complexity approach helped reduce the MPB-invaded area by 16%−77% compared to the conventional short-sighted approach. These results demonstrate the substantial benefits of incorporating longer-term pest spread forecasts and feedback from future management decisions into spatial prioritisations of MPB management efforts.

Dynamic financial tail risk networks: A backtesting-based conditional expected shortfall approach

This paper develops a Factor-Copula methodology for constructing high-dimensional dynamic tail risk networks based on the conditional expected shortfall (CoES) in order to overcome the limitations of traditional quantile regression and copula models. We backtest the CoES using cumulative joint violations and conditional coverage tests. The proposed factor‑copula‑CoES model reduces the rejection rates in 10‑th order conditional backtests by 56.37%, 47.72%, and 1.96% relative to the static‑copula‑CoES, time‑varying‑copula‑CoES, and factor‑copula‑CoVaR models, respectively, with all reductions being statistically significant. The dynamic analysis of the tail risk network of Chinese listed financial institutions indicates that the network topology characteristics align with real market risk events. Finally, the quantitative analyses show different effects of institutional and market factors on network-measured risk spillovers and contagion among Chinese financial institutions.

Calibration and sensitivity enhancement of AI rate integrator through EKF and impact on raman spectroscopy optimal wavelengths

Developments in atomic rate sensors have opened new opportunities for achieving high-precision rate integration. This study investigates the measurement principles and calibration parameters of an atomic interferometry (AI) rotation-rate sensor, modeled through Raman beam interactions and validated using real trajectory data. The primary objective is to identify and estimate the sensor parameters in order to improve orientation accuracy. To this end, real test data were collected from GPS-aided inertial systems (AIDIS 16407 and Vitans) along a predefined route. These data were incorporated into a software-in-the-loop AI gyroscope model, in which Raman laser beams acted as the excitation signal. A two-stage cascaded Extended Kalman Filter (EKF) was developed to simultaneously estimate sensor bias and calibration parameters. This approach enables parameter identification without requiring a physical AI gyroscope, thereby providing a cost-effective calibration framework. The experimental results demonstrate a significant reduction in bias uncertainty from 10 −6 to 10 −10 rad/s, along with a 35% improvement in orientation accuracy compared to the uncalibrated case. Furthermore, the calibrated AI parameters were applied to Raman spectroscopy of Rubidium (Rb), where an optimal wavelength of 1100 nm increased the Raman peak intensity by 20%, enhancing molecular bond resolution and spectral clarity.These findings confirm that the proposed calibration approach not only improves navigation accuracy but also enhances Raman spectroscopic analysis, highlighting the interdisciplinary potential of AI-based sensors.

A comparative analysis of the oral microbiome of Amish and non-Amish individuals to strengthen our understanding of variation within the oral microbiome

More than 700 phylotypes associated with the oral cavity collectively comprise the oral microbiome. Study of microbiomes has advanced our understanding of human health. Little is known about the oral microbiome of the Old Order Amish population, a distinct ethnoreligious group who choose to stay separate from mainstream society to preserve their traditional, faith-based way of life. This research was to generate a novel characterization of the Amish oral bacterial microbiome and, using a comparative study design, provide metagenomic analyses of potential variations between generated profiles of the Amish and non-Amish. Next-generation sequencing of 16S rRNA genes of supragingival plaque and saliva samples was used. Analysis between oral health habits from surveys (e.g., fluoride use, frequency of dental visits) and markers within the microbiomes were used to assess the extent of variation due to oral health habits or other factors. Samples were analyzed from 14 Amish and 13 non-Amish individuals. Using non-parametric analyses, alpha and beta diversity were measured to assess core microbiomes, abundance, and sample dissimilarity. Compared to non-Amish, Amish experienced significantly lower frequency of dental visits (p < 0.001) and fluoride use (p < 0.001), but no difference in frequency of teeth brushing (p = 0.198) was observed. Alpha-diversity of observed species differed significantly between Amish and non-Amish samples (H = −3.89, p = 0.002). Beta-diversity which accounted for relative taxon abundance and presence, as well as other metadata such as fluoride use, frequency of dental visits, and teeth brushing indicated, for both saliva and plaque, samples clustered by grouping and their covariates. The five primary phyla typically associated with the oral microbiome were the dominant phyla in both Amish and non-Amish individuals, although Proteobacteria were proportionally fewer in Amish samples. We conclude the oral microbiome between the Old Order Amish and rural non-Amish are distinctly different, which may reflect observed differences in lifestyle and oral health habits.

Alternate RNA decoding results in stable and abundant proteins in mammals

Positive childhood experiences and subjective well-being among university students in Yunnan Province, China: The mediating role of psychological resilience

This study examines the impact of Positive Childhood Experiences (PCEs) on the Subjective Well-Being (SWB) of university students in Yunnan Province, China, with psychological resilience as a mediating factor. Data were collected from 1,104 students across four universities using the Positive Childhood Experiences Scale, the Connor-Davidson Resilience Scale (CD-RISC), and measures of SWB. Descriptive statistics and mediation analysis were conducted using SPSS and the PROCESS macro. The results indicate that PCEs have a significant positive effect on SWB, and this relationship is partially mediated by psychological resilience. The indirect effect of PCEs on SWB through resilience was statistically significant. These findings highlight the importance of fostering resilience to enhance well-being in educational contexts. Given the regional focus on Yunnan Province, caution is warranted when generalizing the findings. Future research should examine broader and more diverse populations.

Optimally sequencing semantic search predicts creativity

Creativity is a fundamental human activity which drives progress and innovation. Extant research has documented the link between creative performance and the efficient navigation of semantic space during the creative process, but we argue that an important aspect of efficiency has been overlooked: the optimal sequence of concepts retrieved. We develop a simple cognitive test, the Shortest Semantic Path Task, and an associated automatic measure (circuitousness), which capture this insight. Five initial validation studies demonstrate that this novel measure predicts creativity above and beyond existing measures, providing a more complete picture of creative performance. The psychometric properties of the specific instruments tested also reveal opportunities to develop more robust and consistent instruments to measure optimal sequencing in semantic search.

Food for thought? The effects of the Healthy Primary School of the Future on children’s educational outcomes

Background There is limited empirical evidence regarding the effects of school-based health-promoting interventions on educational outcomes, highlighting a need for further research in this area to understand their broader impact on academic performance. The Healthy Primary School of the Future (HPSF) is a Dutch intervention aimed at improving children’s health by providing healthy school lunches and structured physical activity (PA) sessions. While HPSF’s positive impact on physical health has been well-documented, its effects on academic outcomes, particularly in mathematics and reading comprehension, remain less understood. This study evaluated the influence of HPSF on children’s performance by using national standardised tests for these academic domains. Methods A longitudinal quasi-experimental design was employed involving eight Dutch primary schools over a four-year period. Schools included two full HPSF schools (implementing both PA and healthy lunches), two partial HPSF schools (PA only), and four control schools (all in the same region). Scores from biannually taken national standardised tests in mathematics and reading comprehension were combined within the same year to decrease the number of missing values. The data were analysed using mixed model for repeated measures to assess the intervention effects over time. Results Children in full HPSF schools demonstrated significant improvements in mathematics performance compared to control schools (standardised effect size (ES) = 0.30, p = 0.011 after one year, increasing to ES = 0.66, p < 0.001 after four years). Mathematics gains in partial HPSF schools were smaller and non-significant (ES ≤ 0.23, p ≥ 0.137). For reading comprehension, small but significant improvement was observed in year 1 for full HPSF schools compared with control schools (ES = 0.28, p < 0.001), but this effect was diminished by year 4. Partial HPSF schools showed sustained small gains in reading comprehension over time (ES between 0.06 and 0.39). Conclusions The HPSF intervention significantly enhanced mathematics performance when both healthy lunches and PA sessions were implemented, underlining the importance of a holistic approach to health promotion in schools. However, the intervention’s impact on reading comprehension was limited, indicating the need for additional and/or more targeted strategies to improve literacy outcomes. These findings highlight the potential of integrated health interventions to boost academic performance and address both health and educational inequities.