bluuebunny/crossref_metadata_2025 huggingface dataset:
A randomized controlled, trial on effects of mobile phone text messaging in combination with motivational interviewing versus standard infant feeding counselling on breastfeeding and child health outcomes, among women living with HIV
Authors: Zunza Moleen , Thabane Lehana , Kuhn Louise , Els Christine , Lombard Carl , Cotton Mark F. , Young Taryn
Published: November 2024

Introduction
Breastfeeding improves child health, development and survival, particularly in regions where diarrhoea, pneumonia and undernutrition are leading causes of death in children under five. Despite awareness of exclusive breastfeeding benefits and efforts to promote optimal infant feeding practices, the practice of exclusive breastfeeding is low. We conducted a trial to determine whether text messaging plus motivational interviewing increase uptake of exclusive breastfeeding, prolong exclusive breastfeeding and improve child health outcomes.
Methods
We conducted the trial between July 2022 and May 2024, at a secondary-level healthcare facility in South Africa. Mothers living with HIV and their infant, initiating breastfeeding, on cART, 18 years or older, were randomly assigned to either weekly text messaging plus in-person motivational interviewing or standard infant feeding counselling for six months. The randomization allocation sequence was computer generated and assignment was done centrally.
Results
Mother-child pairs (n = 276) were randomly allocated to receive intervention (n = 138) or standard infant feeding counselling. One hundred five and 101 mother-child pairs in the intervention group and standard care group, respectively, had primary outcome data evaluation across all four study visits. We found no significant effect of the intervention on exclusive breastfeeding rates at week 24, (6% versus 7%), rate difference − 1% (95% CI -6–4%). Sixty-two of 276 (22%) mother-child pairs completely stopped breastfeeding before week 24. The most common reasons for stopping breastfeeding were the mother needing to return to work or look for work, 66% (n = 41). We also found that early breastfeeding cessation increased risk of child hospitalization or death compared to any form of breastfeeding to week 24, 10% (5/48) versus 3% (5/158), p = 0.055.
Conclusion
Socio-economic pressures influenced mothers’ decision to stop breastfeeding. Introduction of other foods before six months while breastfeeding remains common. Early cessation of breastfeeding is a public health threat. While finding interventions to enhance exclusive breastfeeding remains critical, HIV services should reliably offer cART, consistently monitor viral load, and support mothers cART adherence, especially where mixed feeding is common. Innovative interventions responsive to socio-economic challenges faced by mothers of reproductive and working age may be more effective in improving exclusive breastfeeding rates.
Feasibility exploration of nano-thermal rod applied to deicing of tunnel pavement in cold region
Authors: Wu Jianguo , Wang Qingzhi , Luo Dongliang , Fang Jianhong , Sun Tian
Published: November 2024

In order to study the feasibility of a nano-thermal rod applied to deicing of tunnel pavement in cold region, the performance of the nano-thermal rod was investigated through experiments and numerical simulation methods. First, the heating efficiency of the nano-thermal rod and the carbon fiber heating wire under low voltage conditions were compared. Then, the tunnel pavement model was prepared and experimentally studied in the environmental simulation box. The effects of the arrangement parameters (including arrangement spacing, embedding depth, and input power) and environmental factors (including ambient temperature and immersion state) on the heating rate and heating effect were studied. Finally, COMSOL Multiphysics was employed to verify the experimental results. The experimental results indicate that the working temperature of the nano-thermal rod is approximately 60°C at a low voltage of 3.9V, with a higher heating efficiency compared to the carbon fiber heating wire. Additionally, both the layout form and ambient temperature of the nano-thermal rod significantly impact tunnel pavement temperature and heating rate. Among these factors, input power and ambient temperature have the most pronounced effects. Furthermore, surface moisture adversely affects the deicing. The experimental results were verified by numerical simulation, demonstrating that the error is within 1.48°C. It is concluded that the use of nano-thermal rod for deicing on tunnel pavements in cold areas is feasible.
Knockout IL4I1 affects macrophages to improve poor efficacy of CD19 CAR-T combined with PD-1 inhibitor in relapsed/refractory diffuse large B-cell lymphoma
Authors: Zhang Rui , Zhang yi , Xiao Hairong , Liu Qingxi , Zhao Mingfeng
Published: November 2024

Chimeric antigen receptor (CAR) T-cell therapy plays a critical role in the treatment of B-cell hematologic malignancies. The combination of PD-1 inhibitors and CAR-T has shown encouraging results in treating patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL). However, there are still cases where treatment is ineffective. This study aimed to investigate the role of IL4I1 in the poor efficacy of CD19 CAR-T combined with PD-1 inhibitors in R/R DLBCL and to explore potential mechanisms. Transcriptomic and metabolomic correlation analyses were performed on tumor tissue from DLBCL patients. We employed an in vitro co-culture system consisting of Pfeiffer cells, CD19 CAR-T and macrophages to investigate the underlying mechanisms. It was found that IL4I1 levels were significantly increased in the tumor tissues of R/R DLBCL patients compared to responders. Correlation analysis revealed a positive association between IL4I1 and tryptophan (Trp)-kynurenic acid (Kyn) related metabolites. In the in vitro co-culture model, the presence of IL4I1 inhibited the cytotoxicity of CAR-T cells. Depletion of IL4I1 disrupted the IDO-AHR-Kyn signaling pathway, thereby enhancing the effectiveness of PD-1 inhibitors in combination with CD19 CAR-T for DLBCL treatment. CAR-T-mediated cytotoxicity was significantly inhibited when IL4I1 was present in the in vitro co-culture model. These findings suggest that IL4I1 may be a contributing factor to poor prognosis in R/R DLBCL patients. IL4I1 expression enhances immunosuppression via the IDO-AHR-Kyn pathway, inhibiting the effectiveness of PD-1 inhibitors combined with CD19 CAR-T. Therefore, suppression of IL4I1 may represent a potential target for combination therapy in DLBCL.
Changes in daily precipitation extremes over the Fiji Islands (1905-2021)
Authors: Fernández-Duque B. , Kumar R. , Kenawy A. El , Vicente-Serrano S.M.
Published: November 2024

Changes in daily precipitation extremes were assessed for the Fiji Islands from 1905 to 2021 using quality-controlled and homogenized series. A set of 23 daily precipitation indices, spanning a wide variability of daily precipitation characteristics, including the frequency, magnitude and the duration, were employed. Changes in extreme events were assessed using the nonparametric Kendall’s Tau-based slope estimator, while the significance of these changes was tested using the Mann Kendall statistic at the 95% confidence interval (p < 0.05). Results are presented for different time periods over the study domain, including 1905–2021, 1935–2021, and 1960–2021. Results suggest a general increase in most of the daily precipitation indices, as evidenced by a significant trend in the intensity of extreme precipitation events (rx1day and rx5d) for the period 1905–2021, albeit with a strong spatial and temporal variability. Most of these observed changes were statistically non-significant (p > 0.05). Stations on the leeward side of Viti Levu showed drier conditions, compared with those on the windward side. Changes in daily precipitation indices like dr1mm and dr3mm show varied patterns across different time periods, with substantial spatial variability between windward and leeward sides of Viti Levu. Our findings can contribute to the development and planning of sustainable strategies to assure water security and adaptative responses to extreme events over the Fiji Islands. Also, due to their isolation and relatively undisturbed ecosystems, our results can serve as early indicators of climate change and variability in remote oceanic islands.
Automatic and accurate determination of defect size in shearography using U-Net deep learning network
Authors: Wu Rong , Wei HaiBo , Lu Chao , Liu Yuan
Published: November 2024

Shearography, an effective non-destructive testing tool, is widely employed for detecting defects in composite materials. It detects internal defects by detecting deformation anomalies, offering advantages such as full-field, non-contact measurement, and high accuracy. Defect size is a critical parameter determining structure performance stability and service life. However, manual inspection is the primary method for defect size measurement in this technique, leading to inefficiency and low accuracy. To address this issue, this study established a defect recognition and high-precision automatic measurement method based on the U-Net deep learning network. First, a high-precision one-time calibration method for all system parameters was developed. Second, U-Net was employed to segment the measured image, identifying defect location and subimage. Finally, defect size was accurately calculated by combining calibration parameters and segmented defect subimage. The proposed method yielded a measurement error of less than 5% and a real-time dynamic detection rate of 14 fps, demonstrating potential for automated quantitative defect detection.
Biodegradation of olive mill solid waste by Anthracophyllum discolor and Stereum hirsutum: Effect of copper and manganese supplementation
Authors: Benavides V. , Serrano A. , Ibieta Fernanda Pinto , Rubilar O. , Ciudad G.
Published: November 2024

Anthracophyllum hirsutum and Stereum hirsutum produce manganese peroxidase (MnP) and laccase to break down lignin, a potential biological pretreatment for lignocellulosic biomass. This work aimed to evaluate the effect of copper (Cu) and manganese (Mn) added to olive mill solid waste (OMSW) inoculated with A. discolor and S. hirsutum on ligninolytic enzyme activity, lignin degradation, and phenolic compound removal. Different optimal metal dosages were determined for each fungal strain. For S. hirsutum, the addition of 6.1 mg Cu kg⁻¹ and 7.3 mg Mn kg⁻¹ resulted in 173 ± 5 U·L⁻¹ MnP activity (an 863% increase compared to no metal addition) and 42 ± 3% lignin degradation (183% higher than with no metal addition, 15 ± 3%). These conditions also led to 65–75% phenol removal efficiencies in OMSW at 25 days and 80–95% in leachates between 20 and 30 days. For A. discolor, 14.6 mg Mn kg⁻¹ yielded 37 ± 7 U·L⁻¹ MnP activity (a 142% increase compared to no metal addition) and 38 ± 7% lignin degradation (150% higher than with no metal addition, 15 ± 3%). Under these conditions, A. discolor achieved 80–90% phenol removal in leachates at 20 days. These results demonstrate the positive effect of optimised metal supplementation, highlighting the potential of S. hirsutum and A. discolor for effective lignocellulosic biomass pretreatment and future mycoremediation processes.
WITHDRAWN: Optimal Planning of Smart Parking Lot based Combined Hydrogen, Heat and Power (SPL-CHHP) to improve Eco-Techno-Emission Constraints
Authors: Mohammad Suleiman Ibrahim , Oraini Badrea Al , Alkhayyat Ahmad , Vasudevan Asokan , Vaghela Krunal , Dey Protyay , Aggarwal Ankita , Sri G. , Jaafar Mahdi Sh. , Al-KHAFAJI MUSTAFA
Published: November 2024

Three main concerns of energy administrators in the distribution network, includes satisfying economic, technical and environmental constraints in the acceptable region. Installing several kinds of distributed generators (DGs) is done to achieve these aims. In this paper, optimal planning of smart parking lot- based combined hydrogen, heat and power (SPL-CHHP) is studied to improve all of technical, economic and environmental issues. SPL-CHHP includes: wind turbine (WT), photovoltaic system (PV) batteries and CHHP. The CHHP includes: electrolyzer, fuel cell, thermal exchanger, electric, hydrogen and thermal storage units. Due to multi-index form of proposed planning problem, fuzzy logic and energy valley optimizer are used to solve it. By applying proposed method’s optimal solution, the annual cost saving from importing electricity is $1.36 million with penetration of 65.63% of SPL-CHHPs, total power loss is cut up to 84.64%, the voltage range is improved from [0.909, 1.00] to [0.965, 1.031] and generated emission is also decreased by 68.55%. Thereby, connecting SPL-CHHPs can bring many different benefits and the introduced method is truly a powerful method for addressing optimization problems related to the integration of SPL-CHHPs in the DS.
Achieving 5% 13C nuclear spin hyperpolarization in high-purity diamond at room temperature and low magnetic field
Authors: Kavtanyuk Vladimir Vladimirovich , Lee Changjae , Jeon Keunhong , Shim Jeong Hyun
Published: November 2024

Optically polarizable nitrogen-vacancy (NV) centers in diamond enable hyperpolarization of 13C nuclear spins at a low magnetic field and room temperature. However, it remains a challenge to achieve a high level of polarization, comparable to that of conventional dynamic nuclear polarization. In this paper, we demonstrate that a 13C polarization of 5\% can be attained at less than 10 mT, equivalent to an enhancement ratio of over 7 x 10^6. We used a high-purity diamond with an initial nitrogen concentration below 1 ppm, resulting in a storage time exceeding 100 minutes. Aligning the magnetic field along [100] increased the number of NV spins involved in polarization transfer by a factor of four. For this field orientation, a comprehensive optimization of the field intensity and microwave (MW) frequency-sweep parameters has been performed. The optimum MW sweep width suggests that polarization transfer occurs primarily to the bulk 13C spins through the integrated solid effect, followed by nuclear spin diffusion.
Cross Cultural Validity and Reliability of the Five-Item Social Frailty Scale
Authors: Karakurt Nermin , Çataltepe Esra , Çeker Eda , Fadıloğlu Ayşe , Güngör Fatih , Varan Hacer Dogan
Published: November 2024

OBJECTIVES
The validity and reliability of social frailty indexes among different populations have been scarcely investigated. The aim of this study is to investigate the validity and reliability of the Five-Item Social Frailty Scale (SFS) in the Turkish population.
METHODS
A total of 308 patients aged 65 years or over were included in the study. All participants underwent a comprehensive geriatric assessment. The SFS was translated into Turkish using the forward-backward translation method. After translation, it was tested for intelligibility on 15 patients. For the inter-rater reliability assessment, questions were asked to 55 patients by two experts, sequentially on the same day in different rooms. For intra-rater reliability, 50 patients were asked the same questions by the same expert at one-week intervals. Intra-rater and inter-rater consistency were evaluated. Correlations between the SFS and geriatric assessment tests were performed.
RESULTS
The mean age of the 308 participants was 74.8 ± 6.2. Of these, 151(49%) were female. In terms of social frailty, 143 (46.4%) of the participants were robust, 105 (34.1%) were prefrail, and 60 (19.5%) were frail. Social frailty was less common in women, married individuals and those who exercised at least 1–2 days a week for 30 minutes. Intra-rater and inter-rater concordance were found to be perfect with Kappa values of 0.90 (p:<0.001) and 0.85 (p:<0.001), respectively. The SFS had statistically significant correlations with geriatric assessment tests.
DISCUSSION
The Five-Item Social Frailty Scale is a reliable and valid tool for assessing social frailty in the Turkish population.
Neuromorphic system using capacitor synapses
Authors: Oshio Reon , Kuwahara Takumi , Aoki Takeru , Kimura Mutsumi , Nakashima Yasuhiko
Published: November 2024

Artificial intelligences are indispensable social infrastructures, neural networks are embodiment methodologies, and neuromorphic systems are promising solutions for compact size and low energy. Memristors were first prepared for the synapse devices but incur energy consumption, and memcapacitors were next prepared but have small dynamic ranges of capacitance. In this research, we have developed a neuromorphic system using capacitor synapses. Here, multiple capacitors have binary-weighted capacitances and are controlled to be connected to intermediate signals. They are discharged through transistors, and when they fall below the threshold voltage, the output signals are inverted. After all, electric charges in the multiple capacitances are summed and measured by the inverting intervals, which is the same as multiply–accumulate operation. A large-scale integration chip is actually fabricated. The working is confirmed by MNIST, and the circuit-aware rounding improves the accuracy to 96 %, indicating a sufficient possibility for practical applications, and the energy efficiency is 163 GOPS/W even by the 180 nm technology, indicating a great potential for low energy consumption.
DPKT: Difficulty-Aware Programming Knowledge Tracing with Large Language Models
Authors: Yang Lina , Sun Xinjie , Li Hui , Xu Ran , Wei Xuqin
Published: November 2024

Knowledge Tracing ( KT ) assesses students’ mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. Although recent years have seen significant improvements in tracking accuracy with the introduction of deep learning and graph neural network techniques, existing research has not sufficiently focused on the impact of difficulty on knowledge state. The text understanding difficulty and knowledge concept difficulty of programming problems are crucial for students’ responses; thus, accurately assessing these two types of difficulty and applying them to knowledge state prediction is a key challenge. To address this challenge, we propose a Difficulty-aware Programming Knowledge Tracing with Large Language Models ( DPKT ) to extract the text understanding difficulty and knowledge concept difficulty of programming problems. Specifically, we analyze the relationship between knowledge concept difficulty and text understanding difficulty using an attention mechanism, allowing for dynamic updates to students’ s. This model combines an update gate mechanism with a graph attention network, significantly improving the assessment accuracy of programming problem difficulty and the spatiotemporal reflection capability of knowledge state. Experimental results demonstrate that this model performs excellently across various language datasets, validating its application value in programming education. This model provides an innovative solution for programming knowledge tracing and offers educators a powerful tool to promote personalized learning.
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

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

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

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

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

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

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

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

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

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

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

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

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

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.
SPACE: A Novel Digital Tool for Assessing Hippocampal Structural Integrity in Older Adults
Authors: Minta* Karolina , Colombo* Giorgio , Tee* Mervin , Low Marcus , Grübel Jascha , Wiener Jan , Chen Christopher P. , Hilal Saima , Schinazi Victor R.
Published: November 2024

Hippocampal atrophy is a hallmark of Alzheimer’s disease and is associated with deficits in navigation. We investigated whether a novel digital assessment, the Spatial Performance Assessment for Cognitive Evaluation (SPACE), can predict hippocampal integrity beyond traditional neuropsychological tests in older adults. Forty older male participants underwent structural MRI and completed the spatial and navigation tasks in SPACE along with a battery of neuropsychological tests typically used to detect cognitive impairment. A regression model revealed that worse performance in the path integration and mapping tasks significantly predicted smaller hippocampal volume above age, education and all neuropsychological tests. Critically, participants who successfully learned the positions of landmarks in a virtual environment during a path integration task and accurately reproduced the configuration in a mapping task had larger hippocampal volumes. These findings indicate that SPACE can serve as a valuable addition to traditional cognitive assessments by providing greater sensitivity for assessing hippocampal volume.

*Karolina Minta, Giorgio Colombo, and Mervin Tee contributed equally.
Genomic Sequencing of the Bacteria involved in the Fermentation of Ogiri and Okpehe
Authors: Ogbu Joy Chinyere , Ugoh S.C. , Etuk-Udo Godwin , Odusanya O.O. , R.A. Jimba , Prince Ogbu Chukwuemeka
Published: November 2024

This research focuses on the genomic sequencing of the bacteria involved in the fermentation of Okpehe and Ogiri. Isolation of the bacteria was done on the fermented samples by innoculating the samples on Nutrient Agar and MacConkey Agar and Biochemical tests (Gram staining, citrate, Oxidase and Catalase) was carried out on the organisms obtained. DNA extraction, Polymerase Chain Reaction, Gel electrophoresis and Genomic sequencing was done on the bacteria.
A Novel Software Reliability Growth Model Based on Stochastic Differential Equations for Open-Source Software
Authors: Hameed Fatimah Saad , Asker Hussein K.
Published: November 2024

In this study, we discussed the reliability assessment for Open Source Software by introducing a new software reliability growth model based on stochastic differential equations. We considered that the software failure rate depends on time (t), and the reporting of faults is irregular. First, we determined its probability distribution by transforming the fundamental differential equation describing fault detection into a stochastic differential equation of Itˆo type. Second, we calculated several software reliability measures, such as the expected number of faults and the mean time between cumulative and instantaneous software failures. Using data from actual software testing procedures, we used the maximum likelihood method to estimate the model’s unknown parameters. Finally, we compared the new model with two other reliability growth models (the exponential and the delayed S-shaped), using a set of software quality standards to identify the best model. The results demonstrated that the proposed model can enhance the quality of OSS systems.
ESG Integration in Corporate Governance: A Comparative Study of Practices in Emerging Markets
Authors: Kouam Arthur William Fodouop
Published: November 2024

This study investigates integrating environmental, social, and governance (ESG) factors into corporate governance practices among companies in emerging markets. Despite the growing global importance of ESG considerations, significant gaps remain in understanding how these practices are adopted within the unique contexts of these economies. The study uses a quantitative research paradigm to analyze data from a sample of 250 companies across diverse sectors, including finance, manufacturing, energy, and technology. To establish a robust empirical foundation, data is sourced from reliable secondary materials, specifically corporate disclosures, industry reports, and financial databases. Employing Ordinary Least Squares (OLS) regression analysis, the study explores the impacts of governance practices, drivers, and barriers on ESG integration, effectively capturing the complexities of these interactions. The research identifies several effective ESG integration practices, including structured policies, comprehensive stakeholder engagement, and transparent reporting mechanisms. Critical drivers for integration include solid regulatory support, active stakeholder influence, and rising market demand for sustainability. Conversely, significant barriers to effective ESG integration include weak regulatory frameworks, cultural resistance, and a lack of high-quality ESG data. Notably, regional disparities emerged, with Asian firms exhibiting superior ESG integration compared to those in Eastern Europe, underscoring the importance of localized economic and regulatory conditions. This research contributes to the literature by comprehensively analyzing ESG integration practices in emerging markets. It emphasizes the need for tailored strategies that account for contextual factors, thus offering valuable insights for corporate leaders navigating sustainability in dynamic environments.