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Determinants of coexistence of undernutrition and anemia among children aged 6–59 months in Nepal: Evidence from the 2022 Nepal Demographic and Health Survey

Introduction Undernutrition and anemia among children aged 6–59 months are significant public health issues in developing countries like Nepal. The coexistence of these conditions impacts childhood development. This study aimed to determine the prevalence of undernutrition and anemia, assess their coexistence, and identify contributing factors among children aged 6–59 months in Nepal. Methods We conducted a secondary analysis using data from 2022 Nepal Demographic and Health Survey (NDHS 2022), a nationally representative cross-sectional survey. The outcome variables were undernutrition, anemia, and their coexistence. Undernutrition was defined as the presence of stunting, wasting, underweight, or any combination of these conditions. Anemia was defined as hemoglobin levels <11.0 g/dL (adjusted for altitude). We applied multivariable multinomial logistic regression to determine factors associated with coexistence, and multivariable logistic regression to assess factors associated with undernutrition and anemia separately. We presented the results from the regression analysis using adjusted odds ratio (aOR) and 95% confidence intervals (CI). Results Among 2,395 children, the weighted prevalence of undernutrition was 33.5%, anemia was 43.4%, and coexistence of undernutrition and anemia was 16.0%. Children from the richest wealth quintile, whose mothers had at least secondary education, and those whose mothers participated in household decision-making had 53% (aOR: 0.47; 95% CI: 0.26 to 0.86), 48% (aOR:0.52; 95% CI: 0.32 to 0.86), and 34% (aOR: 0.66; 95% CI: 0.47 to 0.94) lower odds of experiencing the coexistence of undernutrition and anemia compared to their counterparts. Children with underweight mothers had 80% (aOR:1.80; 95% CI:1.20 to 2.70) higher odds of coexistence compared to their counterparts. Conclusion The prevalence of undernutrition, anemia, and their coexistence among children was high in Nepal. Interventions that improve mothers’ education, strengthen their roles in the family, and enhance the household financial condition are essential to reduce these conditions and improve children’s nutritional status.

Erratum: “Relativistic and quantum electrodynamics effects on NMR shielding tensors of Tl <i>X</i> ( <i>X</i> = H, F, Cl, Br, I, At) molecules” [J. Chem. Phys. 161, 064307 (2024)]

Br-Mediated Spin-State Control in Nickelocene and Cobaltocene

Impaired stem cell migration and divisions in Duchenne muscular dystrophy revealed by live imaging

Abstract Dysregulation of stem cell properties is a hallmark of many pathologies, but the dynamic behaviour of stem cells in their microenvironment during disease progression remains poorly understood. Using the mdx mouse model of Duchenne Muscular Dystrophy, we developed innovative live imaging of muscle stem cells (MuSCs) in vivo, and ex vivo on isolated myofibres. We show that mdx MuSCs have impaired migration and precocious differentiation through unbalanced symmetric divisions, driven by p38 and PI3K signalling pathways, in contrast to the p38-only dependence of healthy MuSCs. Cross-grafting shows that MuSC fate decisions are governed by fibre-independent cues, whereas their migration behaviour is determined by the myofibre niche. This study provides the first dynamic analysis of dystrophic MuSC properties in vivo, reconciling conflicting reports on their function. Our findings establish DMD as a MuSC disease with niche dysfunctions, offering strategies to restore stem cell functions for improved muscle regeneration.

Straightforward model-based approach using only field data and open-source maps to improve carbon stock estimates for REDD + projects

When the outcome is compositional: A method for conducting compositional response linear mixed models for physical activity, sedentary behaviour and sleep research

Time use is compositional in nature because time spent in sleep, sedentary behaviour and physical activity will always sum to 24 h/day meaning any increase in one behaviour will necessarily displace time spent in another behaviour(s). Given the link between time use and health, and its modifiable nature, public health campaigns often aim to change the way people allocate their time. However, relatively few studies have investigated how movement-behaviour compositions change longitudinally (with repeated measures), due to experimental design elements (e.g., intervention effects), or differences due to participant socio-demographic characteristics (e.g., sex, socio-economic status) within clustered sampling designs. This may be because most mixed-model packages that account for the random effects do not natively support a multivariate outcome such as movement-behaviour composition. In the current paper we provide a practical framework of how to implement a compositional multivariate-response linear mixed model that can be used to model the entire 24h movement-behaviour composition as the dependent variable within a multilevel framework. The method accounts for covariances across and within response variables at the grouping (individual, cluster etc.) and covariance between response variables at the observation level. Results are therefore invariant to the chosen log-ratio basis used to construct the response variables (i.e., mathematically equivalent models). The method outlined is applicable to many designs including longitudinal cohort studies, intervention trials, and clustered cross-sectional designs (e.g., students within schools, patients within clinics). In a worked example we show how this approach can be used to investigate how time is reallocated in children across the school year.

Machine-learning graph convolutional electronic propagators

We present a graph-based machine-learning framework for simulating the time evolution of electronic wavefunctions and densities in quantum systems. Inspired by parallels between time-dependent quantum propagators and spectral graph convolutions, we employ a recursive Chebyshev graph neural network architecture capable of learning the dynamics of electronic processes across a range of external potentials and Hamiltonians. Two model variants are introduced: one that evolves the full complex-valued wavefunction and the other that propagates only the electron density. Both models are trained on trajectory data generated from tight-binding Hamiltonians and a time-dependent electron–phonon coupled system. Our results demonstrate that wavefunction-based models achieve near-exact long-time propagation across static and dynamic regimes, while density-only models maintain strong performance using physics-informed loss functions, even in the absence of phase information. This work lays the foundation for coarse-grained, resolution-independent propagators for electronic dynamics and opens new pathways for scalable quantum simulations in complex molecular and condensed-phase systems.

Water-Soluble Micelles with a Polyferrocenylsilane Core for Reductive Synthesis of Nanomaterials

Rejection-based choices discourage people from opting out of voting

Abstract Representing the will of voters is challenging when many opt out of indicating their preferences. Such opt-out behavior has been explained by voters lacking a preference and/or disliking their options. We provide evidence for a third account: people opt out of choosing between undesirable candidates because bad options are incongruent with their typical goal of selecting the best one. Using a voting task, we show across two lab-based studies that the tendency to opt out of choices between bad candidates is eliminated when participants are asked to reject the worst candidate. Leveraging our experimental findings, we simulate elections and show that rejection-based voting can produce election outcomes that are more representative of the preferences of the electorate. To validate this prediction, we conduct two Prolific surveys of self-reported US Independents before the 2024 US presidential election, and show that people are less likely to respond “undecided” when asked who they will vote against rather than who they will vote for. Our findings help understand when and how people vote, and how to better reveal the preferences of voters who know which candidates they like least but are unwilling to endorse the one they like most.

Genome-wide association analysis reveals natural genetic variations controlling canopy architecture traits in bread wheat

Effects of happy and angry human voice recordings on postural stability in dogs: An exploratory biomechanical analysis

Auditory stimuli are known to induce biomechanical balance responses, influencing postural stability in humans. These responses provide valuable insights into the interaction between auditory perception and physical balance. This study investigates the effect of human voices on postural stability in dogs during static stance. Twenty-three healthy pet dogs were assessed standing on a pressure plate under three auditory conditions: happy voice, angry voice, and no sound. Five conventional Center of Pressure (COP) parameters were analyzed, mediolateral displacement, craniocaudal displacement, support surface (SS_%), average speed (AS) and statokinesiogram length. A significant main effect of condition on SS_% ( F (2) = 4.35, p = 0.019, η² p  = 0.165) was found; SS_% values in the angry voice condition (mean = 0.12 ± 0.06) were significantly higher than in the no sound condition (mean = 0.08 ± 0.03; p = 0.026). A K-means cluster analysis of relative COP changes (ΔCOP_%, increase/decrease relative to the no sound condition) revealed two distinct reaction patterns within both sound conditions (ANOVA, all ΔCOP_% parameters, p &lt; 0.01, except AS within happy condition). For happy voices, 57% of dogs exhibited increases across all ΔCOP_% parameters, while 43% showed decreases. In contrast, angry voices led to increased ΔCOP_% parameters in 30% of dogs, with 70% remaining unaffected. A significant difference in Support Surface (ΔSS_%) was found between clusters 1 for happy and angry voices (F = 8.75, p = 0.008). The largest absolute and relative ΔSS_% changes occurred in the angry voice condition. These exploratory findings suggest that the emotional arousal triggered by human voices can have both stabilizing and destabilizing effects on canine balance. Angry human voices were associated with the greatest destabilizing effect.

A JKR/Griffith model transition to slip in frictional contact between layered surfaces with roughness

In a recent study, Liang et al. developed an analytical framework, termed the “Johnson–Kendall–Roberts (JKR)–Griffith model,” to describe how an energetic model of friction between nominally flat rough surfaces leads to the onset of slip governed by elastic instability. In the present study, this approach is extended to the case of a layered solid. By combining Persson’s contact mechanics theory, a JKR-type approximation, and the Cattaneo–Mindlin superposition principle, the model captures the transition from sticking to sliding under tangential loading. The analysis shows that the static friction can exceed the kinetic friction, with this enhancement depending on the ratio of elastic moduli, surface roughness, and normal load. The model further predicts that the maximum discrepancy between static and kinetic friction occurs at an intermediate layer thickness. This framework provides useful guidance for the design of layered surfaces to mitigate stick–slip phenomena, which are often responsible for undesirable machine vibrations and wear.

Terminal Conjugation Enables Nanopore Sequencing of Peptides

Author Correction: UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data

Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques

Abstract Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and accurate early diagnosis plays a critical role in improving patient survival. In this study, a comparative analysis of multiple lightweight Convolutional Neural Network (CNN) variants is presented for multi-class lung cancer classification using histopathological images. Four CNN architectures were designed to systematically explore the trade-off between model complexity and classification performance. Each variant was trained and evaluated within a unified experimental framework incorporating data augmentation, class balancing via computed class weights, and a custom macro-F1-based early stopping callback to ensure stable and fair performance comparison. The models were trained on three histopathological classes, Lung Benign Tissue, Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma. The training process involved automated generation of accuracy, loss, and validation F1 curves, along with confusion matrices for both validation and test datasets. To assess robustness, the best-performing model was evaluated across multiple random seeds and statistical significance was established using paired McNemar’s tests against competing variants. Among the proposed variants, one model (Lite-V2) achieved superior macro-F1 performance and demonstrated strong generalization capability on unseen test data, confirming the effectiveness of lightweight CNNs in achieving high accuracy with reduced computational cost. This work highlights the potential of custom lightweight CNN architectures for efficient and reliable lung cancer classification, offering a reproducible framework that can be extended to larger datasets or adapted for clinical diagnostic applications.

Co-developing SHELTER (Safe, Healthy Environments and Local Transformation for Equity and Resilience) with families with lived experience of homelessness in the New York City shelter system: A community needs assessment and data collection protocol

In January 2025, the nightly census revealed that over 120,000 people were staying in New York City (NYC) shelters, including more than 41,000 children, of whom almost half were aged 0–5 years. Children under five years old (under-5s) experiencing homelessness are especially vulnerable because the first five years of life are a critical period for child growth, including approximately 90% of brain development. Furthermore, under-5s experiencing homelessness have a higher risk for multiple adverse childhood experiences, developing chronic health conditions, and recurrent homelessness across the life course. Data available for under-5s experiencing homelessness is generally lacking, and what is available is of notably poor quality in the United States, leaving a wide evidence gap and an inability to determine the actual needs of this population. This proposed protocol employs community-based participatory research and was co-developed with families with under-5s who have lived experience of homelessness in NYC shelters. The aim is to determine what barriers exist in the physical and social environments to optimizing health and wellbeing (e.g., milestones, child mental health, parental mental health, safety) among under-5s living in NYC shelters. Using a sequential mixed-methods design, we propose to address a gap in the current literature by conducting an assets- and deficits-based health needs assessment comprising a quantitative survey and qualitative semi-structured interviews. In the long term, our objective is to enhance the quality and quantity of data for this vulnerable population, thereby laying the groundwork for the future co-development of a comprehensive, optimized intervention addressing the needs of under-5s experiencing homelessness.

Unitary transformation with double polaronic generator for spin-boson model: Ground state, thermodynamics and dynamical evolution

Unitary transformation with a double polaronic generator is proposed for the unbiased spin-boson Hamiltonian, which leads to a new ground state with lower ground state energy, as well as an effective Hamiltonian without the counter-rotating coupling. The infrared divergence associated with the nonadiabatic modes in Ohmic bath plays an important role in determining the quantum critical point αc of the ground state, which separates the non-degenerate ground state for α&amp;lt;αc from the degenerate one for α&amp;gt;αc and leads to logarithmic divergence of the bosonic numbers. The thermodynamic properties, the dynamical evolution, and time correlation of the SBM model are studied by the effective Hamiltonian obtained from the unitary transformation, which can be solved easily and transparently by the perturbation theory. As a check of our theoretical treatment and numerical results, we have calculated the Wilson ratio and shown that the spectral sum rule and Shiba’s relation are exactly fulfilled.

Scaling crossover in droplet impact force on elastic substrates

Abstract Droplet impacts are fundamental to fluid-structure interactions, shaping processes from erosion to bioprinting. While previous scaling laws have provided insights into droplet dynamics, force scaling laws remain insufficiently understood, particularly for soft substrates where both the droplet and substrate deform significantly. Here, we show that droplet impacts on elastic substrates exhibit a scaling crossover in maximum impact force, transitioning from inertial force scaling, typical for rigid substrates under high inertia, to Hertzian impact scaling, characteristic of rigid spheres on elastic substrates. Using high-speed photoelastic tomography, we captured high-resolution dynamic stress fields and identified a similarity parameter governing the interplay between droplet inertia, substrate elasticity, and deformation time scales. Our findings redefine how substrate properties influence impact forces, demonstrating that droplets under high inertia-long thought to follow inertial force scaling-can instead follow Hertzian impact scaling on soft substrates. This framework provides practical insights for designing soft, impact-resistant materials.

Effect of xanthan gum on mechanical strength and microstructure of Cu (II)-contaminated soil subjected to freeze–thaw cycles

LHAT-YOLO: Study on intelligent monitoring algorithm for helmets at construction sites

Construction sites in civil engineering projects are prone to sudden accidents. In particular, head injuries pose a significant safety threat to construction workers. Helmets play a vital role in protecting the heads of construction workers. Most construction sites still rely on manual methods to monitor workers’ helmet compliance, which is not only inefficient but also incapable of real-time monitoring. While traditional models can achieve intelligent monitoring, their lack of real-time capability fails to meet practical demands. However, the introduction of deep learning has transformed this situation. Therefore, this study proposes an intelligent monitoring method for helmet wearing at civil engineering construction sites based on deep learning theory. The study uses GSConv to improve the convolution module of the YOLOv11 deep learning model and adds a lightweight detection head: FCD (Fast Convolutional Detection) to establish the LHAT-YOLO model (Lightweight YOLO model for detecting helmets). While reducing the complexity of the model, it maintains accuracy and achieves efficient and intelligent detection of helmet wearing at construction sites. Experimental results show that on the dataset comprising 19,780 images in the training set, 2,473 images in the validation set, and 2,473 images in the test set, the LHAT-YOLO model reduces GFLOPs by 11% and Params by 9.5% compared to the YOLOv11 model. Overall, the LHAT-YOLO model achieves a Precision value of 93.95%, a Recall value of 88.99%, an mAP50 value of 94.92%, and an mAP50-95 value of 65.28%. This demonstrates that the LHAT-YOLO model maintains high accuracy even while being lightweight.