bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and validation of endoplasmic reticulum stress-related LncRNA signature as a novel prognosis model for LUAD
Authors: Yu Weijie , Miao Zhoulin , Ainiwaer Julaiti , Qiao Bingzhang , Jumai Kawuli , Sheyhidin Ilyar 
Published: May 2023

Abstract
Background Adenocarcinoma of the lung is the most common pathological subtype. This paper is to investigate the relationship between endoplasmic reticulum-related LncRNA and prognosis of patients with lung adenocarcinomaMethod A total 295 ER stress-related genes are downloaded from the GSEA website and the TCGA website was obtained for TCGA-LUAD patient profiles and mutation data. Co-expression analysis, the univariate cox regression and Least Absolute Shrinkage and Selection Operator Cox regression was performed on significantly expressed LncRNAs. Next, a risk model was developed using multifactorial analysis to classify patients into different risk groups. Survival analysis and nomogram were also constructed and evaluated for patients in different groups. Finally, the tumor microenvironment and immune landscape were explored.Result 14 genes found to be strongly associated with patient prognosis. Risk score models were created and patients were grouped using risk score. Clinical variables and signature were also used to build the nomogram with perfect prediction function. The AUC value for 1,3,5 are respectively 0.739,0.734,0.753. The human immune response and Neutrophil extracellular trap formation are related to signature. Immune responses, immune cells, and immunological checkpoints were found to be considerably different. Last, We screened for sensitive drugs.Conclusion Collectively, we found that 14 ERS-related Lncrna signature was meaningful in predicting patient response to immunotherapy.
New Trends and Knowledge Domains in the Influence of Glutamine Metabolism on the Development of Diabetes From 2001 to 2022：A Scientometric Investigation and Visualization Study
Authors: Zhao Meina , Wang Kaiyan , Mu Fei , Lin Rui , Wang Jingwen 
Published: May 2023

Abstract
"Metabolism affects function" is the consensus of researchers at present. It has potential clinical application value to study the effects of regulating glutamine (Gln) metabolism on diabetes physiology or pathology. Our research aimed to summarize the latest research progress, frontier hot topics and future development trends in this field from the perspective of scientometrics. Relevant literature and reviews were obtained from the Web of Science Core Collection (WoSCC) between January 1, 2001 and May 31, 2022. An Online Analysis Platform of Bibliometrics (http://bibliometric.com/), CiteSpace, and VOS viewer software were used to generate visual knowledge network graphs, including publication countries, institutions and authors partnership analysis, co-occurrence analysis, co-citation analysis, as well as citations and keywords burst detection to acquire research trends and hotspots. Our results show that a total of 945 publications in the WoSCC database met the analysis requirements, with articles being the main type. The overall characteristics showed an increasing trend in the number of publications and citations. The United States was leading the way in this research and was a hub for aggregating collaborations across countries. Vanderbilt University delivered high-quality impact with the most published articles. DeBerardinis. RJ in this field was the most representative author and his main research contents were Gln metabolism and mitochondrial glutaminolysis. Significantly, there was a relative lack of collaboration between institutions and authors. In addition, "type 2 diabetes", "glutamine", "metabolism", "gene expression" and "metabolomics" were the keywords categories with high frequency in co-citation references and co-occurrence cluster keywords. Analysis of popular keywords burst detection showed that "branched chain", "oxidative phosphorylation", "kinase", "insulin sensitivity", "tca cycle", "magnetic resonance spectroscopy" and "flux analysis" were new research directions and emerging methods to explore the link between Gln metabolism and diabetes. Overall, Exploring Gln metabolism showed a gradual upward trend in the field of diabetes. This comprehensive scientometric study identified the general outlook for the field and provided valuable guidance for ongoing research. Strategies to regulate Gln metabolism hold promise as a novel target to treat diabetes, as well as integration and intersection of multidisciplinary provides cooperation strategies and technical guarantees for the development of this field.
Initial non-adherence to lipid-lowering medication: a systematic literature review
Authors: Cooke Catherine E. , Robertson Teisha 
Published: May 2023

Abstract

Background: The impact on cardiovascular health is lost when a patient does not obtain a newly prescribed lipid-lowering medication, a situation termed “initial medication nonadherence” (IMN). This research summarizes the published evidence on the prevalence, associated factors, consequences, and solutions for IMN to prescribed lipid-lowering medication in the United States.
Methods: A systematic literature search using PubMed and Google Scholar, along with screening citations of systematic reviews, identified articles published from 2010 to 2021. Studies reporting results of IMN to lipid-lowering medications were included. Studies that evaluated non-adult or non-US populations, used weaker study designs (e.g., case series), or were not written in English were excluded.
Results: There were 19 articles/18 studies that met inclusion and exclusion criteria. Estimates of the prevalence of IMN to newly prescribed lipid-lowering medications ranged from 10% to 18.2% of patients and 1.4% to 43.8% of prescriptions (n=9 studies). Three studies reported demographic and clinical characteristics associated with IMN and four studies described patient-reported reasons for IMN, including preference for lifestyle modifications, lack of perceived need, and side effect concerns. Four intervention studies reported mixed results with automated calls, live calls, or letters. One study reported worse clinical outcomes in patients with IMN: higher levels of low-density lipoprotein and greater risk of emergency department visits.
Conclusions: Up to one-fifth of patients fail to obtain a newly prescribed lipid-lowering medication but there is limited information about the clinical consequences. Future research should assess outcomes and determine cost-effective approaches to address IMN to lipid-lowering therapy.
End-to-End Training of VAE-GAN Network for Text Detection
Authors: Naveen Palanichamy 
Published: May 2023

Abstract
Scene text detection is challenging due to variations in text appearance, backgrounds, and orientations. It is necessary to improve robustness, accuracy, and efficiency for applications like OCR, image understanding, and autonomous vehicles. The combination of Generative Adversarial Network (GAN) and Network Variational Autoencoder (VAE) has the potential to create a more robust and powerful text detection network. The proposed network comprises three modules: the VAE module, the GAN module, and the text detection module. The VAE module generates diverse and variable text regions, while the GAN module refines and enhances these regions to make them more realistic and accurate. The text detection module is responsible for detecting text regions in the input image and assigning a confidence score to each region. During training, the entire network is trained end-to-end to minimize a joint loss function, which includes the VAE loss, the GAN loss, and the text detection loss. The VAE loss ensures that the generated text regions are diverse and variable, while the GAN loss ensures that the generated text regions are realistic and accurate. The text detection loss guarantees that the network can detect text regions in the input image with high accuracy. The proposed method employs an encoder-decoder structure in the VAE module and a generator-discriminator structure in the GAN module. The generated text regions are refined and enhanced by the GAN module to produce more accurate results. The text detection module then identifies the text regions with high confidence scores. The proposed network is tested on several datasets, including Total-Text, CTW1500, ICDAR 2015, ICDAR 2017, ReCTS, TD500, COCO-Text, SynthText, Street View Text, and KIAST Scene Text and achieved promising results.
The lateral photovoltaic effect in the Ni-SiO2 -Si structure with bias
Authors: Ling Xiang , Zhu Peng Fei , Zhu Kun , Song Pei , Li Xiong 
Published: May 2023

Abstract
We had designed a clamping device to study lateral photovoltaic effect (LPE) in Ni-SiO2-Si structure due to the appropriate barrier height. We studied LPE with different laser wavelengths and power in Ni-SiO2-Si structures, the LPE have a prominent sensitivity and linearity. The most suitable laser wavelength is 532nm for studying the LPE in Ni-SiO2-Si structure, and LPE will increase with the laser power within a certain range and reach the threshold ultimately. The transient response time is 450 μs and the relaxation time is 2250 μs in Ni-SiO2-Si structure without bias. The LPE sensitivity have a significant improvement with bias. However, the LPE have a poor linearity between two electrodes when bias was applied on electrodes position. We change the bias application method, the linearity and sensitivity of LPE obtained a significant progress when bias was applied in middle of electrodes connection from -4V to -7V. The transient response time is 6 μs and the relaxion time is 47μs with -7V bias. Not only improve the LPE sensitivity but also increase response speed with bias. The research can provide a method for obtaining high sensitivity and response speed based on LPE sensors.
Predicting Contraceptive Usage for Married African Women Residing in Rural Areas: A Comparative Study of Deep Learning and Machine Learning Models with XAI Insights
Authors: Nuri Kaleab Wondemu , Kanda Michee Sanza , Justine Elikana Kulwa , Panda Amiya Ranjan , Pradhan Himanshu Sekhar 
Published: May 2023

Abstract
Background
The use of modern contraceptives is a crucial aspect of family planning, especially for women residing in rural areas of Africa. This study seeks to address the issue of contraceptive usage among married African women living in rural areas by exploring the effectiveness of machine learning and deep learning models for predicting this usage.
Methodology
The data used in the study was obtained from the Multiple Indicator Cluster Survey 6 (MICS6) to develop and compare machine learning and deep learning models for predicting contraceptive usage among married African women residing in rural areas. In addition to predictive accuracy, the study also focused on incorporating explainable Artificial Intelligence (XAI) insights to enhance the interpretability of the models.
Result
Artificial Neural Network (ANN) with BFloat16 and Convolutional Neural Network (CNN) + Long Short-Term Memory (LSTM) were the best predictive models with 75% accuracy in both models and F1-score of 73% and 74% respectively. Additionally, XAI techniques provide valuable insights into the factors that influence contraceptive usage in the target population.
Conclusion
The results of this study indicate that deep learning models outperform traditional machine learning models in predicting contraceptive usage among married African women residing in rural areas. The implications of this research are significant, as the findings could inform policy and intervention strategies aimed at improving family planning services in rural areas of Africa.
Multi-classifier fusion base on belief-value for the diagnosis of neuropsychiatric disorders
Authors: Zhao Feng , Ye Shixin , Lv Ke , Wang Qin , Li Yuan , Mao Ning , Ren Yande 
Published: May 2023

Abstract

Neuropsychiatric disorders seriously affect the health of patients, and early diagnosis and treatment are crucial to improve the quality of patients’ life. Machine learning and other related methods can be used for disease diagnosis and prediction, among which multi-classifier fusion method has been widely studied due to its significant performance over single classifiers. In this paper, we propose a multi-classifier fusion classification framework based on belief-valuefor the neuropsychiatric disorders diagnosis. Specifically, the belief-value measures the belief level of different samples by considering information from two perspectives, which are distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples). The proposed belief-value is more representative compared to the belief-value which only uses a single type of information. Further, based on the concept of multi-view learning, we performed the calculation of the belief-values under the sample space with different features, and the complementary relationship between different belief-values was captured by a multilayer perceptual (MLP) network. Compared with majority voting and linear fusion methods, the MLP network can better capture the nonlinear relationship between belief-values, which produces better diagnostic results. Experimental results show that the proposed method outperforms single classifier and multi-classifier linear fusion methods for the diagnosis of neuropsychiatric disorders.
Design of Accurate Multi-class Optimized Lightweight Convolution Neural Network for Rice Varieties Classification
Authors: Selvaraj Deepika , Venkatesan Arunachalam 
Published: May 2023

Abstract
Worldwide, more than 40k rice varieties are existing, each with different nutritional content and quality. Identifying these has to be consistent, automated, and accurate. Considering the feature extraction process, convolution Neural Networks (CNN) are preferred over machine learning (ML) for this classification. Transfer learning approaches help to optimize the CNN model; therefore, it fits in an FPGA. Seven different CNN models were proposed to classify five rice varieties, each model differs based on the: kernel depth; the number of convolution layers (CL); the number of fully connected layers (FCL); and the number of neurons per FCL. These were analyzed considering 70% and 30% for training and testing respectively. A dataset of 15,000 images/variety with each image of resolution. This results in an Optimized Lightweight Convolutional Neural Network (OpLW-CNN) model, having a CL, followed by two FCLs. This model is further analyzed using a random set of images: 500, 5000, and 75000 to fit the model optimally. This model achieves 99%, 98.13%, and 98.14% of specificity, F1-score, and accuracy for a set of 5000 images. These metrics are approximately 1% to 2% lesser than the performance of the benchmark model, and 81.5% fewer computations. Also, this model requires less than a second to classify an image.
The association between ambient temperature and antimicrobial resistance of high-priority bacterial pathogens in China nationwide: A modeling study with difference-in-difference approach
Authors: Shi Lin , Li Weibin , Yang Shifang , Li Jia , Liu Xu , Zeng Yingchao , Xia Haohai , Yang Xinyi , Zhao Manzhi , Yang Lianping 
Published: May 2023

Abstract
Background Climate warming not only affects humans, megafauna and other flora but also the world’s microbial organisms. However, the relationship between ambient temperature and antimicrobial resistance (AMR) of important clinical bacteria is still unclear; therefore, we explored the association between ambient temperature and AMR in China.Methods Six bacteria-antibacterial combination, identified by the World Health Organization as high priority, were collected from the China Antimicrobial Resistance Surveillance System (CARSS) database for 30 provinces from 2014 to 2020. Meteorological factor, socioeconomic and health care factors were obtained from the China Statistical Yearbook. A revised Difference-in-Difference (DID) approach was applied to quantitatively assess the association between ambient temperature and AMR, and exploring the modified effects of socioeconomic and health care factors.Results Ambient temperature had a significant protective effect on Methicillin-Resistant Staphylococcus Aureus (MRSA) (Relative risk (RR) = 0.959, p = 0.042) and Vancomycin Resistant Enterococcus Faecium (VRefm) (RR = 0.798, p < 0.001), and was positively associated with Penicillin Resistant Streptococcus Pneumoniae (PRSP) (RR = 1.556, p < 0.001). Whereas higher socioeconomic levels (higher GDP, higher income per capita, higher consumption per capita) and increased hospitalization rate will eliminate the protective effect of ambient temperature on them. The frequency of medical visit, institution per province did not have an interaction effect on MRSA and VRefm. Interestingly, PRSP was sensitive to the socioeconomic and health care factors, which almost modify its positive association with ambient temperature.Conclusions Ambient temperature was significantly associated with AMR, having varied effects for different bacteria-antibacterial combination. Some certain socioeconomic and health care factors will influence the association between temperature and antimicrobial resistance, thereby increasing the risk of bacterial resistance. The effect of temperature on antimicrobial resistance, and interactions with socioeconomic and hospitalization rate should be seriously considered in antimicrobial stewardship within a changing climate.
Towards an understanding of global brain data governance: perceptions on the ethical and legal principles that influence brain data governance
Authors: Eke Damian , Ochang Paschal , Stahl Bernd Carsten 
Published: May 2023

Abstract

Advances in neuroscience and other disciplines are producing large-scale brain data that consists of datasets from multiple organisms, disciplines, jurisdictions in different formats. However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and legal principles which may influence key stakeholders involved in the generation, collection, processing and sharing of brain data thereby raising ethical and legal challenges. Also, although calls for a culturally informed brain data governance framework have been made, practical steps and studies to understand the global perceptions of key stakeholders using neuroscientists who are influenced by these ethical legal principles is currently lacking. Therefore, using the research question how do ethical and legal principles influence data governance in neuroscience? we attempt to understand the perceptions of key actors on the principles, issues and concerns that can arise from brain data research. We carried out interviews with 21 leading international neuroscientists. The analytical insights revealed key ethical and legal principles, areas of convergence, visibility, and the issues and concerns that arise in brain data research around these principles. These issues and concerns mainly circulate around intimately connected areas which include ethics, human rights, regulations, policies and guidelines, and participatory governance. The research answers the call for a cross cultural study of global brain data governance and the results of the study will assist in understanding the issues and concerns that arise in brain data governance.
Evolution and concept of environmental flows (e-flows): meta-analysis
Authors: Gebreegziabher Gizaw Abera , Degefa Sileshi , Furi Wakgari , Legesse Gudina 
Published: May 2023

Abstract

River ecosystems have become fragile due to river flow alterations in the wake of expending structural controls triggered by the ever-increasing demands of freshwater. The efforts to alleviate the effects of river flow alterations have paved the way for the emergence and expansion of the concept of e-flows. The role of e-flows in ecosystem protection and water resources management is not yet fully understood and practiced. This review summarizes the historical evolution of the concept of e-flows and recaps its assessment methods; analyzes the impacts of river flow alterations; indicates the challenges e-flows have been facing and identifies the gaps in the focus of e-flows research. A total of 303 articles published since 2010 were collected and systematically assessed. Most of the publications are focused on describing the effects and management of river flow alterations. According to the findings of this meta-analysis, less attention is given to basin-scale, social and policy aspects of e-flows. Furthermore, little consideration is given to the part played by groundwater in e-flows along with the roles of e-flows in maintaining the health of floodplains and e-flows of intermittent rivers and ephemeral streams.
Constructed wetland technology for the treatment and reuse of urban household greywater under conditions of Africa's Sahel region
Authors: Compaoré Cheik Omar Tidiane , Maiga Ynoussa , Nikiéma Mahamadi , Mien Oumarou , Nagalo Issa , Panandtigri Hermann Tambila , Mihelcic James R. , Ouattara Aboubakar Sidiki 
Published: May 2023

Abstract

Three pilot systems of a constructed wetland were assessed to advance the urban reuse of greywater for household gardening in areas of the world representative of Africa's Sahel (one unplanted and two planted with local species, namely Andropogon gayanus and Chrysopogon zizanioides). Principal component analysis showed planted systems provided higher removal efficiencies than the unplanted system and A. gayanus performed better for treating most water quality parameters. As expected, removal efficiencies for suspended solids (SS) and chemical oxygen demand (COD) were greater than 90% in all filters. The removal of five-day biochemical oxygen demand (BOD5) was, however, significantly greater in the filter planted with A. gayanus than in the unplanted control. For plant-beneficial parameters such as nutrients (NO3−, NO2−, NH4+, and PO43−), the removal in the planted filters was significantly higher than in the unplanted filter (&gt;90% versus 73%–78%). The reduction of fecal coliforms was significantly greater in the two planted systems and exceeded 2.5 log10 removal. Analysis of the microbial water quality characteristics showed the concentration of fecal microbial indicators would achieve WHO guidelines for restricted irrigation with retention of some embedded nutrients, thus reducing pressures on areas experiencing climate variability, water scarcity, and land degradation.
Synthesis of Ferrocenylimidazo[1,2-a]pyridine-3-amines Using Catalyst
ZrO(NO3)2.2H2O and their Theoretical Studies
Authors: Shajari Nahid , Yahyaei Hooriye , Zafarani Seyyede Sanaz 
Published: May 2023

Abstract:
In this study, synthesis of ferrocenylimidazo[1,2-a]pyridine-3-amines was reported from
the condensation reaction of ferrocenecarboxaldehyde, 2-aminopyridine, and isocyanides using
ZrO(NO3)2.2H2O as an efficient, new, and reusable catalyst under reflux conditions in ethanol. The
structures of the products were confirmed by IR, 1H NMR, 13C NMR, and elemental analysis. Quantum
theoretical calculations for the structure of compounds (4a, 4b, and 4c) were done using the Def2
with the TZVPPD basis set. Geometric parameters were obtained from the optimized structures, and
experimental results were analyzed with the calculated data. The structures of the products were
proved by IR, 1H NMR, 13C NMR, and elemental analysis. Computations were carried out for the IR
spectra data and 1H NMR and 13C NMR chemical shifts of the ferrocenylimidazo[1,2-a]pyridine-3-
amine derivatives in the ground state. Ultimately, a great agreement was found between experimental
and theoretical results.