bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Non-Alcoholic Fatty Liver Disease on Sepsis Inpatient Outcomes: A Nationwide Sample Analysis(2000-2019)
Authors: Lyu Xiuhong , Liu Bolun , Li Yiting , Wang Yichen , Miskovsky John , Gaitanis Melissa , Promrat Kittichai , Wu Wen-chih 
Published: July 2024

Abstract

Background It was reported that patients with Non-Alcoholic Fatty Liver Disease (NAFLD) were at increased risk of contracting severe infections requiring hospitalization which could end up with sepsis. However, data regarding the impact of comorbid NAFLD on the in-hospital outcomes of sepsis admission is scarce.
Methods This nationwide retrospective observational study included 21,057,911 adult patients who were admitted into hospitals in the United States between 2000 and 2019 with a primary discharge diagnosis of sepsis. These patients were categorized according to the presence or absence of comorbid NAFLD. The twenty-year trend of nationwide NAFLD prevalence among sepsis inpatients was elucidated. Multivariate logistic regression analysis was used to analyze NAFLD's impact on in-hospital outcomes of sepsis.
Results In the twenty-year study period, the prevalence of NALFD among sepsis inpatients trended up from 1.2% in 2000 to 4.2 % in 2019. While sepsis mortality had trended down, comorbid NAFLD in sepsis patients was consistently associated with a higher adjusted in-hospital all-cause mortality rate (adjusted odds ratio (OR), 1.45; 95% confidence interval (CI), 1.42-1.48), higher odds of developing septic shock, and multi-organ dysfunctions (including cardiovascular-, renal-, and hepatic dysfunction). They were more likely to be discharged to another acute hospital. The length of stay of sepsis patients with NAFLD who died in the hospital was significantly shorter than those without NAFLD.
Conclusions Comorbid NAFLD is associated with higher in-hospital all-cause mortality and worse clinical outcomes in sepsis inpatients. Addressing this rising epidemic will be of paramount importance to improve sepsis in-hospital outcomes.
Fully Automated CFD Simulation System Research Based on Design Scheme Tree
Authors: Liu Zijun 
Published: July 2024

Abstract

In comparison to remarkable achievements of computer-aided drug discovery system for drug discovery, the role of computational fluid dynamics (CFD) played for flow channel design requires further enhancement. While CFD has undergone rapid evolution, the absence of integrated geometry and mesh processing hinders potential development of advanced applications of CFD. This paper presents JIACFD toolset to overcome the limitation and establishes fully automated CFD simulation system with it. The simulation system is also constructed on design scheme tree which is more in accordance with engineering thinking logic. The control parameter trend analysis method is introduced to select appropriate candidates from design scheme tree. Additionally, the control parameter trend assumption which will be proved by Spearman method is proposed to improve efficiency of the system. During verification process on study case, two independent control parameters are found to have trend correlation, and one control parameter converged when number of meshes increasing, indicating lack of trend sensitivity. Subsequently, the design scheme tree and trend curve are utilized to effectively analyze flow field characteristics of different schemes. Finally, control parameter trend analysis method will be employed to rank design scheme tree and verify that ranking for candidates is not dependent on number of meshes. This paper investigates and verifies presented system, method, and assumption and explores possibility of established system playing more critical role in performance design work.
Research on Stock Prediction Based on CED-PSO-StockNet Time Series Model
Authors: Chen Xinying , Yang Fengjiao , Sun Qianhan , Yi Weiguo 
Published: July 2024

Abstract

In view of the complexity and uncertainty of the stock market, especially the noise interference in the stock data, the traditional single prediction method has been difficult to meet the needs of investors. This paper innovatively proposes the CED-PSO-StockNet time series model to improve the accuracy of stock forecasting. The model first introduces the complete ensemble empirical mode decomposition (CEEMDAN) technology, decomposes the original stock data, estimates the frequency of each component through the extreme point method, and recombines it, so as to effectively remove the noise. Then, the model uses the Encoder-Decoder framework which integrates the attention mechanism to accurately predict the reconstructed components, so as to better extract and use the data features. In addition, this paper also uses the improved particle swarm optimization algorithm to optimize the model parameters. Through five groups of comparative experiments, the effectiveness of each part of CED-PSO-StockNet model is verified, showing its significant advantages in stock forecasting.
Adaptive Toeplitz Convolution- enhanced Classifier for Anomaly Detection in ECG Big Data
Authors: Wu Lili , Ali Majid Khan Majahar , Li Tao , Ni Chenmin , Tian Ying , Zhou Xiaojie 
Published: July 2024

Abstract

The anomaly detection of electrocardiogram (ECG) data is crucial for identifying deviations from normal heart rhythm patterns and providing timely interventions for high-risk patients. Various autoencoder (AE) models within machine learning (ML) have been proposed for this task. However, these models often do not explicitly consider the specific patterns in ECG time series, thereby impacting their learning efficiency. In contrast, we adopt a method based on prior knowledge of ECG time series shapes, employing multi-stage preprocessing, adaptive convolution kernels, and Toeplitz matrices to replace the encoding part of the AE. This approach combines inherent ECG features with the symmetry of Toeplitz matrices, effectively extracting features from ECG signals and reducing dimensionality. Our model consistently outperforms state-of-the-art models in anomaly detection, achieving an overall accuracy exceeding 99.6%, with Precision and Area Under the Receiver Operating Characteristic Curve (AUC) reaching 99.8%, and Recall peaking at 99.9%. Moreover, the runtime is significantly reduced. These results demonstrate that our technique effectively detects anomalies through automatic feature extraction and enhances detection performance on the ECG5000 dataset, a benchmark collection of heartbeat signals.
Overexpression of CIRP in astrocytes contributes to Alzheimer’s disease via downregulation of uPA
Authors: Li Ze , Liu Jing-Peng , Yao Feng-Hua , Chao Yang , Li Shou-Chun , Liu Yuan-Yang , Su Xin-Wen , Liu Yu-Xiao , Liu Ai-Jun 
Published: July 2024

Abstract

Background: Cold inducible RNA-binding protein (CIRP) is an important danger-associated molecular pattern involved in tissue-specific and systemic inflammation and is also regarded as a potential regulator of Alzheimer’s disease (AD). However, the precise roles and mechanism of CIRP in the functional changes in astrocytes during the development of AD are still unknown. This study aimed to assess gene expression alterations in astrocytes after they overexpress CIRP (oe-CIRP) and to explore the relationship between abnormal CIRP expression and AD.
Methods: We established astrocyte cell lines that stably expressed CIRP or control vectors using 3 different kinds of human glioma cell lines, namely, U87, U251 and H4, and analyzed the mRNA expression profiles of 3 pairs of cells via microarray. Then, the significantly differentially expressed mRNAs between the CIRP-overexpressing (ov-CIRP) group and the control group were identified by bioinformatics analysis and validated by quantitative real-time PCR (q-PCR) and western blotting (WB). Finally, the effect of CIRP overexpression in astrocytes on neurons was observed in a coculture system.
Results: We identified 119 mRNAs with obvious fold changes between the ov-CIRP and control groups for all 3 pairs of human glioma cell lines. These mRNAs are associated with diseases such as asthma IgE, dehydroepiandrosterone, rheumatoid arthritis, autism spectrum disorder, AD and so on. The biological functional analysis indicated that urokinase plasminogen activator (uPA), a gene whose expression significantly decreased after CIRP overexpression, was closely associated with AD. The results from q-PCR and WB assays confirmed that overexpression of CIRP significantly inhibited uPA at both the mRNA and protein levels in U87, U251 and H4 cells. Moreover, compared with those cocultured with control astrocytes, SH-SY5Y cells cocultured with CIRP-overexpressing astrocytes exhibited a significant increase in the expression of amyloid-β (Aβ)1-42 and the hyperphosphorylated microtubule-associated protein tau (Tau).
Conclusion: CIRP overexpression inhibited the expression of uPA in human astrocytes, which promoted the expression of Aβ1‒42 and the phosphorylation of tau in neurons, thus increasing the risk of AD. These results suggest that the overexpression of CIRP in astrocytes contributes to the development of AD.
Environmental Management of Poultry Houses: Controlling the Proliferation of Insect Pests in Togo
Authors: VINAKPON Guénnolé , AGBOKA Komi , MENSAH Serge Edgard. Paulin , GUIDI Hoeness NASSI , MLAGA Kodjo Gnatepe , TONA Kokou 
Published: July 2024

Abstract

Modern poultry farming in West Africa is crucial for food security and financial security, but it coexists with insects, which can be harmful and vectors of diseases. This study aims to explore the diversity of insects in the litter and poultry farms, and to determine the factors behind their proliferation in the Maritime Region of Togo. Ninety (90) layer farms were sampled for this study. Insect collection was carried out in two phases: collection of insects around and inside the poultry house, and in the litter. Insects were identified using entomological keys.
In the litter, the results showed that Alphitobius diaperinus (black beetle) is the most abundant (92.28%), with an average population of 270.5 ± 10.8 (n=120) and an average density of 164.87 ± 8.47 per square meter. Around and inside the poultry houses, Diptera were identified as the most abundant (91.99%), with an average population of 3472.2 ±31.2 (n=120). The density of insects in the litter was higher under the drinkers and feeders than in other areas (p<0.0001). The proliferation of insects in the litter is strongly linked to the duration of use of this litter (p<0.0001). The moisture content of the litter also facilitates the proliferation of insects under the drinkers and feeders (p=0.010 and p=0.001, respectively).
In conclusion, poultry farms harbour a diversity of insects, some of which could be vectors of pathologies. These results highlight the importance of environmental and litter management to limit the proliferation of insects in these farms.
Electrochemiluminescence Biosensor for MMP-2 Analysis Using CRISPR/Cas13a and EXPAR Amplification: A Novel Approach for Anti-Aging Research
Authors: Tang Qiang , Wang Jie , Zhang Jiayi , Zeng Hongyu , Su Zhixue , Zhu Xiying , Wei Jihua , Gong Yuanxun , Tang Qianli , Zhang Kai , Liao Xianjiu 
Published: July 2024

Abstract

Matrix metalloproteinase-2 (MMP-2) plays a pivotal role in anti-aging research. Developing advanced detection platforms for MMP-2 with high specificity, sensitivity, and accessibility is crucial. This study introduces a novel electrochemiluminescence (ECL) biosensor for MMP-2 analysis, leveraging the CRISPR/Cas13a system and Exponential Amplification Reaction (EXPAR). The biosensor operates by utilizing the T7 RNA polymerase to transcribe RNA from a DNA template upon MMP-2 interaction. This RNA activates Cas13a, leading to signal amplification and ECL detection. The incorporation of the "photoswitch" molecule [Ru(phen)2dppz]2+ streamlines the process by eliminating the need for extensive electrode modification and cleaning. Under optimized conditions, the biosensor achieved an impressive detection limit of 12.8 aM for MMP-2. The platform demonstrated excellent selectivity, reproducibility, and stability, making it highly suitable for detecting MMP-2 in complex biological samples. This innovative approach shows great potential for applications in molecular diagnostics and anti-aging research.
Enhanced Approximation Techniques: Stancu-type (λ,μ)-Bernstein-Kantorovich Operators
Authors: Cai Qing-Bo , Aslan Resat , Ozger Faruk , Mohiuddine Syed Abdul 
Published: July 2024

Abstract

The principal aim of this study is to investigate a range of approximation characteristics exhibited by Stancu-type (λ, μ)-Bernstein-Kantorovich operators denoted as Fλ,μ r,α,β(ϕ; z). In the initial stage, we conduct a comprehensive examination of diverse moment estimates pertaining to operators Fλ,μ r,α,β(ϕ; z). Subsequently, we delve into the exploration of several facets of direct results, including the order of convergence with respect to the usual modulus of continuity, Lipschitz-type continuous functions, and the Peetre’s K-functional. Furthermore, in order to gain insights into the asymptotic characteristics of the operators Fλ,μ r,α,β(ϕ; z), we derive a Voronovskaya-type asymptotic theorem. Additionally, we provide an analysis of the A-statistical convergence behavior and pointwise estimates associated with the operators Fλ,μ r,α,β(ϕ; z). Finally, we have included graphical representations and numerical error value tables to demonstrate the efficiency and accuracy of our proposed operator. Our analysis reveals that our operator yields significantly superior approximation results compared to certain other linear positive operators documented in the existing literature. Keywords and
Exploring Graphite-Based Thermal Greases For Optimal Microelectronic Device Cooling
Authors: Shishkin Roman , Arkhipova Vicktoria , Zhirenkina Nina , Fattakhova Zillara , Leshok Andrey 
Published: July 2024

Abstract

The quest for effective thermal management solutions for microelectronic devices, catering to the escalating heat flows, necessitates innovative strategies. The significance of thermal interface materials, especially thermal greases, in minimizing thermal resistance within the "microelectronic device – heat-dissipating element" interface, has been widely acknowledged across industries such as microelectronics, aviation, and space engineering. Despite the promising reported values, a crucial consideration entails the method of ascertaining thermal conductivity, necessitating measurements in bulk samples to ensure accurate representations. Graphite, owing to its commercial accessibility and commendable thermal conductivity, emerges as a standout candidate for composite material development, as demonstrated in recent research. We observed that the use of graphite-based fillers, particularly in the form of well-crystallized graphite particles, effectively reduced processor temperatures and enhanced thermal conductivity, outperforming industrially utilized thermal pastes. Our findings accentuate the potential of these materials in contributing to the development of cutting-edge composite materials for microelectronics, highlighting their high prospects for future applications in high-performance devices.
Ionospheric TEC variations during an intense geomagnetic storm on 11 May 2024
Authors: P Chaithra , Nagaraja Kamsali 
Published: July 2024

Abstract

On May 11, 2024, a severe geomagnetic storm occurred, causing significant disruptions to Earth’s magnetosphere and ionosphere. These storms, primarily caused by solar activity, are distinguished by variations in solar wind that increase energy flow from the Sun to Earth. The present research aims to analyze the latitudinal response of ionospheric Total Electron Content (TEC) during this event. The analysis is based on data collected from three different geographic locations: IISC (13.02°N, 77.57°E), POL2 (42.68°N, 74.69°E), and NOVM (55.03°N, 82.91°E). The electron density of the ionosphere is crucial for satellite-based communication and navigation systems. Geomagnetic storm strength is measured by the Kp-index and Disturbance storm time (Dst) index. During a geomagnetic storm and X-class flare of magnitude 5.89, a maximum Dst index of -412 nT and Kp index of 9 are observed during the storm day. TEC values increased at IISc during the storm period compared to quiet days, but decreased over POL2 and NOVM stations. A good correlation was observed between the observed TEC during a storm day and the predicted TEC by the IRI-2020 model over IISC and NOVM, while moderate correlation was observed over POL2.
Empowering Family Caregivers Through Managing Care Recipients’ Behavioral Challenges: A Qualitative Study
Authors: Yen Chia-Ming 
Published: July 2024

Abstract

Objective
Informal caregivers offer care outside a paid or professional capacity may completely lack the necessary knowledge and skills for dementia care. This study focused on examining the effectiveness of educational training to informal caregivers in terms of identifying positive changes.
Design and Intervention:
This study analyzed the effects of 24-hr home-based intervention—Program for Coping with Behavioral and Psychological Symptoms of Dementia (PCBPSD)—on the family caregivers (FCGs) of people with dementia (PWD) in Taiwan. The program included eight thematic lessons
Participants:
Eight FCGs were interviewed four times to identify likely positive changes in terms of reduced care burden and self-adjustment.
Results
Five themes identified included: (1) Reported BPSD by caregivers, (2) Encountered challenges from caregiving. (3) Barriers, (4) Gains and positive changes resulted from training programmes, and (5) Effective coping strategies.
Conclusion
The major challenges encountered by the FCGs not only from the problem behaviors themselves but the difficulty in pleasing care recipients and reluctance to employ foreign paid caregivers. Problem-focused strategies seem to be helpful when seeking information, such as participating in PCBPSD. Emotional-focused strategies should be implemented to prevent the feelings of exasperation in caregivers and the gradual fading of patients’ previous self-image and dignity, particularly as someone’s parent. PCBPSD has helped FCGs deliberately focus on the strengths of PWD by identifying the capabilities they still possess, complying with their preferences in safe situations, encouraging engagement in activities they enjoy, and affording them greater personal freedom.
Unraveling the Impact of Genome Assembly on Bacterial Typing: A One Health Perspective
Authors: Merda Déborah , Vila-Nova Meryl , Bonis Mathilde , Boutigny Anne-Laure , Brauge Thomas , Cavaiuolo Marina , Cunty Amandine , Regnier Antoine , Sayeb Maroua , Vingadassalon Noémie , Yvon Claire , chesnais virginie 
Published: July 2024

Abstract

Background
In the context of pathogen surveillance, it is crucial to ensure interoperability and harmonized data. Several surveillance systems are designed to compare bacteria and identify outbreak clusters based on core genome MultiLocus Sequence Typing (cgMLST). Among the different approaches available to generate bacterial cgMLST, our research used an assembly-based approach (chewBBACA tool).
Methods
Simulations of short-read sequencing were conducted for 5 genomes of 27 pathogens of interest in animal, plant, and human health to evaluate the repeatability and reproducibility of cgMLST. Various quality parameters, such as read quality and depth of sequencing were applied, and several read simulations and genome assemblies were repeated using three tools: SPAdes, Unicycler and Shovill. In vitro sequencing were also used to evaluate assembly impact on cgMLST results, for 6 bacterial species: Bacillus thuringiensis, Listeria monocytogenes, Salmonella enterica, Staphylococcus aureus, and Vibrio parahaemolyticus.
Results
The results highlighted variability in cgMLST, which appears unrelated to the assembly tools, but rather induced by the intrinsic composition of the genomes themselves. This variability observed in simulated sequencing was further validated with real data for five of the bacterial pathogens studied.
Conclusion
This highlights that the intrinsic genome composition affects assembly and resulting cgMLST profiles, that variability in bioinformatics tools can induce a bias in cgMLST profiles. In conclusion, we propose that the completeness of cgMLST schemes should be considered when clustering strains.
In-Depth Examination of CIGS (Copper, Indium, Gallium, Selenium) Layer Properties for Enhanced Solar Cell Performance
Authors: Elmelouky Abderrahmane , Hairch Youssef , Dahlous Kholood A. , Islam Mohammad Shahidul , Acharjee Nivedita , Salah Mohammed 
Published: July 2024

Abstract

The analysis and modeling of the CIGS layer using impedance spectroscopy is crucial for understanding and enhancing the performance of CIGS-based solar cells. Impedance spectroscopy characterizes the electrical and optical properties of materials across a range of frequencies, providing insights into charge transport processes, recombination mechanisms, and diffusion phenomena within the CIGS layer. This technique enables the determination of key electrical and optical parameters, such as resistance, capacitance, and impedance of the layer. By applying this model to experimental impedance spectroscopy data, researchers can extract specific parameters related to cell performance, including series resistance, shunt resistance, and charge carrier mobility. Ultimately, the comprehensive analysis and modeling of the CIGS layer through impedance spectroscopy deepens our understanding of charge transport mechanisms and its electrical properties. This knowledge is instrumental in continuously improving their performance and advancing photovoltaic technology.
Forecasting Model of Microsoft’s Daily Return and Its Volatility
Authors: Rong Runsheng 
Published: July 2024

Abstract

Stock returns tend to float sharply. It is vital for financial analysts to build appropriate model to forecast the returns of stocks and their volatility. This paper builds an initial AR model to predict the daily return of Microsoft, then checks both the ARCH effect in the residual terms by ARCH-LM test method and the autocorrelation relationship between the square of the residual terms by drawing the ACF and PACF plots, finally builds the full model, which consists of an AR model for forecasting the daily return of Microsoft and a GARCH model to predict its volatility.