bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bisphenols Exposure Correlated with Metabolic Syndrome Incidence via Inflammatory Response: Evidence from NHANES 2013-2015
Authors: Li Xiaoting , Dong Zhiyuan , He Liping , Ji Xiaoming 
Published: November 2024

Abstract

Background
Few studies have demonstrated a correlation between bisphenol (BP) exposure and the occurrence of metabolic syndrome (Mets). In the present study, we aimed to identify individuals at high risk of developing MetS who were exposed to bisphenol A (BPA) or bisphenol S (BPS) by their systemic immune-inflammation index (SII) and oxidative balance score (OBS) and the related molecular mechanism.
Methods
In the present study, we analyzed data from 1135 adults in the National Health and Nutrition Examination Survey (NHANES) database (2013–2015). Logistic regression analyses were used to analyze the associations between the SII/OBS or BPA/BPS and Mets risk. Network pharmacology was used to predict and verify the molecular targets and pathways of BPS in the treatment of Mets.
Results
Mets had a positive correlation with the SII. Although there was no association between the OBS score and the risk of MetS, a higher OBS score has the potential to reduce the incidence of MetS. BPS exposure, but not BPA exposure, exhibited a positive relationship with the risk of MetS even after adjusting for partial covariates. There was an interaction between BPS and OBS. Furthermore, a total of 87 core targets of BPS that bind to Mets were identified by Cytoscape, and they were found to be involved mainly in metabolic process regulation. AKT and INS were the key genes affected by BPS in the presence of Mets.
Conclusion
This study confirmed that the interaction between BPS exposure and OBS increased the risk of Mets and that AKT and INS play key roles in the regulation of BPS-induced Mets.
Non-Abelian topological quantum light source
Authors: Xu Ping , Wu Chao , Yang Biao , Zhu Pingyu , Yu Miaomiao , Liu Jiacheng , Luo Weihong , Zheng Qilin , Zhang kaikai , Wang Kun , Xia Gongyu , Zhu Zhihong 
Published: November 2024

Abstract

Non-Abelian topological charges herald a new epoch in topological classification, featuring multiple tangled-bandgaps that promise conceptual breakthroughs. Here, we engineered the first photonic non-Abelian topological insulators by the integrated waveguide and demonstrating a novel quantum light source that exhibits non-Abelian topological features. We experimentally observed the generation of a discrete-band two-photon source with extended wavelengths of signal and idler photons lying in S and L bands, respectively, exhibiting an enhanced coincidence-to-accidental ratio. Additionally, we demonstrated the robustness of this quantum light sources by introducing significant disorders. Such non-Abelian two-photon source can be engineered within the transmission window of the material as well as provides a natural scheme for optimizing the spectral purity towards robust indistinguishable photons. Our scheme paves the way for non-Abelian topological quantum device applications in quantum light sources engineering, quantum computing and quantum simulation as well as shedding light on interdisciplinary studies of non-Abelian physics and quantum optics.
An interpretable predictive framework based on rules and machine learning: rule extraction, validation and adaptive integration with machine learning
Authors: Zhang Huiran , Dai Mengmeng , Lin Yudian , Wu Pin , Huang Lei , Xu Huanyu , Li Shengzhou , Xu Yan , Tang Zheng , Zhang Jinchang , Che Renchao , Xu Tao , Dai Dongbo 
Published: November 2024

Abstract

Traditional knowledge extraction methods often rely on human expertise, which can be time-consuming and prone to cognitive biases. This work presents a comprehensive predictive framework that integrates rule extraction with machine learning (ML) to enhance knowledge discovery in materials science. We used subgroup discovery algorithms to extract rules based on their significance and categorized the datasets accordingly. These rules are considered implicit knowledge within the datasets. Then, the validation strategies are tailored to assess their effectiveness. Through case studies on high-entropy alloys and piezoelectric ceramics, we demonstrate that our rule-based subgrouping and rule embedding feature can significantly enhance model performance and interpretability compared to baseline models. It shows that the approach facilitates the interpretation of complex data, proving the potential of integrating interpretable rule-based insights with ML, and paving the way for future advancements in material design and predictive modeling.
Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise
Authors: Wang Rose , Ribeiro Ana , Robinson Carly , Loeb Susanna , Demszky Dora 
Published: November 2024

Abstract

Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education quality at scale. This challenge disproportionately hurts students from under-served communities, who stand to gain the most from high-quality education and are most likely to be taught by inexperienced educators. We introduce Tutor CoPilot, a novel Human-AI approach that leverages a model of expert thinking to provide expert-like guidance to tutors as they tutor. This study presents the first randomized controlled trial of a Human-AI system in live tutoring, involving 900 tutors and 1,800 K-12 students from historically under-served communities. Following a preregistered analysis plan, we find that students working on mathematics with tutors randomly assigned to have access to Tutor CoPilot are 4 percentage points (p.p.) more likely to master topics (p<0.01). Notably, students of lower-rated tutors experienced the greatest benefit, improving mastery by 9 p.p. relative to the control group. We find that Tutor CoPilot costs only $20 per-tutor annually, based on the tutors’ usage during the study. We analyze 550,000+ messages using classifiers to identify pedagogical strategies, and find that tutors with access to Tutor CoPilot are more likely to use strategies that foster student understanding (e.g., asking guiding questions) and less likely to give away the answer to the student, aligning with high-quality teaching practices. Tutor interviews qualitatively highlight how Tutor CoPilot’s guidance helps them to respond to student needs, though tutors flag common issues in Tutor CoPilot, such as generating suggestions that are not grade-level appropriate. Altogether, our study of Tutor CoPilot demonstrates how Human-AI systems can scale expertise in real-world domains in real time, bridge gaps in skills and create a future where high-quality education is accessible to all students.1
Magnitude of Anxiety, Sleep Disturbances, and Associated Factors among Bachelor Students of Edna Adan University in Hargeisa, Somaliland
Authors: Gideon Peiter , Bayray Alemayehu 
Published: November 2024

Abstract

Background
970 million people around the world are living with mental illness. As regard, mental illness also is the one of chronic diseases that happening in Somaliland. Anxiety and sleep disturbances are the major types of mental illness that most happening in young adult aged.
Methods
This study has used a cross-sectional analysis with 307 bachelor students of Edna Adan University as its participants from May to July 2024. They answered questionnaires related to anxiety, sleep disturbance, and mobile phone usage. Pre-test for associated factors questionnaire was carried to assess the validity and reliability of questionnaire. The acquired data was then univariate analyzed in SPSS version 26.
Results
Female students are majority of the participants and mostly they are in 17–30 years old age group, in fourth-year school, still single, and as a nursing student. Overall, the participants are Somaliland citizens who live with their parents and siblings. They do not smoke, drink coffee, or chew khat (Catha edulis). The average daily internet usage is 4–6 hours. 61.20% of the students were found with minimal to no anxiety, 63.50% had no mobile phone addiction problem, and 71.30% reported a good sleep quality. Academic stressor, financial pressure, and interpersonal relationships are the most commonly experienced in very serious frequency (all of the time), followed by several other factors.
Conclusion
This study shows that the students mainly have minimal to no anxiety, no addiction to mobile phone, while maintaining a good sleep quality. However, sleep disturbance and anxiety-associated factors such as academic stressor, financial pressure, and interpersonal relationships are commonly experienced in a very serious frequency (all of the time).
Harnessing the Synergy of Urochloa brizantha and Amazonian Dark Earth Microbiomes for Enhanced Pasture Recovery
Authors: de Freitas Anderson Santos , Zagatto Luís Felipe Guandalin , Rocha Gabriel Silvestre , Muchalak Franciele , Martins Guilherme Lucio , Silva-Zagatto Solange Santos , Hanada Rogério Eiji , Muniz Aleksander Westphal , Tsai Siu Mui 
Published: November 2024

Abstract

Amazonian Dark Earths (ADEs) are fertile soils from the Amazon rainforest that harbor microorganisms with biotechnological potential. This study aimed to investigate the individual and potential synergistic effects of a 2% portion of ADEs and Urochloa brizantha cv. Marandu roots (Brazil's most common grass species used for pastures) on soil microbial communities and overall soil attributes in degraded soil. We conducted a comprehensive plant succession experiment, utilizing next-generation sequencing for 16S rDNA metabarcoding, enzymatic activity assays, and soil chemical properties analysis. Univariate and multivariate analyses were performed to understand better the microbial interactions within soil environments influenced by ADEs and U. brizantha roots, including differential abundance, diversity, and network analyses. Our findings reveal a complementary relationship between U. brizantha and ADEs, each contributing to distinct positive aspects of soil microbial communities and quality. The combined influence of U. brizantha roots and ADEs exhibited synergies that enhanced microbial diversity and enzyme activity. This balance supported plant growth and increased the general availability of beneficial bacteria in the soil, such as Chujaibacter and Curtobacterium, while reducing the presence of potentially pathogenic taxa. This research provided valuable insights into the intricate dynamics of plant-soil feedback, emphasizing the potential for complementary interactions between specific plant species and unique soil environments like ADEs. The findings highlight the potential for pasture ecological rehabilitation and underscore the benefits of integrating plant and soil management strategies to optimize soil characteristics.
An explainable machine learning model in predicting vaginal birth after cesarean section
Authors: Yang Ming , Long Dajian , Li Yunxiu , Liu Xiaozhu , Ji Yanjie , Bai Zhi , Li Zhongjun 
Published: November 2024

Abstract

Background Vaginal birth after cesarean section（VBAC） is recommended by international and domestic guidelines or expert consensuses.However ,no valid tools can exactly predict who can succeed in trying vaginal birth among eligible women with a history of cesarean section.Machine learning is gradually used to develop models in obstetrics and midwifery.This study aimed to develop an explainable machine learning model to predict the chance of successful VBAC.
Methods The data were collected to establish 7 predicting models from two tertiary hospitals in Guangdong province of China.Training and internal validation data were collected from the First Dongguan Affiliated Hospital Of Guangdong Medical University from January 2012 to December 2022.External validation data were collected from Shenzhen Longhua District Central Hospital from Januray 2011 to December 2017. 7 predicting models based on machine learning were developed and evaluated by area under the operating characteristic curve (AUC).The optimal one was picked out from 7 models according to its AUC and other indices.The outcome of the predictive model was interpreted by Shapley Additive exPlanations(SHAP).
Results A total of 2438 pregnant women with trial of labor after cesarean （TOLAC）were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.725 (95% CI: 0.653–0.792), the accuracy for 0.611 (95% CI: 0.557–0.672), sensitivity 0.69 (95% CI: 0.551–0.829), and specificity 0.69 (95% CI: 0.72–0.76). Cervical Bishop score and interval of pregnancy showed the greatest impact on successful vaginal birth, according to SHAP results.
Conclusion Models based on machine learning algorithms can be used to predict whether a trail of vaginal birth succeeds. CatBoost model showed more significant performance compared with traditional logistic regression and other machine learning algorithms in this study. Cervical Bishop score and interval of pregnancy are important factors for successful VBAC. More researchs still need to be undertaken to promote the accuracy of ML algorithms and overcome their shortcomings.
Semi-Perennial Rice Sustains Yield and Food Safety Under Heatwaves and Beyond
Authors: Chen Zheng , Zhang Sha , Song Jing , Wu Longhua , Zhu Yong-Guan 
Published: November 2024

Abstract

Heatwaves threaten global rice security and challenge the United Nations’ Zero Hunger goals. Semi-perennial rice, with its ratooning ability to regrow from stubble after hot seasons, has shown potential for sustainable production. Here, we investigated the vulnerability and resilience of ratoon cropping system through two-year manipulative experiments of realistic heatwaves and analyzed experimental and global datasets. Our findings reveal that ratoon crops can compensate for up to 100% of heatwave-induced yield losses, maintain nutritional quality, and reduce arsenic (As) contamination by half. These benefits arise from widespread yield trade-offs between main and ratoon crops, and the seasonal mismatch between soil As availability and plant uptake. Ratoon practice can increase annual rice production by 3.9 million Mg in marginal regions, feeding 80 million people while concurrently reducing As exposure by 535 kg in China. This underscores the importance of integrating semi-perennial rice into climate-resilient agriculture.
Investigation of Seam Performance and Biodegradability of Organic Cotton Clothes for Their Sustainability
Authors: ÖZBEY Hatice Kübra , GÜRARDA Ayça 
Published: November 2024

Abstract

In recent years, it has become important for a sustainable environment that clothes are biodegradable, and that unused clothing does not pollute the environment by decomposing in nature. Cotton, especially organic cotton, can decompose very quickly in nature. Since organic clothing manufacturing is a new field, unlike conventional products, seam performance and biodegradability to be conducted on these products are essential in creating new data for the textile and clothing sector. This paper presents an experimental study of the seam performance and biodegradability properties of organic clothes for their sustainability. In this study, six groups of fabrics with a plain weave structure were produced to examine the seam performance and biodegradability of clothes made of organic cotton woven fabrics. Each group includes three different samples of the fabric: conventional cotton fabric dyed with reactive dyes, organic cotton fabric dyed with reactive dyes, and organic cotton fabric dyed with natural dye. Therefore 18 samples having different properties were obtained. Fabric breaking strength, seam strength, seam efficiency and biodegradability tests ​​of these fabric samples were determined. Sample women's blouses were sewn from the fabric samples used in the experimental study, and the blouse appearances were visually presented on the mannequin.
Comparison of the effects of hydroalcoholic extract of Dracocephalum kotschyi and silymarin on diclofenac-induced hepatotoxicity in rats: inflammation signaling HMGB1, NLRP3, IL1B
Authors: Soogh Erfan Hoseyni , Saghaei Firoozeh , Saghaei Firoozeh 
Published: November 2024

Abstract

Background and Objective: Excessive and prolonged use of NSAIDs causes hepatotoxicity.
The hydroalcoholic extract of Dracocephalum kotschyi 's anti-inflammatory and antioxidant properties are known. This study investigates the potential of Dracocephalum kotschyi in inhibiting diclofenac hepatotoxicity in rat liver.
Materials and Methods: 42 male Wistar rats were divided into 6 groups control, diclofenac, 3 groups of diclofenac + hydroalcoholic extract of Dracocephalum kotschyi, and the diclofenac + silymarin group. The control group did not receive any medication. In the other groups, diclofenac (50 mg/kg) was first injected, then in three groups the hydroalcoholic extract of Dracocephalum kotschyi (HEDK) at doses of 40, 80, and 120, in the last group, silymarin was administered at 100 mg/kg for 7 days. Liver enzymes (ALT, AST, ALP), antioxidant enzyme activities, and inflammatory factors were measured and the histopathological changes were assessed.
Results: Diclofenac remarkably increased the levels of ALT, AST, ALP, MDA, IL1-β, NLRP3, and HMGB1 and simultaneously reduced the amount of SOD, CAT, and GPX. Treatment with the HEDK reduced Liver biochemical parameters, inflammatory markers, enhanced antioxidant defense and improved histological outcomes.
Conclusion: HEDK, with its antioxidant and anti-inflammatory effects, provided significant liver protection against diclofenac-induced toxicity.
Methane production from hydrocarbons by consortia dominated by ANME archaea
Authors: Baker Brett , Sarno Natalie , Montgomery Andrew , Zhuang Guang-Chao , Martinez Brennan , De Anda Valerie , Joye Samantha 
Published: November 2024

Abstract

Understanding microbial production and consumption of methane, a potent greenhouse gas in the atmosphere, is critical for bridging knowledge gaps in global carbon cycling. In anoxic environments, methane is known to be produced through hydrogenotrophic, acetoclastic or methylotrophic mechanisms. Methane production from hydrocarbons may also be important, especially in hydrocarbon-rich environments, like the Gulf of California, but the mechanism of this hydrocarbonoclastic methanogenesis remains unclear. The activity of consortia of anaerobic methane oxidizing (ANME) archaea and bacteria limits the release of methane to the atmosphere by consuming methane in anoxic environments globally. Here we used isotopic-labeling to track the conversion of hydrocarbons (hexadecane and naphthalene) to methane in enrichments from hydrothermally impacted, hydrocarbon-rich sediments from the Gulf of California. Methane was produced directly from hexadecane and naphthalene, in both the presence and absence of sulfate. We reconstructed metagenomic assembled-genomes (MAGs) from these experiments which revealed a mixture of bacteria dominated by Desulfobacteriota and Bacteroidota, and archaea dominated by Aeinigmarchaeota, Thermoplasmatota, and ANME group 2c. The ANME-2c were the only MAGs that encoded methyl coenzyme M reductases (McrA) and complete Wood-Ljungdahl pathways (WLP). This suggests that ANME-2c archaea may be involved in the production of methane along the seafloor, and that our understanding of the roles of these globally important microbes is not yet fully appreciated.
Dynamic Embedding Capacity Adjustment for Reversible Data Hiding
Authors: NUJUMUDEEN FAIZAL , Mubarak D Muhammad Noorul , Hussain Tarak 
Published: November 2024

Abstract

Reversible data hiding (RDH) methodologies are designed to embed confidential information within a cover signal while ensuring the complete restoration of the original signal following the extraction of the concealed information, without inducing irreversible distortions. Nevertheless, modern RDH methodologies encounter numerous challenges, encompassing restricted embedding capacity and insufficient resilience against diverse forms of assaults. To remedy these shortcomings, we propose a dynamic embedding capacity adjustment mechanism for RDH. The proposed methodology adaptively regulates the volume of embedded information predicated on the attributes of the cover signal and the requisite security level. The fundamental concept entails dynamically modulating the embedding capacity across various regions of the cover signal, thereby enabling augmented data embedding in less critical zones while safeguarding essential signal characteristics in high-impact domains through edge and texture feature analysis. This methodology scrutinizes the statistical properties and perceptual significance of assorted signal components to allocate embedding capacity more proficiently. The efficacy of this approach is appraised through a comprehensive array of innovative experiments that encompass a heterogeneous assortment of cover signals, including images, audio, and video, under varied embedding scenarios and assault conditions. This investigation aspires to enhance both the embedding capacity and resilience of RDH methodologies, thereby propelling their pragmatic applications in secure data communication and multimedia content safeguarding.
Aseismic slip and seismic swarms leading up to the 2024 M7.3 Hualien earthquake
Authors: Chen Kate , Peng Wei , Bürgmann Roland 
Published: November 2024

Abstract

Recognizing the locations and timing of aseismic fault slip during the earthquake cycle may be critical for short-term earthquake forecasting and hazard assessment. Abundant M ≥ 6 earthquakes and widespread aseismic slip in a double-vergence suture in eastern Taiwan provide a rare opportunity to better understand the role of aseismic slip in earthquake cycle deformation and triggering relationships. By tracking aseismic slip captured by repeating earthquake sequences and earthquake swarms in eastern Taiwan from 2000 to mid 2024, we observed an acceleration of aseismic slip rate that occurred approximately three years preceding the April 3, 2024 Mw7.3 Hualien earthquake. The accelerated aseismic slip revealed by the repeating earthquake data was accompanied by a four-month-long active seismic swarm starting on April 18, 2021, in the 2024 Mw7.3 epicentral area. The swarm occurred on the west-dipping Central Range fault exhibited a northward and upward migration pattern with a diffusion rate of ~ 6 m2/s. After 2021 swarm sequence, the regional aseismic slip rate initially decreased until an Mw 6.1 event in June 2022, followed by a gradual increase in the aseismic slip rate leading up to the mainshock over approximately 1.5 years. Concurrent with the accelerated aseismic slip, the Central Range Fault was characterized by a heightened seismicity rate until the 2024 Mw7.3 mainshock. The resulting static Coulomb stress change reveals that accumulated aseismic and seismic slips from swarms, RESs, and M6+ events on the CRF from mid-2021 to mid-2022 generally produce positive stress changes (up to 20 kPa) on the same fault. The sequential seismic-aseismic interplay of faulting in the epicentral area of the 2024 Hualien event provides a rare example of aseismic-slip-induced stress triggering leading up to the 2024 Mw7.3 earthquake.