Browse Articles

Fall armyworm infestation, maize production and nutrition security: Evidence from Uganda

We study the impact of fall armyworm (FAW) infestation on nutrition security outcomes in eastern Uganda, measuring nutrition security by dietary diversity scores of vulnerable household members––children under 5 years and their mothers. We use different regression models and aim to take the endogeneity of FAW infestation seriously. We also analyse FAW’s impact pathway to nutrition status and ask whether impacts are caused by reduced maize yields and sales or increased costs associated with insecticide use. The main results are that high FAW infestation reduces maize yields and sales, and adversely affects dietary diversity.

A brain implant that could rival Neuralink’s enters clinical trials

Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering of frequency domain information

“They recognize me as a doctor”: A peer mobilisation training programme to promote oral HIV self-testing and referral for acute HIV infection screening among gay and bisexual men and transgender women in coastal Kenya, an exploratory study

Background Targeted peer mobilisation can improve access to HIV testing and care and may impact onward HIV transmission. We describe a qualitative exploration of the experience with a peer mobilisation training programme for oral HIV self-testing (OST) and referral for acute HIV infection (AHI) testing among gay and bisexual men (GBMSM) and transgender women (TGW) in coastal Kenya. Methods The training programme covered five modules: 1) safe sex, 2) OST, 3) AHI, 4) HIV partner notification services, and 5) mobilisation skills. Mobilisers attended two training sessions and weekly meetings between March and June 2019. Mobilisers offered OST to GBMSM and TGW peers and extended an AHI referral card for point-of-care HIV-RNA testing when peers reported AHI symptoms. Two focus group discussions with 18 mobilisers and 15 in-depth interviews with mobilised clients who were newly HIV diagnosed were conducted to explore the experiences of the training programme. Results Mobilisers felt empowered through the training programme, which enhanced their mobilisation skills across two areas: (1) networking skills and (2) client empowerment. Facilitators for HIV testing were confidentiality of the OST, presence of STI symptoms, and building trust between mobilisers and clients. Mobilisers and clients reported challenges as: (1) misconceptions regarding OST and symptoms of AHI, (2) logistical and financial issues, and (3) stigma and security concerns. Discussion Our training programme facilitated peer mobilisers to extend OSTs among GBMSM and TGW in coastal Kenya while it was more difficult to refer clients directly for AHI testing. Mobilisers felt empowered through enhanced mobilisation skills which helped them to mobilise clients for HIV testing. A targeted training programme was helpful in mobilising peers to take up HIV testing.

Integrating ecosystem service trade-offs and bundles for ecological zoning and management optimization: A case study of the Danjiangkou reservoir area, China

Validation and comparison of GC-MS, FT-MIR, and FT-NIR techniques for rapid bromoform quantification in Asparagopsis taxiformis extracts

Influence of nitrogen fertilization rate and application frequency on physical and phytochemical quality of ‘Owari’ Satsuma Mandarin across fruit developmental stages in North Florida

Experimental and machine learning prediction of compressive strength of chemically activated RHA based RAC using SHAP and PDP analysis

Abstract The increasing demand for sustainable construction materials necessitates the effective reuse of industrial and agricultural waste in high-performance concrete (HPC). However, challenges such as strength loss due to recycled concrete aggregates (RCA) and variable performance of supplementary cementitious materials hinder widespread adoption. This study addresses these challenges by investigating the synergistic effect of chemically activated rice husk ash (RHA), RCA (0–100%), and foundry sand on the compressive strength and durability of HPC. Six experimental groups were prepared: one with inactivated RHA and five with chemically activated RHA using 3.5% sodium sulfate (Na 2 SO 4 ), combined with RCA replacement levels of 0%, 40%, 60%, 80%, and 100%. All mixes included 20% FS as partial fine aggregate replacement and constant silica fume. Compressive strength was measured at 3, 7, 14, 28, 56, 90, and 120 days, while durability was evaluated through acid exposure tests over 4 months. To complement the experimental study, machine learning models including K-Nearest Neighbors, Random Forest, Artificial Neural Networks, and Extreme Gradient Boosting were applied to predict compressive strength. Among them, XGB outperformed others with an R 2 of 0.951, RMSE of 3.222 MPa, and MAE of 1.862 MPa. SHAP and Partial Dependence Plot (PDP) analyses revealed curing age, RCA, and Na 2 SO 4 content as key influencing factors. This study concludes that up to 40% RCA can be effectively used in HPC with activated RHA and FS without compromising long-term strength and acid resistance. The integration of interpretable ML models with detailed experimental validation provides a robust framework for sustainable concrete design.

Azo-Bridged Metal–Organic Frameworks with Robust Zr ₆ -Cluster Nodes: A Dual-Functional Design for Suppressing Polysulfide Shuttling in Lithium–Sulfur Batteries

A multi-technique ensemble model leveraging attention mechanism and image processing for enhanced colorectal tumor detection

Abstract This research introduces an improved method for identifying colorectal tumors through a combination of deep convolutional neural networks (CNNs), transfer learning, and sophisticated image processing techniques used on histopathological images. The suggested ensemble—based on ResNet50 and enhanced with a dual attention mechanism—surpasses individual model architectures by enhancing both accuracy and interpretability, allowing the model to emphasize crucial tissue areas pertinent to diagnosis. Segmentation techniques, such as watershed and distance transform, are utilized to define tumor margins and possible lesion regions. The dataset, obtained from Kather et al. (2019), includes 5,000 histopathological images spanning eight unique categories (tumor, stroma, complex, lymph, debris, mucosa, adipose, empty). The experimental findings demonstrate impressive results, achieving a training accuracy of 98.74%, a validation accuracy of 94.35%, an F1-score of 0.94, a recall of 0.94, a precision of 0.95, a specificity of 0.96, and a Cohen’s kappa score of 0.9354, signifying outstanding inter-class consensus. These results showcase the model’s strength across different class distributions and emphasize its possible clinical value in aiding the early identification and management of colorectal cancer.

Feedback-based training reduces ensemble perception bias of facial emotions in individuals with high social anxiety: A single-session randomized controlled trial

People can efficiently extract summary statistics from a set of objects—a process known as ensemble perception—including the average emotion of a facial crowd. Individuals with high social anxiety, however, tend to perceive facial crowds as more negative, reflecting a systematic perceptual bias. Because the ability to interpret group emotions is important to adaptive social functioning, this study examined whether a feedback-based training paradigm could reduce ensemble perception bias, and whether its effects varied by social anxiety. A total of 120 Korean university students were randomly assigned to either a training ( n  = 60) or control ( n  = 60) condition. Participants first completed baseline questionnaires assessing trait and state social anxiety and depression, then performed an ensemble perception task. In the training condition, participants rated the mean emotional intensity of a facial crowd on a continuous scale and received visual feedback displaying both their rating and the actual mean intensity. The control group performed the same task without feedback. Ensemble perception bias and absolute error were assessed before and after training. State social anxiety was reassessed after the task. Overall, feedback training significantly reduced bias and marginally reduced error but did not affect state social anxiety. Although formal moderation by trait social anxiety as a continuous variable was non-significant, exploratory subgroup analyses revealed that participants with high social anxiety showed notable reductions in bias, whereas no such changes were observed in the low social anxiety group. These effects remained after controlling for depressive symptoms. These findings suggest that individuals with high social anxiety may be particularly responsive to corrective feedback, enabling recalibration of their perceptual tendencies. Accordingly, feedback-based training may represent a promising approach for reducing perceptual bias in social perception among socially anxious individuals.

Comparative metabolomic profiling and chemometric correlation of Salvia rosmarinus Spenn. and Origanum vulgare L. with antibacterial, antioxidant and anti-inflammatory activities

Abstract The Lamiaceae plants are recognized in folk medicine for their antibacterial and anti-inflammatory properties. This study reports the first MS-based metabolomics analysis, integrating with chemometrics to explore metabolome heterogeneity in Salvia rosmarinus Spenn. (rosemary) and Origanum vulgare L. (oregano), and to pinpoint the key metabolites driving their antibacterial, antioxidant, and anti-inflammatory activities. UPLC–QTOF–MS/MS facilitated the identification of 164 metabolites, including flavonoids and hydroxycinnamic acids, which were reported for the first time in these species. For instance, salvianolic acid D and quercetin coumaroylhexoside were detected in rosemary, while salvianolic acid K, cleroden J, and flavonoids like nepitrin were newly reported in oregano. In biological evaluation, rosemary strongly inhibited methicillin-resistant Staphylococcus aureus and Escherichia coli , exhibited the highest radical scavenging capacity in DPPH assay, and showed superior anti-inflammatory effects through COX-II inhibition and TNF-α and NF-κB suppression. In contrast, oregano displayed the strongest reducing power in FRAP assay. Chemometric analyses revealed that flavonoids, hydroxycinnamic acids, and terpenes were the principal discriminating classes. Partial least squares analysis correlated rosemary’s antibacterial and radical scavenging activity with hydroxycinnamic acids, flavonoids, and terpenes, while its anti-inflammatory effects were linked to flavonoids and diterpenes. In oregano, FRAP reducing power correlated with benzyl derivatives, organic acids, and hydroxycinnamic acids.

Exploring the effect of menstrual loss and dietary habits on iron deficiency in teenagers: A cross-sectional study

Adolescent girls are particularly susceptible to iron deficiency due to increased iron requirements during the pubertal growth spurt, combined with iron loss following menarche. This study aimed to investigate the prevalence of heavy menstrual bleeding in an adolescent population using the SAMANTA questionnaire and to explore its relationship with dietary habits and iron deficiency. This cross-sectional study was conducted in two Swedish high schools in 2023. Post-menarchal female students, aged 15 and older, were included (n = 394). Data were collected on-site through a patient-reported survey, including the SAMANTA questionnaire for heavy menstrual bleeding, and by blood sampling. Meat-restricted diet was analyzed in relation to iron status. Descriptive analysis and regression analysis were used to assess the prevalence of heavy menstrual bleeding and its relationship with dietary habits and iron deficiency, defined as ferritin <15 µg/L. The prevalence of heavy menstrual bleeding and iron deficiency in the cohort was 53% (208/394) and 40% (157/394), respectively. In univariate analysis, heavy menstrual bleeding (OR 3.0, 95% CI [2.0, 4.6]) and a meat-restricted diet (OR 3.5, 95% CI [2.2, 5.6]) were both associated with increased odds of iron deficiency. When assessing the joint effect of having heavy menstrual bleeding and a meat-restricted diet, the odds of iron deficiency were 13.5 times higher compared to omnivore individuals with normal menstruation (OR 13.5, 95% CI [6.4, 28.7]). Overall, the prevalence of iron deficiency in this population of adolescent girls was very high. Heavy menstrual bleeding and a meat-restricted diet were both independently associated with increased odds of iron deficiency. However, odds for iron deficiency were monumentally higher when combining these two variables, thus highlighting the importance of assessing and addressing both excessive output and low intake of iron.

Fluorogenic Covalent Probes for RNA

Climate-driven shifts in marine habitat explain recent declines of Japanese Chum salmon

Advancing training effectiveness prediction in mass sport through longitudinal data: A mathematical model approach based on the Fitness-Fatigue Model

Despite the critical need for scientific training load assessment in mass sports, the Fitness-Fatigue Model (FFM) requires further mathematical optimization and practical output indicators. The aim of this study was to optimize the mathematical relationship between “adaptation” and “fatigue” in the FFM, identify generalizable model output indicators, and evaluate its performance in predicting training effectiveness in mass sport. To account for the nonlinear and time-varying characteristics of training effectiveness, this study proposed new mathematical assumptions and optimized parameters against individual longitudinal data. The external load (speed and wattage) and internal load (wearable-compatible heart rate variability [HRV] and heart rate recovery [HRR] related indicators) of each training day were collected for 28–42 days per person (420 paired data from 13 subjects during 12 weeks of medium-intensity continuous cycling). The longitudinal data were used to perform parameter estimation and model evaluation for each individual separately. When the optimal model output indicator was selected, the R 2 values of the optimized model ranged from 0.61–0.95, with fitting root mean square error (RMSE) at 0.07–0.37, and mean absolute percentage error (MAPE) in predictive ability assessment at 3.99%−31.99%. However, a few individuals had larger fitting errors (minimum R 2 of 0.32, maximum RMSE of 0.90) and predictive errors (maximum MAPE of 86.57%) when the output indicator was inappropriate. The original model generally has lower R 2 and higher RMSE and MAPE. This shows the optimization of functional relationships and the application of individual longitudinal data have resulted in better performance of the model, but optimal indicator selection varies by individual. Furthermore, HRV and HRR related indicators are generalizable model output indicators that can be used to predict training effectiveness in mass sports through wearable devices and machine learning technology. However, the study has limitations including the homogeneous sample and single training type. Future research should validate the model across different sports and populations, investigating the factors affecting model fitting and prediction.

Ultrasonographic predictors of residual acetabular dysplasia in high-risk infants

Factors influencing effective decrease of controlled attenuation parameters in metabolic-associated steatotic liver disease: A multilevel linear regression analysis at Vajira Hospital

Backgrounds Metabolic-associated steatotic liver disease (MASLD) is a growing global health concern. Although several studies have demonstrated associations between baseline metabolic factors and hepatic steatosis, the quantitative influence of these characteristics on the extent of liver fat reduction following lifestyle modification remains unclear. This study aims to analyse the relationship between baseline factors and the modulation of controlled attenuation parameter (CAP) from baseline to 6 months and compare the mean difference in CAP changes of individuals at a telemedicine-based clinic. Methods A cohort of MASLD who had hepatic steatosis (CAP ≥ 215 dB/m) with metabolic risk were enrolled. (30 August 2023–30 April 2024). Baseline characteristics, diet and exercise were collected. Multivariable multilevel random intercepts and slope linear regression models were used to analyse the mean difference in CAP change over time for each characteristic adjusted for other variables in the model. Results The mean age was 46.93 years, 70% were females. The baseline CAP value was 319.13 ± 42.33 dB/m. Individual baseline age ≥ 45 (−44.52 dB/m [CI −84.86 to −4.17], p = 0.031); higher waist circumference (−87.85 dB/m [CI −153.23 to −22.47], p = 0.008); and a lower BMI (−78.31 dB/m [CI −139.94 to −16.67], p = 0.013) were associated with greater reductions in the mean difference of CAP change. Notably, participants with diabetes (−61.31 dB/m [CI −100.25 to −22.36], p = 0.002) and better glycemic control (−43.49 dB/m [CI −74.00 to −12.99], p = 0.005) exhibited greater liver fat reductions. Conclusion Lifestyle modification led to significant reductions in liver fat, and the extent of improvement was influenced by baseline metabolic characteristics. These findings suggest that metabolic profiles, rather than weight loss alone, determine treatment responsiveness and support the use of individualized lifestyle strategies for MASLD management.

Accurate modeling of microwave structures in constrained domains using global sensitivity analysis and performance-based pre-screening

Abstract The significance of behavioral models gradually increases in the design and analysis of microwave components. They are mainly used to replace CPU-heavy full-wave electromagnetic (EM) simulations and to expedite EM-driven procedures, especially optimization. Unfortunately, constructing accurate surrogates is a challenging task. In the case of highly nonlinear frequency characteristics of microwave passives, it is normally feasible only when the structures are parametrized by a small number of parameters belonging to narrow ranges. Design utility of such models is limited. Therefore, we developed a novel methodology for computationally efficient and reliable microwave modeling. The presented approach incorporates dimensionality reduction as well as spatial confinement to lower the cost of training data acquisition and to improve the model predictive power. The former is enabled by rapid global sensitivity analysis, which identifies the directions having major influence on the circuit response variability. These directions span the model domain, which is further confined using the pre-screening mechanism focusing on better-quality designs, as well as the spectral analysis of the selected design subset. The surrogate established in the reduced domain still covers the parameter space parts of primary importance, thereby retaining its design applicability. Excellent accuracy of the proposed technique has been validated through extensive benchmarking against several state-of-the-art methods, whereas design readiness has been demonstrated through circuit optimization under various sets of performance requirements. Physical measurements of fabricated circuit prototypes provide auxiliary yet essential validation of the relevance of the proposed modeling technique.

Differences in physical fitness levels by adherence to the 24-hour movement guidelines among Japanese elementary school children

Currently, physical fitness levels of Japanese children are lower than in the 1980s. Investigating the relationship between adherence to the 24-hour movement guidelines (24-h MG)—which include moderate-to-vigorous physical activity (MVPA), screen time (ScT), and sleep duration (Sleep)—and physical fitness is crucial for improving children’s fitness. This cross-sectional study examined differences in physical fitness by 24-h MG adherence patterns among children in grades 1–6. Eight fitness components were assessed using standardized tests: handgrip strength (muscle strength), sit-up (trunk muscle strength and endurance), sit-and-reach (flexibility), repeated side jump (agility), 20-meter shuttle run (cardiorespiratory fitness), 50-meter sprint (speed), standing long jump (explosive power), and softball throw (explosive power and dexterity). A total of 307 participants (41.4% male) were included in the analysis. Analysis of covariance was performed, with sex, grade, BMI, and other guideline adherence as covariates. Total fitness scores were significantly higher in those meeting the MVPA (Cohen’s d = 0.47; standardized effect size), both the MVPA and ScT (d = 0.63), both the MVPA and Sleep guidelines (d = 0.65), or with all three guidelines (d = 0.59) compared to those not meeting them. Children meeting the MVPA guideline—either alone or in combination with ScT or Sleep guidelines—showed significantly higher scores in multiple fitness components compared to those not meeting them. When comparing effect sizes, differences in total and most of the individual fitness scores were greater among those who met both the MVPA and either the ScT or Sleep guideline. In contrast, those who did not meet the MVPA guideline but adhered to one or both of the ScT and Sleep guidelines showed no significant differences in total and individual fitness scores. These findings suggest that promoting MVPA adherence is crucial for enhancing physical fitness, and that additionally encouraging appropriate ScT and Sleep behaviors may further improve children’s physical fitness.