bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge, attitude, and practice toward sleep hygiene and cardiovascular health: a cross-sectional survey among healthcare workers
Authors: Sun Weixin , Pan Renyou , Song Xiaolong , Gu Tingting , Ni Qimeng , Gu Yuexing
Published: October 2024

Background

Healthcare workers grapple with distinct challenges, inherent to their profession, making them susceptible to irregular sleep patterns and insufficient sleep, which may further impact their cardiovascular health. This study aimed to investigate the knowledge, attitude and practice (KAP) of healthcare workers concerning sleep hygiene and cardiovascular health.

Methods

A cross-sectional survey was conducted at Yancheng TCM Hospital Affiliated with Nanjing University of Chinese Medicine between July, 2023 and September, 2023. Demographic characteristics, KAP scores, sleep health awareness and habits, and sleep quality were assessed through the questionnaires.

Results

A total of 423 valid questionnaires were included in the study. Among these, 215 (50.83%) were aged 35 or below, and 128 (30.26%) reported suffering from chronic insomnia. The mean knowledge, attitude, and practice scores were 43.23 ± 5.67 (possible range: 10–50), 24.53 ± 4.59 (possible range: 9–45), and 20.22 ± 4.46 (possible range: 6–30), respectively. Multivariate analyses indicated that attitude score (OR = 0.890, 95% CI: [0.807–0.981], p = 0.019), sleep health awareness & habits score (OR = 1.847, 95% CI: [1.284–2.656], p = 0.001), experienced chest tightness or heart palpitations (OR = 6.084, 95% CI: [2.172–17.042], p = 0.001), and the presence of chronic insomnia (OR = 5.017, 95% CI: [2.428–10.368], p < 0.001) were independently associated with sleep quality.

Conclusion

Healthcare workers had adequate knowledge, negative attitude and moderate practice toward sleep hygiene and cardiovascular health. The findings highlight the need for targeted interventions to address the observed gaps in attitudes and practices, aiming to enhance overall awareness and promote healthier sleep habits among healthcare professionals.
Investigation of hospital-acquired infections prevalence and analysis of influencing factors: a case study of a specialized infectious disease hospital in Chongqing, 2017–2023
Authors: Deng Bing , Li Peilin , Liu Yalan , Xie Juan , Huang Yaling , Sun Qingyun , Su Shifang , Deng Wenwen
Published: October 2024

Objective

This study aimed to investigate the prevalence of hospital-acquired infections (HAIs) and their main influencing factors in a specialized infectious disease hospital in Chongqing from 2017 to 2023, providing reference for the formulation of precise infection control measures.

Methods

A retrospective cross-sectional survey method was employed, combining bedside investigations with medical record reviews. Surveys were conducted on all hospitalized patients on a certain day of the last week of October each year from 2017 to 2023. Data collected included patients’ basic information, diagnosis, and hospital infection status. Statistical analysis, including retrospective case–control and multivariable logistic regression analysis, was performed to identify the risk factors for hospital infections.

Results

The investigation compliance rate for the prevalence of HAIs in the specialized infectious disease hospital in Chongqing from 2017 to 2023 was greater than 96% each year. The prevalence rate of HAIs ranged from 0.89 to 2.52%. Hospital infection departments were mainly concentrated in general internal medicine, tuberculosis, and HIV/AIDS departments, accounting for 31.25, 26.25, and 23.75%, respectively. The most common infection site was the lower respiratory tract (54.22%), followed by bloodstream and urinary tract infections, each accounting for 9.64%. The predominant pathogens of hospital infections were Klebsiella pneumoniae and fungi. The utilization rate of antimicrobial drugs ranged from 20.75 to 33.25%, primarily for monotherapy. The rate of pathogen testing for therapeutic antimicrobial drug use was 82.84%, meeting national requirements. Multivariable logistic regression analysis revealed that the use of antibiotic (OR = 7.46, 95%CI 2.54–21.89, p < 0.001) and the presence of cardiovascular diseases (OR = 26.69, 95%CI 6.69–106.54, p < 0.001) increased the risk of HAIs.

Conclusion

The prevalence of HAIs in specialized infectious disease hospitals remains stable, primarily concentrated in departments such as general internal medicine, tuberculosis, and HIV/AIDS. The lower respiratory tract is the main infection site, and comorbid cardiovascular diseases and antibiotic use are risk factors for HAIs. Therefore, to reduce the risk of hospital infections, it is necessary to strengthen the daily monitoring of key departments and the care of key patients. Further implementation of precise and effective infection control measures, including rational antibiotic use, regular infection monitoring and pathogen culture is warranted.
Association of physical activity, screen time and sleep with substance use in children and adolescents: a large sample cross-sectional study
Authors: Su Huadong , Lyu Dongye , Huang Ke , Yan Jin
Published: October 2024

Background

24-h movement guidelines (24-HMG) play an important role in various demographics such as early years, children, youth, and the older adult. Nevertheless, most existing research exploring the links between socioeconomic factors, dietary intake, and substance use with sleep patterns, physical activity (PA), and sedentary behavior (SB) has been conducted in high-income Western countries.

Purpose

Hence, this study seeks to investigate the relationship between adherence to the 24-HMG and smoking and alcohol use behaviors among children and adolescents in China.

Methods

A comprehensive survey, in collaboration with the Municipal Education Commission, was carried out across primary and middle schools in Shenzhen, China. Sleep duration was gauged using the Pittsburgh Sleep Quality Index (PSQI), screen time was assessed with items adapted from the Health Behavior of School-aged Children (HBSC) survey, and PA was measured using a single item adapted from the HBSC survey. Results were presented as odds ratios (ORs) with 95% confidence intervals (CIs), considering p-values below 0.05 as statistically significant.

Results

Among the sample, 51.9% were boys and 48% were girls. Those who did not meet any guidelines had a higher probability of smoking (OR = 1.62 [95% CI: 1.03, 2.56], p = 0.037) among children and adolescents. Conversely, meeting one (OR = 0.94 [95% CI: 0.61, 1.52], p = 0.874) or two guidelines (OR = 0.84 [95% CI: 0.52, 1.34], p = 0.459) showed no significant impact. The data displayed an inverse correlation between the number of guidelines adhered to and the likelihood of alcohol use among children and adolescents: none (OR = 2.07, p < 0.001), one guideline (OR = 1.40, p = 0.006), and two guidelines (OR = 1.22, p = 0.106).

Conclusion

Not meeting guidelines elevates smoking and alcohol use risks in children and adolescents, whereas following more guidelines lowers these risks, highlighting the importance of guideline adherence in reducing substance use.
A hybrid self-supervised model predicting life satisfaction in South Korea
Authors: Nguyen Hung Viet , Byeon Haewon
Published: October 2024

Objective

Life satisfaction pertains to an individual’s subjective evaluation of their life quality, grounded in their personal criteria. It stands as a crucial cognitive aspect of subjective wellbeing, offering a reliable gauge of a person’s comprehensive wellbeing status. In this research, our objective is to develop a hybrid self-supervised model tailored for predicting individuals’ life satisfaction in South Korea.

Methods

We employed the Busan Metropolitan City Social Survey Data in 2021, a comprehensive dataset compiled by the Big Data Statistics Division of Busan Metropolitan City. After preprocessing, our analysis focused on a total of 32,390 individuals with 51 variables. We developed the self-supervised pre-training TabNet model as a key component of this study. In addition, we integrated the proposed model with the Local Interpretable Model-agnostic Explanation (LIME) technique to enhance the ease and intuitiveness of interpreting local model behavior.

Results

The performance of our advanced model surpassed conventional tree-based ML models, registering an AUC of 0.7778 for the training set and 0.7757 for the test set. Furthermore, our integrated model simplifies and clarifies the interpretation of local model actions, effectively navigating past the intricate nuances of TabNet’s standard explanatory mechanisms.

Conclusion

Our proposed model offers a transparent understanding of AI decisions, making it a valuable tool for professionals in the social sciences and psychology, even if they lack expertise in data analytics.
Can salivary and skin microbiome become a biodetector for aging-associated diseases? Current insights and future perspectives
Authors: Nurkolis Fahrul , Utami Trianna Wahyu , Alatas Aiman Idrus , Wicaksono Danar , Kurniawan Rudy , Ratmandhika Satria Rafi , Sukarno Kartika Taufani , Pahu Yehezkiel Gian Pradipta , Kim Bonglee , Tallei Trina Ekawati , Tjandrawinata Raymond Rubianto , Alhasyimi Ananto Ali , Surya Reggie , Helen Helen , Halim Princella , Muhar Adi Muradi , Syahputra Rony Abdi
Published: October 2024

Growth and aging are fundamental elements of human development. Aging is defined by a decrease in physiological activities and higher illness vulnerability. Affected by lifestyle, environmental, and hereditary elements, aging results in disorders including cardiovascular, musculoskeletal, and neurological diseases, which accounted for 16.1 million worldwide deaths in 2019. Stress-induced cellular senescence, caused by DNA damage, can reduce tissue regeneration and repair, promoting aging. The root cause of many age-related disorders is inflammation, encouraged by the senescence-associated secretory phenotype (SASP). Aging’s metabolic changes and declining immune systems raise illness risk via promoting microbiome diversity. Stable, individual-specific skin and oral microbiomes are essential for both health and disease since dysbiosis is linked with periodontitis and eczema. Present from birth to death, the human microbiome, under the influence of diet and lifestyle, interacts symbiotically with the body. Poor dental health has been linked to Alzheimer’s and Parkinson’s diseases since oral microorganisms and systemic diseases have important interactions. Emphasizing the importance of microbiome health across the lifetime, this study reviews the understanding of the microbiome’s role in aging-related diseases that can direct novel diagnosis and treatment approaches.
Bye-bye, bye advantage: estimating the competitive impact of rest differential in the National Football League
Authors: Lopez Michael J. , Bliss Thompson
Published: October 2024

The National Football League (NFL) sets its regular season schedule to optimize viewership and minimize competitive inequities. One inequity assumed to impact team performance is rest differential, defined as the relative number of days between games. Using Bayesian state space models on both game outcomes and betting market data, we estimate the competitive effect of rest differential in American football. We find that the most commonly referred to inequities—both the bye week rest advantage and the mini-bye week rest advantage— currently show no significant evidence of providing the rested team a competitive edge. Further, we trace a decline in the advantage of a bye week to a 2011 change to the NFL's Collective Bargaining Agreement, which represents a natural experiment to test the relevance of rest and preparation in football. Prior to the agreement, NFL teams off a bye week received a significant advantage (+2.2 points per game), but since 2011, that benefit has been mitigated. Our findings imply that extra days with practice time, and not extra days off alone, are the primary driver of any NFL rest advantage.
The Mellow Babies parenting programme: role of group processes and interpersonal change mechanisms
Authors: Tanner Jessica , Wilson Philip , Wight Daniel , Thompson Lucy
Published: October 2024

Introduction

Group-based parenting programmes have specific mechanisms of change compared to individual delivery. The Mechanisms of Action in Group-based Interventions framework (MAGI); distinguishes between interpersonal and intrapersonal mechanisms of change. This paper articulates a theory of change for Mellow Babies, a 14-week attachment-based group parenting programme for mothers of infants aged under 18 months, identifying the inter and intrapersonal change processes.

Methods

Thirty-two semi-structured interviews were conducted with mothers and practitioners who participated in Mellow Babies, including twenty post-group interviews and nine telephone fidelity checks. Data were analysed using Deductive Qualitative Analysis based on the components identified within the MAGI framework.

Results

Key interpersonal change mechanisms included: 1. Normalisation through social comparisons; 2. Validation and cognitive reframing through group feedback; 3. Peer support, offering accountability for the implementation of new habits, and providing opportunities to give and receive advice; and 4. Social and experiential learning, including internalisation of group responses leading to increased self-compassion. Intrapersonal change mechanisms were: 1. Developing new self-insight, including parenting self-awareness; 2. Increasing parenting knowledge and understanding of infant development; 3. Having time and space for self; 4. Motivation to implement new habits. Interpersonal change mechanisms had a moderating role on intrapersonal change mechanisms and subsequent programme outcomes.

Discussion

The contribution of group processes and interpersonal mechanisms of change are often overlooked within programme evaluations. Findings from this study implicate their mediating role on intrapersonal change mechanisms and subsequent programme outcomes. It is important for programme deliverers and evaluators to understand the interrelationships between group processes, change mechanisms and programme outcomes to optimise efficacy and ensure cross-contextual replicability.
Machine learning algorithms applied for drone detection and classification: benefits and challenges
Authors: Mrabet Manel , Sliti Maha , Ammar Lassaad Ben
Published: October 2024

In recent years, the increasing use of drones for both commercial and recreational purposes has led to heightened concerns regarding airspace safety. To address these issues, machine learning (ML) based drone detection and classification have emerged. This study explores the potential of ML-based drone classification, utilizing technologies like radar, visual, acoustic, and radio-frequency sensing systems. It undertakes a comprehensive examination of the existing literature in this domain, with a focus on various sensing modalities and their respective technological implementations. The study indicates that ML-based drone classification is promising, with numerous successful individual contributions. It is crucial to note, however, that much of the research in this field is experimental, making it difficult to compare results from various articles. There is also a noteworthy lack of reference datasets to help in the evaluation of different solutions.
Stakeholder perceptions of physical literacy: results from a national consultation in England
Authors: Duncan Michael J. , Essiet Inimfon , Hurter Liezel , Roberts William M. , Lewis Kiara , Goss Hannah , Morris Jade L. , Bingham Daniel D. , O’Brien Wesley , Barnett Lisa M. , Shearer Cara , Daly-Smith Andrew , Foweather Lawrence
Published: October 2024

Background

There is a lack of evidence of stakeholder perspective and understanding of physical literacy among relevant stakeholders from England. As part of research commissioned by Sport England to develop a physical literacy consensus statement for England, this study presents findings from the first national consultation with stakeholders in England.

Methods

One hundred and ninety-three individual stakeholders (50.3%) from education, community sport, national governing bodies of sport, physical activity and sport coaching sectors completed an online survey consisting of fixed item and open ended questions designed to examine their knowledge, understanding, perceptions and practices relating to physical literacy.

Results

Responses from stakeholders suggested there was confusion in use of the term physical literacy in practice and confusion regarding the definition of physical literacy. Most respondents suggested they were involved in physical literacy related activity and understood the term. However, when probed the physical literacy related activity they referred to was likely not actually physical literacy related. Understanding of the term physical literacy was inconsistent in general. Stakeholders considered the affective, social, physical and cognitive areas (domains) of learning to be most important for developing a positive relationship with movement, sport and physical activity for life.

Conclusions

While stakeholders are aware of the term “physical literacy” and hold value of it within their practice, there remain key misconceptions relating to what physical literacy is, and debate as to whether any existing definitions truly capture the construct of physical literacy.
The Adjusting Effects of Trees on Cfa-Climate Campus Acoustic Environments and Thermal Comforts in the Summer
Authors: Lu Wen , Chen Yanyi , Zhou Tianru , Zhang Jian , Xiao Aoyan , Zhu Feng , Yin Hui , Liu Ting
Published: October 2024

This study explores the effects of trees on the acoustic and thermal environment in addition to people’s responses to trees in different contexts. Through field measurements conducted during the summer of 2023 at the campus of the Southwest University of Science and Technology in Mianyang, residents’ neutral points were locally found to be 52.2 dBA (acoustic) and 23.8 °C (thermal). Further, at their maximum, the trees were able to reduce heat stress by 4 °C (indicated by the physiologically equivalent temperature—PET) and the noise level by 10 dBA (indicated by the A-weighted sound pressure—LAeq); this was achieved by trees with a crown diameter of 20 m. Subjective acoustic and thermal responses varied depending on the context. Acoustically, their neutral LAeq values toward the sounds of traffic, teaching, sports, and daily life were 46.9, 52.5, 51.0, and 52.7 dBA, respectively. Thermally, pedestrians’ neutral PET values were 24.2, 26.1, 22.3, and 25.1 °C, respectively, under the same conditions. These phenomena might be a consequence of the effects of sound frequencies. Future urban forestry research should focus on planting for environmental quality improvement.
Southern South American Long-Distance Pollen Dispersal and Its Relationship with Atmospheric Circulation
Authors: Pérez Claudio F. , Ulke Ana G. , Gassmann María I.
Published: October 2024

This paper addresses the study of synoptic-scale meteorological conditions that favor long-range pollen transport in southern South America combining airborne pollen counts, modeled three-dimensional backward trajectories, and synoptic and surface meteorological data. Alnus pollen transport trajectories indicate origins predominantly in montane forests of the Yungas between 1500 and 2800 m altitude. The South American Low-Level Jet is the main meteorological feature that explains 64% of the detected pollen arrival at the target site. Podocarpus and Nothofagus pollen instead are linked primarily to the widespread Subantartic forests in southern Patagonia. Their transport patterns are consistent with previous studies, which show an association with synoptic patterns related to cold front passages carrying pollen in the free atmosphere (27% for Nothofagus and 25% for Podocarpus). These results show the significance of understanding long-distance pollen transport for disciplines such as climate change reconstruction and agriculture, emphasizing the need for further research to refine atmospheric circulation models and refine interpretations of past vegetation and climate dynamics.
The Role of Geographical Indication Products in Promoting Agricultural Development—A Meta-Analysis Based on Global Data
Authors: Li Chunyan , Ban Qi , Gao Jianmei , Ge Lanqing , Xu Rui
Published: October 2024

As an intellectual property product that is highly farmer-friendly, geographical indication (GI) products have always garnered significant attention. In recent years, research on how GI products promote agricultural development has been increasing, yet the academic community remains divided on this issue. On one hand, some studies point out that GI products can drive agricultural development; on the other hand, other studies suggest that the impact of GI products is not significant or varies. Meta-analysis is a method that leverages statistical techniques to integrate the findings of multiple studies with a common research objective, addressing controversial issues and arriving at generalizable conclusions. Therefore, to more precisely uncover the intrinsic relationship between GI products and agricultural development and to delve deeper into the root causes of the aforementioned discrepancies, this study employed a meta-analytic approach. We extracted 478 correlation coefficients (r) as effect sizes from 82 empirical articles worldwide. Using these coefficients, we calculated the overall effect size and moderating effects of GI products on promoting agricultural development. Research indicates that GI products exert a positive influence on agricultural development. There is a low positive correlation between the two (r = 0.197). Further analysis reveals that various factors at the sample, data, literature, and methodology levels all impact the outcomes of GI products’ promotion of agricultural development. Research has shown that, in pursuit of sustainable agricultural development goals, it is further recommended that governments should accord high priority to the cultivation and development of GI products. This is aimed at providing practical insights to facilitate the sustainable advancement of GI products and bolster agricultural competitiveness.
Can Different Cultivars of Panicum maximum Be Identified Using a VIS/NIR Sensor and Machine Learning?
Authors: Difante Gelson dos Santos , Monteiro Gabriela Oliveira de Aquino , Santana Juliana Caroline Santos , Frontado Néstor Eduardo Villamizar , Rodrigues Jéssica Gomes , Chaves Aryadne Rhoana Dias , Santana Dthenifer Cordeiro , Oliveira Izabela Cristina de , Ítavo Luis Carlos Vinhas , Baio Fabio Henrique Rojo , Oliveira Gabriela Souza , Silva Junior Carlos Antonio da , Longhini Vanessa Zirondi , Dias Alexandre Menezes , Teodoro Paulo Eduardo , Teodoro Larissa Pereira Ribeiro
Published: October 2024

Panicum maximum cultivars have distinct characteristics, especially morphological ones related to the leaf structure and coloration, and there may be differences in the spectral behavior captured by sensors. These differences can be used in classification using machine learning (ML) algorithms to differentiate biodiversity within the same species. The objectives of this study were to identify ML models able to differentiate P. maximum cultivars and determine which is the best spectral input for these algorithms and whether reducing the sample size improves the response of the algorithms. The experiment was carried out at the experimental area of the Forage Sector of the School Farm belonging to the Federal University of Mato Grosso do Sul (UFMS). The leaf samples of the cultivars Massai, Mombaça, Tamani, Quênia, and Zuri were collected from experimental plots in the field. Analysis was carried out on 120 leaf samples from the P. maximum cultivars using a VIS/NIR hyperspectral sensor. After obtaining the spectral data and separating them into bands, the data were submitted for ML analysis to classify the cultivars based on the spectral variables. The algorithms tested were artificial neural networks (ANNs), REPTree and J48 decision trees, random forest (RF), and support vector machine (SVM). A logistic regression (LR) was used as a traditional classification method. Two input models were evaluated in the algorithms: the entire spectrum band provided by the sensor (ALL) and another input configuration using the calculated bands. The reflectances from the P. maximum cultivars showed different behavior, especially in the green and NIR regions. RL and ANN algorithms using all information in the spectrum are able to accurately classify the cultivars, reaching accuracies above 70 for CC and above 0.6 for kappa and F-score. VIS/NIR leaf reflectance can be a powerful tool for low-cost, non-destructive, and high-performance analysis to distinguish P. maximum cultivars. Here, we achieved better model accuracy using only 40 leaf samples. In the present study, the J48 decision tree model proved to have good classification performance regardless of the sample size used, which makes it a strategic model for forage cultivar classification studies in smaller or larger datasets.
Non-Destructive Measurement of Rice Spikelet Size Based on Panicle Structure Using Deep Learning Method
Authors: Deng Ruoling , Liu Weisen , Liu Haitao , Liu Qiang , Zhang Jing , Hou Mingxin
Published: October 2024

Rice spikelet size, spikelet length and spikelet width, are very important traits directly related to a rice crop’s yield. The accurate measurement of these parameters is quite significant in research such as breeding, yield evaluation and variety improvement for rice crops. Traditional measurement methods still mainly rely on manual labor, which is time-consuming, labor-intensive and error-prone. In this study, a novel method, dubbed the “SSM-Method”, based on convolutional neural network and traditional image processing technology has been developed for the efficient and precise measurement of rice spikelet size parameters on rice panicle structures. Firstly, primary branch images of rice panicles were collected at the same height to build an image database. The spikelet detection model using convolutional neural network was then established for spikelet recognition and localization. Subsequently, the calibration value was obtained through traditional image processing technology. Finally, the “SSM-Method” integrated with a spikelet detection model and calibration value was developed for the automatic measurement of spikelet sizes. The performance of the developed SSM-Method was evaluated through testing 60 primary branch images. The test results showed that the root mean square error (RMSE) of spikelet length for two rice varieties (Huahang15 and Qingyang) were 0.26 mm and 0.30 mm, respectively, while the corresponding RMSE of spikelet width was 0.27 mm and 0.31 mm, respectively. The proposed algorithm can provide an effective, convenient and low-cost tool for yield evaluation and breeding research.
Optimal Methods for Estimating Shortwave and Longwave Radiation to Accurately Calculate Reference Crop Evapotranspiration in the High-Altitude of Central Tibet
Authors: Liu Jiandong , Du Jun , Wang Fei , Liu De Li , Tang Jiahui , Lin Dawei , Tang Yahui , Shi Lijie , Yu Qiang
Published: October 2024

The FAO56 Penman–Monteith model (FAO56-PM) is widely used for estimating reference crop evapotranspiration (ET0). However, key variables such as shortwave radiation (Rs) and net longwave radiation (Rln) are often unavailable at most weather stations. While previous studies have focused on calibrating Rs, the influence of large Rln, particularly in high-altitude regions with thin air, remains unexplored. This study investigates this issue by using observed data from Bange in central Tibet to identify the optimal methods for estimating Rs and Rln to accurately calculate ET0. The findings reveal that the average daily Rln was 8.172 MJ m−2 d−1 at Bange, much larger than that at the same latitude. The original FAO56-PM model may produce seemingly accurate ET0 estimates due to compensating errors: underestimated Rln offsetting underestimated net shortwave radiation (Rsn). Merely calibrating Rs does not improve ET0 accuracy but may exacerbate errors. The Liu-S was the empirical model for Rs estimation calibrated by parameterization over the Tibetan Plateau and the Allen-LC was the empirical model for Rln estimation calibrated by local measurements in central Tibet. The combination of the Liu-S and Allen-LC methods showed much-improved performance in ET0 estimation, yielding a high Nash–Sutcliffe Efficiency (NSE) of 0.889 and a low relative error of −5.7%. This strategy is indicated as optimal for ET0 estimation in central Tibet. Trend analysis based on this optimal strategy indicates significant increases in ET0 in central Tibet from 2000 to 2020, with projections suggesting a continued rise through 2100 under climate change scenarios, though with increasing uncertainty over time. However, the rapidly increasing trends in precipitation will lead to decreasing trends in agricultural water use for highland parley production in central Tibet under climate change scenarios. The findings in this study provide critical information for irrigation planning to achieve sustainable agricultural production over the Tibetan Plateau.
Identification and Location Method of Bitter Gourd Picking Point Based on Improved YOLOv5-Seg
Authors: Jiang Sheng , Wei Yechen , Lyu Shilei , Yang Hualin , Liu Ziyi , Xie Fangnan , Ao Jiangbo , Lu Jingye , Li Zhen
Published: October 2024

Aiming at the problems of small stems and irregular contours of bitter gourd, which lead to difficult and inaccurate location of picking points in the picking process of mechanical arms, this paper proposes an improved YOLOv5-seg instance segmentation algorithm with a coordinate attention (CA) mechanism module, and combines it with a refinement algorithm to identify and locate the picking points of bitter gourd. Firstly, the improved algorithm model was used to identify and segment bitter gourd and melon stems. Secondly, the melon stem mask was extracted, and the thinning algorithm was used to refine the skeleton of the extracted melon stem mask image. Finally, a skeleton refinement graph of bitter gourd stem was traversed, and the midpoint of the largest connected region was selected as the picking point of bitter gourd. The experimental results show that the prediction precision (P), precision (R) and mean average precision (mAP) of the improved YOLOv5-seg model in object recognition were 98.04%, 97.79% and 98.15%, respectively. Compared with YOLOv5-seg, the P, R and mA values were increased by 2.91%, 4.30% and 1.39%, respectively. In terms of object segmentation, mask precision (P(M)) was 99.91%, mask recall (R(M)) 99.89%, and mask mean average precision (mAP(M)) 99.29%. Compared with YOLOv5-seg, the P(M), R(M), and mAP(M) values were increased by 6.22%, 7.81%, and 5.12%, respectively. After testing, the positioning error of the three-dimensional coordinate recognition of bitter gourd picking points was X-axis = 7.025 mm, Y-axis =5.6135 mm, and Z-axis = 11.535 mm, and the maximum allowable error of the cutting mechanism at the end of the picking manipulator was X-axis = 30 mm, Y-axis = 24.3 mm, and Z-axis = 50 mm. Therefore, this results of study meet the positioning accuracy requirements of the cutting mechanism at the end of the manipulator. The experimental data show that the research method in this paper has certain reference significance for the accurate identification and location of bitter gourd picking points.
Digital Technologies Impact on Healthcare Delivery: A Systematic Review of Artificial Intelligence (AI) and Machine-Learning (ML) Adoption, Challenges, and Opportunities
Authors: Okwor Ifeanyi Anthony , Hitch Geeta , Hakkim Saira , Akbar Shabana , Sookhoo Dave , Kainesie John
Published: October 2024

Recent significant advances in the healthcare industry due to artificial intelligence (AI) and machine learning (ML) have been shown to revolutionize healthcare delivery by improving efficiency, accuracy, and patient outcomes. However, these technologies can face significant challenges and ethical considerations. This systematic review aimed to gather and synthesize the current knowledge on the impact of AI and ML adoption in healthcare delivery, with its associated challenges and opportunities. This study adhered to the PRISMA guidelines. Articles from 2014 to 2024 were selected from various databases using specific keywords. Eligible studies were included after rigorous screening and quality assessment using checklist tools. Themes were identified through data analysis and thematic analysis. From 4981 articles screened, a data synthesis of nine eligible studies revealed themes, including productivity enhancement, improved patient care through decision support and precision medicine, legal and policy challenges, technological considerations, organizational and managerial aspects, ethical concerns, data challenges, and socioeconomic implications. There exist significant opportunities, as well as substantial challenges and ethical concerns, associated with integrating AI and ML into healthcare delivery. Implementation strategies must be carefully designed, considering technical, ethical, and social factors.
Urinary Neutrophil Gelatinase-Associated Lipocalin as a Predictor of COVID-19 Mortality in Hospitalized Patients
Authors: Švitek Luka , Zlosa Mihaela , Grubišić Barbara , Kralik Kristina , Perić Nora , Berišić Bernarda , Lišnjić Dubravka , Mandić Sanja
Published: October 2024

Neutrophil gelatinase-associated lipocalin (NGAL) is a protein secreted by activated neutrophils and certain tissues. The aim of this study was to investigate the prognostic role of urinary neutrophil gelatinase-associated lipocalin (uNGAL) in hospitalized COVID-19 patients with regard to mortality. The prospective observational cohort study involved 86 hospitalized adult COVID-19 patients. Patients’ urine samples were collected upon admission and 48–72 h after admission. General anamnesis and clinical status information were obtained from medical records. Within 24 h of sample collection, urine aliquots were centrifuged, the supernatant was separated, and samples were frozen at −80 °C until uNGAL concentration measurement, which was performed within two years of sample collection. The patients were categorized into groups based on disease outcome (survived/deceased). Data analysis was performed to identify predictive factors and diagnostic indicators for the unfavorable outcome group using logistic regression and ROC curve methods. Logistic regression associated age, cardiomyopathy, invasive mechanical ventilation, and uNGAL concentration (follow-up sampling) with mortality. ROC analysis identified uNGAL concentration (follow-up sampling) as an indicator of mortality, with a cut-off value of >23.8 ng/mL. This study concludes that there is an association between disease outcome and uNGAL concentration in COVID-19 patients. However, understanding the dynamics of uNGAL concentration during COVID-19 remains limited, which is crucial for its effective clinical application.
Assessing Solutions for Resilient Dairy Farming in Europe
Authors: Kuipers Abele , Østergaard Søren , Loges Ralf , Zijlstra Jelle , Brocard Valerie
Published: October 2024

The objective of the EU project Resilience for Dairy (R4D) is to develop and strengthen a self-sustainable Thematic Network on resilient dairy farms in 15 European countries. This article focusses on those solutions (practices and techniques) that are assessed contributing to a resilient dairy farming sector. The opinions of experts, farmers, and stakeholders were collected and scored through surveys and in a series of local workshops. Six key contributing knowledge fields are included: economic and social resilience, technical efficiency, environment, animal welfare and health, and societal perception. Assessing these knowledge fields proved to be a good predictor for measuring resilience. Only the impact fields of animal welfare and health and societal perception overlapped each other in response. This study shows differences in the choice of solutions across Europe. Experts from South and East Europe are more positive about the contribution of solutions to resilience than their colleagues from North and West Europe, except for social life items. Expert and farmer/stakeholder opinions differ regarding several of the solutions. Technical efficiency is a leading strategy. Priority topics of interest are communication with society, renewable energy production, strategic hoof trimming, early detection of diseases, monitoring fertility and health, and calf rearing. Besides resilience, attractiveness and readiness of the solutions were also assessed.
Extreme-Phenotype Genome-Wide Association Analysis for Growth Traits in Spotted Sea Bass (Lateolabrax maculatus) Using Whole-Genome Resequencing
Authors: Zhou Zhaolong , Shao Guangming , Shen Yibo , He Fengjiao , Tu Xiaomei , Ji Jiawen , Ao Jingqun , Chen Xinhua
Published: October 2024

Spotted sea bass (Lateolabrax maculatus) is an important marine economic fish in China, ranking third in annual production among marine fish. However, a declined growth rate caused by germplasm degradation has severely increased production costs and reduced economic benefits. There is an urgent need to develop the fast-growing varieties of L. maculatus and elucidate the genetic mechanisms underlying growth traits. Here, whole-genome resequencing technology combined with extreme phenotype genome-wide association analysis (XP-GWAS) was used to identify candidate markers and genes associated with growth traits in L. maculatus. Two groups of L. maculatus, consisting of 100 fast-growing and 100 slow-growing individuals with significant differences in body weight, body length, and carcass weight, underwent whole-genome resequencing. A total of 4,528,936 high-quality single nucleotide polymorphisms (SNPs) were used for XP-GWAS. These SNPs were evenly distributed across all chromosomes without large gaps, and the average distance between SNPs was only 175.8 bp. XP-GWAS based on the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (Blink) and Fixed and random model Circulating Probability Unification (FarmCPU) identified 50 growth-related markers, of which 17 were related to body length, 19 to body weight, and 23 to carcass weight. The highest phenotypic variance explained (PVE) reached 15.82%. Furthermore, significant differences were observed in body weight, body length, and carcass weight among individuals with different genotypes. For example, there were highly significant differences in body weight among individuals with different genotypes for four SNPs located on chromosome 16: chr16:13133726, chr16:13209537, chr16:14468078, and chr16:18537358. Additionally, 47 growth-associated genes were annotated. These genes are mainly related to the metabolism of energy, glucose, and lipids and the development of musculoskeletal and nervous systems, which may regulate the growth of L. maculatus. Our study identified growth-related markers and candidate genes, which will help to develop the fast-growing varieties of L. maculatus through marker-assisted breeding and elucidate the genetic mechanisms underlying the growth traits.
In Vivo Evaluation of Demineralized Bone Matrix with Cancellous Bone Putty Formed Using Hydroxyethyl Cellulose as an Allograft Material in a Canine Tibial Defect Model
Authors: Yoo Donghyeok , Oh Minha , Kim Minkyung , Lee Dongbin
Published: October 2024

Demineralized bone matrix (DBM) is a widely used allograft material for bone repair, but its handling properties and retention at defect sites can be challenging. Hydroxyethyl cellulose (HEC) has shown promise as a biocompatible carrier for bone graft materials. This study aimed to evaluate the efficacy of DBM combined with cancellous bone putty formed using HEC as an allograft material for bone regeneration in a canine tibial defect model. Experiments were conducted using dogs with proximal tibial defects. Four groups were compared: empty (control group), DBM + HEC (DH), DBM + cancellous bone + HEC (DCH), and DBM + cancellous bone + calcium phosphate + HEC (DCCH). Radiographic, micro-computed tomography (CT), and histomorphometric evaluations were performed 4 and 8 weeks postoperatively to assess bone regeneration. The Empty group consistently exhibited the lowest levels of bone regeneration throughout the study period, indicating that DBM and cancellous bone with HEC significantly enhanced bone regeneration. At week 4, the DCCH group showed the fastest bone regeneration on radiography and micro-computed tomography. By week 8, the DCH group showed the highest area ratio of new bone among all experimental areas, followed by the DH and DCCH groups. This study demonstrated that HEC significantly enhances the handling, mechanical properties, and osteogenic potential of DBM and cancellous bone grafts, making it a promising carrier for clinical applications in canine allograft models. When mixed with allograft cancellous bone, which has high porosity and mechanical strength, it becomes a promising material offering a more effective and reliable option for bone repair and regeneration.
Insights into the Gut Microbial Diversity of Wild Siberian Musk Deer (Moschus moschiferus) in Republic of Korea
Authors: Kim Nari , Do Kyung-Hyo , Cho Chea-Un , Seo Kwang-Won , Jeong Dong-Hyuk
Published: October 2024

The gut microbiota plays a crucial role in the health and well-being of wildlife. However, its composition and diversity remain unexplored, particularly in threatened species such as the Siberian musk deer (SMD). This study aimed to elucidate the gut microbiota composition within different wild SMD communities for assessing their health status. We conducted the first comprehensive fecal microbiome analysis of wild SMD inhabiting three distinct locations in Gangwon Province, Republic of Korea (Korea). Fecal samples were collected non-invasively and 16S rRNA gene sequencing was performed for gut microbiota characterization. Consistent with previous research, Firmicutes and Bacteroidetes were the dominant phyla in the gut microbiota of wild SMD. Planctomycetota was a prevalent phylum in wild SMD gut microbiota, warranting further investigation of its ecological significance. While significant differences were observed in the gut microbiota richness among the three groups, no significant disparities were detected in the beta diversity. Additionally, certain genera exhibited distinct relative abundances among the groups, suggesting potential associations with geographic factors, gut disorders, and dietary habits. Our findings provide valuable insights into the gut microbiome of wild SMD and offer a foundation for future microbiome-based conservation efforts for this vulnerable species.
A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective
Authors: Rodríguez Alejandro , Gómez Josep , Martín-Loeches Ignacio , Claverias Laura , Díaz Emili , Zaragoza Rafael , Borges-Sa Marcio , Gómez-Bertomeu Frederic , Franquet Álvaro , Trefler Sandra , González Garzón Carlos , Cortés Lissett , Alés Florencia , Sancho Susana , Solé-Violán Jordi , Estella Ángel , Berrueta Julen , García-Martínez Alejandro , Suberviola Borja , Guardiola Juan J. , Bodí María
Published: October 2024

Background: Bacterial/fungal coinfections (COIs) are associated with antibiotic overuse, poor outcomes such as prolonged ICU stay, and increased mortality. Our aim was to develop machine learning-based predictive models to identify respiratory bacterial or fungal coinfections upon ICU admission. Methods: We conducted a secondary analysis of two prospective multicenter cohort studies with confirmed influenza A (H1N1)pdm09 and COVID-19. Multiple logistic regression (MLR) and random forest (RF) were used to identify factors associated with BFC in the overall population and in each subgroup (influenza and COVID-19). The performance of these models was assessed by the area under the ROC curve (AUC) and out-of-bag (OOB) methods for MLR and RF, respectively. Results: Of the 8902 patients, 41.6% had influenza and 58.4% had SARS-CoV-2 infection. The median age was 60 years, 66% were male, and the crude ICU mortality was 25%. BFC was observed in 14.2% of patients. Overall, the predictive models showed modest performances, with an AUC of 0.68 (MLR) and OOB 36.9% (RF). Specific models did not show improved performance. However, age, procalcitonin, CRP, APACHE II, SOFA, and shock were factors associated with BFC in most models. Conclusions: Machine learning models do not adequately predict the presence of co-infection in critically ill patients with pandemic virus infection. However, the presence of factors such as advanced age, elevated procalcitonin or CPR, and high severity of illness should alert clinicians to the need to rule out this complication on admission to the ICU.
Identifying Potential Natural Antibiotics from Unani Formulas through Machine Learning Approaches
Authors: Nasution Ahmad Kamal , Alqaaf Muhammad , Islam Rumman Mahfujul , Wijaya Sony Hartono , Ono Naoaki , Kanaya Shigehiko , Altaf-Ul-Amin Md.
Published: October 2024

The Unani Tibb is a medical system of Greek descent that has undergone substantial dissemination since the 11th century and is currently prevalent in modern South and Central Asia, particularly in primary health care. The ingredients of Unani herbal medicines are primarily derived from plants. Our research aimed to address the pressing issues of antibiotic resistance, multi-drug resistance, and the emergence of superbugs by examining the molecular-level effects of Unani ingredients as potential new natural antibiotic candidates. We utilized a machine learning approach to tackle these challenges, employing decision trees, kernels, neural networks, and probability-based methods. We used 12 machine learning algorithms and several techniques for preprocessing data, such as Synthetic Minority Over-sampling Technique (SMOTE), Feature Selection, and Principal Component Analysis (PCA). To ensure that our model was optimal, we conducted grid-search tuning to tune all the hyperparameters of the machine learning models. The application of Multi-Layer Perceptron (MLP) with SMOTE pre-processing techniques resulted in an impressive accuracy precision and recall values. This analysis identified 20 important metabolites as essential components of the formula, which we predicted as natural antibiotics. In the final stage of our investigation, we verified our prediction by conducting a literature search for journal validation or by analyzing the structural similarity with known antibiotics using asymmetric similarity.
Guanethidine Restores Tetracycline Sensitivity in Multidrug-Resistant Escherichia coli Carrying tetA Gene
Authors: Zhao Xiaoou , Zhang Mengna , Zhang Zhendu , Wang Lei , Wang Yu , Liu Lizai , Wang Duojia , Zhang Xin , Zhao Luobing , Zhao Yunhui , Jin Xiangshu , Liu Xiaoxiao , Ma Hongxia
Published: October 2024

The worrying issue of antibiotic resistance in pathogenic bacteria is aggravated by the scarcity of novel therapeutic agents. Antibiotic adjuvants offer a promising solution due to their cost-effectiveness and high efficacy in addressing this issue, such as the β-lactamase inhibitor sulbactam (a β-lactam adjuvant) and the dihydrofolate reductase inhibitor trimethoprim (a sulfonamide adjuvant). This study aimed to discover potential adjuvants for tetracyclines from a list of previously approved drugs to restore susceptibility to Escherichia coli carrying the tetA gene. We have screened guanethidine, a compound from the Chinese pharmacopoeia, which effectively potentiates the activity of tetracyclines by reversing resistance in tetA-positive Escherichia coli, enhancing its antibacterial potency, and retarding the development of resistance. Guanethidine functions via the inhibition of the TetA efflux pump, thereby increasing the intracellular concentration of tetracyclines. Our findings suggest that guanethidine holds promise as an antibiotic adjuvant.