bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Yoga Training on Oxidative Stress: A Randomized Controlled Trial
Authors: Sagre Siddharth , Kumari Neetu , Bhardwaj Priya , Maruthi Mulaka , Jangra Ashok , Kumar Dinesh , Ahlawat Ravinder Pal 
Published: February 2024

Abstract

Background: Oxidative stress, a critical health parameter, occurs because of an imbalance of free radicals and antioxidants and resulting in the onset of chronic diseases. Yoga is being used widely as a complementary medicine, recognized for its preventive effects on age-related chronic diseases, but the underlying effects in relation to oxidative stress are not well known.
Aim: The current research aimed to find the impact of yoga training on oxidative stress by assessing the levels of Malondialdehyde (MDA), Ferric Reducing Ability of Plasma (FRAP), and Free radical scavenging capacity by 2,2-diphenyl-1-picrylhydrazyl(DPPH) assay.
Method: Thirty sedentary university students with an average age of 23.93±1.73 years were selected and randomly allocated into control (n=15) and experimental (n=15) group. The study employed a pre-test post-test random group design, with the 12-week yoga treatment given to the experimental group while the other group serves as control. The training program consisted of asana (body postures), pranayama (breathing exercises), and relaxation exercises. The blood samples were collected in fasting conditions at the beginning and end of the intervention to measure the level of oxidative stress biomarkers.
Results and Conclusion: At baseline, statistically insignificant differences were found in the level of oxidative stress level and demographic characteristics in both groups. After the training intervention, in only experimental group statistically significant changes were observed in the level of oxidative stress biomarkers (p<0.05). The results of the study suggest that integration of yoga practice may have beneficial effect in advancing health and wellness into the lives of college students.
Analyzing Multilingual Conversations During COVID-19: An Imbalanced Class-Ensemble Learning Approach with Reweighted AdaBoost-SVM for Code-Switched Text Classification
Authors: Jaballi Samawel , Zrigui Salah , Nicolas Henri , Zrigui Mounir 
Published: February 2024

Abstract
This study confronts the challenge of analyzing multilingual, code-switched conversations during the COVID-19 pandemic, a context where traditional classifiers often fall short. We developed a cost-sensitive ensemble learning approach that combines a reweighted AdaBoost-SVM model with an SVM as its base learner, specifically designed to effectively manage the imbalanced dataset common in code-switched communication scenarios. A key innovation of our approach is the novel rebalancing of AdaBoost weights. By incrementally adjusting the weights of misclassified samples from both minority and majority classes, we achieve a more balanced classification in each iteration. This strategy significantly improves the accuracy for minority class classification, a common issue with existing models. In the testing phase, we employed a comprehensive selection of both machine and deep learning classifiers, including Naive Bayes, Decision Trees, SMOTEBoost, CNN, Bi-LSTM, etc. These classifiers underwent comprehensive evaluation across two different multilingual datasets, assessed using six distinct metrics, including P-mean. The results from our experiments demonstrate that our proposed ensemble learning approach, fine-tuned with optimal hyperparameters and leveraging M-BERT for feature extraction, achieved remarkable accuracies of 78.84%, 86.56% and 83.96% on the test sets of the CTSA, TUNIZI and combined CTSA-TUNIZI datasets, respectively. This performance not only surpassed traditional classification methods but also outperformed advanced deep learning models, such as Bi-LSTM.
Analytical and Numerical Solution for Inverse Coefficient Non-Linear Hyperbolic Equation with Periodic Boundary Condition
Authors: Yernazar Akbala , Aslan Erman , Bağlan İrem 
Published: February 2024

Abstract

This study investigates an inverse problem of unknown time-dependent coefficients in the one-dimensional nonlinear hyperbolic equation with periodic boundary conditions. The generalized Fourier method is employed to construct the Fourier coefficient for the solutions, and using iteration method convergence, the uniqueness and stability of the solution to the nonlinear problem are proved. Additionally, in order to solve the inverse problem numerically Finite Difference Method (FDM) with Gauss Seidel Iteration process is proposed. Two different implicit finite difference schemes are applied, namely, implicit and Crank-Nicolson. A numerical example is presented to illustrate the method's behavior. Both numerical predictions are close to experimental results, however, estimation of implicit scheme has lower true error and relative true error than Crank-Nicolson scheme.
Subsoiling combined with layered nitrogen application optimizes root distribution and increases summer maize grain yield
Authors: Li Xiang Ling , WANG Rui , Ji peng tao , Wang jian , Dong wei xin , Tao pei jun , Zhang yue chen 
Published: February 2024

Abstract
Background No-tillage of summer maize after the harvest of winter wheat is the primary agricultural practice on the North China Plain. However, prolonged no-tillage without deep tillage practices negatively impacts soil properties, which is detrimental to the growth and yield of summer maize.
Methods Xianyu 688 and Jifeng 2 were used as test materials, no-tillage and surface fertilizing with normal N (BC240), no-tillage and surface fertilizing with N reduction (BC180), subsoiling layered fertilization with normal N (FC240) were designed, and subsoiling layered fertilization with N reduction (FC180), assess root distribution, dry matter accumulation, N absorption and utilization, and grain yield of summer maize.
Results The FC240 and FC180 treatments significantly reduced soil bulk density in the 10-50 cm depth soil layer compared to the BC240 and BC180 treatments, and also increased total N content in the 20-50 cm depth soil layer. Root length in the 20-40 cm soil layer significantly increased with the FC240 treatment, along with a notable increase in root exudates, which improved root vitality during later growth stages. Additionally, the FC240 and FC180 treatments enhanced plant dry matter accumulation, N accumulation, and grain yield. Xianyu 688 exhibited increased N uptake efficiency, N fertilizer partial factor productivity, and N harvest index, while Jifeng 2 showed increased N uptake efficiency and N fertilizer partial factor productivity.
Conclusion It provided useful enlightenment for selecting suitable maize varieties, formulating effective management strategies and cultivating high N-efficient varieties under the condition of subsoiling combine with layered N application.
Progress in the Development of Broadly Reactive Norovirus Therapeutics
Authors: Hansman Grant S. , Kher Gargi , Svirina Anna D. , R.H.Tame Jeremy , Hartley-Tassell Lauren , Irie Hiro , von Itzstein Mark , Rudd Penny A. , Pancera Marie 
Published: February 2024

Abstract
Discovered over five decades ago, norovirus is frequently reported as the leading cause of outbreaks of acute gastroenteritis. No vaccines or antivirals are currently available. We further analyzed two of our leading norovirus inhibitors that either indirectly or directly obstructed norovirus from binding to histo-blood group antigen receptors. Using X-ray crystallography, we showed that both inhibitors engage highly conserved capsid residues amongst genetically diverse noroviruses. The indirect inhibitor, a modified Fc-linked Nanobody, showed improved binding affinity and neutralization capacity compared with the native Nanobody. More strikingly, we showed that the direct inhibitor, a chewable prebiotic tablet containing human milk oligosaccharide 2’-fucosyllactose, worked as an inhibitor for a leading norovirus. These new discoveries are expected to promote the development of effective norovirus treatments.
An Adaptive Margin Semi-supervisedLearning for Industrial Surface Inspection
Authors: Zheng Xiaoqing , Zhou Lixiang , Liu Yeqing , Xue Anke , Kong Yaguang , Wang Yaru 
Published: February 2024

Abstract
To meet the continuously increasing quality standards in industrial man-ufacturing processes, machine vision-based surface inspection systems arewidely used for checking surface quality and removing defective products.Automated Surface Inspection (ASI) algorithms are the core of the soft-ware for these systems, and their accuracy and effectiveness are crucial forthe success of these visual inspection systems. Compared with traditionalimage processing techniques-based ASI algorithms, deep learning-basedASI algorithms enable highly accurate automatic defect detection andare widely adopted by researchers in academia and industry. With alarge amount of labeled data, supervised deep learning can extract fea-tures from training samples and detect different types of surface defects.However, obtaining a large number of labeled samples is costly and oftenimpractical. Therefore, this paper proposes a semi-supervised learningapproach based on FixMatch, which requires only a limited numberof labeled samples but achieves performance comparable to supervisedlearning. This paper further introduces an uncertainty-adaptive marginmechanism to balance the learning weights between labeled and unla-beled samples, enhancing the model’s generalization ability under limitedlabeled data and preventing overfitting. Experiments on two public sur-face defect datasets (DAGM and NEU) and one industrial defect dataset(CCL) were conducted. The experimental results show that the proposedmethod, even with a small number of labeled samples, achieves high accu-racy rates of 99.83%, 99.81%, and 97.99% on the DAGM, NEU, and CCLdatasets, respectively, outperforming some supervised learning methodsand benchmark semi-supervised learning methods.
A novel measure of time series data pattern based on persistent homology that can detect the trend change of the daily all-cause mortality in Tokyo after the temperature rise
Authors: Oda Haruhisa , Ng Chris Fook Sheng , Hashizume Masahiro 
Published: February 2024

Abstract
Background: Evaluating the effect of temperature rise on mortality has been an important theme in environmental epidemiology. However, few studies examine the problem: ''Are there any differences in the mortality data pattern between recent years and former years that can be linked to climate change?" We might think we can investigate peak patterns. However, we do not have a complete list of peak types relevant to our problem. We can still try to take into account as many types of peaks as possible, but this microscopic method should not be the most straightforward way, given that our problem deals with macroscopic trends in data patterns. We will construct a new measure of data pattern based on persistent homology. With this measure, we will see the change in the mortality trends in Tokyo that took place after the climate change.
Methods: We circularly plot the time series data and apply persistent homology to this point cloud. The main output of our analysis is the hole structures represented by triangles connecting points in our data. We define our measure of data pattern to be whether multiple holes are detected. This single measure can behave like simplified versions of various peak analysis methods without the information on what peaks to focus on.
Results: With our measure, we succeed in detecting the change in the mortality trends preceded by the temperature rise in Tokyo.
Conclusions: We propose that we should regard the holes detected by our novel methodology as a fundamental characteristic of time series data.
Trial registration: Not applicable.
A Comprehensive Machine Learning Approach for COVID-19 Target Discovery in the Small-Molecule Metabolome
Authors: Sumon Md. Shaheenur Islam , Hossain Md. Sakib Abrar , Al-Sulaiti Haya , Yassine Hadi M. , Chowdhury Muhammad E. H. 
Published: February 2024

Abstract
Respiratory viruses, including influenza, RSV, and COVID-19, cause various respiratory infections. Distinguishing these viruses relies on diagnostic methods such as PCR testing. Challenges stem from overlapping symptoms and the emergence of new strains. Advanced diagnostics are crucial for accurate detection and effective management. This study leveraged nasopharyngeal metabolome data to predict respiratory virus scenarios including control vs RSV, control vs influenza A, control vs COVID-19, control vs all respiratory viruses, and COVID-19 vs influenza A/RSV. Our advanced machine learning models, including linear discriminant analysis, support vector machine, random forest, and logistic regression, exhibited superior accuracy, sensitivity, and specificity to previous supervised machine learning approaches. Key techniques such as feature ranking, standard scaling, and SMOTE were used to address class imbalances, thus enhancing model robustness. SHAP analysis identified crucial metabolites influencing positive predictions, thereby providing valuable insights into diagnostic markers. Our approach not only outperformed existing methods but also revealed top dominant features for predicting COVID-19, including Lysophosphatidylcholine acyl C18:2, Kynurenine, Phenylalanine, Valine, tyrosine, and aspartic Acid (Asp). These compounds play critical roles in metabolic pathways and have been identified as top contributors to predictive models in COVID-19 respiratory virus scenarios.
Long-lasting intense Cut-oﬀ Lows to become more frequent in the Northern Hemisphere
Authors: Mishra Aditya , Maraun Douglas , Schiemann Reinhard , Hodges Kevin , Zappa Giuseppe 
Published: February 2024

Abstract

Cut-off Lows (COLs) are mid-latitude storms that are detached from the main westerly flow. They tend to propagate slower than other mid-latitude storms and are often harbingers of heavy and persistent rainfall. The assessment of COLs in climate models is relatively limited, in fact, there are no studies conducted on the future changes of COLs within climate models. Given the importance of COLs in leading to severe hazards, here we study them in CMIP6's future (SSP5.85) simulations. The results show that COLs with high intensity and longer lifetimes are projected to become more frequent in spring over the land regions of the Northern Hemisphere. Such an increase in frequency indicates a substantial increase in related hazard as high-intensity long-lasting COLs are known to cause heavy rainfall. The effect may partly be compensated by the increasing COL propagation velocity with climate change. However, the overall increase in hazard is still high with 80% of all models indicating a surge in severe COL frequency. Lastly, the results are discussed in relation to the projected changes in COL drivers. The study forms a basis for future work on COL-associated rainfall and consequent impacts.
Benefits of intravenous iron supplementation in heart failure
Authors: Kotit Susy 
Published: February 2024

Introduction: Iron deficiency (ID) is one of the most frequent comorbidities in patients with heart failure (HF) and is estimated to be present in up to 80% of acute patients regardless of their ejection fraction. Randomized controlled trials have shown that supplementary intravenous iron results in improved clinical outcomes; however, the current understanding of the effects of intravenous iron on morbidity and mortality remains limited.
Study and results: The meta-analysis pooled individual participant data from three randomized placebo-controlled trials of ferric carboxymaltose (FCM) in adult patients (n=4,501) with heart failure and iron deficiency (CONFIRM-HF, AFFIRM-AHF, and HEART-FID). FCM therapy significantly reduced the co-primary composite endpoint of total cardiovascular hospitalizations and cardiovascular death, with a rate ratio (RR 0.86; 95% CI 0.75 to 0.98; p=0.029). FCM therapy was associated with a 17% relative rate reduction in total cardiovascular hospitalizations (RR 0.83; 95% CI 0.73 to 0.96; p=0.009) and a 16% relative rate reduction in total heart failure hospitalizations (RR 0.84; 95% CI 0.71 to 0.98; p=0.025).
Lessons learned: The meta-analysis shows that in iron-deficient patients with heart failure and reduced or mildly reduced left ventricular ejection fraction, intravenous ferric carboxymaltose (FCM) is associated with a reduced risk of total cardiovascular hospitalization...
Estimating critical depth and discharge over sloping rough end depth using machine learning
Authors: Mohammed Ahmed Y. , Sihag Parveen 
Published: February 2024

Abstract
This study uses machine learning (ML) to predict the end-depth structure's discharge and critical depth (yc). Linear regression, M5P, random forest, random tree, reduced error pruning tree, and Gaussian process (GP) are the ML methods used in this investigation. The findings indicate that the radial kernel function-based GP model is most suitable compared to other applied models with the lowest root-mean-square error = 0.0021, 0.007, normalized root-mean-square error = 0.0361, 0.0516 representing mean absolute error = 0.0015, 0.004 and the highest coefficient of correlation = 0.9912, 0.9916, Legates and McCabe's index = 0.8839, 0.9026 Willmott's index = 0.9956, 0.9956, and Nash Sutcliffe model efficiency = 0.9823, 09830 for yc for the end-depth structure (yc) and discharge (Q) with the testing stage, respectively. Results of the sensitivity study indicate that the friction coefficient is the most significant input variable compared to other parameters for predicting (yc) and flow running via the thickness model's last stage (Q) using this dataset.
Risks and farmers' behavior change towards water conservation: a study in the Southeast of Iran
Authors: Ataei Pouria , Karimi Hamid , Hallaj Zeynab , Mottaghi Dastenaei Afshin 
Published: February 2024

Abstract
Frequent droughts in Iran have influenced farmers' social and economic lives and have entailed extensive negative consequences. This research aimed to study the process by which farmers adopt water conservation behavior and explore the intervention of perceived risks and risk attitude. This survey was conducted among farmers in the Sistan region in the southeast of Iran (N = 6,000). A sample of 361 farmers was selected by multistage cluster randomization. The research instrument was a researcher-made questionnaire whose reliability was checked by Cronbach's alpha and composite reliability in a pilot study and whose content validity was confirmed by a panel of agricultural sociologists. The data were analyzed using mean, percentage, and structural equation modeling in the SPSSwin26 and AMOS24 software suites. The results reveal that perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action influence farmers' water conservation behavior positively and significantly. Also, most components of the health belief model are influenced by farmers' perceived risks and risk attitude. It can be concluded that it is imperative to focus on socio-psychological components to promote water conservation behavior and use water scarcity-coping strategies in Iran.
Clinicians’ Decision-Making Regarding Telehealth Services: Focus Group Study in Pediatric Allied Health
Authors: Thomas Donna Claire , Litherland Eva Frances , Masso Sarah , Raymundo Gianina , Keep Melanie 
Published: February 2024

Background
Many allied health services now provide both telehealth and in-person services following a rapid integration of telehealth as a response to the COVID-19 pandemic. However, little is known about how decisions are made about which clinical appointments to provide via telehealth versus in person.

Objective
The aim of this study is to explore clinicians’ decision-making when contemplating telehealth for their clients, including the factors they consider and how they weigh up these different factors, and the clinicians’ perceptions of telehealth utility beyond COVID-19 lockdowns.

Methods
We used reflexive thematic analysis with data collected from focus groups with 16 pediatric community–based allied health clinicians from the disciplines of speech-language pathology, occupational therapy, social work, psychology, and counseling.

Results
The findings indicated that decision-making was complex with interactions across 4 broad categories: technology, clients and families, clinical services, and clinicians. Three themes described their perceptions of telehealth use beyond COVID-19 lockdowns: “flexible telehealth use,” “telehealth can be superior to in-person therapy,” and “fear that in-person services may be replaced.”

Conclusions
The findings highlight the complexity of decision-making in a community-allied health setting and the challenges experienced by clinicians when reconciling empirical evidence with their own clinical experience.
Usability of Health Care Price Transparency Data in the United States: Mixed Methods Study
Authors: Maleki Negar , Padmanabhan Balaji , Dutta Kaushik 
Published: February 2024

Background
Increasing health care expenditure in the United States has put policy makers under enormous pressure to find ways to curtail costs. Starting January 1, 2021, hospitals operating in the United States were mandated to publish transparent, accessible pricing information online about the items and services in a consumer-friendly format within comprehensive machine-readable files on their websites.

Objective
The aims of this study are to analyze the available files on hospitals’ websites, answering the question—is price transparency (PT) information as provided usable for patients or for machines?—and to provide a solution.

Methods
We analyzed 39 main hospitals in Florida that have published machine-readable files on their website, including commercial carriers. We created an Excel (Microsoft) file that included those 39 hospitals along with the 4 most popular services—Current Procedural Terminology (CPT) 45380, 29827, and 70553 and Diagnosis-Related Group (DRG) 807—for the 4 most popular commercial carriers (Health Maintenance Organization [HMO] or Preferred Provider Organization [PPO] plans)—Aetna, Florida Blue, Cigna, and UnitedHealthcare. We conducted an A/B test using 67 MTurkers (randomly selected from US residents), investigating the level of awareness about PT legislation and the usability of available files. We also suggested format standardization, such as master field names using schema integration, to make machine-readable files consistent and usable for machines.

Results
The poor usability and inconsistent formats of the current PT information yielded no evidence of its usefulness for patients or its quality for machines. This indicates that the information does not meet the requirements for being consumer-friendly or machine readable as mandated by legislation. Based on the responses to the first part of the experiment (PT awareness), it was evident that participants need to be made aware of the PT legislation. However, they believe it is important to know the service price before receiving it. Based on the responses to the second part of the experiment (human usability of PT information), the average number of correct responses was not equal between the 2 groups, that is, the treatment group (mean 1.23, SD 1.30) found more correct answers than the control group (mean 2.76, SD 0.58; t65=6.46; P<.001; d=1.52).

Conclusions
Consistent machine-readable files across all health systems facilitate the development of tools for estimating customer out-of-pocket costs, aligning with the PT rule’s main objective—providing patients with valuable information and reducing health care expenditures.