bluuebunny/crossref_metadata_2025 huggingface dataset:
Conversation-based AI for anxiety disorders might lower the threshold for traditional medical assistance: a case report
Authors: Grosshans Martin , Paul Torsten , Fischer Sebastian Karl Maximilian , Lotzmann Natalie , List Hannah , Haag Christina , Mutschler Jochen
Published: September 2024

Artificial intelligence (AI) offers a wealth of opportunities for medicine, if we also bear in mind the risks associated with this technology. In recent years the potential future integration of AI with medicine has been the subject of much debate, although practical clinical experience of relevant cases is still largely absent. This case study examines a particular patient’s experience with different forms of care. Initially, the patient communicated with the conversation (chat) based AI (CAI) for self-treatment. However, over time she found herself increasingly drawn to a low-threshold internal company support system that is grounded in an existing, more traditional human-based care structure. This pattern of treatment May represent a useful addition to existing care structures, particularly for patients receptive to technology.
Sleeping <6.55 h per day was associated with a higher risk of low back pain in adults aged over 50 years: a Korean nationwide cross-sectional study
Authors: Hu Dexin , Zhang Yihui , Liu Xingkai , Yang Xin , Liang Xichao , Hu Xu , Yuan Hua , Zhao Chenguang
Published: September 2024

Background

Patients with low back pain (LBP) often suffer from sleep disorder, and insufficient sleep duration was recognized as a potential risk factor for LBP. Our aim was to explore the exact effect of sleep duration on LBP and the optimal sleep duration to reduce the risk of LBP.

Methods

Analyzing data from the Korean National Health and Nutrition Examination Survey (KNHANES), we investigated the association between sleep duration and LBP in individuals aged 50 years and older. We used logistic regression models, interaction stratification analysis, and threshold effect assessment to analyze the relationship between sleep duration and LBP.

Results

A total of 6,285 participants, comprising 3,056 males and 3,229 females with a median age of 63.1 years, were enrolled in the study. The association between sleep duration and LBP risk exhibited an L-shaped curve (p < 0.015) in RCS analysis. In the threshold analysis, the OR of developing risk of LBP was 0.864 (95% CI:0.78–0.957, p = 0.005) in participants with sleep duration <6.55 h. Each additional hour of sleep was associated with a 13.6% decrease in the risk of LBP. No significant association was observed between sleep duration ≥6.55 h and the risk of LBP. The risk of LBP did not decrease further with increasing sleep duration. Results remain robust across subgroups.

Conclusion

Our findings indicate that shorter sleep duration is a risk factor for LBP in adults aged over 50 years. We revealed an L-shaped association between sleep duration and LBP, with an inflection point at approximately 6.55 h per day. These results underscore the significance of sleep duration as a factor in the risk assessment for LBP.
A new approach to a wicked problem: development of a cross-sector community-centered learning network to tackle childhood food inequity
Authors: Schuh Aaron M. , Alexander Christopher , Gasperetti Kristen , Gorecki Michelle C. , Cutler Kimberly , Hoffman Charles , Kahn Robert S. , Okano Chika , Riley Carley L.
Published: September 2024

Food insecurity is a complex societal problem that disproportionately impacts households with children and those led by minoritized populations, with negative impacts on health across the life course. System to Achieve Food Equity adapted the learning systems model, used to address similarly complex problems, to tackle food insecurity at a neighborhood level. SAFE, born out of the COVID-19 pandemic, leverages a family-centered, community-based, cross-sector network fundamentally aimed at changing the food system so that all children in Cincinnati have the food they need to thrive. Through the following principles, Community-Led Network, Co-Production with Community, Equitable Sustainability, Learning to Learn Together, Distributing Leadership and Power, and Shared Data and Governance, SAFE has grown to over 300 individuals and 100 organizations, funded 9 novel interventions, distributed over 270,000 meals, and created a collaborative of motivated like-minded stakeholders. Future work includes improved data collection and sharing, support for increased stakeholder engagement and greater distribution of leadership and power, advocacy for policy change, refining measurement tools of network maturity for community settings, and collaboration with other efforts that contribute to food security indirectly.
CovRoot: COVID-19 detection based on chest radiology imaging techniques using deep learning
Authors: Niloy Ahashan Habib , Fahim S. M. Farah Al , Parvez Mohammad Zavid , Shiba Shammi Akhter , Faria Faizun Nahar , Rahman Md. Jamilur , Hussain Emtiaz , Tamanna Tasmi
Published: September 2024

The world first came to know the existence of COVID-19 (SARS-CoV-2) in December 2019. Initially, doctors struggled to diagnose the increasing number of patients due to less availability of testing kits. To help doctors primarily diagnose the virus, researchers around the world have come up with some radiology imaging techniques using the Convolutional Neural Network (CNN). Previously some research methods were based on X-ray images and others on CT scan images. Few research methods addressed both image types, with the proposed models limited to detecting only COVID and NORMAL cases. This limitation motivated us to propose a 42-layer CNN model that works for complex scenarios (COVID, NORMAL, and PNEUMONIA_VIRAL) and more complex scenarios (COVID, NORMAL, PNEUMONIA_VIRAL, and PNEUMONIA_BACTERIA). Furthermore, our proposed model indicates better performance than any other previously proposed models in the detection of COVID-19.
Sleep quality and emotional reactivity in patients with borderline personality disorder
Authors: Socci Valentina , Festucci Fabiana , Barlattani Tommaso , Salfi Federico , D'Aurizio Giulia , Rossi Rodolfo , Ferrara Michele , Rossi Alessandro , Pacitti Francesca , Tempesta Daniela
Published: September 2024

Background/objective

Emotional dysregulation is bidirectionally associated with sleep disturbances, with potentially critical implications for emotional reactivity, in patients with borderline personality disorder (BPD). This study evaluated subjective and objective sleep quality, emotional regulation, and emotional reactivity in 20 patients with BPD compared to 20 non-clinical individuals.

Methods

Subjective and objective sleep quality was assessed using the Pittsburgh Sleep Quality Index and a 3-day actigraphic measurement. Emotional regulation was evaluated using the Difficulties in Emotion Regulation Scale and the Berkeley Expressivity Questionnaire. Furthermore, each participant underwent an emotional reactivity task selected from the International Affective Picture System.

Results

Compared to control subjects, individuals with BPD reported poor subjective sleep quality and objective sleep continuity disturbances, with more sleep fragmentation and decreased sleep efficiency. Moreover, BPD patients showed emotional dysregulation and altered subjective reactivity to emotional stimuli, particularly positively valenced stimuli.

Conclusion

These results suggest the importance of further clarifying the specific direction of sleep-dependent emotional modulation in individuals with BPD, with significant clinical implications for patients with co-occurring sleep disturbances.
Effects of physical training combined with transcranial direct current stimulation on maximal strength and lower limb explosive strength in healthy adults
Authors: Liu Jintong , Li Chunlei , Fang Junhui , Xu Haokai , Zhang Xingyue , Zhao Fan
Published: September 2024

Objective

The purpose of this systematic review and meta-analysis was to investigates whether transcranial direct current stimulation applied during physical training increases muscle strength in comparison with sham tDCS combined with physical training.

Methods

Randomized controlled trials of the effects of tDCS combined physical training intervention on muscle strength and cortical excitability were collected by searching Web of Science, Pubmed, EBSCO, CNKI. The retrieval date ends in April 2024. 11 randomized controlled trials are finally included. The total sample size of the study is 338. The experimental group was subjected to tDCS combined with physical training intervention, and the control group was physical training combined with sham tDCS intervention.

Results

There is a significant increase in maximal strength (SMD = 0.38; 95% CI: 0.09, 0.67; p = 0.01) and lower limb explosive strength (MD = 2.90; 95% CI: 1.06, 4.74; p = 0.002) when physical training was performed with tDCS, but not following physical training combined with sham tDCS. Subgroup analysis of the subject population showed an increase in muscle strength in those with training experience following tDCS combined with physical training (SMD = 0.39; 95% CI: 0.08, 0.70; p = 0.01), but not for those without training experience (SMD = 0.29; 95% CI: −0.06, 0.63; p = 0.10). Motor evoked potential (MEP) wave amplitude increased significantly following physical training with tDCS (SMD = 0.71; 95% CI: 0.18, 1.24; p = 0.008), but was not different between groups (SMD = 0.16; 95% CI: −0.33, 0.65; p = 0.52).

Conclusions

tDCS combined with physical training intervention can improve muscle strength, lower limb explosive strength and cerebral cortex excitability. Compared to tDCS combined with training of small muscle groups, tDCS combined with training of large muscle groups was more effective in improving muscle strength. Muscle strength was more likely to improve after tDCS combined with physical training in people with physical training experience compared with people without physical training experience. The combination of tDCS with physical training intervention and the sham-tDCS with physical training intervention both increased cortical excitability.

Systematic Review Registration

https://www.crd.york.ac.uk/, PROSPERO, identifier (CRD42024550454).
Genomic comparison between Mycobacterium bovis and Mycobacterium microti and in silico analysis of peptide-based biomarkers for serodiagnosis
Authors: Moens Charlotte , Bogaerts Bert , Lorente-Leal Victor , Vanneste Kevin , De Keersmaecker Sigrid C. J. , Roosens Nancy H. C. , Mostin Laurent , Fretin David , Marché Sylvie
Published: September 2024

In recent years, there has been an increase in the number of reported cases of Mycobacterium microti infection in various animals, which can interfere with the ante-mortem diagnosis of animal tuberculosis caused by Mycobacterium bovis. In this study, whole genome sequencing (WGS) was used to search for protein-coding genes to distinguish M. microti from M. bovis. In addition, the population structure of the available M. microti genomic WGS datasets is described, including three novel Belgian isolates from infections in alpacas. Candidate genes were identified by examining the presence of the regions of difference and by a pan-genome analysis of the available WGS data. A total of 80 genes showed presence-absence variation between the two species, including genes encoding Proline-Glutamate (PE), Proline-Proline-Glutamate (PPE), and Polymorphic GC-Rich Sequence (PE-PGRS) proteins involved in virulence and host interaction. Filtering based on predicted subcellular localization, sequence homology and predicted antigenicity resulted in 28 proteins out of 80 that were predicted to be potential antigens. As synthetic peptides are less costly and variable than recombinant proteins, an in silico approach was performed to identify linear and discontinuous B-cell epitopes in the selected proteins. From the 28 proteins, 157 B-cell epitope-based peptides were identified that discriminated between M. bovis and M. microti species. Although confirmation by in vitro testing is still required, these candidate synthetic peptides containing B-cell epitopes could potentially be used in serological tests to differentiate cases of M. bovis from M. microti infection, thus reducing misdiagnosis in animal tuberculosis surveillance.
Mapping the Frontier: A Bibliometric Analysis of Artificial Intelligence Applications in Local and Regional Studies
Authors: Delcea Camelia , Nica Ionuț , Ionescu Ștefan , Cibu Bianca , Țibrea Horațiu
Published: September 2024

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development.
Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy
Authors: Haljasmaa Kristina I. , Bramm Andrey M. , Matrenin Pavel V. , Eroshenko Stanislav A.
Published: September 2024

Together with the growing interest towards renewable energy sources within the framework of different strategies of various countries, the number of solar power plants keeps growing. However, managing optimal power generation for solar power plants has its own challenges. First comes the problem of work interruption and reduction in power generation. As the system must be tolerant to the faults, the relevance and significance of short-term forecasting of solar power generation becomes crucial. Within the framework of this research, the applicability of different forecasting methods for short-time forecasting is explained. The main goal of the research is to show an approach regarding how to make the forecast more accurate and overcome the above-mentioned challenges using opensource data as features. The data clustering algorithm based on KMeans is proposed to train unique models for specific groups of data samples to improve the generation forecast accuracy. Based on practical calculations, machine learning models based on Random Forest algorithm are selected which have been proven to have higher efficiency in predicting the generation of solar power plants. The proposed algorithm was successfully tested in practice, with an achieved accuracy near to 90%.
Munificent Environment Factors Influencing Entrepreneurial Intention and Behaviour: The Moderating Role of Risk-Taking Propensity
Authors: Mothibi Nkosinathi Henry , Malebana Mmakgabo Justice , Rankhumise Edward Malatse
Published: September 2024

This study examined the effect of munificent environment factors on the antecedents of entrepreneurial intention and risk-taking propensity by means of the theory of planned behaviour. The study also assessed the effect of perceived behavioural control and entrepreneurial intention on entrepreneurial behaviour, as well as the moderating role of risk-taking propensity on the association between the antecedents of entrepreneurial intention, entrepreneurial intention, and entrepreneurial behaviour. Data were gathered from 127 SME owners in the Gauteng metropolitan cities of Ekurhuleni, Johannesburg, and Tshwane using a structured self-administered questionnaire. Partial least squares structural equation modelling (PLS-SEM) was employed to test the relationships. Findings revealed the varying effects of the munificent environment factors on the antecedents of entrepreneurial intention and risk-taking propensity. Perceived behavioural control had a significant effect on entrepreneurial intention, while attitude towards behaviour and subjective norms were non-significant. Risk-taking propensity weakened the link between entrepreneurial behaviour and entrepreneurial intention and did not exhibit a significant moderating effect on the association between attitude towards the behaviour and entrepreneurial intention or between subjective norms and entrepreneurial intention. Risk-taking propensity had a significant effect on both entrepreneurial intention and entrepreneurial behaviour. Perceived behavioural control had a direct positive significant effect on entrepreneurial behaviour, whereas entrepreneurial intention did not. Efforts to promote access to entrepreneurial role models and social capital are vital in regard to stimulating risk-taking propensity and entrepreneurial behaviour. Thus, interventions that are directed at the enhancement of perceived behavioural control could help shape the formation of entrepreneurial intentions and stimulate entrepreneurial activity.
Dynamic RCS Modeling and Aspect Angle Analysis for Highly Maneuverable UAVs
Authors: Sen Kerem , Aksimsek Sinan , Kara Ali
Published: September 2024

Unmanned aerial vehicles (UAVs) are increasingly significant in modern warfare due to their versatility and capacity to perform high-risk missions without risking human lives. Beyond surveillance and reconnaissance, UAVs with jet propulsion and engagement capabilities are set to play roles similar to conventional jets. In various scenarios, military aircraft, drones, and UAVs face multiple threats while ground radar systems continuously monitor their positions. The interaction between these aerial platforms and radars causes temporal fluctuations in scattered echo power due to changes in aspect angle, impacting radar tracking accuracy. This study utilizes the potential radar cross-section (RCS) dynamics of an aircraft throughout its flight, using ground radar as a reference. Key factors influencing RCS include time, frequency, polarization, incident angle, physical geometry, and surface material, with a focus on the complex scattering geometry of the aircraft. The research evaluates the monostatic RCS case and examines the impact of attitude variations on RCS scintillation. Here, we present dynamic RCS modeling by examining the influence of flight dynamics on the RCS fluctuations of a UAV-sized aircraft. Dynamic RCS modeling is essential in creating a robust framework for operational analysis and developing effective countermeasure strategies, such as advanced active decoys. Especially in the cognitive radar concept, aircraft will desperately need more dynamic and adaptive active decoys. A methodology for calculating target aspect angles is proposed, using the aircraft’s attitude and spherical position relative to the radar system. A realistic 6DoF (6 degrees of freedom) flight data time series generated by a commercial flight simulator is used to derive aircraft-to-radar aspect angles. By estimating aspect angles for a simulated complex flight trajectory, RCS scintillation throughout the flight is characterized. The study highlights the importance of maneuver parameters such as roll and pitch on the RCS measured at the radar by comparing datasets with and without these parameters. Significant differences were found, with a 32.44% difference in RCS data between full maneuver and no roll and pitch changes. Finally, proposed future research directions and insights are discussed.
Experimental Investigation of Pulse Detonation Combustion Characteristics via Atomizer Geometry
Authors: Oh Yoojin , Choi Myeung Hwan , Park Sungwoo
Published: September 2024

Recent studies have increasingly focused on integrating detonation processes into engine technologies, advancing beyond the fundamental research phase of detonation research. The present study investigates the detonability and combustion characteristics of liquid fuels, specifically ethanol, with an emphasis on the effects of atomization properties facilitated by different atomizer designs to implement pulse detonation combustion engines. Oxygen was used as the oxidizer. We employed internal injectors (I45, I90, IB4) and atomizer venturis (VA, VB, VR) to examine how variations in liquid fuel atomization and atomizer configurations influence detonation. The occurrence of detonation was assessed using predicted Sauter mean diameters (SMDs) and exit velocities for different atomizer setups. Additionally, we evaluated the effects of nitrogen dilution at concentrations of 0%, 25%, and 50% on velocity variations and changes in detonation characteristics. The findings suggest that while higher exit velocities decrease SMD, facilitating detonation, excessively high velocities hinder detonation initiation. Conversely, lower exit velocities emphasize the role of SMD in initiating detonation. However, the introduction of nitrogen, which reduces the SMD, was found to decrease reactivity and impede detonation.
An Attitude Determination and Sliding Mode Control Method for Agile Whiskbroom Scanning Maneuvers of Microsatellites
Authors: Yang Xinyan , Li Zhaoming , Li Lei , Liao Yurong
Published: September 2024

Microsatellites have significantly impacted space missions by offering advanced technology at a low cost. This study introduces an attitude determination and control algorithm for agile whiskbroom scanning maneuvers in microsatellites to enable wide-swath target detection for low-Earth-orbit microsatellites. First, an angular velocity calculation model for agile whiskbroom scanning is established. A methodology has been developed to calculate the maximum available time for whiskbroom scanning from one side of the sub-satellite point to the other while ensuring the seamless joining of adjacent strips to avoid missing targets. Thereafter, a gyro- and magnetometer-based cubature Kalman filter is put forward for microsatellite attitude estimation. Furthermore, for attitude control, a hybrid manipulation law capable of preventing singularities and escaping singularity surfaces is designed to ensure high-precision torque output from the control moment gyroscopes (CMGs) used as actuators. The benefits of the linear sliding mode and fast terminal sliding mode are integrated, and a non-singular sliding surface is designed, yielding a non-singular fast terminal sliding mode attitude control algorithm for tracking the desired trajectory. This algorithm effectively suppresses chattering and enhances dynamic performance without using a switching term. A semi-physical simulation experiment system is also conducted on the ground to validate the proposed algorithm’s high-precision tracking of the planned whiskbroom scanning path. The experimental results demonstrate an attitude angle control accuracy of 4 × 10−2 degrees and angular velocity control accuracy of 0.01°/s and thus the effectiveness of the proposed algorithm.
Fermentation and Nutritive Value of Pineapple Stubble Silage Supplemented with Leucaena Hay
Authors: Silva Raphael Ramos , Rodrigues Rosane Cláudia , Rodrigues Marcônio Martins , Abdalla Adibe Luiz , da Silva Cabral Luciano , da Costa Araújo Daniel Louçana , Olivera-Viciedo Dilier , da Silva Eduarda Castro , de Moura Zanine Anderson , de Jesus Ferreira Daniele , Santos Francisco Naysson de Sousa , Costa Kátia Aparecida de Pinho , Santos Antonio Marcos da Penha , Rocha Pedro Lucas Oliveira , Perazzo Alexandre Fernandes
Published: September 2024

The increase in pineapple production has generated a large amount of waste, which, when incorporated into the soil, can lead to contamination and environmental degradation. In this context, this study highlights the innovative combination of pineapple waste and Leucaena hay, offering benefits for animal nutrition while reducing input costs. The objective of this study was to determine the nutritional value of silage from ‘Pérola’ pineapple waste (PS) with different levels of Leucaena hay. A completely randomized design was adopted with six treatments (pre-wilted PS and PS with 0%, 10%, 20%, 30%, and 40% inclusion of Leucaena hay (LH)). The dry matter content of the silage, crude protein, lignin, and pH increased linearly (p < 0.05) as a function of the inclusion of Leucaena hay, while buffering capacity, soluble carbohydrate content, and effluent losses showed an inverse relationship. As LH levels increased, ruminal DM disappearance decreased. Wilting the pineapple plant did not alter the in vitro gas production, in vitro digestibility of organic matter, partition factor (PF), and methane production when compared to the silages added with Leucaena hay. It is recommended to add 20% Leucaena hay in PS silage.
Research and Experiment on a Chickweed Identification Model Based on Improved YOLOv5s
Authors: Yu Hong , Zhao Jie , Xi Xiaobo , Li Yongbo , Zhao Ying
Published: September 2024

Currently, multi-layer deep convolutional networks are mostly used for field weed recognition to extract and identify target features. However, in practical application scenarios, they still face challenges such as insufficient recognition accuracy, a large number of model parameters, and slow detection speed. In response to the above problems, using chickweed as the identification object, a weed identification model based on improved YOLOv5s was proposed. Firstly, the Squeeze-and-Excitation Module (SE) and Convolutional Block Attention Module (CBAM) were added to the model’s feature extraction network to improve the model’s recognition accuracy; secondly, the Ghost convolution lightweight feature fusion network was introduced to effectively identify the volume, parameter amount, and calculation amount of the model, and make the model lightweight; finally, we replaced the loss function in the original target bounding box with the Efficient Intersection over Union (EloU) loss function to further improve the detection performance of the improved YOLOv5s model. After testing, the accuracy of the improved YOLOv5s model was 96.80%, the recall rate was 94.00%, the average precision was 93.20%, and the frame rate was 14.01 fps, which were improved by 6.6%, 4.4%, 1.0%, and 6.1%, respectively, compared to the original YOLOv5s model. The model volume was 9.6 MB, the calculation amount was 13.6 GB, and the parameter amount was 5.9 MB, which decreased by 29.4%, 14.5%, and 13.2% compared with the original YOLOv5s model, respectively. This model can effectively distinguish chickweed between crops. This research can provide theoretical and technical support for efficient identification of weeds in complex field environments.
Responses of Leaf Senescence for Stipa krylovii to Interactive Environmental Factors
Authors: Song Xingyang , Zhou Guangsheng
Published: September 2024

The effects of temperature, and photoperiod on autumn phenology are well established for many species. However, the impact of multiple environmental factors and their interactions on regulating autumn phenology remains insufficiently explored. A large-scale controlled experiment in an artificial climate chamber was conducted from April to October 2021 at the Hebei Gucheng Agricultural Meteorology National Observation and Research Station, Hebei Province. This study aimed to investigate the interactive effects of temperature [T1.5, (1.5 °C above the control), T2, (2 °C above the control)], photoperiod [LP, long photoperiod (4 h photoperiod above the control), SP, short photoperiod (4 h photoperiod below the control)], and nitrogen addition [LN, low nitrogen, (nitrogen at 5 g N·m−2·a−1), MN, medium nitrogen, (nitrogen at 10 g N·m−2·a−1), HN, high nitrogen, (nitrogen at 20 g N·m−2·a−1), control for temperature and photoperiod was the mean monthly temperature and average photoperiod (14 h) from 1989–2020 for Stipa krylovii, while the control for nitrogen treatment was without nitrogen addition] on leaf senescence in Stipa krylovii. A three-way analysis of variance (ANOVA) revealed significant effects of temperature, photoperiod, and nitrogen addition on leaf senescence (p < 0.01), with effects varying across different levels of each factor. Increased temperature notably delayed leaf senescence, with delays averaging of 4.0 and 6.3 days for T1.5 and T2, respectively. The LP treatment advanced leaf senescence by an average of 4.0 days, while the SP treatment delayed it by an average of 6.2 days; nitrogen addition advanced leaf senescence, with the effect intensifying as nitrogen levels increased, resulting in average advancements of 1.5, 1.9, and 4.3 days for LN, MN, and HN, respectively. Additionally, we observed that temperature altered the sensitivity of leaf senescence to the photoperiod, diminishing the advancement caused by LP at 2 °C and amplifying the delay caused by SP. These findings underscore the differential impacts of these three factors on the leaf senescence of Stipa krylovii and provide critical insights into plant phenology in response to varying environmental conditions.
Pleiotropic Gene HMGA2 Regulates Myoblast Proliferation and Affects Body Size of Sheep
Authors: Cao Xiukai , Ling Chen , Liu Yongqi , Gu Yifei , Huang Jinlin , Sun Wei
Published: September 2024

Uncovering genes associated with muscle growth and body size will benefit the molecular breeding of meat Hu sheep. HMGA2 has proven to be an important gene in mouse muscle growth and is associated with the body size of various species. However, its roles in sheep are still limited. Using sheep myoblast as a cell model, the overexpression of HMGA2 significantly promoted sheep myoblast proliferation, while interference with HMGA2 expression inhibited proliferation, indicated by qPCR, EdU, and CCK-8 assays. Furthermore, the dual-luciferase reporter system indicated that the region NC_056056.1: 154134300-154134882 (-618 to -1200 bp upstream of the HMGA2 transcription start site) was one of the habitats of the HMGA2 core promoter, given the observation that this fragment led to a ~3-fold increase in luciferase activity. Interestingly, SNP rs428001129 (NC_056056.1:g.154134315 C>A) was detected in this fragment by Sanger sequencing of the PCR product of pooled DNA from 458 crossbred sheep. This SNP was found to affect the promoter activity and was significantly associated with chest width at birth and two months old, as well as chest depth at two and six months old. The data obtained in this study demonstrated the phenotypic regulatory role of the HMGA2 gene in sheep production traits and the potential of rs428001129 in marker-assisted selection for sheep breeding in terms of chest width and chest depth.
Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores
Authors: Bussiman Fernando , Alves Anderson A. C. , Richter Jennifer , Hidalgo Jorge , Veroneze Renata , Oliveira Tiago
Published: September 2024

Gait scores are widely used in the genetic evaluation of horses. However, the nature of such measurement may limit genetic progress since there is subjectivity in phenotypic information. This study aimed to assess the application of machine learning techniques in the prediction of breeding values for five visual gait scores in Campolina horses: dissociation, comfort, style, regularity, and development. The dataset contained over 5000 phenotypic records with 107,951 horses (14 generations) in the pedigree. A fixed model was used to estimate least-square solutions for fixed effects and adjusted phenotypes. Variance components and breeding values (EBV) were obtained via a multiple-trait model (MTM). Adjusted phenotypes and fixed effects solutions were used to train machine learning models (using the EBV from MTM as target variable): artificial neural network (ANN), random forest regression (RFR) and support vector regression (SVR). To validate the models, the linear regression method was used. Accuracy was comparable across all models (but it was slightly higher for ANN). The highest bias was observed for ANN, followed by MTM. Dispersion varied according to the trait; it was higher for ANN and the lowest for MTM. Machine learning is a feasible alternative to EBV prediction; however, this method will be slightly biased and over-dispersed for young animals.
Optimized Machine Learning Models for Predicting Core Body Temperature in Dairy Cows: Enhancing Accuracy and Interpretability for Practical Livestock Management
Authors: Li Dapeng , Yan Geqi , Li Fuwei , Lin Hai , Jiao Hongchao , Han Haixia , Liu Wei
Published: September 2024

Heat stress poses a significant challenge to livestock farming, particularly affecting the health and productivity of high-yield dairy cows. This study develops a machine learning framework aimed at predicting the core body temperature (CBT) of dairy cows to enable more effective heat stress management and enhance animal welfare. The dataset includes 3005 records of physiological data from real-world production environments, encompassing environmental parameters, individual animal characteristics, and infrared temperature measurements. Employed machine learning algorithms include elastic net (EN), artificial neural networks (ANN), random forests (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and CatBoost, alongside several optimization algorithms such as Bayesian optimization (BO) and grey wolf optimizer (GWO) to refine model performance through hyperparameter tuning. Comparative analysis of various feature sets reveals that the feature set incorporating the average infrared temperature of the trunk (IRTave_TK) excels in CBT prediction, achieving a coefficient of determination (R2) value of 0.516, mean absolute error (MAE) of 0.239 °C, and root mean square error (RMSE) of 0.302 °C. Further analysis shows that the GWO–XGBoost model surpasses others in predictive accuracy with an R2 value of 0.540, RMSE as low as 0.294 °C, and MAE of just 0.232 °C, and leads in computational efficiency with an optimization time of merely 2.41 s—approximately 4500 times faster than the highest accuracy model. Through SHAP (SHapley Additive exPlanations) analysis, IRTave_TK, time zone (TZ), days in lactation (DOL), and body posture (BP) are identified as the four most critical factors in predicting CBT, and the interaction effects of IRTave_TK with other features such as body posture and time periods are unveiled. This study provides technological support for livestock management, facilitating the development and optimization of predictive models to implement timely and effective interventions, thereby maintaining the health and productivity of dairy cows.
Effect of Urolithin A on Bovine Sperm Capacitation and In Vitro Fertilization
Authors: Jorge Manuela , Ferreira Filipa C. , Marques Carla C. , Batista Maria C. , Oliveira Paulo J. , Lidon F. , Duarte Sofia C. , Teixeira José , Pereira Rosa M. L. N.
Published: September 2024

Reactive oxygen species (ROS) play a critical role in the functional competence of sperm cells. Conversely, excessive generation of ROS can impair sperm function, including their fertilization ability. Urolithin A (UA), a gut bacteria-derived metabolite produced from the transformation of ellagitannins, with anti-aging and antioxidant properties, was investigated for the first time in bovine sperm cells in the present study. Firstly, different doses of UA (0, 1, and 10 μM; 8–16 sessions) were used during the capacitation process of frozen-thawed bovine sperm. Sperm motility was assessed using optical microscopy and CASA. Sperm vitality (eosin-nigrosin), ROS, and ATP levels, as well as mitochondrial membrane potential (JC1) and oxygen consumption were evaluated. A second experiment to test the effect of different doses of UA (0, 1, and 10 μM; 9 sessions) in both the capacitation medium, as above, and the fertilization medium, was also implemented. The embryonic development and quality were evaluated. UA, at a concentration of 1 μM, significantly improved sperm movement quality (p < 0.03). There was a trend towards an increase in the oxygen consumption rate (OCR) of capacitated sperm with 1 μM and 10 μM UA supplementation. Moreover, an increase in ATP levels (p < 0.01) was observed, accompanied by a reduction in ROS levels at the higher UA concentration. These results suggest that UA may enhance spermatozoa mitochondrial function, modifying their metabolic activity while reducing the oxidative stress. Also, the number of produced embryos appears to be positively affected by UA supplementation, although differences between the bulls may have mitigated this effect. In conclusion, presented results further support previous findings indicating the potential therapeutic value of UA for addressing reproductive sub/infertility problems and improving ART outcomes. In addition, our results also reinforce the important bull effect on ART and that male sperm bioenergetic parameters should be used to predict spermatozoa functionality and developmental potential.
Preventing and Controlling Healthcare-Associated Infections: The First Principle of Every Antimicrobial Stewardship Program in Hospital Settings
Authors: Sartelli Massimo , Marini Corrado P. , McNelis John , Coccolini Federico , Rizzo Caterina , Labricciosa Francesco M. , Petrone Patrizio
Published: September 2024

Antimicrobial resistance (AMR) is one of the main public health global burdens of the 21st century, responsible for over a million deaths every year. Hospital programs aimed at improving antibiotic use, referred to as antimicrobial stewardship programs (ASPs), can both optimize the treatment of infections and minimize adverse antibiotics events including the development and spread of AMR. The challenge of AMR is closely linked to the development and spread of healthcare-associated infection (HAIs). In fact, the management of patients with HAIs frequently requires the administration of broader-spectrum antibiotic regimens due to the higher risk of acquiring multidrug-resistant organisms, which, in turn, promotes resistance. For this reason, even before using antibiotics correctly, it is necessary to prevent and control the spread of HAIs in our hospitals. In this narrative review, we present seven measures that healthcare workers, even if not directly involved in the tasks of infection prevention and control, must know, support, and embrace. We hope that this review may raise awareness among all healthcare professionals about the issues with the increasing rate of AMR and the ongoing efforts towards minimizing its rise.
Clinopodium gracile Alleviates Metabolic Dysfunction-Associated Steatotic Liver Disease by Upregulating Peroxisome Proliferator-Activated Receptor α and Inhibiting Mitochondrial Oxidative Damage
Authors: Ren Mingshi , Ren Jiayue , Zheng Jianmei , Sha Xiaotong , Lin Yining , Wu Feihua
Published: September 2024

The most prevalent chronic liver disease, known as metabolic dysfunction-associated steatotic liver disease (MASLD), is characterized by an excessive accumulation of lipids and oxidative damage. Clinopodium gracile, a natural herbal medicine widely used by Chinese folk, has antioxidative, anti-inflammatory, and lipid metabolism-regulating effects. Here, we explored the effect of C. gracile extract (CGE) on MASLD using palmitic acid (PA)-induced hepatocytes and high-fat diet (HFD)-fed mice. In vitro, CGE could promote fatty acid oxidation and inhibit fatty acid synthesis and uptake to reduce lipid accumulation by regulating PPARα activation. Moreover, CGE could inhibit reactive oxygen species production and maintain mitochondrial homeostasis in PA-induced HepG2 cells. In vivo, animal study results indicated that CGE could effectively reduce lipid metabolism disorder, inhibit oxidative stress, and upregulate PPARα protein in the liver of HFD-fed mice. Molecular docking results also showed that active compounds isolated from CGE had low binding energy and highly stable binding with PPARα. In summary, these findings reveal that CGE may be a potential therapeutic candidate for MASLD and act by upregulating PPARα to reduce lipid accumulation and suppress mitochondrial oxidative damage.
Comparative Study on Hepatoprotective Effects of Traditional Herbs, Roots of Angelica gigas Nakai, Glycyrrhiza uralensis Fischer, Zizyphus jujuba Mill., and Fruits of Paeonia lactiflora Pall., on Ethanol-Induced Liver Injury in Mice
Authors: Kim So-Yeon , Oh Kyung-Jin , Seo Yu-Ri , Kim Young-Woo , Song Phil Hyun , Song Chang-Hyun
Published: September 2024

Alcohol-associated liver disease (ALD) is a major cause of chronic liver disease, with few effective treatments besides alcohol abstinence. Angelicae Gigantis Radix (AG), Glycyrrhizae Radix et Rhizoma (GR), Paeoniae Radix (PR), and Zizyphi Fructus (ZF) are traditional herbs used to treat various ailments, including liver diseases. While several studies have reported the beneficial effects of GR on ALD, the effects of AG, PR, and ZF remain underexplored. Therefore, their efficacy and mechanisms against ALD were investigated using an alcohol-related liver injury model. The model was induced by ethanol gavage in C57BL/6J mice for 14 days, followed by oral administration of AG, GR, PR, and ZF one hour post-induction. The administration of these herbs reduced liver weight, and improved serum biomarkers of liver injury (ALT, AST, albumin). The herbs enhanced hepatic antioxidant capacity (GSH, SOD, catalase) and suppressed the production of proinflammatory cytokines (TNF-α, IL-1β) and apoptotic changes (caspase-3). The mechanisms of action involved lipid-lowering gene modulation through regulation of the cytochrome P450 2E1/Sirtuin 1/Nrf2 pathways. Histopathological and immunohistochemical analyses revealed that these herbs attenuated hepatocyte damage and steatosis via antioxidant, anti-inflammatory, and antiapoptotic effects. These findings suggest that traditional herbs, particularly AG, could be promising alternative therapies for treating ALD.
Plastic Pollution in the Aquatic Ecosystem of the High-Mountain Lake Markakol (Kazakhstan): First Observations and Conclusions
Authors: Madibekov Azamat , Ismukhanova Laura , Opp Christian , Sultanbekova Botakoz , Zhadi Askhat , Zhumatayev Serik , Madibekova Aisha
Published: September 2024

The primary data of micro- and macroplastics in the aquatic environment of Lake Markakol, located in the mountainous area of East Kazakhstan, are presented. The determination of micro- and macroplastics in water is based on sieving, drying, liquid oxidation, density separation and visual sorting using a microscope with a magnification of 40×. The detected plastic fragments in the aquatic environment include fishing line nets, Styrofoam balls, plastic bags, plastic bottles, wrappers, food labels and packages and other types of plastic waste. The sizes of the plastic fragments were ˃25 mm, 1.0–5.0 mm and 0.315–1.0 mm. The concentration of plastic in Lake Markakol was 837.5 µg/m3 in the tributaries and 482.1 µg/m3 in the lake water. The detected plastic mainly corresponded to sieve mesh sizes of 1.0–5.0 mm and 0.315–1.0 mm. The main sources of plastic pollution are fishing, tourism and the lack of adequate infrastructure for household waste management. These data emphasize the importance of measures to regulate plastic waste management in order to preserve the Lake Markakol ecosystem and maintain human health.
Impact of Kitchen Natural Gas Use on Indoor NO2 Levels and Human Health: A Case Study in Two European Cities
Authors: Barros Nelson , Fontes Tânia
Published: September 2024

Natural gas (NG) is commonly used in kitchens, powering stoves, ovens, and other appliances. While it is known for its efficiency and convenience, NG contributes to the release of nitrogen dioxide (NO2) and can have significant implications for human health. In this study, the importance of the use of NG in kitchens on human exposure to NO2 was analyzed. An extensive literature review in the field was conducted, and the NO2 levels were assessed in kitchens with NG cookers in Aveiro and electric cookers in Porto, both in Portugal. Higher levels of NO2 were found in kitchens in Aveiro, where NO2 levels outdoors are lower than in Porto. This pollutant can spread to other rooms, especially when ventilation is lacking, which is particularly concerning during colder seasons and at night. As around 70% of the time is spent at home, this can have a significant impact on human exposure to NO2. Therefore, although Aveiro has low levels of NO2 outdoors, its population may be exposed to much higher levels of this pollutant than the Porto population, a city with air quality issues, but predominantly using electric cookers. This finding emphasizes the need for the stricter regulation of NG use indoors to protect human health and also suggests a shift in human health protection policies from mere monitoring/control of outdoor air quality to a comprehensive assessment of human exposure, including exposure to indoor air quality.
Global Health Emergencies of Extreme Drought Events: Historical Impacts and Future Preparedness
Authors: Mani Zakaria A. , Khorram-Manesh Amir , Goniewicz Krzysztof
Published: September 2024

This study examines the global health implications of extreme drought events from 2000 to 2023. Utilizing data from the International Disaster Database (EM-DAT), we analyzed the number of people affected and the total deaths attributed to drought. Our findings reveal that over 1.6 billion people have been impacted by drought globally, with Southern Asia and Sub-Saharan Africa being the most severely affected regions. India and China account for a significant portion of the affected population, with 688.2 million and 327.35 million impacted people, respectively. Drought-related mortality has also been substantial, with over 24,000 deaths recorded globally, including more than 20,000 in Somalia alone. The study highlights the uneven distribution of drought impacts, underscoring the need for targeted interventions and comprehensive drought preparedness strategies. Our analysis also reveals the critical role of socio-economic factors in exacerbating the health impacts of drought, particularly in regions with inadequate healthcare infrastructure and limited access to resources. This study provides novel insights into the specific health impacts of drought, including the correlation between drought frequency and mortality rates, and offers actionable recommendations for improving future emergency responses and health system preparedness. These recommendations are tailored to address the unique challenges faced by the most vulnerable regions, emphasizing the importance of context-specific strategies to enhance resilience against the growing threat of climate-induced droughts.