bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bioinformatical Enrichment Analysis Reveals Key Differentially Expressed Genes in Endometriosis Pathogenesis
Authors: Kusum¹ Kusum , Ashish Ashish , Singh Royana , Bhushan Ravi , Chaube Radha , Rai Sangeeta 
Published: September 2024

Abstract

Endometriosis is a gynecological disease characterized by the presence of uterine (eutopic) endometrial glands and tissues outside the intra-uterine locations, in ectopic regions such as the pelvic peritoneum, fallopian tubes, or ovaries. Approximately 5–10% of reproductive and 20–50% of infertile women are affected by endometriosis. The pathogenesis of endometriosis involves various factors, including hormonal, environmental, genetic, and immune system components, directly or indirectly altering estrogen levels and impacting women's reproductive health. This study aimed to identify novel and potential biomarkers for endometriosis using mRNA seq analysis. Differentially expressed genes (DEGs) were identified from raw gene expression profiles, and their functional analysis was subsequently conducted. A total of 552 DEGs (312 upregulated and 240 downregulated) were identified in samples from women with endometriosis compared to control subjects. Major DEGs, such as C3, PSAP, APP, GNG12, were identified as hub nodes and found to be involved in various functions, including epithelial cell differentiation and development, proteolysis, gland development, muscle fiber development, and response to hormone stimulus. These DEGs may play a direct or indirect role in the pathogenesis of endometriosis, serving as potential biomarkers for ectopic endometrium. While this study provides a preliminary insight into the mechanism of endometriosis, further detailed studies are necessary to fully understand its path of action.
The impact of salt consumption on cardiometabolic and cognitive health in aged female rats
Authors: SUN FEN , Zhao Lu-Ping , Jin Qi , Wang Qiu-Xiang , Jin Shi-Han , Xie Ji-Zhi , Xu Jun-Tao , Yin Meng-Jia , Jin Chao , Wang Jing-Hua 
Published: September 2024

Abstract

Health concerns about excess dietary salt have traditionally focused on its relationship with hypertension and the increased risk of cognitive impairment. However, research has often overlooked the unique health concerns and physiological differences between men and women, leading to gaps in knowledge, particularly regarding disease prevention and treatment strategies for women. The present study examined aged female rats over 12 weeks, using control, low, and high salt diets to mimic the post-menopausal phase in human females when cardiovascular risks typically increase. Cardiometabolic parameters and cognition were monitored. The findings revealed the impact of varying salt diets on blood lipids, blood pressure (BP) and heart rate (HR) levels and variability, anxiety, and cognition. Specifically, intake of a low-salt diet led to a significant reduction in BP levels but an increase in BP variability starting from the eighth week of the diet onset. Moreover, HR levels and variability were notably higher with the low-salt diet. Aged female rats exhibited increased anxiety on the low-salt diet at the fourth week, but the anxiety began to improve starting from the eighth week. Additionally, a trend suggested that the low salt intake worsened short-term memory while improving long-term memory. Furthermore, plasma lipids decreased significantly in aged female rats on a high-salt diet compared to those on a low-salt diet. The study provides valuable insights into the effects of salt intake on cardiometabolic parameters and cognitive function in aged female rats, highlighting the importance of considering sex-specific dietary guidelines for cardiometabolic and cognitive health.
An improved CNN model in image classification application on water turbidity
Authors: Nie Ying , Chen Yuqiang , Guo Jianlan , Li Shufei , Xiao Yu , Gong Wendong , Lan Ruirong 
Published: September 2024

Abstract

Water turbidity is an important indicator for evaluating water clarity and plays an important role in environmental protection and ecological balance. Due to the subtle changes in water turbidity images, the differences captured are often too subtle to be classified. Convolutional neural networks (CNNs) are widely used in image classification and perform well in feature extraction and classification. This study explored the application of convolutional neural networks in water turbidity classification. The innovation lies in applying CNN to water turbidity images, focusing on optimizing the CNN model to improve prediction accuracy and efficiency. The study proposed four CNN models for water turbidity classification based on artificial intelligence, and adjusted the number of model layers to improve prediction accuracy. Experiments were conducted on noise-free and noisy datasets to evaluate the accuracy and running time of the models. The results show that the CNN-10 model with a dropout layer has a classification accuracy of 96.5% under noisy conditions. This study has opened up new applications of CNN in fine-grained image classification, and further demonstrated the effectiveness of convolutional neural networks in water turbidity image classification through experiments.
Two distinct oscillatory auxin signals define the plasticity of lateral rooting in Arabidopsis thaliana
Authors: Kleine-Vehn Jürgen 
Published: September 2024

Abstract

The so-called root clock defines lateral root pre-branch sites (PBS) but how these sites contribute to the root system architecture remains incompletely understood1,2,3,4. Here we reveal in the model plant Arabidopsis thaliana that two distinct oscillatory systems for the phytohormone auxin coordinate the spatial and temporal identity of PBS, jointly defining the lateral root density. We followed auxin signalling dynamics for days and thereby detected a systemic auxin signal oscillating in the mature primary root, which is distinct from the previously described root clock at the root tip. While the root clock spatially primes the PBS in the growing root tip, the systemic oscillatory auxin signal temporally controls the auxin-dependent identity of these PBS. Light perception in the shoot defines the strength of the systemic auxin signal and thereby controls the auxin-reliant ability of PBS to develop into lateral roots. Moreover, PHYB and CRY1 mediate the light-dependent integration of other environmental signals, such as ambient temperature, into the control systemic auxin signalling and lateral root density. Our work reveals how two spatially distinct oscillatory auxin signals define the plasticity of plant root development in response to fluctuating conditions.
Understanding chinstrap penguin and elephant seal migrations in the Southern Ocean
Authors: Wilson Cara , Hinke Jefferson T. , Mazloff Matthew 
Published: September 2024

Abstract

Characterizing the high-latitude winter foraging habitats of migratory marine predators is necessary for conservation and management in Antarctica. Tracking data from chinstrap penguins (Pygoscelis antarcticus) and southern elephant seals (Mirounga leonina), key Antarctic predators with different diets and foraging habits, indicate that approximately 12% and 5%, respectively, of tagged penguins and seals undertake long-distance winter migrations to remote regions south of 60°S between 120°W and 170°W. Tracking data revealed reduced daily swimming speeds and two hotspots of increased use, consistent with increased foraging efforts, for both species in this region. Light limitation during winter, however, precludes the use of optical satellite data to characterize marine productivity here, but biogeochemical-Argo floats can provide year-round chlorophyll data. These data inform the Biogeochemical Southern Ocean State Estimate (B-SOSE), which provides year-round estimates of marine productivity. Overlapping the predator hotspots, B-SOSE predicts two areas with year-round elevated surface chlorophyll levels, consistent with previous studies indicating enhanced mixing in those areas. We hypothesize that persistent areas of elevated chlorophyll centered near 160°W and 120°W near the boundaries of the Ross Gyre and the southern boundary of the Antarctic circumpolar current support a productive food web capable of supporting the diverse foraging niches of pelagic species.
Non-cooperative Inference Method for the IoBT Topology Based on Flow Rate Estimation
Authors: Huang Jun , Dong Rongcheng , Niu Zhao , Peng Chao , Chen Jie 
Published: September 2024

Abstract

The widespread integration of Internet of Things (IoT) technology in the military domain has brought significant attention to the security concerns surrounding the Internet of Battlefield Things(IoBT). Given the limited communication resources within IoBT, there's a growing focus on conducting network security detection without disrupting normal network operations. Topology serves as a crucial foundation for conducting network security detection in IoBT, facilitating the discovery of abnormal devices and unauthorized access detection. Security detection based on topology can effectively enhance the information security and operational levels of IoBT. This paper utilizes matching analysis of time series for information exchanged between neighboring nodes and implements IoBT topology inference based on flow rate estimation, and a threshold parameter adaptive adjustment strategy is innovatively proposed to improve the accuracy of topology inference. The non-cooperative inference method proposed in this paper enables network topology inference without network access and information parsing, exhibiting strong generality and independence from the discovery of acknowledgment frames during information exchange processes. Simulation results demonstrate the feasibility and superiority of this method.
Cardiometabolic Index Correlations with Coronary Heart Disease among U.S. Adults: A NHANES 1999–2018 Analysis
Authors: Lei Yu , Luo Yu , Wang Yi , Luo Lei , Yang Yanhui , Chen Xin , Li Ji 
Published: September 2024

Abstract

Background
Coronary heart disease (CHD) is a prevalent global health challenge, resulting in substantial morbidity and mortality, with atherosclerosis being its principal pathological characteristic. Recently, the interrelationship between CHD and cardiometabolic diseases has garnered significant attention in the research community. The cardiometabolic index (CMI), a composite indicator of lipid metabolism and obesity, has emerged as a potential predictor of cardiometabolic disorders. The principal aim of the research is to elucidate the correlation between CHD and CMI within a large-scale, nationally representative sample.
Methods
This study employed a cross-sectional methodology, examining datasets derived from the National Health and Nutrition Examination Survey (NHANES) conducted over the period of 1999 to 2018.. After applying the exclusion criteria, the study ultimately included 33,917 individuals. The CMI was calculated via the following formula: CMI = waist-to-height ratio (WHtR) × [triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C)]. CHD status was determined by self-reported physician diagnosis. To evaluate the link between CMI and CHD, several weighted generalized linear models were utilized, adjusted for potential confounding variables.
Results
Considering the weights, the prevalence of CHD was 3.45%. The CMI values were significantly greater among participants with CHD than among those without CHD. Upon covariate adjustment, a significant and positive correlation was identified between elevated CMI and the incidence of CHD. Subgroup analysis elucidated a modification of the association by hypertension status, demonstrating a more pronounced correlation in non-hypertensiveindividuals.
Screening and validation of optimal real-time PCR reference genes for Abelmoschus manihot
Authors: Wu Qixuan , Deng Meixin , Zhao Xiaolan , Long Jianmei , Zhang Jianxia 
Published: September 2024

Abstract

Abelmoschus manihot is an important medicinal and edible plant known for its functional secondary metabolites. However, little is known about the key genes involved in production of secondary metabolites in A. manihot. This is largely due to a lack of effective gene expression detection systems for A. manihot, and thus screening of real-time PCR reference genes is a prerequisite. In this study, 11 candidate reference genes were screened and cloned from A. manihot, and their expression stability was evaluated in different tissues using four algorithms: geNorm, NormFinder, BestKeeper, and RefFinder. The expression stability of eIF and PP2A1 was the highest, while that of tubulin alpha (TUA) was the lowest. The combined use of the two most stable reference genes, eIF and PP2A1, met the experimental requirements for normalizing gene expression in A. manihot. Furthermore, the gene expression of transcription factors bHLH147 and bHLH148 was further validated by data normalization. This study identified potential reference genes in different A. manihot tissues, paving the way for functional gene analysis and dissecting metabolite regulation mechanisms in A. manihot.
CBRM: A Causal Approach to BalancingPopularity Bias with Global Quality inRecommendation Systems
Authors: Dong Rongsheng , Lv Dongting , Li Fengying 
Published: September 2024

Abstract

The popularity bias in recommendation systems leads to the over-recommendation of popular items, thereby neglecting high-quality but less popular items. This bias reduces both recommendation accuracy and user satisfaction. To address this issue, this paper proposes leveraging global quality to balance the popularity bias in recommendation systems, thereby improving recommendation accuracy. The key lies in how to eliminate the adverse effects of global quality on items during the training process and how to incorporate global quality in the recommendation phase to generate the top-K recommendations. To this end, this paper introduces the (Causal Balance Recommendation System) CBRM, which adopts a new training and inference strategy. CBRM utilizes the backdoor adjustment technique to cut off the backdoor path of global quality, thereby eliminating the bias introduced by global quality during model training. Additionally, it adjusts recommendation scores through causal intervention to introduce the desired global quality. Compared to four recommendation models—BPRMF, BPR-PC, MACR, and PDA,CBRM demonstrates superior performance on the Douban Movie and Netflix datasets, and particularly excels on the ML-10M dataset. Specifically, compared to the best-performing model, PDA, CBRM achieves improvements of 14.5%, 7.1%, 3.3%, and 8.9% in recall, precision, hit rate, and normalized discounted cumulative gain (NDCG), respectively. The experimental results demonstrate the effectiveness of CBRM in practical applications and validate the potential of causal theory in enhancing the accuracy of recommendation systems.
Prevalence And Risk Factors Of Depression In College Students in Northeast China During the COVID-19 Pandemic: A Cross-sectional Study
Authors: Cui Yanze , Yang Liying , Yang Wanqiu , Wang Xiaohong , Liu Jiazhou , Wang Yanqing , Liu Jiacheng , Leng Dan , Yang Borui , Zhao Na , Kang Chuanyi 
Published: September 2024

Abstract

Background
The Coronavirus disease 2019 pandemic spread rapidly worldwide, posing a serious health challenge to the global public. However, few studies exist on the association between health risk behaviors and depression among Chinese college students during the stage. Therefore, this study aimed to explore associated factors including health risk behaviors and life satisfaction among Chinese college students with depression.
Methods
A cross-sectional study was conducted in northeast China from April to June 2020 on purposively sampled from two colleges. Data were collected through the Adolescent Students' Life Satisfaction Scale, Health Risk Behavior Scale, Self-rating Anxiety Scale, and Self-rating Depression Scale. Binary logistic regression was conducted to analyze the risk factors of depression.
Results
The prevalence of depressive state of college students was 32.1%. Students with health-compromising behavior (OR = 1.128, p < 0.001) were positively correlated with depression. Furthermore, satisfaction with friendships (OR = 0.941, p < 0.001), freedom (OR = 0.955, p = 0.004), school (OR = 0.966, p = 0.010), and the environment (OR = 0.933, p < 0.001) were protective factors for students’ depression.
Conclusions
To reduce this depression, schools should increase publicity and education to promote a regular diet among college students, channel adolescents' destructive emotions to prevent them from self-injury and self-harm, enrich their after-school life to strengthen their interpersonal communication, and enhance friendship- building among them.
An Intelligent Framework for Modeling Nonlinear Irreversible Biochemical Reactions Using Artificial Neural Networks
Authors: Bilal Hazrat , shah Rehan Ali , Ahmad Hijaz , Jan Akhter , Radwan Taha 
Published: September 2024

Abstract

The objective of this paper is to perform a comparative performance analysis of three different algorithms, namely Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG), for nonlinear irreversible biochemical reaction (NIBR). These algorithms are based on artificial neural network (ANN) techniques. Nonlinear irreversible biochemical reactions are described by a set of nonlinear ordinary differential equations (ODE’s) that take into account the concentration of different compounds, such as products , substrate, enzyme-substrate complex, and enzyme-product complex. To develop the Backpropagation Levenberg Marquardt artificial neural networks (BLM-ANN) model, a dataset is created using the RK4 method, incorporating diverse NIBR models with varying reaction rates. A comparative analysis is presented, focusing on the performance of algorithms in handling the trained dataset. The design of the ODE’s system of the proposed model is done using the MATLAB/Simulink environment. To analyze the developed model, the artificial neural network toolbox is employed. The reliability and efficiency of the BLM-ANN model were evaluated by training, validation, and testing phase, it demonstrated an accurate prediction of irreversible biochemical reaction when compared to approximate result. The effectiveness of the proposed BLM-ANN model is measured using various metrics, such as state transition (ST), error-histograms (EH), mean square error (MSE), and regression analysis (R). These evaluation showed the value of the numerical solution provided by the BLM-ANN model for irreversible biological reactions. A comparison between the proposed BLM-ANN model and reference solution for the NIBR model demonstrated its effectiveness with observed convergence up to 10 −8 to 10 −3 .
Euregional Technology Award: An approach to inspire students for STEM
Authors: Breukelen Dave van , Smeets Maurice 
Published: September 2024

Abstract

The economy in the Meuse-Rhine Euroregion, encompassing parts of Belgium, Germany, and The Netherlands, strongly relies on scientific and industrial activities. However, the region faces a major shortage of employees, partly due to a low number of graduates in the fields of science, technology, engineering, and mathematics (STEM). This mixed-methods research studied the pedagogical design and impact of the Euregional Technology Award (EUTech Award). The EUTech Award is a technological design competition, based on inquiry- and design-based learning, aimed at promoting STEM among lower secondary school students. Utilizing pre- and post-testing, non-participatory observations, and focus groups, the study reveals a positive shift in students' STEM perceptions, making them more positive about STEM. Results show that emphasizing the social and creative aspects of STEM is crucial in fostering a positive mindset towards STEM, especially for student with an implicit STEM interest. It also highlights how divergent and creative thinking can enhance problem-solving skills. Furthermore, the study underscores the importance of design- and inquiry-based learning in STEM education, the need for a balance between different learning experiences, and the vital role of well-trained STEM teachers. Overall, this research unveils the potential of programs like the EUTech Award to positively influence STEM education.
Effect of moisture change on water retention behavior of unsaturated silty soil under loading- unloading conditions
Authors: Shah Syed Kamran Hussain , Malik Adnan Anwar , Haq Shay , Ahmad Waqar , Ali Umair , Chaudhry Rabia 
Published: September 2024

Abstract

Earthen embankments either natural or constructed artificially often comprise of unsaturated soils being considered stronger in terms of compressibility and permeability. However, the internal nomenclature of unsaturated soils due to the presence of air, water and soil particles makes it more complex while such soils interact with moisture under the circumstances of applied stress history during the earthen structure’s life span. To investigate this behavior this study is designed to understand the soil water interaction and quantify the retention behavior during loading and unloading conditions. To serve this idea custom designed static compaction tests were carried out under un-drained water and drained air condition at applied stress history. The results show sufficient impact on the soil water retention behavior with the application of loading unloading with varying soil water contents. During the initial stages of the loading process, suction increases with decreasing void ratio due to the heterogeneous distribution of water in the soil at the initial stage. Whereas there is a unique increasing trend in suction at the reloading or wetting phase of the water retention curve at all moisture contents. To examine the impact on void ratio the experimental data has been compared with the selected model incorporating suction, void ratio and degree of saturation for prediction of soil water retention behaviors. The model prediction deviates from the experimental data and shifts away from the model prediction indicating that loading-unloading sequence coupled with the variation in moisture does affect the soil behavior.
Detecting Human Activities In COMA Patients Using ML Algorithm
Authors: Singh Aditi , Singh Utkarsh , Pandey Vivek , Deotale Nilesh , Talekar Poorva 
Published: September 2024

Abstract

Health monitoring systems employing machine learning facilitate the collection and collaboration of data from comatose patients, leveraging sensors in hospitals via IoT technology. This data aids doctors in enhancing and managing the health of comatose patients during emergency situations. The hardware platform for this project integrates sensors and cameras capable of internet communication, enabling remote access for doctors via smartphones. This innovative approach empowers doctors to access and monitor patients' health statuses globally. Sensors gather vital medical data including heart rate, blood pressure, and other relevant parameters, which is then transmitted to a medical server via the internet. A series of cameras further monitor the patient, with data being securely stored for access by doctors and patient relatives through a dedicated application. In the event of sensor values exceeding predefined thresholds, urgent alarms are triggered, alerting both patients and medical staff. Continuous monitoring of patient health parameters ensures prompt intervention when necessary. Routine monitoring data sent to doctors via servers aids in diagnostics, facilitating precise treatment strategies. By harnessing IoT for data collection, patients benefit from enhanced medical care at reduced costs, promoting easier recovery. The project boasts an impressive execution rate of 99.7%, indicative of its efficacy and reliability in real-world applications.