bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset
Authors: Thai Thanh Tuan , Ku Ki-Bon , Le Anh Tuan , Oh San Su Min , Phan Ngo Hoang , Kim In-Jung , Chung Yong Suk
Published: December 2024

This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm. The models underwent comprehensive evaluation, considering accuracy, precision, and recall as key parameters. Notably, YOLOv8 demonstrated superior performance over Mask R-CNN, particularly in accurately calculating stomata pore dimensions. Beyond this comparative study, the implications of our findings extend across diverse biological research, providing a robust foundation for advancing our understanding of plant physiology. Furthermore, the preprocessing enhancements offer valuable insights for refining image analysis techniques, showcasing the potential for broader applications in scientific domains. This research marks a significant stride in unraveling the complexities of plant structures, offering both theoretical insights and practical applications in scientific research.
Predicting PTSD and complex PTSD from interpersonal violence in Japanese school-based extracurricular sports activities: using the International Trauma Questionnaire (ITQ)
Authors: Toyoda Hayato , Ishikawa Katsuhiko , Omi Yasuhiro
Published: December 2024

Introduction

Victims of interpersonal violence in sports show various mental health concerns. However, no studies have quantitatively examined their primary complaints, considering psychological symptoms such as denial of self-concept and interpersonal challenges not captured by conventional post-traumatic stress disorder (PTSD). Recently, an association between interpersonal violence victimization and complex PTSD (CPTSD) has been noted in Japanese sports coaching situations, specifically for extracurricular sports activities. This study aimed to examine the applicability of the International Trauma Questionnaire (ITQ) and determine whether interpersonal violence victimization and related risk factors predicted PTSD and CPTSD in extracurricular sports activities in Japan.

Methods

This study included 651 adults aged 18–25 who had previously participated in extracurricular sports activities in junior high and high school. The ITQ was examined using confirmatory factor analysis with maximum likelihood with robust standard errors, fit indices comparisons, a graded response model, differential item functioning, and rank correlation designs. A binomial logistic regression model with robust standard errors examined the association of PTSD and CPTSD with interpersonal violence victimization and related risk factors.

Results

The optimal factor structure, measurement precision, and validity of the ITQ were confirmed. Physical and psychological violence victimization and the ITQ were positively correlated with PTSD, difficulties in emotion regulation, self-disgust, and interpersonal problems subscales, respectively. A high frequency of psychological and physical violence victimization experiences and self-identified LGB (lesbian, gay, or bisexual) were associated with PTSD and CPTSD diagnosability. Additionally, being a woman and in school life away from parents were associated solely with PTSD diagnosability.

Discussion

This is the first quantitative study to examine CPTSD in a study on interpersonal violence in sports. Our findings can provide insights into desirable victim support and enhanced clinical care in interpersonal violence in a sports context.
Female and male treatable mortality: socioeconomic and public finance related factors across European countries
Authors: Tavares Aida Isabel
Published: December 2024

Background

About 36.5% of premature deaths in European Union countries could have been avoided through prompt and effective medical treatment. This treatable mortality is even a priority established in Sustainable Development Goal (SDG) target 3.4. Given the gap in the literature about the socioeconomic drivers of this type of mortality, as well as the increasing importance of public financial management in defining priority policies, this study aims to analyze the socioeconomic and public finance drivers associated with treatable mortality for women and men across European countries.

Methods

Eurostat data is collected for 31 countries for the period 2011–2019 stratified by sex. Panel data quantile regression with fixed effects and conditional mean panel data model using feasible generalized least squares are estimated to explain treatable mortality in women and men.

Results

Key findings point to a positive association between the public finance indicator proxying health priority and the treatable mortality rate for both sexes; a difference between drivers of treatable mortality between men and women; and a different set of drivers across the different quantiles of treatable mortality.

Conclusion

Drivers of male and female treatable mortality may differ according to the country’s level of mortality rate. Government health priority seems to account for previous treatable mortality rates as a reactive measure. Policymakers aiming to reduce treatable mortality are likely to use instruments such as health expenditure, improved employment, education levels, and perhaps proactive policy-setting priorities concerning health.
The emotional path to influencing decision-making: harnessing emotions for better financial choices
Authors: Born Nadja , Ashebir Sophia , Brady Samantha , D'Ambrosio Lisa , Coughlin Joseph
Published: December 2024

Introduction

As life expectancy and expected years in retirement rise and family structures change, the need for personal financial protections, such as long term care (LTC) insurance, in managing financial risks associated with later life is expected to increase. Roughly half of American households are at risk of not being able to maintain their present standards of living post-retirement. Because both public and private health insurance programs typically do not cover LTC needs, which are associated with significant costs, the potential demand for LTC further exacerbates the retirement savings crisis.

Methods

Using an original survey experiment, in this high-powered study (N = 1,450), we examine the impact of a 2×3 framing intervention on participants' attitudes, emotions, and behavioral intentions toward LTC insurance.

Results

Results indicated that direct framing effects were present for people's reported emotions: those who received a loss frame (compared to a gain frame) were more likely to report anxiety-related emotions, and those who were exposed to a care choice narrative frame (compared to a family or a financial frame) were more likely to report calmness-related emotions. There were no significant interaction effects between loss/gain and narrative frames. A mediation analysis suggested that the framing impacts acted through these two different emotional pathways to yield more positive attitudes toward and behavioral intentions around LTC.

Implications

The study results underscore the need to examine how different frames affect emotional arousal as a potential pathway to impacting attitudes and behaviors. We found that both a loss framing and a narrative framing, operating through different emotional pathways, have the potential to be helpful to nudge people to hedge against a financial risk associated with older age.
Spatiotemporal characterization and hydrological impact of drought patterns in northwestern Morocco
Authors: Ait Dhmane Latifa , Saidi Mohamed Elmehdi , Moustadraf Jalal , Rafik Abdellatif , Hadri Abdessamad
Published: December 2024

Drought assessment and management, intensified by global warming, present critical challenges in semi-arid Mediterranean regions, impacting environmental sustainability and economic stability. This study evaluates spatiotemporal drought risk in the Bouregreg watershed in northwest Morocco by integrating remote sensing data with various drought indices. The Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Standardized Temperature Index (STI) were utilized to assess meteorological drought over a 12-month period. The Temperature Conditions Index (TCI) was used to evaluate temperature-related conditions for agricultural drought, while the GRACE Drought Severity Index (GRACEDSI) assessed hydrological drought on a monthly scale. Additionally, trend analysis was performed using Mann-Kendall and Sen’s slope methods, and Pearson correlations were conducted among the indices. The findings revealed an overall downward trend in drought indices, with evapotranspiration (SPEI) being the primary drought driver. Over the study period, there was a significant increase in total evaporation demand, largely attributed to rising temperatures (STI and TCI). Meanwhile, precipitation conditions (SPI) remained relatively stable, highlighting the impact of global warming on agricultural and hydrological drought severity patterns in recent years. The results further indicated that drought risk is more pronounced in the plateau and plain areas of the Bouregreg compared to the mountainous regions. In evaluating water reserves, total water storage (TWS) data obtained from the Gravity Recovery and Climate Experiment (GRACE) was utilized. Comparisons were made between in situ groundwater level (GWL) data and those from GRACE TWS at a resolution of 0.25°. Our results reveal concordant trends between the two datasets, despite the differences in resolution. The TWS appears to be strongly correlated with GWL measurements and precipitation data with a lag of 1–4 months. The findings underscored a significant decline in water reserves and worsening drought conditions in recent years. Correlation analyses also revealed a moderate relationship between this decline and the systematic temperature rise, suggesting shared trends influenced by other anthropogenic factors not accounted for in the analysis. In summary, these results underscore the vulnerability of the entire study area to various forms of drought, ranging from mild to extreme severity.
The “FitBrain” program: implementing exergaming & dual-task exercise programs in outpatient clinical settings
Authors: Glatt Ryan M. , Patis Corwin , Miller Karen J. , Merrill David A. , Stubbs Brendon , Adcock Manuela , Giannouli Eleftheria , Siddarth Prabha
Published: December 2024

Dual-task training and exergaming interventions are increasingly recognized for their potential to enhance cognitive, physical, and mood outcomes among older adults and individuals with neurological conditions. Despite this, clinical and community programs that use these interventions are limited in availability. This paper presents the “FitBrain” program, an outpatient clinical model that combines dual-task and exergaming interventions to promote cognitive and physical health. We review the scientific rationale supporting these methods, detail the structure and methodology of the FitBrain program, and provide examples of session designs that integrate dual-tasking through exergaming. The paper also addresses implementation considerations, such as tailoring interventions to specific populations, ensuring user-centered design, and leveraging accessible technologies. We discuss key challenges, including limited research on programs utilizing multiple technologies and cost constraints, and propose directions for future research to refine best practices and evaluate the comparative effectiveness of multimodal vs. singular interventions. This paper aims to inform clinicians and program developers on implementing dual-task and exergaming interventions within diverse clinical and community settings by offering a structured model and practical guidelines.
Biopsy-Proven T-Cell Mediated Rejection After Belatacept Rescue Conversion: A Multicenter Retrospective Study
Authors: Bertrand Dominique , Chavarot Nathalie , Olagne Jérôme , Greze Clarisse , Gatault Philippe , Danthu Clément , Colosio Charlotte , Jaureguy Maïté , Duveau Agnès , Bouvier Nicolas , Le Meur Yannick , Golbin Léonard , Thervet Eric , Thierry Antoine , François Arnaud , Laurent Charlotte , Lemoine Mathilde , Anglicheau Dany , Guerrot Dominique
Published: December 2024

After kidney transplantation, conversion to belatacept is a promising alternative in patients with poor graft function or intolerance to calcineurin inhibitors. The risk of acute rejection has not been well described under these conditions. Here we present a retrospective multicenter study investigating the occurrence of acute rejection after conversion in 901 patients (2011–2021). The incidence of cellular and humoral rejection was 5.2% and 0.9%, respectively. T-cell mediated rejection (TCMR) occurred after a median of 2.6 months after conversion. Out of 47 patients with TCMR, death-censored graft survival was 70.1%, 55.1% and 50.8% at 1 year, 3 years and 5 years post-rejection, respectively. Eight patients died after rejection, mainly from infectious diseases. We compared these 47 patients with a cohort of kidney transplant recipients who were converted to belatacept between 2011 and 2017 and did not develop rejection (n = 238). In multivariate analysis, shorter time between KT and conversion, and the absence of anti-thymocyte globulin induction after KT were associated with the occurrence of TCMR after belatacept conversion. The occurrence of rejection after conversion to belatacept appeared to be less frequent than with de novo use. Nevertheless, the risk of graft loss could be significant in patients with already low renal function.
From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
Authors: Ahmed Muhammad Waqas , Adnan Muhammad , Ahmed Muhammad , Janssens Davy , Wets Geert , Ahmed Afzal , Ectors Wim
Published: December 2024

The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems (RTMs), the combination of UAVs and vision-based methods has shown great potential. Currently, most solutions focus on analyzing traffic footage captured by hovering UAVs due to the inherent georeferencing challenges in video footage from nonstationary drones. We propose an innovative method capable of estimating traffic speed using footage from both stationary and nonstationary UAVs. The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. The geographic positions of these moving vehicles over time are logged in JSON format. The accuracy of this method was validated with reference measurements recorded from a laser speed gun. The results indicate that the proposed method can estimate vehicle speeds with an absolute error as low as 0.53 km/h. The study also discusses the associated problems and constraints with nonstationary drone footage as input and proposes strategies for minimizing noise and inaccuracies. Despite these challenges, the proposed framework demonstrates considerable potential and signifies another step towards automated road traffic monitoring systems. This system enables transportation modelers to realistically capture traffic behavior over a wider area, unlike existing roadside camera systems prone to blind spots and limited spatial coverage.
Single-Stage Entity–Relation Joint Extraction of Pesticide Registration Information Based on HT-BES Multi-Dimensional Labeling Strategy
Authors: Dong Chenyang , Xi Shiyu , Che Yinchao , Xiong Shufeng , Ma Xinming , Xi Lei , Xiong Shuping
Published: December 2024

Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high information density, complex logical structures, large spans between entities, and heterogeneous entity lengths, as well as to overcome the challenges faced when using traditional joint extraction methods, including triplet overlap, exposure bias, and redundant computation, we propose a single-stage entity–relation joint extraction model based on HT-BES multi-dimensional labeling (MD-SERel). First, in the encoding layer, to address the complex structural characteristics of pesticide registration texts, we employ RoBERTa combined with a multi-head self-attention mechanism to capture the deep semantic features of the text. Simultaneously, syntactic features are extracted using a syntactic dependency tree and graph neural networks to enhance the model’s understanding of text structure. Subsequently, we integrate semantic and syntactic features, enriching the character vector representations and thus improving the model’s ability to represent complex textual data. Secondly, in the multi-dimensional labeling framework layer, we use HT-BES multi-dimensional labeling, where the model assigns multiple labels to each character. These labels include entity boundaries, positions, and head–tail entity association information, which naturally resolves overlapping triplets. Through utilizing a parallel scoring function and fine-grained classification components, the joint extraction of entities and relations is transformed into a multi-label sequence labeling task based on relation dimensions. This process does not involve interdependent steps, thus enabling single-stage parallel labeling, preventing exposure bias and reducing computational redundancy. Finally, in the decoding layer, entity–relation triplets are decoded based on the predicted labels from the fine-grained classification. The experimental results demonstrate that the MD-SERel model performs well on both the Pesticide Registration Dataset (PRD) and the general DuIE dataset. On the PRD, compared to the optimal baseline model, the training time is 1.2 times faster, the inference time is 1.2 times faster, and the F1 score is improved by 1.5%, demonstrating its knowledge extraction capabilities in pesticide registration documents. On the DuIE dataset, the MD-SERel model also achieved better results compared to the baseline, demonstrating its strong generalization ability. These findings will provide technical support for the construction of pesticide knowledge bases.
A Systematic Review of Occupational Shoulder Exoskeletons for Industrial Use: Mechanism Design, Actuators, Control, and Evaluation Aspects
Authors: Tian Jin , Wei Baichun , Luo Suo , Yang Chifu , Chen Changbing , Liu Yingjie , Feng Jiadong , Li Ping , Zhu Haiqi , Yi Chunzhi
Published: December 2024

Prolonged awkward arm postures and repetitive tasks in industrial environments can easily lead to worker injuries. The occupational shoulder exoskeleton is a promising solution to reducing the incidence of work-related musculoskeletal disorders, especially in the shoulder. In general, the device has to be compatible with the human shoulder, which is one of the most complex joints, thus posing significant challenges in existing efforts to design occupational shoulder exoskeletons. The purpose of this paper is to present a comprehensive review of exoskeletons designed to support human shoulders and arms in industrial applications. Recent developments and advancements in mechanism design, actuators, and control aspects are discussed in detail. A brief overview of the evaluation (i.e., participants, task design, objective, and subjective assessment) is also reported, and the experiment protocol to assess performance is summarized. Finally, existing challenges and possible future research directions for occupational shoulder exoskeletons are provided.
Design and Simulation of Steering Control Strategy for Four-Wheel Steering Hillside Tractor
Authors: Zhu Jiawang , Feng Tianci , Kang Shuyi , Chen Du , Ni Xindong , Wang Ling
Published: December 2024

Steering control is vital for hillside tractors, and four-wheel-steering technology significantly enhances stability and flexibility. However, existing methods often overlook the slope–stability relationship. In order to address this issue, considering the impact of tractor wheels on the steering characteristics, a nonlinear three-degree-of-freedom dynamic model based on the basic requirements within a 15° range of slope angles was derived. Subsequently, a four-wheel steering control strategy for hillside tractors was designed based on this model, incorporating a fuzzy PID control algorithm. The dynamics model was validated on the high-fidelity Carsim/Simulink co-simulation platform, and relevant experiments were conducted in Matlab/Simulink. Results showed that fuzzy PID control reduced the yaw rate’s average settling time from 0.36 s to 0.1 s and the response value from 0.28 rad/s to 0.038 rad/s. In addition, the lateral velocity and sideslip angle responses closely matched ideal values. Thus, the tractor with four-wheel steering under fuzzy PID control exhibited improved wheel angle flexibility and higher tracking accuracy. Finally, hardware-in-the-loop-experiments were conducted, confirming the algorithm’s effectiveness in ensuring stability and meeting the requirements of semi-physical simulation scenarios. This research provides a foundation for potential applications in tractor manufacturing to improve control performance on hilly terrains.
Biochar Influences Polyethylene Microplastic-Contaminated Soil Properties and Enzyme Activities
Authors: Su Jie , Zhu Yuanchen , Chen Xu , Lu Xinchun , Yan Jun , Yan Lei , Zou Wenxiu
Published: December 2024

Microplastics (MPs) have emerged as a significant pollutant, threatening agricultural ecosystem sustainability and global food security. However, relatively few studies have investigated biochar remedial effects on plant growth and biochemical properties in soils contaminated with MPs. In polyethylene microplastic (PE-MP)-contaminated soil, we investigated corn stover biochar effects on soybean growth, soil nutrient content, enzyme activity, and microbial biomass and assessed its impact on soil microbial resource limitations. The addition of MPs inhibited soybean growth in various forms across four stages. Conversely, the addition of biochar to MPs improved soybean growth to some extent, where above-ground biomass increased by 5.82% after adding biochar to soils containing microplastics. In soil treated with MPs and biochar, nitrate nitrogen (N), available phosphorus (P), and available potassium (K) increased by 20.1, 27.4, and 57.2%, respectively, while available nitrogen significantly decreased to 128.3 mg kg−1 compared to the MP-only treatment. PE-MPs alone significantly reduced soil carbon (C), N, and P enzyme activities, as well as microbial biomass, with β-glucosidase, leucine aminopeptidase, and acid phosphatase activities decreasing by 29.9, 27.8, and 25.5%, respectively. Interestingly, biochar addition to MPs significantly alleviated these detrimental effects. Microbial biomass C, N, and P increased by up to 56.0, 22.5, and 96.6%, respectively, following biochar addition to soils containing MPs. Analysis of vector lengths, vector angles, and scatter plots indicated that the presence of MPs reduced soil N and P availability. Overall, while MPs inhibited soybean development, biochar addition alleviated this effect to some extent. Furthermore, partial least-squares path modeling revealed that MPs negatively affected soil chemical properties, microbial biomass, and enzyme activities, whereas biochar positively influenced soil enzyme activities.
Impacts of Air Velocity Treatments Under Summer Conditions: Part III—Litter Characteristics, Ammonia Emissions, and Leg Health of Heavy Broilers
Authors: West Derek , Akter Suraiya , Cheng Bin , Oviedo Edgar , Classen John , Wang-Li Lingjuan
Published: December 2024

The broiler industry is crucial for rural economies, but it faces challenges in enhancing animal performance and well-being, particularly due to heat stress and ammonia (NH3) in grow-out houses. This study investigates the effectiveness of air velocity (AV) treatment in reducing heat stress and NH3 emissions while also improving litter conditions to enhance animal welfare. Conducted at the North Carolina State University’s poultry engineering laboratory, the research used six identical controlled chambers, exposing broilers to High and Low AV treatments from 28 to 61 days across four flocks during the summers of 2017 and 2018. The nitrogen mass balance (NMB) method quantified NH3 emissions, while litter quality was assessed for moisture content, pH, total nitrogen, and total ammoniacal nitrogen. Additionally, leg health was evaluated through assessments of footpad dermatitis (FPD) and hock burns (HB) at 42 and 61 days. Results showed higher NH3 emissions in Low AV chambers, while High AV conditions improved litter quality and resulted in greater nitrogen retention. Broilers in High AV chambers also had higher individual body weights and better leg health scores. Thus, optimizing AV effectively mitigates heat stress, controls NH3 emissions, and enhances overall broiler performance and welfare, offering insights for sustainable industry practices.
The Interactive Effects of Nutrient Density and Breed on Growth Performance and Gut Microbiota in Broilers
Authors: Jia Meiting , Lei Jiaqi , Dong Yuanyang , Guo Yuming , Zhang Bingkun
Published: December 2024

This study investigated whether variations in growth response to low nutrient density across breeds are linked to microbiota regulation. Arbor Acres (AA) and Beijing-You (BY) were fed high- (HN) and low-nutrient (LN) diets from day (d) 0 to d42. Body weight, feed intake, and intestinal measurements were recorded, and microbiota from the ileum and cecum were analyzed on d7, d21, and d42. Results showed that AA broilers had greater growth performance with a lower feed conversion ratio (FCR) and greater average daily gain (ADG) than BY chickens. The LN diet negatively affected AA broiler growth due to impaired intestinal development, while BY chickens compensated by increasing feed intake. Microbiota composition was primarily affected by breed than by nutrient density, with AA broilers having more beneficial bacteria and BY chickens having more short-chain fatty acid (SCFA)-producing bacteria. The LN diets reduced anti-inflammatory bacteria such as Shuttleworthia and Eisenbergiella in the cecum on d7. By d21, LN diets decreased Lactobacillus and increased proinflammatory Marvinbryantia, potentially impairing growth. However, LN diets enriched SCFA-producing bacteria like Ruminococcaceae_UCG.013, Eisenbergiella, and Tyzzerella in BY chickens and Faecalitalea in AA broilers by d21, which may benefit gut health. By d42, LN diets reduced genera linked to intestinal permeability and fat deposition, including Ruminococcus_torques_group, Romboutsia, Erysipelatoclostridium, and Oscillibacter. Additionally, LN diets enriched Christensenellaceae_R-7_group in AA broilers, associated with intestinal barrier integrity, and increased anti-inflammatory bacteria Alistipes and Barnesiella in AA broilers and BY chickens, respectively, by d42. Overall, AA broilers were more susceptible to reduced nutrient density due to impaired intestinal development, while BY chickens adapted better by increasing feed intake. The microbiota responses to low nutrient density varied over time, potentially negatively affecting gut health in the early stage and growth in the middle stage but possibly improving lipid deposition and gut health in the middle and late stages.
Melatonin Treatment at Dry-off Reduces Postpartum Shedding of Coccidia in Primiparous Dairy Cows and Their Calves
Authors: López-Gatius Fernando , Ganau Sergi , Mora-García María , Garcia-Ispierto Irina
Published: December 2024

Coccidiosis is a protozoan disease that causes diarrhea in cattle. This study examines the impact of treating pregnant cows at dry-off with melatonin on postpartum coccidia excretion in dams and their calves. The study population comprised 106 primiparous lactating dairy cows: 53 controls and 53 receiving melatonin on days 220–226 of gestation, plus 99 calves: 52 born from control and 47 from treated dams. Feces samples were collected from each dam on gestation days 220–226 and on days 10–16 and 30–36 postpartum; and from each calf on days 10–16 and 30–36 of age. Postpartum rates of high excretion of oocysts per gram (OPG) (feces counts > 5000) were significantly lower (p < 0.01) in treated than control dams. Low excretion rates of OPG (<2000) were significantly higher (p < 0.01) in the melatonin treatment than control groups in dams on days 30–36 of lactation and in calves at 10–16 and 30–36 days of life. In conclusion, melatonin treatment in lactating cows at dry-off reduced coccidia shedding in dams and their calves during the early postpartum period.
Antibiotics Usage and Avoidance in Germany and Poland: Attitudes and Knowledge of Patients, Physicians, and Pharmacists
Authors: Fal Andrzej M. , Stelzmüller Ingrid , Kardos Peter , Klimek Ludger , Kuchar Ernest , Gessner André
Published: December 2024

Introduction: Antimicrobial resistance poses a significant global health threat, partly due to the overprescription of antibiotics. Understanding prescribers’ behaviors and identifying knowledge gaps and misconceptions are essential for addressing antibiotic misuse and inappropriate use. Methods: Through online questionnaires, this study surveyed key stakeholders in outpatient antibiotic use in Germany (DE) and Poland (PL), including patients, physicians, and pharmacists. Results: Despite generally good knowledge about antibiotics, discrepancies exist between physicians’ perceptions and patients’ actual expectations regarding antibiotic prescriptions. Physicians often misjudge patients’ attitudes toward antibiotics, with many patients having a neutral stance. This study found a strong physician interest in non-antibiotic treatments and patient willingness to engage with information about antibiotics. Conclusions: Improved communication between healthcare providers and patients was identified as a potential measure for enhancing antimicrobial stewardship, with education on effective alternative treatments, such as symptomatic therapies, as a likely strategy to reduce antibiotic reliance.
Knowledge, Attitudes, and Practices Toward Antibiotic Use in Food-Producing Animals Among University Students in Seven Cities in Southern and Central China: A Cross-Sectional Study
Authors: Sun Hui , Zhang Jiajia , Zhu Junjie , Xu Boya , Gao Yinyan , Zhang Dexing , Wu Irene X. Y. , Hu Yanhong Jessika , Deng Shuzhen
Published: December 2024

Background: The misuse of antibiotics in both humans and food-producing animals poses significant risks to human health and contributes to the rise of antibiotic resistance. Raising public awareness is crucial to managing antibiotic resistance, particularly among university students, as they represent a future force in tackling this global issue. Methods: A cross-sectional study was conducted from July 2022 to May 2024 in seven cities in Southern and Central China to assess university students’ knowledge, attitude, and practice regarding antibiotic use in humans and food-producing animals. Binary logistic regression was used to identify associated factors. Results: A total of 6357 students from 72 universities participated. Less than half of the students answered the knowledge items appropriately. Only 21.47% to 29.98% had a proper understanding of basic antibiotic concepts and their use in humans and food-producing animals. Respectively, 21.49% and 28.50% students paid attention to antibiotic content in food from food-producing animals and refused to buy food containing antibiotics. Factors associated with higher knowledge, attitude, and practice total scores included being male, being of older age, having a postgraduate education, majoring in the medical science discipline, studying at a double-first-class university, having a higher family monthly income, having parents in the medical area, and using antibiotics in the past year (p < 0.001). Conclusions: Given students’ insufficient knowledge—particularly in identifying antibiotics and understanding their functions—and inappropriate practices related to purchasing food from food-producing animals, targeted education programs are suggested. These programs should address the fundamental concepts of antibiotic use in both humans and food-producing animals while providing practical guidance on individual behaviors to help mitigate antibiotic resistance.
A Practical Approach to Modeling and Performance Analysis of a Turboshaft Engine Using Matlab
Authors: Jakubowski Robert , Jakliński Piotr
Published: December 2024

This article presents a detailed approach to constructing a numerical model of a free power turbine engine specifically designed for performance analysis in the Matlab R2024b environment. The core innovation of this model lies in its integration of precise engine geometry parameters, calculated at the design point, and performance characteristics of key components such as the compressor and power turbine. These components are modeled to reflect significant variations in their performance based on changing operational conditions, including rotor speed and environmental factors. To validate the model’s assumptions, data from the PZL-3W engine were used. The model was created by meticulously incorporating the engine’s specific design characteristics, allowing for an in-depth examination of how performance characteristics shift with adjustments to high-pressure rotor settings or changes in ambient conditions. The resulting calculations demonstrated a high level of agreement between the model’s output and empirical data available for the PZL-3W engine, as well as with data found in the relevant scientific literature. This alignment underscores the model’s robustness and reliability in simulating engine performance across a range of operating scenarios, making it a valuable tool for further engineering analyses and optimization of free power turbine engines and their possible application.
SAITI-DCGAN: Self-Attention Based Deep Convolutional Generative Adversarial Networks for Data Augmentation of Infrared Thermal Images
Authors: Wu Zhichao , Wei Changyun , Xia Yu , Ji Ze
Published: December 2024

Defect detection plays a crucial role in industrial production, and the implementation of this technology has significant implications for improving both product quality and processing efficiency. However, the limited availability of defect samples for training deep-learning-based object detection models within industrial processes poses challenges for model training. In this paper, we propose a novel deep convolutional generative adversarial network with self-attention mechanism for the data augmentation of infrared thermal images for the application of aluminum foil sealing. To further expand its applicability, the proposed method is designed not only to address the specific needs of aluminum foil sealing but also to serve as a robust framework that can be adapted to a wide range of industrial defect detection tasks. To be specific, the proposed approach integrates a self-attention module into the generator, adopts spectral normalization in both the generator and discriminator, and introduces a two time-scale update rule to coordinate the training process of these components. The experimental results validated the superiority of the proposed approach in terms of the synthesized image quality and diversity. The results show that our approach can capture intricate details and distinctive features of defect images of aluminum foil sealing. Furthermore, ablation experiments demonstrated that the combination of self-attention, spectral normalization, and two time-scale update rules significantly enhanced the quality of image generation, while achieving a balance between stability and training efficiency. This innovative framework marks a notable technical breakthrough in the field of industrial defect detection and image synthesis, offering broad application prospects.
Quantifying the Influence of Cloud Seeding on Ice Particle Growth and Snowfall Through Idealized Microphysical Modeling
Authors: Mehdizadeh Ghazal , Erfani Ehsan , McDonough Frank , Hosseinpour Farnaz
Published: December 2024

Cloud seeding is a weather modification technique for enhancing precipitation in arid and semi-arid regions, including the Western U.S. However, designing an optimal cloud seeding operation based on comprehensive evaluation metrics, such as seeding agent dispersion and atmospheric conditions, has yet to be thoroughly explored for this region. This study investigated the impacts of cloud seeding, particularly utilizing silver iodide, on ice particle growth within clouds through numerical modeling. By leveraging the Snow Growth Model for Rimed Snowfall (SGMR), the microphysical processes involved in cloud seeding across five distinct events were simulated. The events were in the Lake Tahoe region, nestled within the Sierra Nevada Mountain ranges in the Western U.S. This model was executed based on six primary variables, including cloud top height, cloud base height, cloud top temperature, cloud base temperature, liquid water content, and ice water content. This study incorporated datasets from the Modern-Era Retrospective Analysis for Research and Applications Version 2 and the Clouds and the Earth Radiant Energy System. The findings revealed the importance of ice nucleation, aggregation, diffusion, and riming processes and highlighted the effectiveness of cloud seeding in enhancing ice particle number concentration, ice water content, and snowfall rates. However, event-specific analyses indicated diverse precipitation responses to cloud seeding based on initial atmospheric conditions. The SGMR modeling hints at the importance of improving ice microphysical processes and provides insights for future cloud seeding research using regional and global climate models.
A Novel Sine Step Size for Warm-Restart Stochastic Gradient Descent
Authors: Soheil Shamaee Mahsa , Fathi Hafshejani Sajad
Published: December 2024

This paper proposes a novel sine step size for warm-restart stochastic gradient descent (SGD). For the SGD based on the new proposed step size, we establish convergence rates for smooth non-convex functions with and without the Polyak–Łojasiewicz (PL) condition. To assess the effectiveness of the new step size, we implemented it across several datasets, including FashionMNIST, CIFAR10, and CIFAR100. This implementation was compared against eight distinct existing methods. The experimental results demonstrate that the proposed sine step size improves the test accuracy of the CIFAR100 dataset by 1.14%. This improvement highlights the efficiency of the new step size when compared to eight other popular step size methods.
LysR-Type Transcriptional Regulator Contributes to Pseudomonas cannabina pv. alisalensis Virulence by Regulating Type Three Secretion System
Authors: Yamamoto Kanon , Fujikawa Takashi , Uke Ayaka , Usuki Giyu , Ishiga Yasuhiro , Sakata Nanami
Published: December 2024

Pseudomonas cannabina pv. alisalensis (Pcal) causes bacterial blight on cabbage. In a previous study, we screened for reduced virulence using Tn5 transposon mutants and identified a LysR-type transcriptional regulator (LTTR) as a potential virulence factor in Pcal. However, the role of LTTR in Pcal virulence has not been thoroughly investigated. In this study, we demonstrated that the Pcal NN14 mutant (with Tn5 insertion in the LTTR-encoding gene) showed reduced disease symptoms and bacterial populations in cabbage, indicating that LTTR contributes to Pcal virulence. RNA-seq analysis identified 39 LTTR-dependent genes. Genes associated with 13 of the type three secretion system (T3SS), two of flagellar apparatus, ABC transporters, and transcription factors were expressed at lower levels in the NN14 mutant compared to the wild type. Conversely, tssH and hcp, type six secretion system (T6SS)-related genes, showed higher expression in NN14. Furthermore, these differences in gene expression were observed in minimal medium, but not in nutrient-rich medium, suggesting that LTTR acts as a global regulator responsive to nutrient conditions. Additionally, LTTR activated the expression of T3SS-related genes during Pcal infection. We also demonstrated that NN14 showed a reduced ability to induce hypersensitive reaction (HR) cell death in non-host plants. Collectively, these results suggest that LTTR contributes to Pcal virulence by regulating T3SS in response to environmental changes.
Modeling Thermal Runaway Mechanisms and Pressure Dynamics in Prismatic Lithium-Ion Batteries
Authors: Ayayda Mohammad , Benger Ralf , Reichrath Timo , Kasturia Kshitij , Klink Jacob , Hauer Ines
Published: December 2024

Lithium-ion batteries play a vital role in modern energy storage systems, being widely utilized in devices such as mobile phones, electric vehicles, and stationary energy units. One of the critical challenges with their use is the thermal runaway (TR), typically characterized by a sharp increase in internal pressure. A thorough understanding and accurate prediction of this behavior are crucial for improving the safety and reliability of these batteries. To achieve this, two new combined models were developed: one to simulate the thermal runaway and another to simulate the internal cell pressure. The thermal model tracks a chain of decomposition reactions that eventually lead to TR. At the same time, the pressure model simulates the proportional increase in pressure due to the evaporation of the electrolyte and the gases produced from the decomposition reactions. What sets this work apart is the validation of the pressure model through experimental data, specifically for prismatic lithium-ion cells using NMC chemistries with varying stoichiometries—NMC111 and NMC811. While the majority of the literature focuses on the simulation of temperature and pressure for cylindrical cells, studies addressing these aspects in prismatic cells are much less common. This article addresses this gap by conducting pressure validation experiments, which are hardly documented in the existing studies. Furthermore, the model’s accuracy and flexibility are tested through two experiments, conducted under diverse conditions to ensure robust and adaptive predictions of cell behavior during failure scenarios.
Amino Acid Metabolism and Autophagy in Atherosclerotic Cardiovascular Disease
Authors: Wu Yuting , Avcilar-Kücükgöze Irem , Santovito Donato , Atzler Dorothee
Published: December 2024

Cardiovascular disease is the most common cause of mortality globally, accounting for approximately one out of three deaths. The main underlying pathology is atherosclerosis, a dyslipidemia-driven, chronic inflammatory disease. The interplay between immune cells and non-immune cells is of great importance in the complex process of atherogenesis. During atheroprogression, intracellular metabolic pathways, such as amino acid metabolism, are master switches of immune cell function. Autophagy, an important stress survival mechanism involved in maintaining (immune) cell homeostasis, is crucial during the development of atherosclerosis and is strongly regulated by the availability of amino acids. In this review, we focus on the interplay between amino acids, especially L-leucine, L-arginine, and L-glutamine, and autophagy during atherosclerosis development and progression, highlighting potential therapeutic perspectives.
Two Different Brain Injury Patterns Associated with Compound Heterozygosis of the PIGO Gene in a Term Newborn: A Case Report
Authors: Dellepiane Francesco , Moltoni Giulia , Ronci Sara , Guarnera Alessia , Rossi-Espagnet Maria Camilla , Digilio Maria Cristina , Martinelli Diego , Campi Francesca , Longo Daniela
Published: December 2024

The glycosylphosphatidylinositol (GPI) is a glycol–lipid that anchors several proteins to the cell surface. The GPI-anchor pathway is crucial for the correct function of proteins involved in cell function, and it is fundamental in early neurogenesis and neural development. The PIG gene family is a group of genes involved in this pathway with six genes identified so far, and defects in these genes are associated with a rare inborn metabolic disorder manifesting with a spectrum of clinical phenotypes in newborns and children. Among them, the PIGO gene encodes for phosphatidylinositol glycan anchor biosynthesis class O protein (PIGO), an enzyme participating in this cascade, and the loss of its function often leads to a severe clinical picture characterized by global developmental delay, seizures, Hirschsprung disease, and other congenital malformations. To date, 19 patients with confirmed PIGO deficiency have been described in the literature with a host of clinical and radiological manifestations. We report a case of a male term newborn with two compound heterozygous variants of the PIGO genes, presenting with encephalopathy, drug-resistant epilepsy, and gastrointestinal abnormalities. Brain MRI first showed diffusion restriction in the ponto-medullary tegmentum, ventral mesencephalon, superior cerebellar peduncles, cerebral peduncles, and globi pallidi. This pattern of lesion distribution has been described as part of the neuroradiological spectrum of PIG genes-related disorders. However, after one month of life, he also showed a previously undescribed MRI pattern characterized by extensive cortical and subcortical involvement of the brain hemispheres. The presence of two different mutations in both the PIGO genes may have been responsible for the particularly severe clinical picture and worse outcome, leading to the death of the newborn in the sixth month of life despite therapeutic attempts. This case expands the neuroradiological spectrum and may bring new insights on glycosylation-related disorders brain manifestations.
Dehydroepiandrosterone and Bone Health: Mechanisms and Insights
Authors: Mohamad Nur-Vaizura , Razali Nur-Syahirah Che , Shamsuddin Nur-Amira Mohd
Published: December 2024

Background/Objectives: Dehydroepiandrosterone (DHEA), a steroid hormone produced by the adrenal glands, plays a key role in various physiological processes, including bone health. Its age-related decline is linked to reduced bone density, though the mechanisms by which DHEA affects bone metabolism remain complex. This review summarises the diverse effects of DHEA on bone metabolism and density, highlighting its therapeutic potential; Methods: A literature search on the effects of DHEA on bone-related parameters was conducted from PubMed and Scopus using a specific search string, and after removing duplicates and irrelevant articles, 36 relevant full-text studies were included; Results: DHEA promotes osteoblast differentiation and proliferation, regulates the RANKL/OPG ratio, and inhibits osteoclastogenesis and bone resorption. Its osteogenic effects are mediated through multiple signalling pathways. In ovariectomised rat models, DHEA enhances trabecular bone volume, stimulates osteoblast proliferation, and increases oestradiol production and aromatase activity. In elderly individuals with low androgen levels, DHEA supplementation increases sulphated DHEA and oestradiol levels and improves bone mineral density, particularly in the ultra-distal radius of women and the femoral neck of men. However, the clinical use of DHEA remains debated due to inconsistent study results. Its effects on bone health may vary based on factors such as age, gender, and health conditions, emphasising the need for further research to clarify its mechanisms and optimise its use; Conclusions: In conclusion, while DHEA shows potential as a modulator of bone health, comprehensive clinical trials are required to assess its efficacy and safety, particularly in at-risk populations.