bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of injectable mineral supplementation on health, metabolic stress, and performance in Holstein cows during the transition period
Authors: Marques Raquel Souza , Pinheiro Filipe Aguera , Mori Clara Satsuki , Suárez-Retamozo Susan , Almeida Rodrigo , Lima Bruno Sivieri , Durel Luc , Gomes Viviani 
Published: February 2025

Abstract

Metabolic distress in dairy cows during the transition period is associated with a high incidence of infectious diseases and reduced performance. This experimental field trial evaluated the use of injectable mineral supplementation (IMS) to prevent metabolic distress in Holstein cows during the transitional period. The IMS group (n = 189) received three injections (10 mL) of a multi-mineral supplement (Fosfosal, Virbac Brazil, São Paulo, Brazil) at days − 14, 0 (calving day) and + 14 days after parturition. The non-supplemented group (NIMS; n = 123) received three placebo injections. Productive, health, and reproductive performances were assessed along with metabolic distress biomarkers in a subset of cows (NIMS, n = 32; and IMS, n = 34). IMS cows had lower incidences of persistent hypocalcemia and metritis, along with reduced haptoglobin, higher glucose, and lower beta-hydroxybutyric acid (BHB) concentrations. The IMS group had higher enzyme activities for glutathione peroxidase (GPx), reduced glutathione (GSH), and higher immunoglobulin G (IgG) concentrations. Mineral supplementation did not affect milk production, somatic cell count (SCC), or reproductive performance. In conclusion, IMS-supplemented cows showed improved immunity, characterized by an anti-inflammatory profile, higher IgG concentrations, reduced lipid metabolism, and oxidative stress, positively affecting their overall health.
Adaptive Multilayer Ensemble based Real Time Stream data Classification : Architectural Perspectives and Performance
Authors: Jadhav Shailaja , Kodavade Dattatraya 
Published: February 2025

Abstract

Recently, there has been a lot of study focused on large-scale data analysis. Data typically arrives continually in the form of data streams in numerous applications of today’s data intensive world. The analytics engines designed to deal with streaming data need to be capable of acting on data -in- motion . Since the conventional techniques for data mining and machine learning were created for static datasets, data streams present unique difficulties. They are unable to effectively analyse data that is expanding at a quick pace and are less suitable to take into account the representative qualities of data streams. The authors through this research viz. A-MERIT-C(Adaptive Multitiered Ensemble for Real (Integrated with feature engineering) Time Classification), have presented an active learning data stream analysis model through Ensemble Learning Framework ,which is able to learn concepts in near real time streaming data environment. The design of the framework is adaptive to address the dynamism associated with real time data through the classifier pool (i.e. it chooses the best performing classifiers). This design of Adaptive Multitiered Ensemble is composed of prequentially evaluated classifiers rather than traditional hold out evaluation. The distinguishing characteristics of A-MERIT-C give considerable improvements in terms of f1 (87.48), recall (77.79), accuracy (79.27) AUC (88.89) for streaming data analytics as compared to that of similar real time airline FLTRADAR 24 models ; on the other hand, it is able to address the limitations of existing algorithms such as concept evolution , feature drift through incremental learning and feedback .
Vestibular circuit stimulation for retuning locomotor dynamics in Parkinson's disease
Authors: Hartig Johannes , Friedrich Maximilian , Signoret-Genest Jérémy , Tabuchi Sawako , Schöneberg Nina , Peach Robert , Doll Dennis , Petschner Tobias , Knorr Susanne , Volkmann Jens , Tovote Philip , Ip Chi Wang 
Published: February 2025

Abstract

Postural and locomotor dysfunction represent axial symptoms of Parkinson’s disease (PD), remaining poorly treated by medication and deep brain stimulation. Non-invasive neuromodulation of the vestibular system, centered on the vestibular nucleus complex (VNC), offers a novel therapeutic avenue. However, the underlying circuits remain ill-explored. In this study, we found that the VNC in mice feeds extensive Vglut2-defined projections into striato-thalamo-subthalamic and caudal medulla motor hubs, but not the mesencephalic locomotor region. Optoactivation of excitatory VNC neurons below the threshold for vestibular symptoms promoted activity in these basal ganglia-brainstem axis targets. Unbiased analysis of pose dynamics revealed global enhancement of behavioural transitions and locomotion, confirmed by regular kinematic analyses. Therapeutically, it enabled resynchronization of naturalistic gait patterns and improved locomotor performance, but not capacity, in parkinsonian mice. Our data identify excitatory VNC circuit processes for therapeutic retuning of motor dysfunction in the context of PD.
Deficiency of integrin β4 contributes bronchopulmonary dysplasia by compromising cellular stability through the activation of RhoA-(ZO- 1) signaling pathways
Authors: Zhang Dongliang , Wang Xianhui , Han Jingjing , Shao Xiaoyun , Xiang Yang , Chi Yinxiu 
Published: February 2025

Abstract

Alterations in the composition and remodeling of the lung extracellular matrix (ECM) are critical for lung development. Our research identified that mice with a conditional knockout of integrin β4 (ITGB4) exhibit lung dysplasia. In this study, we investigated the expression of collagen IV (IVcol) and matrix metalloproteinase 9 (MMP9) in both normal and ITGB4-deficient mice using Western blot and immunohistochemistry techniques. Our findings indicate that ITGB4 deficiency results in bronchopulmonary dysplasia, which is characterized by increased deposition of IVcol and reduced expression of MMP9. The zonula occludens-1 (ZO-1), on both normal and IV collagen-coated substrates was assessed using laser confocal microscopy. Concurrently, RhoA activities were quantified via fluorescence resonance energy transfer (FRET) microscopy. The findings indicated a significant disruption of ZO-1 in ITGB4-deficient cells, accompanied by an dcrease in RhoA activity.However, RhoA activity was enhanced in ITGB4−/−cells on the type IVcollagen-coated substrate. Furthermore, the application of rhosin resulted in an enhanced expression of ZO-1 in ITGB4−/− cells. These findings indicate that reduced expression of ITGB4 leads to elevated levels of IV collagen and hinders the adaptation of bronchial epithelial cells.
The application of Simulated Annealing Algorithm, Firefly Algorithm, Invasive Weed Optimization, and Shuffled Frog Leaping Algorithm for prediction of Water Quality Index
Authors: Ghadimi Feridon , Moghaddam Sara 
Published: February 2025

Abstract

Groundwater is a vital resource for drinking water, agriculture, and industry worldwide. Effective groundwater quality management is crucial for safeguarding public health and ensuring ecological sustainability. Hydrogeochemical data modeling is widely utilized to predict groundwater quality using various approaches. The method proposed in this study leverages an intelligent model combined with groundwater chemical compositions. Sampling was conducted from 175 drinking and agricultural wells in the Arak Plain. By utilizing hydrogeochemical data and performing correlation and sensitivity analyses, the key groundwater chemical compositions were identified: Ca²⁺, Cl⁻, EC, HCO₃⁻, K⁺, Mg²⁺, Na⁺, pH, SO₄²⁻, TDS, and NO₃⁻.The study predicted the Water Quality Index (WQI) values using the groundwater chemical composition data and an artificial neural network (ANN) model. The chemical compositions of the groundwater served as the model’s input, while the WQI was treated as the model’s output. To enhance the ANN's accuracy, several optimization algorithms were used, including: Simulated Annealing Algorithm (SAA), Firefly Algorithm (FA), Invasive Weed Optimization (IWO), and Shuffled Frog Leaping Algorithm (SFLA).The comparison of results indicated that the ANN-SAA model outperformed the other models. The R² and MSE values for the ANN-SAA model in predicting the WQI were for training data: R² = 0.8275, MSE = 0.0303 and test data: R² = 0.7357, MSE = 0.0371.These results demonstrate that the ANN-SAA model provides a reliable and accurate method for predicting groundwater quality index values, offering a valuable tool for groundwater quality assessment and management.
Advancing Audio-Visual Attention Analysis in 360° Videos Through Real-Time Visualization
Authors: Hirway Amit , Qiao Yuansong , Murray Niall 
Published: February 2025

Abstract

This study presents an open-source, real-time visualization tool designed to analyse audio-visual attention in 360° video environments under varying sound conditions. Traditional methods, such as static saliency maps and post-hoc analyses, often fail to capture the dynamic and participant-specific nature of attention shifts in immersive environments. To address these limitations, the proposed tool dynamically integrates head pose fixation maps with sound intensity heatmaps, enabling real-time tracking of attention patterns across different audio conditions, including No Sound (NS), Stereo (ST), First-Order Ambisonics (FO), and Third-Order Ambisonics (HO). Attention shifts across sound conditions were quantified using the Jaccard Index, which measures the overlap of the top 5% most-viewed regions across participants. The results demonstrate that increasing auditory complexity—from silence to spatial audio—significantly broadens visual exploration. First-Order Ambisonics (FO) led to the most dispersed attention patterns, with a 62.4% reduction in attention overlap indoors and 58.8% outdoors compared to NS. Third-Order Ambisonics (HO) resulted in a 61.2% reduction indoors and 52.0% outdoors, suggesting that while FO encourages broader exploration, HO facilitates a more focused distribution of attention. Notably, HO conditions led to a 3.2% increase indoors and a 16.6% increase outdoors in attention overlap compared to FO, indicating that higher-order spatial audio helps guide attention more precisely in complex environments. Unlike conventional approaches, which rely on static analyses, this tool provides real-time, participant-specific insights into attention shifts, offering a dynamic perspective on how spatial audio influences exploration. These capabilities empower VR content creators and researchers with actionable insights, optimizing spatial audio design and enhancing user engagement. By offering a robust and adaptable framework, this study advances the understanding of audio-visual interactions in immersive media environments.
Quaternion tensor low rank approximation
Authors: Zahir alaeddine , Jbilou Khalide , Ratnani Ahmed 
Published: February 2025

Abstract

In this paper, we propose novel approaches for low-rank approximation of quaternion tensors. The first method employs quasi-norms to approximate a low-rank tensor using the QT-product, which generalizes the L-product to N-mode quaternions. The second method leverages Non-Convex norms to approximate both the Tucker and TT-rank for tensor completion. We demonstrate that the proposed methods provide more accurate tensor approximations compared to traditional convex relaxations of rank, such as the nuclear norm. Furthermore, we establish theoretical guarantees supporting the effectiveness of our models. To validate their performance, we conduct extensive numerical experiments, illustrating the superiority of our methods in inpainting and denoising applications. The results confirm that incorporating Non-Convex surrogate functions and quaternion tensor representations leads to enhanced reconstruction accuracy and robustness, making them valuable tools for high-dimensional data processing.
VIPER: Variational Inference for Pattern Extraction and Recognition in Genome Sequences using State Space Models for Cancer Detection
Authors: Bairwa Amit Kumar , Bhat Siddhanth , Sawant Tanishk , Varma Prabhath , Kushwaha Satpal Singh 
Published: February 2025

Abstract

This study presents VIPER, a novel deep-learning framework for cancer-causing mutation detection from genomic data. VIPER uniquely combines 1D Convolutional Neural Networks (Conv1D) with Mamba blocks, a structured state-space model (SSM) architecture, to capture local and long-range dependencies in genomic sequences. Unlike traditional methods or state-of-the-art models like Transformers, VIPER offers enhanced scalability and computational efficiency, making it well-suited for large-scale genomic analysis. The model was evaluated on the Genome Screen Mutants VCF dataset and achieved a training accuracy of 96.84%, a validation accuracy of 97.30%, and an F1 score of 97.13%. These results outperform conventional deep learning models on the same dataset, including RNNs, CNNs, and Transformers. VIPER also significantly reduced computational overhead while maintaining high precision (97.52%) and recall (96.51%), highlighting its utility in clinical workflows. By focusing on clinically significant mutations, such as driver mutations associated with oncogene activation, VIPER provides actionable insights for precision oncology. This work advances the state of the art in cancer genomics by delivering an accurate, efficient, and interpretable solution for large-scale mutation detection. Future extensions may integrate multiomics data to enhance diagnostic capabilities further.
Association between Prenatal, Pre-pregnancy Rainfall and Adult Obesity: Findings from the Community Behavior and Attitude Survey in Tuvalu (COMBAT)
Authors: Wei Chih-Fu , Tang Lois , Lin Po-Jen , Lee Tai-Lin , Wu Stephanie , Chang Chia-Rui , Shih Chih-Wei , Lo Yuan-Hung , Shiau Shi-Chian , Tausi Selotia , Sosene Vine , Maani Pauke , Tupulaga Malo , López-Gil José Francisco , Hershey Maria , Hsu Yu-Tien 
Published: February 2025

Abstract

Tuvalu has one of the highest obesity prevalence rates globally, and is a Pacific Island nation facing significant climate change challenges. Altered rainfall pattern, as a part of climate change, may influence obesity risk during the critical developmental periods. This study investigated the associations between rainfall exposure during prenatal, pre-pregnancy periods and adult obesity in Tuvalu. A nationwide survey was conducted between February and May 2022, which included 892 adults from Tuvalu. Rainfall data was obtained from ECMWF Reanalysis v5 based on participants’ birth year and birthplace. Rainfall exposure during the first year of birth, the year before birth, and two years before birth was analyzed, and rainfall exposure between three to five years before birth were included as negative control periods. Obesity and severe obesity were defined based on body mass index (BMI) upon the survey, according to the World Health Organization criteria. The results showed association between higher rainfall before birth increased BMI and greater odds of adulthood obesity. These associations were more pronounced among male participants. No significant associations were observed for rainfall three to five years before birth. In conclusion, prenatal exposure to higher rainfall during the year and two years before birth are associated with increased obesity risk in adulthood, reflecting prenatal environmental influences on developmental periods. These findings emphasize the importance of understanding climate-related health exposures and the need for targeted interventions in climate change-vulnerable populations. Further research should explore heterogeneity across Pacific Island nations and the mechanisms linking rainfall, birth weight, and obesity.
Integrated Modeling and Management of Non-Point Source Pollution in the Bailin River Basin: Best Practices for Reducing Nutrient Loads
Authors: Wang Hao , Liu Yifeng , Zhu Shijiang , Liu Yang , Xu Wen 
Published: February 2025

Abstract

The Bailin River, a key tributary of the Yangtze River, faces significant water quality challenges due to agricultural non-point source (NPS) pollution exacerbated by industrial discharge and urban runoff. This study employs the Soil and Water Assessment Tool (SWAT) to analyze the temporal and spatial dynamics of runoff as well as total nitrogen (TN) and total phosphorus (TP) loads in the Bailin River basin from 2020 to 2023. A critical source area analysis was performed to identify regions disproportionately contributing to pollutant loads. Through various simulations, including different Best Management Practices (BMPs) scenarios, the study explores their effectiveness in reducing nutrient loads. The findings reveal that nutrient losses are significantly concentrated during the flood season, with TN and TP losses accounting for 58.61% and 58.92% of annual totals, respectively. Specific BMP scenarios, combining optimized fertilization, vegetation buffer strips, and grass ditches, demonstrated substantial pollutant reduction, with the best combinations exceeding 58% reductions for both TN and TP. The study emphasizes the necessity of targeted interventions in critical source areas to optimize management strategies and achieve better water quality outcomes. Continuous monitoring and adaptive management practices will be crucial to addressing ongoing challenges of non-point source pollution in this basin. Ultimately, this research contributes to a deeper understanding of NPS pollution in mountainous watersheds and highlights effective management pathways for improved ecological health and water quality.
From Piaget to Posthumanism: Critical Review of Conceptualization of AI in Early Childhood Education
Authors: Cai Zhuoyun , Xing Yaxin Grace , Yamamoto Yuya , Wang X. Christine 
Published: February 2025

Abstract

Artificial Intelligence (AI) is increasingly integrated into Early Childhood Education (ECE), reshaping how young children learn and develop. While existing studies have explored AI’s practical applications in ECE, they often neglect the critical conceptualization necessary to deepen our understanding of its role in this field. To address this gap, we critically reviewed 35 empirical studies on AI in ECE, adapting Kafai and Proctor’s (2022) cognitive, situated, and critical framings of computational thinking. Our analysis investigates how these studies conceptualize “learning with AI” and “learning about AI” and address foundational questions of “who,” “what,” and “how” in AI education for young children. The findings reveal a strong emphasis on cognitive and situated framings, which focus on individual and contextual applications of AI. However, there is a notable lack of attention to broader societal impacts and equity concerns from a critical perspective. To bridge this gap, we propose a post-humanist perspective, framing AI as a new ontological category that enhances our understanding of its role in ECE. Additionally, we advocate for an ethical care framework, emphasizing empathy and inclusivity, in designing AI learning experiences. These approaches aim to foster a sustainable and socially responsible ecosystem for early learning with and about AI.
Informatic tools for diagnosis in dentistry. A compilation review
Authors: Gil Alain Manuel Chaple 
Published: February 2025

Abstract

The objective of this study was to compile the computer tools available in the scientific literature aimed at helping the diagnosis in dentistry. A scoping review was conducted using PubMed, Scopus, and Web of Science. Were include articles that reported the usefulness of a computer/technological tool that helps diagnosis in dental practice, published in the last 20 years in English and Spanish. Online tool Rayyan® was used to establish homogeneity in the review of the authors and centralize the results. In total, 12648 records were retrieved from the databases. After decantation, 39 reports described 36 computer tools used to help for diagnosis in dentistry. More informatic tools related to "Restorative Dentistry’ have been developed than the rest of the specialties 14 (40%). Python was the predominant programming language, 83.3% of the tools were validated, and 27.8% were free. Informatics tools in dentistry enhance the diagnosis and treatment planning. However, a robust regulatory framework is required for validation prior to clinical implementation. Continuous training of dental professionals using these technologies is crucial to maximize their benefits and ensure optimal patient care. More research is needed to explore the potential of informatics applications in dentistry, their integration into existing health systems, and their accessibility in resource-limited areas.
Hybridizing persistent homology and machine learning for oceanographic time series
Authors: Lin Zixin , Zulkepli Nur Fariha Syaqina , Kasihmuddin Mohd Shareduwan Mohd , Gobithaasan R. U. 
Published: February 2025

ABSTRACT

Oceanographic phenomena are inherently complex due to factors such as global warming, ice melting, and human activities. Analyzing oceanographic data presents a challenging task, and a quantitative approach is often implemented to address these complexities. Traditional machine learning techniques, such as regression and cluster analysis, are commonly employed for predicting and identifying similarities in oceanographic conditions across different locations. This study explores the integration of qualitative approaches into oceanographic data analysis by developing hybrid models that combine topological data analysis (TDA) with traditional machine learning methods. The focus is on extracting qualitative features using persistent homology, the core tool of TDA, and incorporating these features into machine learning models such as support vector regression (SVR) and hierarchical clustering analysis (HCA). The study applies these methods to the following two oceanographic datasets: wave height data from buoy stations in Canada and sea level data from gauge stations in Japan. The results show that the hybrid models significantly outperform traditional approaches across several performance metrics, including the R2 score, maximum absolute error, mean squared error, and Rand score. This demonstrates the value of adding topological information to machine learning models, which enhances their predictive performance.
Bibliometrics and visualization analysis of microplastics research in water from 2011 to 2023
Authors: Jiang Lei , Wang Meiyi , He Shan , Ren Jiabo , Zhang Tong , Cheng Zhihui 
Published: February 2025

ABSTRACT
Microplastics in water have emerged as a significant public concern in recent years due to their potential adverse impacts on both human and animal health. This study conducts an analysis of articles published in key journals indexed in the Web of Science from 2011 to 2023, employing CiteSpace and VOSviewer for data extraction and visualization. The results elucidate a marked increase in both the number of publications and citations since 2018. Initially, the United Kingdom was at the forefront of research output in this domain, with a publication proportion of 16.59% from 2011 to 2017 and 4.37% from 2018 to 2023. However, the proportion of publications in China has increased from 10.31 to 40.45%. Notably, the Marine Pollution Bulletin has not only been an early contributor to this field but also holds the record for the highest number of published articles. Keyword analysis indicates research trends and hotspots. Recent investigations on microplastic removal techniques have predominantly centered on adsorption. Moreover, studies focusing on microplastics in surface water have also garnered considerable attention. This study offers a comprehensive review of existing research and provides guidance for future directions in microplastic research.