bluuebunny/crossref_metadata_2025_split huggingface dataset:
The validity of single-item measures of health-related quality of life across groups differing in acute respiratory symptom severity
Authors: Smith Adam B. , Ware John E. , Aluko Patricia , Kulasekaran Anuradha 
Published: August 2024

Abstract
Purpose
Practical considerations precluding health-related quality of life (HRQOL) monitoring in population and clinical research have spawned development of improved items for more brief surveys of frequently measured HRQOL outcomes. The aim of this study was to validate the use of the Quality of Life General (QGEN-8), a shorter 8-item alternative to the longer 36-item short form (SF)-36 Health Survey for measuring the same eight HRQOL domains across groups of adults with varying severity of acute respiratory symptoms, such as cough and sore throat.

Methods
National Opinion Research Center (NORC) representative probability (N = 1,648) and supplemental opt-in (N = 5,915) U.S. adult samples were surveyed cross-sectionally online in 2020. Parallel analyses compared QGEN-8 and SF-36 estimates of group means for each of eight matching profile domains and summary physical and mental scores across groups differing in severity of acute symptoms and chronic respiratory conditions using analysis of covariance (ANCOVAs) controlling for socio-demographics and presence of chronic respiratory conditions.

Results
In support of discriminant validity, ANCOVA estimates of QGEN-8 means with SF-36 estimates revealed the same patterns of declining HRQOL with the presence and increasing severity of symptoms and chronic condition severity.

Conclusion
QGEN-8® shows satisfactory validity and warrants further testing in cross-sectional and longitudinal population and clinical survey research as a more practical method for estimating group differences in SF-36 profile and summary component HRQOL scores.
Urban equilibrium: legal imperatives for sustainable development and habitat preservation in Shenzhen, China
Authors: Zhanwen Que , Islam Md. Ziaul 
Published: August 2024

AbstractShenzhen, a bustling metropolis in China, is currently grappling with the delicate balance between urban expansion and environmental preservation. This study finds that the disintegration of habitats due to urban development in Shenzhen city significantly impacts the ecosystem of animals and plants, detrimentally altering their living conditions. The study results reveal that the expansion of construction land in China has surged by an impressive 84.39% between 2000 and 2020, which has come at the cost of diminishing wetlands, farmland, and woodlands by 57.24%, 16.58%, and 1.86%, respectively. Between 1980–2020, the urban expanse of Shenzhen expanded dramatically, growing from 16.0 square kilometres to 850.2 square kilometres. This transformation was primarily driven by the rapid conversion of forested areas, which saw a staggering decrease of approximately 300% to 780 km2 by 2020. Additionally, half of Shenzhen’s expansion can be attributed to the transformation of agricultural land, with an additional 40% stemming from the clearing of forests and approximately 9% resulting from the decrease in water resources. Furthermore, non-urban green spaces, particularly built-up areas, have significantly expanded, now encompassing approximately 60% of the city in 2018. Therefore, based on the existing literature and authoritative legal texts, this study aims to examine the legal frameworks of the Shenzhen government and, along with China’s national legislation, to make room for wildlife and biodiversity conservation in a metropolis like Shenzhen.
Characterising the urinary acylcarnitine and amino acid profiles of HIV/TB co-infection, using LC–MS metabolomics
Authors: Pretorius Charles , Luies Laneke 
Published: August 2024

Abstract
Introduction
The human immunodeficiency virus (HIV) and tuberculosis (TB) co-infection presents significant challenges due to the complex interplay between these diseases, leading to exacerbated metabolic disturbances. Understanding these metabolic profiles is crucial for improving diagnostic and therapeutic approaches.

Objective
This study aimed to characterise the urinary acylcarnitine and amino acid profiles, including 5-hydroxyindoleacetic acid (5-HIAA), in patients co-infected with HIV and TB using targeted liquid chromatography mass spectrometry (LC–MS) metabolomics.

Methods
Urine samples, categorised into HIV, TB, HIV/TB co-infected, and healthy controls, were analysed using HPLC–MS/MS. Statistical analyses included one-way ANOVA and a Kruskal-Wallis test to determine significant differences in the acylcarnitine and amino acid profiles between groups.

Results
The study revealed significant metabolic alterations, especially in TB and co-infected groups. Elevated levels of medium-chain acylcarnitines indicated increased fatty acid oxidation, commonly associated with cachexia in TB. Altered amino acid profiles suggested disruptions in protein and glucose metabolism, indicating a shift towards diabetes-like metabolic states. Notably, TB was identified as a primary driver of these changes, affecting protein turnover, and impacting energy metabolism in co-infected patients.

Conclusion
The metabolic profiling of HIV/TB co-infection highlights the profound impact of TB on metabolic pathways, which may exacerbate the clinical complexities of co-infection. Understanding these metabolic disruptions can guide the development of targeted treatments and improve management strategies, ultimately enhancing the clinical outcomes for these patients. Further research is required to validate these findings and explore their implications in larger, diverse populations.
Machine Learning-based Differentiation of Japanese Plum Cultivars Based on Metabolite Profiling
Authors: Silue Yardjouma , Fawole Olaniyi A. , Majoni Taongashe , Opara Umezuruike L. , Okolie Jude A. 
Published: August 2024

AbstractThis study investigates the metabolite diversity of twelve Japanese plum cultivars grown in South Africa to understand their differential organoleptic characteristics and nutritional properties. The goal is to differentiate or associate these plum cultivars based on their metabolic profiles. Metabolite profiling was conducted using gas chromatography-mass spectrometry (GC-MS) at different postharvest ripening stages. Different unsupervised machine learning algorithms were applied: hierarchical clustering, K-means clustering, Density-Based Spatial Applications with Noise, and principal component analysis (PCA). Results revealed that each cultivar contains a unique combination of 13 amino acids, 4 sugars (contributing to organoleptic characteristics), and numerous phenolic compounds and antioxidant activities (contributing to nutritional value). The levels of these compounds are cultivar-dependent and vary with postharvest stages. The number of clusters of plum cultivars varied with both the clustering algorithm and postharvest stages. However, certain cultivars were consistently grouped regardless of the clustering method, indicating similar characteristics and responses to storage and shelf-life conditions. Similar consistent groupings were observed after cold storage and shelf life. Our findings also showed that K-means clustering is the most effective model for plum cultivar differentiation based on the Silhouette Score and Davies-Bouldin Index. This study enhances our understanding of how metabolites evolve over different postharvest stages and provides a robust framework for differentiating plum cultivars, which can aid in sorting and grading operations. The research offers actionable insights to improve postharvest handling and storage practices, which are critical for maintaining the nutritional quality of plums, an important fruit for human health.
Exercise and resting periods: Thermal comfort dynamics in gym environments
Authors: Avci Ali Berkay , Balci Görkem Aybars , Basaran Tahsin 
Published: August 2024

AbstractPhysical exercise spaces emerged as popular facilities due to recognizing the significance of physical well-being. This study investigates the relationship among physiological responses, human body energy transfer modes, and indoor environmental conditions in influencing thermal comfort perception within indoor physical exercise space. Seven male participants engaged in a 30 min constant-work-rate cycling exercise and a 20 min resting period in a climatic chamber. The physiological and environmental responses were recorded during the experiments, and the body’s energy transfer modes were calculated using the collected data. The dataset was prepared using the 2 min averages of the collected data and calculated parameters across the experiment phases, including the features of skin temperature, core temperature, skin relative humidity, heart rate, oxygen consumption, body’s heat transfer rates through convection, radiation, evaporation, and respiration, net metabolic heat production rate (metabolic rate minus external work rate), indoor air temperature, indoor relative humidity, air velocity, and radiant temperature. Gradient boosting regressor (GBR) was selected as the analyzing method to estimate predicted mean vote (PMV) and thermal sensation vote (TSV) indices during exercise and resting periods using features determined in the study. Thus, the four GBR models were defined as PMV-Exercise, PMV-Resting, TSV-Exercise, and TSV-Resting. In order to optimize the models’ performances, the hyperparameter tuning process was executed using the GridSearchCV method. A permutation feature importance analysis was performed, emphasizing the significance of net metabolic heat production rate (24.2%), radiant temperature (17.0%), and evaporative heat transfer rate (13.1%). According to the results, PMV-Exercise, PMV-Resting, and TSV-Resting GBR models performed better, while TSV-Exercise faced challenges in predicting exercise thermal sensations. Critically, this study addresses the need to understanding the interrelationship among physiological responses, environmental conditions, and human body energy transfer modes during both exercise and resting periods to optimize thermal comfort within indoor exercise spaces. The results of this study contribute to the operation of indoor gym environments to refine their indoor environmental parameters to optimize users’ thermal comfort and well-being. The study is limited to a small sample size consisting solely of male participants, which may restrict the generalizability of the findings. Future research could explore personalized thermal comfort control systems and synergies between comfort optimization and energy efficiency in indoor exercise spaces.
Characteristics of opinions in the societal and non-societal domains
Authors: Singh Loitongbam Gyanendro , Singh Sanasam Ranbir 
Published: August 2024

Abstract
With the increasing availability of user opinions on the web, understanding the distinct nature of opinions in societal and non-societal contexts becomes crucial for opinion mining and sentiment analysis tasks. Societal topics, encompassing social unrest, terrorist acts, and government policies, differ significantly from non-societal topics like product reviews, movie reviews, and restaurant reviews. Given the regional specificity of societal issues and the lack of sentiment-annotated resources for them, this paper highlights the need to comprehend the differences in opinions between these domains for effective sentiment analysis. Through statistical text and network analysis, it investigates word usage, sentiment word association, and homogeneity in societal versus non-societal contexts. The study also explores graph-based analysis as a novel approach to sentiment analysis, considering its advantage in easily expanding context through the addition of nodes, as opposed to the complexity of inserting relevant tokens in text. The findings suggest that while non-societal sentiment resources might not be directly applicable to societal domains, graph-based analysis offers promising avenues for sentiment analysis in diverse societal topics.
A Time-Limited Scoping Review Investigating the Use of Wearable Biosensors in the Substance Use Field
Authors: Jeyadevan Ashani , Grigg Jodie 
Published: August 2024

AbstractPurpose of ReviewThe purpose of this time-limited scoping review was to investigate applications of wearables under investigation in the substance use field and consider the feasibility of these devices as an overdose intervention tool.Recent FindingsWearables were used for drug use detection, overdose detection and/or opioid overdose detection paired with naloxone delivery. Highest accuracy was associated with wrist-worn devices interpreted with machine learning algorithms. Challenges included difficulty in continuous monitoring, optimising algorithms, variability in device accuracy, and differences between lab and community settings. Qualitative studies investigated willingness to wear biosensors, and identified key themes of comfort, security, and aesthetics.SummaryFindings represent promising first steps towards use of wearables as a harm reduction tool for people who use substances. However, few studies investigated overdose detection and real-time intervention, and devices were limited in their biosensing capacities. As technologies advance, systematic reviews are required, and devices should be evaluated and validated to ensure they are fit-for-purpose.
Fuzzy cognitive network process for software reliability and quality measurement: comparisons with fuzzy analytic hierarchy process
Authors: Yuen Kevin Kam Fung 
Published: August 2024

AbstractSoftware reliability and quality measurement has a long-lasting impact on the final products and user experiences. The analytic hierarchy process (AHP) and its various hybrid models, including fuzzy AHP, have been applied to software reliability and quality measurement in various aspects. Related studies indicate that limitations in the paired ratio scales of AHP may lead to misapplications, which most AHP users might not be aware of. To address this issue, the fuzzy cognitive network process (FCNP) is proposed as a promising alternative applied for software reliability and quality measurement. One application based Fuzzy AHP is revisited to demonstrate the feasibility and usability of the FCNP. The paper discusses conversion from the FAHP to the FCNP, examining their reproducibility, comparability, merits, and limitations. The proposed fuzzy CNP method can a valuable tool for software customers, designers, developers, testers, and purchasers, to evaluate the level of software reliability and quality in general applications within intelligent environment.
A Novel Multiple-Network Analysis Integrating Metabolomics and Pharmacology for Mechanism Study of Medicine: A Case Study of CDD-2101 Against Constipation
Authors: Shi Jingchun , Xiang Li , Lam Yan Y. , Ning Ziwan , Liu Jie , Li Yaqi , Zhou Yujuan , Zhai Lixiang , Lin Chengyuan , Zhu Lin , Bian Zhaoxiang 
Published: August 2024

AbstractConstipation affects more than 14% of the global population with complex pathogenesis. Compound Traditional Chinese medicines (TCM), such as CDD-2101, a manufactured version of a time-tested TCM, hold great promise for constipation treatment due to the multiple-component-multiple-target strategy. Here, a novel multiple network analysis based on metabolomics and network pharmacology was developed to decipher the complex interplays between CDD-2101 and constipation. First, CDD-2101 alleviated constipation in a loperamide-induced acute constipation mice model and no adverse effect was observed. Then, fecal metabolomics and phytochemistry profiles were conducted by liquid chromatography-mass spectrometry analysis. The untargeted metabolomics built a metabolomic network related to significantly modulated pathways during the constipation alleviation, including bile acid secretion, steroid hormone biosynthesis, glycerophospholipid metabolism, and linoleic acid metabolism. Based on the phytochemistry, network pharmacology predicted similar modulated pathways and built a hub-target network including retinoid X receptor alpha (RXRA), cytochrome P450 1A1 (CYP1A1), cytochrome P450 1A2 (CYP1A2), and phospholipase A2-IVA (PLA2G4). Third, integrated networks were built to select the key pathways and targets, as well as the key ingredients, including rhein, hesperetin, albiflorin, and magnolol from the TCM, which were corroborated by molecular docking, in vitro enzyme assays, and targeted metabolomics results. Finally, an integrated network was built to elucidate the multi-ingredient-multi-symptom linkage during the treatment. In conclusion, the novel multiple-network analysis revealed the complex “multi-component-multi-target-multi-pathway” mechanisms of CDD-2101 against constipation. Our work provides insight into the understanding of active compounds and pharmacological mechanisms, contributing to the development of natural medicines.
Assessing muscle quality as a key predictor to differentiate fallers from non-fallers in older adults
Authors: Michel Emeline , Zory Raphael , Guerin Olivier , Prate Frederic , Sacco Guillaume , Chorin Fréderic 
Published: August 2024

Abstract
Background
Falling is an important public health issue because of its prevalence and severe consequences. Evaluating muscle performance is important when assessing fall risk. The study aimed to identify factors [namely muscle capacity (strength, quality, and power) and spatio-temporal gait attributes] that best discriminate between fallers and non-fallers in older adults. The hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk.

Methods
184 patients were included, 81% (n = 150) were women and the mean age was 73.6 ± 6.83 years. We compared body composition, mean grip strength, spatio-temporal parameters, and muscle capacity of fallers and non-fallers. Muscle quality was calculated as the ratio of maximum strength to fat-free mass. Mean handgrip and power were also controlled by fat-free mass. We performed univariate analysis, logistic regression, and ROC curves.

Results
The falling patients had lower muscle quality, muscle mass-controlled power, and mean weighted handgrip than the non-faller. Results showing that lower muscle quality increases fall risk (effect size = 0.891). Logistic regression confirmed muscle quality as a significant predictor (p < .001, OR = 0.82, CI [0.74; 0.89]). ROC curves demonstrated muscle quality as the most predictive factor of falling (AUC = 0.794).

Conclusion
This retrospective study showed that muscle quality is the best predictor of fall risk, above spatial and temporal gait parameters. Our results underscore muscle quality as a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the aging population.
Surface Modification of a Zeolite Microfiltration Membrane: Characterization and Application to the Treatment of Colored and oily Wastewaters
Authors: Bahrouni Jamila , Aloulou Hajer , Attia Afef , Dammak Lasaad , Amar Raja Ben 
Published: August 2024

AbstractMembrane-based technologies used for water treatment can be an excellent alternative to handle wastewater including both conventional and emerging pollutants as they can provide technological (e.g., high quality of treated water) and economic (e.g., small footprint and low unit cost of production) advantages over other water treatment processes. Recently, low cost ceramic membranes fabricated from natural resources like kaolinitic clay, bentonite clay, phosphate are increasingly used owing to their low-cost starting materials, low sintering temperature and their excellent additional properties. Moreover, the modification of the surface by grafting process provides membranes appropriate for low UF process (dp < 10 nm) and suitable for micropollutants removal at relatively high permeate flux value which can be maintained during filtration due to antifouling characteristics of the UF active layer. In this work, the surface of microfiltration membranes made from natural zeolite was chemically modified by grafting with 1 H, 1 H, 2 H, 2 H-perfluorodecyltriethoxysilane molecule named PFAS. Various characterization methods and techniques, including scanning electron microscopy (SEM), mercury porosimetry, FTIR, TGA, and contact angle, were used to check the properties of the membranes surface before and after grafting. The grafted membranes pore size and porosity were reduced, as proved by SEM images. The determination of the water permeability shows a reduction from 1218 L.h−1.m−2.bar −1 to 204 L.h−1.m−2.bar −1 which confirm the surface densification. The application of the grafted membrane to the treatment of Indigo Blue (IB) colored solution and oily wastewater was investigated to evaluate the performances of this membrane in terms of permeate flux and pollutants retention. The filtration results revealed a good retention of color and oil, exceeding 95% for both parameters. Therefore, it is interesting to recommend this new low-cost membrane for the treatment of industrial wastewater containing recalcitrant pollutants such as color. The study of the effect of the treated colored solution on plant growth, shows that the presence of some residual nutrients required for crops growth, might make the IB treated water beneficial for irrigation purposes.
Precipitation Control on Weathering Intensity and Depositional Flux of Meteoric 10Be Revealed From Soil Profiles Along a Climate Gradient (Chile)
Authors: Krone Laura V. , Wittmann Hella , von Blanckenburg Friedhelm 
Published: August 2024

AbstractAlong a climate gradient in the Chilean coastal mountains, we investigated denudation rates using the meteoric cosmogenic nuclide 10Be and its ratio to stable 9Be, and chemical depletion fractions (CDFs) in bulk soil samples. We find that the fraction of 9Be released from bedrock is a sensitive indicator of weathering, similar to CDF. Meteoric 10Be decreases exponentially with depth, reflecting the reactive nature of this tracer. We also measured denudation rates by the well‐understood in situ cosmogenic 10Be system on quartz. Assuming that both systems record the same denudation rate we calculated the depositional flux of meteoric 10Be for each study site. The flux agrees to that derived from atmospheric models in the mediterranean and humid areas. In contrast, in the arid and semi‐arid areas, the calculated flux agrees with a precipitation‐derived flux, indicating delivery of 10Be to be affected by small‐scale climatic variations not reflected by current atmospheric models.
Improving Low‐Cloud Fraction Prediction Through Machine Learning
Authors: Zhang Haipeng , Zheng Youtong , Li Zhanqing 
Published: August 2024

AbstractIn this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of “too few” low clouds and “too rapid” SCT in global climate models.