bluuebunny/crossref_metadata_2025_split huggingface dataset:
Turkish Validity and Reliability Study of the Pregnancy Quality of Life Scale
Authors: AYAN Gamze , APAY Serap EJDER 
Published: November 2024

Abstract

Background: Turkish validity and reliability of the Pregnancy Quality of Life Scale
The aim of the study was to bring them into Turkish society.
Methods: The study was carried out methodologically between April 2021 and April 2022 at Bayburt State Hospital, which is the only public hospital operating in the city center of Bayburt. In the study, a total of 355 pregnant women who met the research criteria and volunteered to participate in the study were examined without selecting a sample. Validity and reliability analysis of language and content validity, explanatory and confirmatory factor analysis, cronbach- α coefficient were used.
Results: Content validity of the Quality of ory factor analysis of the scale, it was determined that it had a single factor structure and factor loads were in the appropriate range. As a result of confirmatory factor analysis, it was found that the single factor structure of the scale was valid and the fit indices weLife in Pregnancy Scale was carried out with expert opinions. As a result of the explanatre appropriate. In internal consistency analyses, item-total score correlations were sufficient, and the total cronbach-α coefficient of the scale was 0.628 for the first trimester, and 0.628 for the II. 0.727 for trimester, III. It is 0.698 for trimester.
Conclusion: It has been determined that the Pregnancy Quality of Life Scale is a valid and reliable measurement tool in Turkish.
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies
Authors: Wang Luping , Chen Sheng , Jiang Linnan , Pan Shu , Cai Runze , Yang Sen , Yang Fei 
Published: November 2024

Abstract

The large models, as predicted by scaling law forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. These large models require substantial computational resources and GPU memory to operate. When adapting large models to specific downstream tasks, their massive parameter scale poses a significant challenge in fine-tuning on hardware platforms with limited computational power and GPU memory. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) offers a practical solution by efficiently adjusting the parameters of large pre-trained models to suit various downstream tasks. Specifically, PEFT adjusts the parameters of pre-trained large models to adapt to specific tasks or domains, minimizing the introduction of additional parameters and the computational resources required. This review mainly introduces the preliminary knowledge of PEFT, the core ideas and principles of various PEFT algorithms, the applications of PEFT, and potential future research directions. By reading this review, we believe that interested parties can quickly grasp the PEFT methodology, thereby accelerating its development and innovation.
Determination and Application of a New Drought Resistant Control Water Level for Multi-Reservoir Systems Based on Water Resource Allocation Model
Authors: cheng Yaping , Yan Ziqi , Yan Denghua , Liu Miao , Wei Ruishen 
Published: November 2024

Abstract

With the increasing frequency of global drought events, it is essential to implement effective monitoring and early warning systems. In China, the concept of the "drought-limited water level" has provided a new indicator for drought management and reservoir operations. This study proposes a refined optimization model for determining the drought-limited water level in multi-reservoir systems based on a general water allocation and simulation model. Applied to the typical reservoirs in the North China Plain, the drought-limited water level and corresponding drought management strategies of each reservoir were precisely determined. Additionally, a comparative analysis was conducted between the traditional aggregated optimization approach and the proposed refined optimization method. Results indicate that the application of drought-limited water levels effectively mitigates regional water shortage during drought, and transforms the continuous severe damage into relatively milder wide shallow damage. This adjustment supports efficient spatiotemporal allocation of water resources. Furthermore, this method improves the accuracy of drought-limited water level determination for individual reservoirs and effectively reduces unnecessary water shortage risk. The findings demonstrate the model is well-suited for determining drought-limited water levels in complex multi-reservoir systems, providing important references for the engineering promotion of drought-limited water levels and drought-resistant management of reservoirs.
Soil C and N and specific bacterial functions affect greenhouse gas emissions in dryland rice under water and fertilizer coupling management
Authors: Tan Hao , An Xiuhua , Tuo Yunfei , He Xiahong 
Published: November 2024

Abstract

Background
Dryland rice has obvious advantages in saving water and fertilizer and improving agricultural productivity. However, irrational application of fertilizer and irrigation by farmers is prone to cause a large amount of greenhouse gas (GHG) emissions. It is of great environmental significance to explore the greenhouse gas emissions in dryland rice under water-fertilizer coupling regimes.
Methods
In this study, we set up the three irrigation levels of W1 (200 m3·hm-2), W2 (300 m3·hm-2), and W3 (400 m3·hm-2), the four fertilizer levels of F1 (400·kg-hm-2), F2 (600·kg-hm-2), F3 (800·kg-hm-2), and F4 (1000 kg·hm-2) with a total of twelve water and fertilizer treatments in a completely orthogonal combination as well as a non-irrigated and non-fertilized CK as a control in a dryland rice farmland. The cumulative greenhouse gas (GHG) emissions, global warming potential (GWP), and greenhouse gas intensity (GHGI) of dryland rice in the growth period under different irrigation and fertilization treatments were monitored, the differences in soil physicochemical properties and bacterial structure and function under different water and fertilizer treatments were analyzed, and the effects of the representative C and N as well as the specific functions of bacteria in soil on the average cumulative GHG emissions through the structural equation model were revealed.
Results
The data showed that the increase in irrigation and fertilizer application increased the soil C and N content and yield of dryland rice. The CO2 cumulative emissions also increased by 62.40% compared with CK, but the CH4 and N2O cumulative emissions significantly decreased by 98.20% and 42.67%, respectively. It was worth noting that the GWP of the W3F4 treatment had a maximum value of 22085.64 kg CO2/ha, but the GHGI was lower. The relative abundance in Acidobacteriota was low. However, it had a great importance for water and fertilizer treatments. Although the functional relative abundance in Chemoheterotrophy and aerobic_chemoheterotrophy was more than 30%, the relative abundance in nitrate_ reduction, predatory_or_exoparasitic, and chitinolysis function was less than 3.33% which directly affected the GHG emissions. Moreover, the soil C and N indirectly affected the GHG emissions through soil bacterial functions.
Conclusions
This study can provide a reference basis for the response of the GHG emissions to soil C and N and specific bacterial function in dryland rice under different water and fertilizer regimes, as well as a practical guidance for attenuating the GHG emissions in farmers' production.
“Ethical pharmaceutical practices and beliefs in Lebanon: a national cross-sectional study”
Authors: Issa Aline , Fadel Bahia 
Published: November 2024

Abstract

Background and Objectives Pharmacy practice is in constant evolution. Ethical dilemmas are more often encountered and more complicated therefore the need to reform the code of pharmaceutical ethics in Lebanon is growing. Until today there has been no study investigating the ethical attitude and beliefs among pharmacists and pharmacy students in Lebanon. The primary objective of this present study is to explore and understand pharmacists’ knowledge and attitudes towards ethics and good pharmacy practice in daily experience in Lebanon and to determine the factors affecting these attitudes.
Methods This was a cross-sectional study conducted among pharmacists and pharmacy students in Lebanon using a snowball sampling technique. An online questionnaire based on Google forms was used to collect data. Descriptive and inferential analyses were conducted using SPSS statistics 26. Chi-square and Fisher exact tests were used. Tests were two-tailed and results with a p-value of ≤ 0.05 were considered statistically significant.
Results Six hundred eighty-seven pharmacists and pharmacy students responded to the survey. Participants showed high implication in a patient-centered and a collaborative approach (90%). The study shows financial pressure on pharmacist resulting in selling unneeded products (68%), selling products with the highest profit margin (57%) and below the official price (12%). Collaboration with other healthcare providers was a core standard in pharmacy practice (93%) and 47% assured the need for ongoing continuous education after licensure. The study shows the youngest age category having the highest agreement on continuous education being essential for maintaining licensure (65%, p=0.02). Pharmacists working outside Beirut and Mount Lebanon tend more to refuse overlooking expiry dates compared to other regions (77% vs 67%, p=0.017). Pharmacists working in the community pharmacy field tend to promote self-medication and sell unneeded products significantly more than those working in other pharmacy fields (p=0.001) and are significantly more implicated in personal development compared to other pharmacy fields (p=0.002).
Conclusion The present study investigated the ethical practices of pharmacists in Lebanon shedding the light on challenges and dilemmas facing the pharmacist in daily practice. Further research on a large scale is needed helping to improve the code of ethics and guiding the Lebanese pharmacists in their daily practice.
Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis
Authors: Du Jian , Zhou Tian , Zhang Wei , Peng Wei 
Published: November 2024

Abstract

Background: Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosing OA, providing new avenues for early diagnosis and treatment.
Methods:From the Gene Expression Omnibus (GEO) database, we first obtained OA synovial tissue microarray datasets. Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential signature genes of OA，and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between signature genes and immune cells.
Results: The Limma package and WGCNA identified a total of 72 DEGs related to OA，of which 12 were up-regulated and 60 were down-regulated. Then, the PPI network analysis identified 21 hub genes, and three machine learning algorithms finally screened four feature genes (BTG2, CALML4, DUSP5, and GADD45B). The ANN diagnostic model was constructed based on these four feature genes. The AUC of the training set was 0.942, and the AUC of the validation set was 0.850. Immune cell infiltration analysis revealed B cells memory, T cells gamma delta, B cells naive, Plasma cells, T cells CD4 memory resting, and NK cells The abnormal infiltration of activated cells may be related to the progression of OA.
Conclusions: In this study, BTG2, CALML4, DUSP5, and GADD45B were identified as potential characteristic genes for OA, and an ANN diagnostic model with excellent diagnostic performance has been developed. Therefore, the diagnostic model established in this research can serve as a reliable reference for early OA diagnosis and provide a novel perspective on the pathogenesis of OA.
Bat-specific adaptations in interferon signaling and GBP1 contribute to enhanced viral tolerance
Authors: Banerjee Arinjay , Gonzalez Victoria , Lobb Briallen , Cote Jacob , Bhuinya Arkadeb , Tubb Adriana , Nuthalapati Stephen , Asavajaru Akarin , Zhou Yan , Misra Vikram , Falzarano Darryl , Sweeney Trevor , Gobeil Sophie , Wang Lin-Fa , Doxey Andrew 
Published: November 2024

Abstract

Bats are reservoirs of emerging zoonotic viruses of concern that cause severe disease in humans and agricultural animals. However, it is poorly understood how bats are able to tolerate diverse viral infections, knowledge that could help pave the way for new therapeutic strategies. Here, we characterized antiviral pathways in two divergent bat species, Pteropus alecto and Eptesicus fuscus, identifying unique bat-specific mechanisms underlying their enhanced antiviral tolerance. We demonstrate the critical roles of STAT1 and STAT2 in IFNβ signaling, along with species-specific adaptations that collectively contribute towards a “steady and ready” antiviral state in bat cells. Unlike in humans, we find that bat interferon signalling processes resist the immune antagonistic properties of viruses like MERS-CoV which further explains the ability of bats to tolerate coronavirus infections. Using transcriptomic analysis, we identified canonical and non-canonical interferon stimulated genes (ISGs) including two key bat genes, IFIT1 and GBP1. Compared to their human orthologs, we show that bat IFIT1 and GBP1 exhibit enhanced antiviral activity against a wide range of RNA and DNA viruses, including coronaviruses and additional bat-derived poxviruses (e.g., Eptesipoxvirus). Ultimately, our work provides important insights into the evolution of enhanced interferon-mediated antiviral responses in bats, contributing to their unique ability to resist viral diseases.
TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference
Authors: Lin Zhixiang 
Published: November 2024

Abstract

RNA velocity inference is a valuable tool for understanding cell development, differentiation, and disease progression. However, existing RNA velocity inference methods typically rely on explicit assumptions of ordinary differential equations (ODE), which prohibits them to capture complex transcriptomic expression patterns. In this study, we introduce TIVelo, a novel RNA velocity estimation approach that first determines the velocity direction at the cell cluster level based on trajectory inference, before estimating velocity for individual cells. TIVelo calculates an orientation score to infer the direction at the cluster level without an explicit ODE assumption, which effectively captures complex transcriptional patterns, avoiding potential inconsistencies in velocity estimation for genes that do not follow the simple ODE assumption. We validated the effectiveness of TIVelo by its application to 16 real datasets and the comparison with five benchmarking methods.
Exploring dominant strategies in iterated and evolutionary games: a multi-agent reinforcement learning approach
Authors: Wang Long , Su Qi , Wang Hongyu , Xia Yu 
Published: November 2024

Abstract

Exploring dominant strategies in iterated games holds profound theoretical and practical significance across diverse domains. Previous studies, through mathematical analysis of limited cases, have unveiled classic strategies such as tit-for-tat, generous-tit-for-tat, win-stay-lose-shift, and zero-determinant strategies. While these strategies offer valuable insights into human decision-making, they represent only a small subset of possible strategies, constrained by limited mathematical and computational tools available to explore larger strategy spaces.
To bridge this gap, we propose a novel approach using multi-agent reinforcement learning to delve into complex decision-making processes that go beyond human intuition. Our approach has led to the discovery of a new strategy, i.e., memory-two bilateral reciprocity (MTBR) strategy.
MTBR consistently outperforms a wide range of strategies in pairwise interactions while achieving high payoffs. When introduced into an evolving population with diverse strategies, MTBR demonstrates dominance and fosters higher levels of cooperation and social welfare in both homogeneous and heterogeneous structures, as well as across various game types. This high performance is verified by simulations and mathematical analysis.
Our work highlights the potential of multi-agent reinforcement learning in uncovering dominant strategies in complex environments, offering a new perspective on strategy exploration in iterated games.
Rare Genetic Diseases in Behavioral Health: A Comprehensive Review of Therapeutic Strategies and Diagnostic Delay for Childhood Through Adulthood With Paired Economic Impact Analysis
Authors: Garrison Sheldon R. , Siegel Isaac J. , Takala Christopher R. , Vaithilingam Sarah L. , Yang Gene W. , Zoghbi Anthony W. , Hartig Madeline M. , Vadapalli Sreya , Anderson Margaret E. 
Published: November 2024

Abstract

Rare genetic diseases (RDs) with primary neuropsychiatric symptoms pose unique challenges for diagnosis and management. While the majority of these RDs have neuropsychiatric symptoms that are secondary to the RD, a subset present with primary neuropsychiatric symptoms directly linked to their underlying pathophysiology. This subset has significant unmet medical need with delayed diagnoses leading to prolonged delays in treatment optimization and the trialing of medications that fail to target the underlying pathophysiology. This comprehensive review identifies 108 RDs with central neuropsychiatric symptoms that have a 7.7-year average diagnostic delay. Optimal management strategies for these RDs typically includes non-psychotropic medications, dietary adjustment, avoidance of certain drug classes and other targeted treatments that target the underlying pathophysiology before improvement of the neuropsychiatric symptoms is observed. Surprisingly, despite the limited number of RDs that fit this unique profile, the annual economic burden for these conditions had an annual mental health care-related inpatient charges totally $4.2 billion. Addressing the diagnostic delay and optimizing management for these specific conditions must include increased involvement across multiple specialties, including psychiatry, family medicine, pediatricians, medical genetics, as well as an enhanced strategy for genetic testing to ensure the prompt initiation of condition-specific therapies for affected individuals.
Antibiotic susceptibility situation of environmental Legionella pneumophila isolates in Southern Germany
Authors: Lang Hannah , Marschang Rachel E. , Bismarck Doris , Heusinger Anton , Müller Elisabeth , Felten Martin 
Published: November 2024

ABSTRACT

Antimicrobial resistance is an emerging problem in hospitals and long-term healthcare facilities. Early detection of susceptibility pattern changes in pathogenic bacteria can prevent treatment failures. Therefore, this study chose to investigate the antibiotic susceptibility situation of Legionella pneumophila isolates from hospitals and long-term healthcare facilities in Southern Germany. Serogroups and minimal inhibitory concentrations (MICs) of nine antibiotics were determined from 41 L. pneumophila strains. In total, 28% of the collected strains belonged to the more pathogenic serogroup 1, whereas 72% belonged to serogroups 2–14. Among the tested antibiotics, rifampicin had the lowest MIC90 value. The MIC90 values can be summarized in the following order: rifampicin &lt; levofloxacin &lt; moxifloxacin &lt; ciprofloxacin &lt; clarithromycin &lt; azithromycin &lt; erythromycin &lt; doxycycline &lt; tigecycline.
A catalogue of colour terms: principles of selection and structure
Authors: Rossal V. V. 
Published: November 2024

Author of the article motivates the principles of the formation of the catalogue of colour terms in the Russian language which can serve as an effective tool of pre-translation analysis while translating foreign texts into Russian.The article contains the basis of the principle of vocabulary retrieval from lexicographic sources based on the semantic principles that prove to be significant for translation; the necessity of etymological analysis of the analysed word; the consideration of figurative meanings and connotations.The author explains the choice of the number of dictionaries used for the cataloguing, reasons the necessity of using national text corpora, literary fiction and lexicographic research aimed at colour terms analysis and demonstrates examples of vocabulary description from the catalogue.Twelve basic colour terms are chosen as hypernyms and compose twelve semantic fields. The principles of hyponyms identification established by the author reason the choice of the vocabulary for the catalogue, but the semantic fields are purposefully open and can be equipped with more hyponyms extracted from the historical texts or formed in the process of word formation in the language. The author shows the principle giving an example of the semantic field of the white colour and showing the outline of the analysis of the colour terms алебастровый / alabaster showing the steps of the catalogue entry formation.In the course of the research were used following methods: definitional, componential and etymological analyses, which helped to thoroughly explore all the significant features of the colour terms in the Russian language.The research has a pragmatic scope and can be used while translating into Russian colour terms of different languages.