bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between Alcohol Consumption Frequency, Coffee, Cheese Intake, and Anxiety with the Risk of Gastroesophageal Reflux Disease: A Mendelian Randomization Study
Authors: Zhang Qingqing , Liu Nanyang , Wu Di , Xu Zhengyu , Wang Yichen , Wang Ping 
Published: June 2023

Abstract
Background
In previous observational research, an association has been found between the frequency of alcohol consumption, coffee intake, cheese consumption, and anxiety with the risk of gastroesophageal reflux disease (GERD). However, conflicting conclusions exist among these studies, and the causal relationship between these exposure factors and GERD remains uncertain.
Methods
Independent genetic variants associated with alcohol consumption frequency, coffee intake, cheese consumption, and anxiety at the genome-wide significance level were selected as instrumental variables. Summary-level data for GERD was derived from a genome-wide association meta-analysis, which included 78,707 cases and 288,734 European-ancestry controls. The primary analysis method was Inverse Variance-Weighted (IVW), with Weighted Median (WM), MR-Egger, Simple mode, and Weighted mode methods serving as complementary approaches to IVW. Sensitivity analyses were conducted using Cochran's Q test, the MR-Egger intercept test, and leave-one-out analysis to assess the stability of the results.
Results
The IVW results demonstrate a strong positive causal relationship between the frequency of alcohol intake (OR=1.52, 95%CI=1.25-1.84, P<0.001) and having consulted a general practitioner for nerves, anxiety, tension, or depression (OR=22.60, 95%CI=12.12-42.15, P<0.001) with GERD. A negative causal relationship was observed between genetically predicted cheese consumption and GERD (OR=0.36, 95%CI=0.26-0.50, P<0.001). However, the association between coffee consumption and GERD was not significant in the IVW analysis (OR=1.21, 95% CI, 0.98-1.60, P>0.05).
Conclusions
This study reveals the causal relationships between the frequency of alcohol consumption, cheese intake, and anxiety with GERD, as well as the potential role of high coffee consumption in the development of GERD.
A rapid and positive effect of coastal marine reserves: the example of the Palavas Marine Reserve (France)
Authors: Molinier Pauline , Mallet Alicia , Jouvenel Jean-Yves , Changeux Thomas 
Published: June 2023

Abstract
The aim of this article is to study the early effects of a recent marine reserve on the fish population, the Palavas Coastal Marine Reserve (PCMR), created in 2016 on fishermen initiative. This recent reserve, covering 100 ha and located 2.6 km from the coast at N 43°30.6845, E 3°58.4635, constitutes almost entirely a protected zone, with a ban on any fishing activity. The abundance of the fish populations and the size and biomass of the target species have been studied for the period of the first 4 years of protection. An inventory of fish populations was carried out using the scuba diving transect method in the rocky substrate of three zones: a large enhanced protection zone (EPZ) where only navigation was allowed,, a small restricted zone (RZ), where scuba diving was admitted, and an off-reserve zone (ORZ). Monitoring of this recent reserve shows significant increases in overall abundance and changes in population assemblages within the third year of protection. The biomass of target species tripled in the EPZ showing a refuge effect despite the fact that the fishing ban was not always respected during this implementation period. However, the behaviour patterns of the fish, which are more or less fearful of divers depending on the study area, do not offer an adequate basis for determining differences in populations between the EPZ and the RZ.
Assessing the Statistical Abilities of GPT-4 as a Zero-shot Reasoner: Strengths and Weaknesses
Authors: Costa Douglas , Dias João , Nakano Celso , Monteiro Mario Luiz 
Published: June 2023

Abstract
Background
The field of artificial intelligence (AI) has witnessed remarkable advancements in natural language processing and understanding (NLP and NLU), particularly through the development of large language models like GPT-4. These models have demonstrated promise across various domains, including medicine. However, the extent to which they can reason statistically as zero-shot reasoners remains largely unexplored.
Objective
This study aims to evaluate GPT-4's statistical abilities as a zero-shot reasoner and identify its strengths and weaknesses in addressing statistical inquiries.
Methods
A diverse set of statistical questions was extracted from published articles and inputted into the GPT-4 chatbot. The responses generated by GPT-4 were assessed for accuracy and compared against the correct answers. Logistic regression analyses were performed to determine the impact of statistical topics and AI tasks on the model's performance.
Results
GPT-4 achieved an overall accuracy of 74% in responding to the statistical questions. The analysis revealed that the model excelled at comprehending well-structured and unambiguous questions, showcasing proficiency in entity recognition and information integration. However, it encountered difficulties with complex statistical concepts, data interpretation, and questions involving ambiguity or convoluted structures.
Conclusion
GPT-4 demonstrated strengths in understanding statistical concepts when presented clearly. However, it faced challenges with more intricate tasks and synthesizing information from multiple sources. Suggestions for improvement include fine-tuning the model using advanced statistical datasets, incorporating external knowledge sources, optimizing prompt engineering techniques, and enabling visual information processing.
Implications: This study offers insights into the strengths and weaknesses of GPT-4 as a zero-shot reasoner in statistical tasks. Although the model exhibits potential in basic statistical reasoning, caution should be exercised in relying solely on its responses without human supervision for comprehensive statistical analysis and interpretation. Additionally, given that most researchers in the medical field may lack statistical expertise, leveraging language models can be valuable for addressing their statistical inquiries.
Exercise-primed extracellular vesicles improve cell-matrix adhesion and chondrocyte health
Authors: Iijima Hirotaka , Wang Kai , D'Amico Ella , Tang Wan-Yee , Rogers Renee J. , Jakicic John M. , Ambrosio Fabrisia 
Published: June 2023

Abstract
Extracellular vesicles (EVs) have been suggested to transmit the health-promoting effects of exercise throughout the body. Yet, the mechanisms by which beneficial information is transmitted from extracellular vesicles to recipient cells are poorly understood, precluding a holistic understanding of how exercise promotes cellular and tissue health. In this study, using articular cartilage as a model, we introduced a network medicine paradigm to simulate how exercise facilitates communication between circulating EVs and chondrocytes, the cells resident in articular cartilage. Using the archived small RNA-seq data of EV before and after aerobic exercise, microRNA regulatory network analysis based on network propagation inferred that circulating EVs activated by aerobic exercise perturb chondrocyte-matrix interactions and downstream cellular aging processes. Building on the mechanistic framework identified through computational analyses, follow up experimental studies interrogated the direct influence of exercise on EV-mediated chondrocyte-matrix interactions. We found that pathogenic matrix signaling in chondrocytes was abrogated in the presence of exercise-primed EVs, restoring a more youthful phenotype, as determined by chondrocyte morphological profiling and evaluation of chondrogenicity. Epigenetic reprograming of the gene encoding the longevity protein, α-Klotho, mediated these effects. These studies provide mechanistic evidence that exercise transduces rejuvenation signals to circulating EVs, endowing EVs with the capacity to ameliorate cellular health even in the presence of an unfavorable microenvironmental signals.
Hydropower Scheduling with Power Load Prediction: Optimizing Energy Efficiency and Navigation Performance
Authors: Huang Guangqin , Tan Ming , Meng Zhihang , Yan Jiaqi , Chen Jin , Qu Qiang 
Published: June 2023

Abstract
Hydropower stations integrated into the grid system often suffer from imbalanced scheduling of power generation and shipping benefits due to untimely acquisition of load data. This paper proposes a joint scheduling model for hydropower stations based on power load forecasting and applies it practically to the Shatuo Hydropower Station in Guizhou Province of China. The model uses forecasting algorithms to predict power load data and optimization algorithms to schedule the discharge flow for each time period. Experiments show that the CNN-GRU model (i.e., the Convolutional Neural Network - Gate Recurrent Unit) is superior to other models in load balancing data for water stations. Using predicted power load data and the GA-NSGA-II algorithm (i.e., the improved elite non-dominated sorting genetic algorithm), the scheduling results are optimized, making the Shatuo Hydropower Station achieve both power generation and navigation benefits. This approach solves the problems of untimely load data acquisition and insufficient scheduling, providing an effective way for hydropower generation navigation scheduling.
Detecting faulty lithium-ion cells in large-scale parallel battery packs using current distributions
Authors: Lambert Pierre , Tredenick Eloise , Duncan Stephen , Drummond Ross 
Published: June 2023

Abstract
One of the main issues affecting the uptake of battery packs are safety concerns, particularly with respect to the fires caused by cell faults. Managing the risks from faults requires advances in battery management systems and an understanding of the dynamics of large packs. To address this issue, a machine learning classifier based upon a support vector machine was developed to detect cell faults within large packs using a limited number of current sensors. To train the classifier, a modelling framework for parallel connected packs was introduced and shown to generalise to Doyle-Fuller-Newman electrochemical models. The fault classification performance was found to be satisfactory, with an accuracy of 83% using current information from only 27% of the cells. These results highlight the potential of combining mathematical modelling and machine learning to improve battery management systems and deal with the complexities of large packs.
Improving Drought Resistance by Introduction of the Proline Synthesis Gene P5CSA into Inbred Rice Lines as a New Genetic Resource for Breeding Programs
Authors: Herawati Reny , Masdar Masdar , Sutrawati Mimi , Simarmata Marulak , Fahrurrozi Fahrurrozi 
Published: June 2023

Abstract
This research was aimed to identifying the P5CS gene involved in the drought stress mechanism in upland rice lines which is candidate as new genetic resource for breeding programs. The plant material consisted of 19 breed lines: Salumpikit and IR20 varieties, drought-tolerant and sensitive, respectively. The experiment consisted of 4 stages, including the evaluation of drought stress with 20% PEG 6000 (-0.58 MPa) in the germination and nursery phases, the vegetative phase, and the expression analysis of the P5CS gene. The results showed that the PEG inhibited the growth of roots, shoots, and the ratio of roots to shoot in the germination and nursery phases of all the tested lines, while the Salumpikit and IR20 varieties were confirmed as drought resistant and sensitive, respectively. The proline content under drought stress was significantly different in the lines tested, while Salumpikit and IR20 were confirmed to have high and low proline content, respectively. The proline content in several lines, such as G4, G6, G8, G10, G12, G13, G14, G15, and G17, exceeded the content in the Salumpikit variety. The P5CS gene was amplified in PCR analysis and expressed in the consistency of proline. It was found that the lines of G4, G6, G8, G13, and G17 showed tolerance to drought stress, had high STI values, and showed recovery ability and proline content. These lines have the potential to be released as candidates for new varieties. In addition, these lines have great potential as a new genetic source for upland rice breeding programs.
Does fiscal devaluation always work? A sectoral and business cycle perspective
Authors: Cizkowicz Piotr , Rzońca Andrzej , Wojciechowski Wiktor 
Published: June 2023

Abstract
The study verifies effects of fiscal devaluation on a broad set of sector and firm-level macroeconomic variables among European economies in years 1995-2019. Using local projection and panel data models it finds that fiscal devaluation impact on employment is stronger in services and small businesses, where production is most labour intensive. In turn, in manufacturing, where production is capital intensive, firms may substitute capital for labour in response to fiscal devaluation. Lastly, fiscal devaluation favours employment in recessions more than in times of economic expansions, as only in recessions it reduces labour costs in a more lasting way.
JEL codes: C33, E24, E62, E63, J50, H30, H60
ZmLLG1 gene affects the development of the maize root system
Authors: Sun Siyu , Yang Yulu , Shan Richard , Wu Hao , Xie Hongxue 
Published: June 2023

Abstract
Nutrient uptake by plant roots seriously affects the growth and yield of maize. AUX/IAA encoding genes play an important role in auxin signal transduction and regulating the growth of plant roots.In this study, AUX/IAA gene ZmLLG1 was cloned from maize. The expression level of the ZmLLG1 gene in different tissues was detected by qRT-PCR, and ZmLLG1 was found to be overexpressed in the roots of maize. Arabidopsis thaliana and mutant maize were both analyzed by statistical method. A yeast two-hybrid experiment, BiFC experiment and pull-down experiment were used to verify the interactions between the protein and ZmLLG1.The CoDing Sequence (CDS) of ZmLLG1 is 684 bp and encodes a protein composed of 227 amino acids. A subcellular localization analysis confirmed that ZmLLG1 was localized in the nucleus. The ZmLLG1 gene was expressed in all tissues of maize, but its expression level was highest in the root. The root length and number of lateral roots of zmllg1 mutant maize were much lower than those of wild-type B73 maize, and ZmLLG1 was responsive to auxin. The yeast two-hybrid assay, BiFC experiment and pull-down assay showed that ZmLLG1 could interact with ZmARF5, ZmARF7, and ZmARF25 to regulate the growth and development of the maize root system. Keywords AUX/IAA, Root, ARF, Maize mutant, Protein
Fear of hypoglycaemia among insulin-treated patients with type 2 diabetes mellitus: a cross-sectional study
Authors: Qiu Jiajia , Wang Panxia , Qin Nan , Lv Cheng , Liang Xinyu , Jiang Sheng 
Published: June 2023

Abstract

To analyze the occurrence and influencing factors of fear of hypoglycemia in type 2 diabetes mellitus treated with insulin. From June 2021 to June 2022, hospitalized T2DM patients with a history of hypoglycemia episodes in the past six months were randomly selected as research subjects in the First Affiliated Hospital of Xinjiang Medical University by convenient sampling, and paper questionnaires were distributed on the spot and collected on the spot by researchers who had received unified training, including general information questionnaire, hypoglycemic fear scale (HFS-II.) A total of 300 questionnaires were distributed, and 278 valid questionnaires were recovered, with an effective recovery rate of 92.7%. Among the 278 patients with T2DM, 171 were male, (61.5%), 107 were female, (38.5%), age 58 (53, 66) years, glycated hemoglobin 8.76% (7.4%, 10.32%), HFS-II total score was 19.5 (10.75, 31). The total scores of HFS-II. in T2DM patients with different disease course, BMI, gender ,ethnicity, hypoglycemia type, frequency of blood glucose monitoring, number of hypoglycemia in the past six months, and peripheral neuropathy lesions were significantly compared(P<0.05). The results of multiple linear regression analysis show that the regression equation is significant, F=13.119, P<0.001. Among them, ethnicity(Uygur B=1.036, P<0.001), blood glucose monitoring frequency (>3 times a week, B=1.196, P<0.001; ≤1 time per month, B=0.883, P<0.001; 2~3 times a month B=0.678, P=0.014), the number of occurrences of hypoglycemia in the past six months (3~4 times B=0.958,P<0.001;>6 times B=0.939, P<0.001), hypoglycemia type (severe hypoglycemia B=1.273, P=0.007;pseudohypoglycemia B=1.195, P<0.001;Symptomatic hypoglycemia B=0.592, P=0.029) was an influencing factor in FoH and explained 28.3% of the total variation (R2=0.306, adjusted R2=0.283). Ethnicity, the number of hypoglycemia occurrences in the past six months, the type of hypoglycemia, and the frequency of blood glucose monitoring are the influencing factors of FOH in T2DM patients. Medical staff should screen patients' psychology early, actively provide individualized treatment, strengthen diabetes education.
Prevention and Treatment of Neonatal Hypothermia through an Implementation Science Study in Jaltenango Chiapas, Mexico
Authors: Garcia Alejandro Frade , Azcarraga Lucila Servitje , Olivas María Azucena Espinosa , Ulloa Laila Zulema Garcia , Santisteban Rodrigo Garcia , Sosa Mariana Yulian Montano , Liu Enju , Hansen Anne 
Published: June 2023

Abstract
Introduction: Neonatal Hypothermia is preventable but common, contributing to neonatal morbidity and mortality especially in low resource settings. Kangaroo Mother Care (KMC) is the preferred method to prevent hypothermia, but relying on it as a continuous heat source is challenging. Other sources of external heat designed for low resource settings are an important potential complement to KMC. This study introduced a novel Infant Warmer in a low resource community hospital through an implementation science approach.
Methods: We conducted a prospective, interventional cohort study in Jaltenango Chiapas, Mexico from January to November 2022. Our intervention was 1) an educational program about the importance of euthermia and optimal thermoregulatory practices including KMC, and 2) provision of an Infant Warmer designed for low-resource settings with training of healthcare providers. Our hypothesis was that neonatal hypothermia rates would decrease after our intervention. Our aims were to reduce hypothermia, and increase knowledge and confidence regarding neonatal thermoregulation. The study had three phases: Pre-Intervention, Intervention, and Warmer Use. We collected clinical data during the Pre-Intervention and Warmer Use Phases, including temperature on admission and six hours later (called “Follow-Up”). At three-time points we conducted surveys of healthcare providers regarding their knowledge of hypothermia, confidence in keeping newborns warm, and attitudes regarding the Warmer. We also conducted a Parent Survey.
Results: We studied 372 newborns. Comparing Pre-Intervention to Warmer Use Phases, rates of hypothermia decreased from 62% to 27% on admission, and 59% to 11% on Follow-Up. The mean admission and Follow-Up temperatures increased by 0.06°C and 0.23°C (p=0.003) respectively. Healthcare providers' knowledge of hypothermia and confidence in keeping newborns warm also improved throughout the study.
Conclusion: We found high baseline rates of neonatal hypothermia in this low resourced hospital. We successfully lowered rates and improved knowledge of hypothermia by providing education and appropriate thermoregulatory equipment to complement KMC.
IRB-P00043372.
Trend Analysis for Different Types of Aerosols in Conjugation with Temperatures for the Indian Region During the Post-Monsoon Season (1980-2019)
Authors: Yadav Harsh , Mehta Manu , Jain Shruti , Singh Sandhya , Bhandari Shrishti , Nihlani Seema 
Published: June 2023

Abstract

This study has made an effort to understand the long-term changes of aerosol loadings as well as temperature in the post-monsoon season (October-November) over a 40-year long period (1980–2019) for the Indian region using MERRA-2 reanalysis data. Special emphasis has been given on the highly populated and polluted cities of Delhi, Mumbai, Kanpur, and Jaipur. The focus was put on the total Aerosol Optical Depth (AOD) and on that of Black Carbon (BC), Organic Carbon (OC) and Sulphates. The rapid urbanisation and increasing population of the cities have brought along increasing air pollution levels, and an increase in AOD levels, especially in the recent decades. The changes in temperature were analyzed by studying the air temperature at 2m height and the surface air temperature parameters. The temperatures, although at varying rates, have increased throughout the Indian region in the past decades. The long-term trend analysis of aerosols showed an increase in rapid rise in the AOD levels. Corresponding increase or decrease in temperature trends have been discussed.
Identification of Immune-Related Hub Genes in Multiple Myeloma
Authors: Sheng Xinge , Hong Xiuli , Lu Jingyuan , Huang Fangfang , Deng Huilan , Lu Quanyi 
Published: June 2023

Abstract
Purpose:: Multiple myeloma(MM) is a common malignant tumor in the blood system. Despite recent advances in its treatment, its symptomatic remission rate and survival rate are still not optimistic. In the future, it is necessary to continue to search for different treatment targets and new treatment methods in order to improve the quality of life and survival time of patients with MM. The study aims to explore the potential immune related pivotal genes and immune infiltration patterns in MM.
Methods: The study included peripheral blood samples from patients with MM who our hospital from October 2020 to April 2022. Obtain a gene chip for research from a comprehensive gene expression database, perform differential expression analysis on the processed gene dataset, and then perform functional enrichment analysis, weighted gene co expression network analysis, GSEA immune infiltration analysis, and LASSO regression analysis on the obtained differential expression genes to obtain the hub genes. Finally, the hub gene TNFSF14 (LIGHT) was validated by qRT-PCR.
Results: In the study, three immune-related hub genes (ADAM8, CR2, and TNFSF14) and three main types of peripheral immune cells (activated CD8 T cells, macrophages, and plasma cell like dendritic cells) were obtained, which are closely related to the pathogenesis of MM. Then, by collecting peripheral blood samples from some patients in our hospital and conducting real-time fluorescence quantitative polymerase chain reaction, it was confirmed that the hub gene TNFSF14 (LIGHT) mined in this study was highly expressed in peripheral blood samples from patients with MM, which may indicate that it plays a pathogenic role in MM.
Conclusion: The study found that immune-related hub genes (ADAM8, CR2, and TNFSF14) are closely related to the pathogenesis of MM, and should be further researched.