bluuebunny/crossref_metadata_2025_split huggingface dataset:
Variations in vitamin D status among Chinese children aged 1–6 years during the COVID-19 pandemic
Authors: Qiao Yongfeng , Wang Xiaoqin , Ma Yanfen , Hu Jian 
Published: January 2025

BackgroundVitamin D deficiency has been a critical global health issue within the pediatric population. Closed-off management brought about by the COVID-19 pandemic has drastically impacted outdoor activities and sunlight exposure, however, whether it indirectly further exacerbated the vitamin D deficiency has not been largely investigated, especially among children in China. The purpose of this study was to evaluate 25(OH)D concentrations in children before and during the COVID-19 lockdown and to analyze the factors influencing their vitamin D status.MethodsA cross-sectional survey included children aged 1–6 years from Han Zhong Central Hospital in the southern Shanxi Province of China. This study examined healthy children from a pediatric health care department over two periods: before COVID-19 (March 2019–February 2020), and during COVID-19 (March 2020–February 2021). Total 25(OH)D concentrations were compared between the two observation periods. Vitamin D status was determined by 25(OH)D concentrations: deficient (&lt;20 ng/ml), insufficient (20–29 ng/ml), and sufficient (30–100 ng/ml).ResultsThe study involved 6,780 children, with 52.8% being 1-year-olds, 23.1% being 2-year-olds, and 24.1% being 3 to 6-year-olds. Boys and girls were 52.8 and 47.2%, respectively. The actual prevalence of deficiency in vitamin D nutritional status among children was 2.8%, with 87.1% of cases in those aged 3 to 6 years. Vitamin D insufficiency was 18.3%, affecting 54.8% of the same demographic. The average of 25(OH)D concentration were 38.2 ± 9.8 ng/ml, significantly varying by age and season. 25(OH)D concentrations decreased with age, from 42.3 ± 8.8 ng/ml at 1-year-olds to 37.4 ± 8.2 ng/ml at 2-year-olds, and further to 30.2 ± 8.1 ng/ml at 3 to 6-year-olds. Seasonal variations showed that 25(OH)D concentrations were higher in spring (38.7 ± 10.1 ng/ml), summer (38.7 ± 10.0 ng/ml), and fall (38.6 ± 9.2 ng/ml) in comparison to winter (36.0 ± 9.8 ng/ml). Additionally, the concentrations of 25(OH)D in spring exhibited a decrease during the COVID-19 pandemic (37.9 ± 10.3 ng/ml) in comparison to the pre-pandemic measurements (39.3 ± 9.9 ng/ml) (p = 0.008), while winter concentrations increased from (35.1 ± 10.4 ng/ml) to (37.9 ± 10.3 ng/ml) during the pandemic (p = 0.002).ConclusionThe research indicated that vitamin D deficiency is uncommon among Chinese children, with 25(OH)D concentrations experiencing a notable decline in those aged 3–6 years. The findings suggested a potential need for tailored supplementation strategies and possibly higher doses for this age group, along with monitoring 25(OH)D concentrations to evaluate supplementation effectiveness. COVID-19-related restrictions minimally affected children’s 25(OH)D concentrations, revealing the nutritional implications of the pandemic.
Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease
Authors: Martínez-Enguita David , Hillerton Thomas , Åkesson Julia , Kling Daniel , Lerm Maria , Gustafsson Mika 
Published: January 2025

IntroductionDNA methylation (DNAm) age clocks are powerful tools for measuring biological age, providing insights into aging risks and outcomes beyond chronological age. While traditional models are effective, their interpretability is limited by their dependence on small and potentially stochastic sets of CpG sites. Here, we propose that the reliability of DNAm age clocks should stem from their capacity to detect comprehensive and targeted aging signatures.MethodsWe compiled publicly available DNAm whole-blood samples (n = 17,726) comprising the entire human lifespan (0–112 years). We used a pre-trained network-coherent autoencoder (NCAE) to compress DNAm data into embeddings, with which we trained interpretable neural network epigenetic clocks. We then retrieved their age-specific epigenetic signatures of aging and examined their functional enrichments in age-associated biological processes.ResultsWe introduce NCAE-CombClock, a novel highly precise (R2 = 0.978, mean absolute error = 1.96 years) deep neural network age clock integrating data-driven DNAm embeddings and established CpG age markers. Additionally, we developed a suite of interpretable NCAE-Age neural network classifiers tailored for adolescence and young adulthood. These clocks can accurately classify individuals at critical developmental ages in youth (AUROC = 0.953, 0.972, and 0.927, for 15, 18, and 21 years) and capture fine-grained, single-year DNAm signatures of aging that are enriched in biological processes associated with anatomic and neuronal development, immunoregulation, and metabolism. We showcased the practical applicability of this approach by identifying candidate mechanisms underlying the altered pace of aging observed in pediatric Crohn’s disease.DiscussionIn this study, we present a deep neural network epigenetic clock, named NCAE-CombClock, that improves age prediction accuracy in large datasets, and a suite of explainable neural network clocks for robust age classification across youth. Our models offer broad applications in personalized medicine and aging research, providing a valuable resource for interpreting aging trajectories in health and disease.
Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision
Authors: Waltz Lucas , Katari Sushma , Hong Chaeun , Anup Adit , Colbert Julian , Potlapally Anirudh , Dill Taylor , Porter Canaan , Engle John , Stewart Christopher , Subramoni Hari , Shearer Scott , Machiraju Raghu , Ortez Osler , Lindsey Laura , Nandi Arnab , Khanal Sami 
Published: January 2025

IntroductionAdvancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. In many fields, including agriculture, this progress has outpaced the availability of sufficiently diverse and high-quality datasets, which now serve as a limiting factor. While many agricultural use cases appear feasible with current compute resources and ML algorithms, the lack of reusable hardware and software components, referred to as cyberinfrastructure (CI), for collecting, transmitting, cleaning, labeling, and training datasets is a major hindrance toward developing solutions to address agricultural use cases. This study focuses on addressing these challenges by exploring the collection, processing, and training of ML models using a multimodal dataset and providing a vision for agriculture-focused CI to accelerate innovation in the field.MethodsData were collected during the 2023 growing season from three agricultural research locations across Ohio. The dataset includes 1 terabyte (TB) of multimodal data, comprising Unmanned Aerial System (UAS) imagery (RGB and multispectral), as well as soil and weather sensor data. The two primary crops studied were corn and soybean, which are the state's most widely cultivated crops. The data collected and processed from this study were used to train ML models to make predictions of crop growth stage, soil moisture, and final yield.ResultsThe exercise of processing this dataset resulted in four CI components that can be used to provide higher accuracy predictions in the agricultural domain. These components included (1) a UAS imagery pipeline that reduced processing time and improved image quality over standard methods, (2) a tabular data pipeline that aggregated data from multiple sources and temporal resolutions and aligned it with a common temporal resolution, (3) an approach to adapting the model architecture for a vision transformer (ViT) that incorporates agricultural domain expertise, and (4) a data visualization prototype that was used to identify outliers and improve trust in the data.DiscussionFurther work will be aimed at maturing the CI components and implementing them on high performance computing (HPC). There are open questions as to how CI components like these can best be leveraged to serve the needs of the agricultural community to accelerate the development of ML applications in agriculture.
Associations of sleep quality and fall risk among older adult outpatients at Thai Binh Medical University Hospital, Northern Vietnam
Authors: Diep Nguyen The , Tuan Doan Anh , Van Nguyen Tien , Anh Hoang Minh , Cu Dao Huy 
Published: January 2025

IntroductionPoor sleep quality negatively impact health, reducing quality of life and increasing disease risk. For the elderly, poor sleep quality lead to fatigue and reduced mobility, increasing the risk of falls. In Vietnam, no studies have explored the relationship between sleep quality and fall risk in the elderly. This study aimed to determine the correlation between sleep quality and fall risk among the elderly, providing knowledge for fall prevention in this population in Vietnam.MethodsA cross-sectional study was conducted on 404 elderly patients who visited and were treated as outpatients at Thai Binh University Hospital from October 2023 to June 2024. Direct interviews were conducted using the Fall Risk Questionnaire (FRQ) from STEADI-CDC-2017 (USA), and the Pittsburgh Sleep Quality Index (PSQI) with a cut-off score of 7 to assess sleep quality. The FRQ tool used a cut-off score of 4 to evaluate fall risk.ResultsThe percentage of elderly at risk of falls was 19.6%. The average PSQI score was 11.0 ± 2.7, with 358 (88.6%) elderly patients showing poor sleep quality. Logistic regression analysis showed that higher PSQI scores in the elderly significantly correlated with an increased risk of falls (OR = 1.16, 95% CI: 1.02–1.32, p = 0.03). Elderly individuals with the highest PSQI scores had a higher risk of falls compared to those with the lowest PSQI scores.ConclusionPoor sleep quality are closely related to fall risk among the elderly, alongside other factors such as religion, education, comorbidities, and participation in recreational activities.
Exploring vestibular stimulation to reduce the influence of cybersickness on virtual reality experiences
Authors: Smith Shamus P. , Nalivaiko Eugene , Owen Sam , Depireux Didier , Flynn Mark 
Published: January 2025

IntroductionThere is increasing use of head-worn displays to support immersive virtual reality (VR) experiences. However, users of such technology often encounter motion sickness-like effects, commonly termed cybersickness. The unpleasant effects of cybersickness can limit the duration of VR technology usage and deter return use after only one bad experience. One explanation of cybersickness is that it is a physiological responses to perceived differences between the visual and vestibular stimulation provided via VR technology and the user’s current body positioning and movement, i.e., a mismatch between visual and vestibular senses.MethodsAn exploratory user study is described that investigates the use of technology to influence vestibular cues and change experiences of cybersickness. A vestibular stimulation device using bone conduction was applied to users experiencing cybersickness induced by a VR roller-coaster in a head-worn display. Three conditions were tested: a control group without the device and two groups with the device configured to different vibration force levels.ResultsResults showed that users with the stronger vibration level, when compared to a control group, had different virtual environment experiences with longer ride durations and lower reported nausea scores.DiscussionAlthough limited by participant numbers, the results are promising for applying vestibular stimulation to positively influence cybersickness experiences in head-worn displays. Given the spreading application of VR technologies and the need to mitigate cybersickness, there is a need to further evaluate the efficacy of such devices.
Grazing increased alpine grassland soil respiration rates on the Tibetan Plateau
Authors: Mao Yahui , Du Yangong , Chen Kelong 
Published: January 2025

IntroductionGrazing activities were intensive in alpine grasslands and may alter the soil respiration rates across the Tibetan Plateau. However, grazed alpine grassland soil respiration rates and their underlying driving mechanisms remain unclear across the Tibetan Plateau.MethodsIn this study, we synthesized data from 38 groups to clarify the response of CO2 emission rates to different grazing intensities.ResultsThe effect size was 0.039 ± 0.051 of grazed activity on the ecosystem respiration rates, indicating that grazing increased respiration rates by 3.99%. Furthermore, effect sizes were −0.146 ± 0.209 and 0.076 ± 0.046 in alpine steppes and alpine meadows, respectively, indicating a 13.58% reduction and a 7.90% increase. The effect sizes were 0.051 ± 0.112, 0.029 ± 0.075, and 0.055 ± 0.089 in light, moderate, and high grazing intensity, respectively. Moderate grazing mitigated grassland CO2 emission rates compared with light and high grazing. Both air temperature and soil organic carbon significantly increased grassland ecosystem respiration rate, explaining 38.47% and 22.57% of variance heterogeneity.DiscussionIn addition, both future global warming and increasing soil organic carbon would increase alpine grassland CO2 emission rates on the Tibetan Plateau.
Test-retest reliability of postural control measures in healthy secondary school pupils: insights from the MOVE12 pilot study
Authors: Litleskare Sigbjørn , Barene Svein 
Published: January 2025

IntroductionPostural control is an essential part of human movement. Measurements of center of pressure displacements by force platform are considered the gold standard for assessing postural control. However, the test-retest reliability of these measurements in field-based conditions is unclear.MethodsThis study aimed to investigate the test-retest reliability of center of pressure measures under field-based conditions, and assess the impact of height, weight, and body mass index (BMI) on test-retest reliability. The study sample comprised 215 upper secondary school pupils (114 girls) aged 16–17 years, all drawn from the control group of a larger intervention study. There was a 12-week interval between the initial test and the retest. Measurements of center of pressure displacements was assessed during a bipedal eyes closed and a unipedal eyes open condition. Interclass correlation coefficients (ICCs) with 95% confidence intervals were calculated for six distinct measures in both conditions. Correlations between the change in postural control and height, weight, and BMI were assessed as well.ResultsTrace length exhibited the best test-retest reliability in both conditions, achieving moderate to good reliability in the bipedal eyes closed condition (ICC = 0.73, 95% CI = 0.66–0.78). Test-retest reliability was higher for all measures in the bipedal eyes closed (ICC's = 0.53–0.73) compared to unipedal eyes open condition, and some of these measures approached zero reliability (ICC's = 0.03–0.48). Among body characteristics, only height was significantly correlated with test-retest changes in postural control (r = 0.16, p &lt; 0.05) and only for two of the measures.DiscussionThis study underscores the necessity of careful selection of postural control measures and thorough assessments in field-based research to achieve acceptable test-retest reliability.
Food safety practices and associated factors among food handlers in food establishments in Adama town, Central Ethiopia
Authors: Gemeda Nimona , Yazew Tamiru , Moroda Meseret , Kuyu Chala G. 
Published: January 2025

IntroductionFoodborne infections caused by a variety of variables pose serious public health risks, resulting in significant morbidity and mortality in poor nations like Ethiopia. However, the study contains a deficit in information about food safety practices and associated factors. Thus, this study assessed food safety practices and influencing factors among food handlers in public food establishments in Adama town, Central Ethiopia.MethodsAn institution-based cross-sectional design was used for this study. A total of 319 food handlers were selected through systematic random sampling, with data collected through face-to-face interviews using the Kobo tool. The data were analyzed using descriptive statistics, binary and multivariate logistic regressions using SPSS V-27. Significant factors associated with food safety practices were determined based on Adjusted Odds Ratios (AOR), 95% Confidence Intervals (CI), and a significance level set at p &lt; 0.05.Results and discussionThe findings revealed that 64.79% of food handlers exhibited commendable food safety practices. Factors significantly associated with the food safety practices included knowledge (AOR = 2.106, 95% CI: 1.23–3.606), attitude (AOR = 2.0, 95% CI: 1.169–3.334), ownership status of the establishment (AOR = 2.65, 95% CI: 1.48–4.98), and monthly salary (AOR = 0.516, 95% CI: 0.293–0.906). Food safety practices among food handlers in public food establishments rank as medium compared to other studies conducted in the country. These findings indicates the vital role of education, awareness campaigns, and training programs in promoting safe food handling practices. Therefore, to ensure safe and healthy food, there is a need for targeted interventions, mainly focusing on improving food handler knowledge and attitudes, to enhance food safety standards in public food establishments. It is recommended that authorities and organizations prioritize significant factors identified in this study to reduce foodborne illnesses, ensure public health, and foster a culture of responsible food-handling practices.
The relationship between deltamethrin-induced behavioral changes and acetylcholinesterase activity in zebrafish embryos or larvae based on transcriptome
Authors: Liu Chunyu , Guo Yantong , Zhang Xue , Chen Hongsong , Han Maomao , Wang Han , Niu Jieyao , He Jinfei , Yang Jingfeng , Dong Wu , Xue Jiangdong 
Published: January 2025

BackgroundDeltamethrin (DM) is a broad-spectrum insecticide that is widely used to control agricultural pests. Recently, DM has posed a potential threat to the health of infants and young children, and this is because of the environmental and food pollution that is caused by the extensive use of DM.MethodsIn this study, zebrafish (Danio rerio) embryos were used as experimental animals to quantify the behavioral changes of larvae induced by DM and explore the relationship between DM and acetylcholinesterase activity.ResultsThe results showed that DM caused an increase in coiling movement, heart rate, and apoptosis in the brain in early zebrafish embryos or larvae. It also caused a decrease in the expression of acetylcholinesterase-associated genes and the activity of acetylcholinesterase, which also led to an increase in the acetylcholinesterase content. The transcriptome data also showed that low concentration DM induced acetylcholine-related gene signaling pathways. The above results suggest that low doses of DM may induce neurodegeneration because DM exposure inhibits acetylcholinesterase, leading to brain cell apoptosis and behavioral changes in the zebrafish embryos or larvae. Micro-injection of zebrafish embryos at the 2–4 cell stage knocked down or overexpressed the acetylcholinesterase gene showed that the behavior and enzyme activity of zebrafish had some effects.ConclusionThis study explored the relationship between acetylcholinesterase and the change in zebrafish behavior caused by DM exposure to provide a basis for the treatment of DM poisoning in the aquaculture.
Global prevalence of Eimeria species in goats: a systematic review and meta-analysis
Authors: Ali Endris A. , Ghafar Abdul , Angeles-Hernandez Juan C. , Yaseen Muhammad , Gauci Charles G. , Beveridge Ian , Baxendell Sandra , Jabbar Abdul 
Published: January 2025

BackgroundCoccidiosis is a protozoal disease caused by Eimeria species, the main symptom of which is diarrhea. Eimeria spp. infection can cause weight loss and ill-thrift in goats, and in severe cases, it can lead to mortality in kids, resulting in economic losses for the goat industry. This study aimed to determine the global prevalence of Eimeria spp. in goats and to identify the possible predictors of heterogeneity among selected studies.MethodsData were retrieved from five databases of major global importance (PubMed, Web of Science, CAB Direct, Scopus, and Google Scholar), with 255 studies published between 1963 and 2022 being included. A random-effects model was used to calculate pooled prevalence estimates with 95% confidence intervals (CI), followed by subgroup meta-analysis and meta-regression analysis to identify factors contributing to high prevalence and explore sources of heterogeneity among studies.ResultsThe estimated global prevalence of Eimeria spp. in goats was 62.9% (95% CI: 58.6–67.2). Our results indicated high inter-study variability (inconsistency index (I2) = 99.7%, p &lt; 0.01). Among the variables analyzed, regions and quality of studies were the most significant predictors of heterogeneity. According to the region-based subgroup meta-analysis, North America had the highest estimated prevalence of Eimeria spp. (92.2, 95% CI: 82.7–98.2), followed by Europe (86.6, 95% CI: 79.8–92.3), while Asia had the lowest prevalence (52.0, 95% CI: 45.9–58.1). Most countries (n = 42/56) had an estimated prevalence above the overall pooled estimate (&gt;62.9%). The subgroup of studies conducted in 2000 or later presented a lower prevalence of 59.6% (95% CI: 54.7–64.3). Studies with a score of 5–7 had a significantly higher prevalence (72.4, 95% CI: 66.2–78.2) than studies with low or medium scores (p &lt; 0.01). The prevalence of Eimeria spp. in goats detected with conventional and molecular methods was 67.3% (95% CI: 47.0–84.7). Only 47% (119/255) of the studies provided details on identifying Eimeria at the species level. Overall, more than 26 Eimeria spp. have been identified in goats globally. Among these, the most frequently reported and pathogenic species were E. arloingi (115/119), E. ninakohlyakimovae (108/119), E. christenseni (94/119), and E. caprina (71/119). Other valid species that were reported less frequently include E. alijevi, E. hirci, E. caprovina, E. aspheronica and E. jolchijevi.ConclusionThese findings suggest that the pathogenic Eimeria spp. are widespread in goats globally. Given the high prevalence and the extensive distribution of pathogenic Eimeria spp. in goats, it is recommended that integrated parasite management approaches be implemented for the effective control of coccidiosis in goats.