bluuebunny/crossref_metadata_2025_split huggingface dataset:
PDE4B regulating NOX3/JNK/ALKBH5-mediated GCK methylation in accelerating the development of type 2 diabetes combined with fatty liver
Authors: Guan Yaqing , Luo Jie , Su Man , Yue Lin 
Published: April 2024

Abstract

Objective: This study aims to explore the regulatory mechanism of PDE4B on NOX3/JNK/ALKBH5-mediated GCK methylation and its impact on the acceleration of type 2 diabetes in conjunction with fatty liver development.
Methods: A model of type 2 diabetes combined with fatty liver was established, and a HepG2 cell model stimulated with high glucose was utilized. Western blotting was employed to monitor changes in oxidative stress-related proteins (P22PHOX, P47PHOX, P67PHOX) and signaling pathway proteins (ERK1/2, JNK, ALKBH5). Phosphosite mapping was conducted to assess GCK methylation, while RT-PCR was used to analyze changes in gluconeogenesis-related gene expression. Colorimetric assays were performed to measure glucose, total cholesterol, triglycerides, and glycogen levels, and Oil Red O staining was utilized to detect lipid droplet formation.
Conclusion: Oil Red O staining demonstrated increased lipid droplet content in the liver of type 2 diabetes combined with fatty liver model. High glucose stimulation led to increased lipid droplet content in HepG2 cells. Colorimetric assay results revealed elevated levels of triglycerides, total cholesterol, and glucose in the type 2 diabetes combined with fatty liver model. Western blot results showed that PDE4B knockout reduced the expression of P22PHOX, P47PHOX, P67PHOX, ERK1/2, JNK, and ALKBH5. MSP analysis indicated decreased GCK methylation levels. RT-PCR analysis demonstrated decreased levels of gluconeogenic genes (G6pase, PGC1a, PEPCK). Colorimetric assays indicated a decrease in glycogen synthesis levels, while Oil Red O staining showed a reduction in lipid droplet content.
Survey on the perceptions of Asian endoscopists to artificial intelligence
Authors: Lee Jonathan , QUEK Sabrina Xin Zi , KOO Chieh Sian , LIU Lin , LEUNG Wai Keung , TANG Raymond Shing Yan , Chung Hyunsoo , ZHANG Xuesong , ICHIMASA Katsuro , NGO Denis , MARALIT Ruter , DAO Viet Hang , KHIEN Vu Van , DUSEJA Ajay , PRATAP Nitesh , LEE Yeong Yeh , RENALDI Kaka , SIM Shi Min , WU Clement Chun Ho , LI James Weiquan , TAN Jarrod Kah Hwee , KOH Calvin Jianyi , SO Jimmy Bok Yan 
Published: April 2024

Abstract
OBJECTIVES: Recent studies have demonstrated the potential of artificial intelligence (AI) in diagnostic and therapeutic endoscopy for managing gastrointestinal diseases. However, there is limited knowledge of the perspective of endoscopists towards AI technology, particularly in the Asian community. This study aims to bridge this knowledge gap to guide policymakers, healthcare providers, and technology developers through an Asian AI Task Force.

MATERIALS AND METHODS: An online survey of 45 questions exploring (a) the perceived benefits of AI in endoscopy, (b) the barriers to adopting AI in endoscopy, (c) the priority areas and barriers to research in AI endoscopy, as well as (d) priorities for an Asian AI Task Force were distributed between November 2022 and May 2023 to endoscopists from sixteen medical institutes across ten Asian regions.

RESULTS: 293 participants completed the survey. Two-fifths (41.98%, n=123) reported no prior exposure to AI endoscopy. The majority (73.2%, n=90/123) of those without prior AI exposure expressed concerns about the accountability of AI and its impact on working practices. Almost all participants agreed that AI would enhance quality improvement (90.8%, n=266) and better diagnosis (90.4%, n=265). 69% (n=202) identified “staying up to date with AI advances'' as the top challenge towards clinical adoption of AI. Notably, those without prior exposure expressed high concern regarding accountability for the wrong diagnoses (73.2% vs. 60.6%, p =0.03) and lack of clinical trials (76.4% vs. 57.1%, p <0.01). Most respondents prioritised developing a reference paper guide for clinicians interested in AI (81.2%) and supporting funding applications for AI research (81.5%) as key areas that an Asian AI Endoscopy task force should address.

CONCLUSION: The survey results from Asian endoscopists emphasise the pressing need for collaborative frameworks and educational initiatives, including establishing an Asian AI Task Force, to facilitate the successful integration of AI in endoscopy practice and research across the region.
Eigen-Entropy based Time Series Signatures to Support Multivariate Time Series Classification
Authors: Patharkar Abhidnya , Huang Jiajing , Wu Teresa , Forzani Erica , Thomas Leslie , Lind Marylaura , Gades Naomi 
Published: April 2024

Abstract
Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset. To manage dataset's dynamic nature, we employ preprocessing with dense multi scale entropy. Consequently, the proposed framework, Eigen-entropy-based Time Series Signatures, captures correlations among multivariate time series without losing its temporal and dynamic aspects. The efficacy of our algorithm is assessed using six binary datasets sourced from the University of East Anglia (UEA), in addition to a publicly available sepsis dataset and an institutional sepsis dataset from the Mayo Clinic. We employ recall as a metric for evaluation against the baseline algorithm, Dependent Dynamic Time Warping with 1 Nearest Neighbor. Our algorithm outperforms the baseline algorithm for UEA datasets in terms of recall with an average value of 10% across all datasets, and by 38% for an institutional sepsis dataset. For the public sepsis dataset, the performance of our algorithm is comparable to the baseline algorithm.
Female Legislators Decrease Epidemic Deaths
Authors: Saito Takaharu 
Published: April 2024

Abstract

Recent discourse has lauded the eﬀicacy of female leadership in national governments, particularly during the COVID-19 pandemic. This study employs two-way fixed effect (TWFE) and instrumental variable (IV) regression methodologies to reveal a more pronounced association between the proportion of female legislators and reduced epidemic-related mortality, rather than a direct correlation with female executive leadership. Further analysis elucidates that the effectiveness of female legislative representation is amplified through its interaction with the deliberative processes in governance. This suggests that policy diversity and inclusive decision-making mechanisms are instrumental in enhancing epidemic response eﬀicacy.
Multi-Energy Storage SOC Equalization Strategy Based on Virtual DC Motor Control
Authors: Wang Junrui , Bai Bingchao , Wang Rui , Dou Shuai , Tan Lu , Zhu Xuqiang 
Published: April 2024

Abstract

Droop control is commonly used in DC microgrid to maintain the reasonable distribution of power between DC micro-sources to ensure the stable and safe operation of DC microgrid. However, the traditional state of charge balance control strategy based on droop control will cause problems such as bus voltage drop and unreasonable system power distribution due to its own characteristics. Therefore, a multi-energy storage state of charge balance control strategy based on virtual DC motor control is proposed. Firstly, according to the instantaneous power balance characteristics of DC microgrid, the fluctuation of load or photovoltaic output is controlled by first-order high-pass filtering as an additional amount of rated power controlled by conventional virtual DC motor, which provides additional inertia and damping support for the system to improve the transient stability of the system. Secondly, the state of charge balance strategy based on improved adaptive droop control is used to realize the state of charge balance between multiple sets of batteries and the reasonable distribution of system power. Finally, the virtual DC motor control is combined with adaptive droop control, and the control strategy is verified by building a simulation model of photovoltaic multi-energy storage DC microgrid and an experimental platform. The results show that the proposed control method can reduce the influence of power fluctuation on DC bus voltage and realize the state of charge balance between multiple sets of batteries.
Microscopic detection and molecular characterization of Sarcocystis miescheriana in wild boars (Sus scrofa): first report from Greece
Authors: Dimzas Dimitris , Rubiola Selene , Pacifico Laura , Veneziano Vincenzo , Chiesa Francesco , Chassalevris Taxiarchis , Diakou Anastasia 
Published: April 2024

Abstract

The genus Sarcocystis includes protozoan parasites with an indirect life cycle. Sarcocystis spp. can infect various animal species and humans, causing sarcocystosis, a parasitosis of economic importance and zoonotic concern. Wild boars can act as intermediate hosts for Sarcocystis mieshieriana and the zoonotic Sarcocystis suihominis that infects humans by consumption of raw or undercooked infected swine meat. In the present study, diaphragmatic muscle tissue of 123 wild boars hunted in northern Greece were examined to determine the frequency of Sarcocystis spp. The samples were examined by tissue compression and molecular techniques. Under light microscopy, 34 out of 123 (27.6%) wild boars tested positive for Sarcocystis spp., while a higher infection prevalence (75%) was revealed by multiplex PCR performed in 100 of the samples. The partial mtDNA cox1 gene (~1100 bp) of 20 samples tested positive for S. miescheriana by multiplex PCR was amplified and sequenced. Sarcocystis miescheriana was identified as the only species involved in these infections. This is the first study on the prevalence of Sarcocystis spp. in wild animals in Greece. Further large-scale surveys are needed to assess the prevalence and species of this parasite in Greece and to design efficient control and preventive measures in a One Health perspective.
A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data
Authors: Sánchez-Marqués Raquel , García Vicente , Sánchez José Salvador 
Published: April 2024

Abstract

Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the diagnosis, prognosis and prediction of the response to therapy has aroused the interest of researchers in their use together with machine learning and deep learning models. Most of the research in this field has been model-centric, meaning it has been based on finding better performing algorithms. However, in practice, improving data quality can result in a better model. This study investigates a data-centric machine learning approach to determine their potential benefits in predicting glioma grades. We report six performance metrics to provide a complete picture of model performance. Experimental results indicate that standardization and oversizing the minority class increase the prediction performance of four popular machine learning models and two classifier ensembles applied on a low-imbalanced data set consisting of clinical factors and molecular biomarkers. The experiments also show that the two classifier ensembles significantly outperform three of the four standard prediction models. Furthermore, we conduct a comprehensive descriptive analysis of the glioma data set to identify relevant statistical characteristics and discover the most informative attributes using four feature ranking algorithms.
HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models
Authors: Kumar Shrawan , Thakur Bharti , Mishra Vinaytosh 
Published: April 2024

Abstract
Detecting heart disease is significant for timely intervention and improved patient outcomes. With the rising global burden of cardiovascular disorders, there is an urgent need for accurate and efficient diagnostic tools. Advanced technologies, such as Machine Learning (ML) and Artificial Intelligence (AI), have shown promise in enhancing early detection and risk assessment. Integrating these innovative approaches into clinical practice can significantly contribute to the early identification of heart disease, facilitating timely interventions and personalized patient care. Addressing this need is vital for reducing the impact of heart disease on public health. This research presents a comprehensive examination of heart disease detection models, encompassing neural networks, backpropagation, random crop techniques, Long Short-Term Memory (LSTM), and regression methods. The investigation included a comparative analysis of various models, including Heart Disease Evaluation (HBE), AdaBoost, Support Vector Machine (SVM), Random Forest, and proposed method (HDRP), with a focus on accuracy, error rates, and precision over 5 and 10 epochs. Notably, the HDRP model emerged as a standout performer, consistently demonstrating superior accuracy and the lowest error values, showcasing its efficacy in heart disease prediction. The discussion highlights the unique features of HDRP, attributed to advanced methodologies or distinctive design elements. The study emphasizes the importance of future research to delve into the specific attributes contributing to HDRP's performance, exploring model architecture, optimization strategies, and scalability across diverse datasets.
Cross-border data sharing for research in Africa: An analysis of the data protection and research ethics requirements in 12 jurisdictions
Authors: Staunton Ciara , Edgcumbe Aliki , Abdulrauf Lukman , Gooden Amy , Ogendi Paul , Thaldar Donrich 
Published: April 2024

Abstract

Background In recent years, there has been a notable uptake in genomic and health-related research activities across the African continent. Similarly, there has been increased introduction of data protection legislation that affects the sharing of personal data such as health data and genomic data, including for research. Many of these statutes have stricter requirements when sharing personal data across borders. Consequently, the cross-border sharing of health data that includes genetic data requires careful navigation of the pertinent data protection legislation, in particular concerning the sharing of such data for research purposes. To help researchers navigate these legal frameworks, 12 African countries were analysed to develop country guides on cross-border data sharing.Results Of the 12 countries that were analysed, ten have data protection laws in place (Botswana, Ghana, Kenya, Malawi, Nigeria, Rwanda, South Africa, Tanzania, Uganda, and Zimbabwe), while two countries (Cameroon and The Gambia) do not. With the exception of Ghana, all countries with data protection statutes or bills had additional requirements to be met when sharing personal data across borders. Consent and adequacy are the most common grounds for justifying the sharing of personal data across borders.Conclusion Given the limitations of the current models of consent, consent is not a suitable basis to transfer large quantities of data for research. Adequacy is a common ground, but there are national differences in the implementation of this ground. Researchers must therefore analyse each national legal framework and make decisions on a case-by-case and country-by-country basis.
Molecular characterization and functional analysis of Peptidoglycan recognition protein-L2 in Hexagrammos otakii
Authors: Bai Yifan , Liu Yingying 
Published: April 2024

Abstract

Peptidoglycan recognition proteins (PGRPs) belong to the pattern recognition receptors (PRRs), which are essential for sensing and defending against pathogenic microorganisms during innate immunity pathways. Although an increasing body of research indicates that the PGRP protein in fish has various biological functions such as antimicrobial activity, amidase activity, and the ability to regulate multiple signaling pathways, the molecular mechanisms by which PGRP contributes to the innate immune processes in fish remain relatively limited. In the present study, we have recombinantly expressed a long-type PGRP from fat greenling (Hexagrammos otakii) (HoPGRP-L2) and analyzed its molecular mechanism in the pathogen identification process. The open reading frame (ORF) of HoPGRP-L2 is 1449 bp in length that encodes for a peptide with 482 amino acids. As a PRR, HoPGRP-L2 has a typical PGRP domain that enables HoPGRP-L2 to recognize and conjugate to bacterial peptidoglycan (PGN) on the cell wall. We demonstrated that HoPGRP-L2 could bind to pathogenic microorganisms and promote the agglutination of them. Furthermore, HoPGRP-L2 was confirmed to possess zinc ion-dependent amidase activity and exhibited an effect on the growth inhibition of chosen bacteria. HoPGRP-L2 also prolongs the survival time in carp injected with Aeromonas hydrophila. Taken together, our results indicate that PGRP acts as a PRR involved in recognizing and eliminating pathogens during the innate immune response in Hexagrammos otakii.
Bitter gourd improvement through the selection of attributes contributing to yield by character association and path analysis
Authors: Sriniva B. , Padma S. S. Vijaya , Naidu L. Naram , Rao M. Paratpara , Kumar Ch. Kiran , Sekhar V. 
Published: April 2024

Abstract
The current study was carried out at the Vegetable Experimental Farm of the College of Horticulture in Venkataramannagudem, Andhra Pradesh. The objective was to analyse associations between characters and direct and indirect effects for 12 traits associated with bitter gourd improvement and yield using a half diallel design. The experiment involved seven parents, twenty-one hybrid combinations, and two standard checks which were evaluated in a randomized complete block design with three replications. For every trait, genotypic correlations were found to be higher than phenotypic correlations, demonstrating that various traits have an inherent connection. Notably, at both the phenotypic and genotypic levels, yield per plant was significantly associated with plant height, number of fruits per plant, average fruit weight, fruit length and diameter, internodal length, and seeds per fruit. This shows that all six attributes should be studied together in order to increase bitter gourd yield. The average fruit weight, number of fruits per plant, plant height, days to female flower appearance, fruit length and diameter, internodal length, and sex ratio all had a substantial and positive direct influence on fruit yield per plant, according to path coefficient analysis. This means that direct selection based on these attributes would result in increased bitter gourd fruit yield per plant.
Hyperbolic metamaterials assisted ultrathin Pd films for high-sensitivity hydrogen sensors
Authors: Chen Zhiying , Wu Xiaohu , Liu Haotuo , Baqir Muhammad Abuzar , Yu Kun , Zhang Kaihua 
Published: April 2024

Abstract

Optical hydrogen sensors can use optical fiber as the medium for light transmission or sensing, and realize hydrogen detection based on the relevant physicochemical properties of hydrogen-sensitive materials, which have excellent characteristics such as intrinsic safety, good stability, small size, high specificity, and easy to network. However, traditional hydrogen-sensitive materials such as Pd metal usually require a large thickness to obtain good performance, but this will shorten the service life of the sensor. In response to this issue, a hydrogen sensor based on hyperbolic metamaterials and ultrathin Pd films is proposed. The control of the Pd film thickness to be less than 0.02 μm ensures that mechanical deformation does not occur during the formation of hydrides in the Pd film. The calculations indicate that the sensor sensitivity can be maintained at approximately 2300 even when the thickness of the palladium film is reduced to 0.011 μm. This work provides a promising solution for high-performance optical hydrogen detection, characterized by high sensitivity, low latency, and low cost.
A Compact Parallel Dispensing Machine of High-viscous Adhesive Based on Electrostatic-induced Mechanism
Authors: Xu Zheng , Liu Yuanchun , Sun Zhongping , Zhang Jiaqi , Wang Xiaodong , Xu Xiaoyu 
Published: April 2024

Abstract

Adhesive bonding process has been widely applied in the assembly of micro-electro-mechanical system products, in which the efficient micro-dispensing technique of high-viscous liquid is important for the bonding performance. In this paper, we propose a compact and portable machine for the parallel dispensing task of high-viscous adhesive based on electrostatic-induced mechanism. The designed flexible probe array can reduce the impact of height difference Δh on transferring stage to thereby improve the dispensing consistency. With the developed machine, both parallel and serial dispensing modes are built up with replaceable dispensing head to satisfy various requirements. The dispensing efficiency is increased by 91.44% via parallel dispensing mode, comparing with previous single-probe mode. In addition, smart patterning with Mean square deviation σd less than 0.026 mm is achieved with both the configurable probe-status and the replaceable dispensing head. We believe this machine can be used efficiently and smartly in the adhesive bonding process.
Comparison of Transient Spray Characteristics of N-dodecane and OME 3 Using Large Eddy Simulation
Authors: Al-lehaibi Moaz , Menacer Brahim 
Published: April 2024

Abstract

There have been continuing efforts in the Engine Combustion Network (ECN) community to understand the detailed spray characteristics by investigating several standard diesel injectors. With the increasing interest in the use of alternative and renewable fuels, the present study conducted large eddy simulations with dynamic structure subgrid closure in the Eulerian volume-of-fluid (VOF) framework to identify the characteristics of oxymethylene ethers (OME), which has distinct physical and chemical properties leading to low soot emissions. In particular, the spray features of n-dodecane and OME3 were compared and analyzed in terms of their transient dynamics following the nozzle opening. ECN Spray A, C, and D were considered to assess the effects of nozzle convergence factor and nozzle diameter. The measured nozzle geometries by the Argonne National Laboratory were adopted to mimic the internal flow development. Due to the higher density of OME3, it generates a higher projected density, but a lower injection velocity is obtained. This behavior is attributed to its higher mass inertia. The diverging Spray C injector induces a significantly lower projected density and mass flow rate than the converging Spray D injector owing to cavitation just after the sharp nozzle inlet corner. By comparison, the Spray A injector generates the lowest projected density and mass flow rate for its smallest nozzle diameter. Various fuels demonstrate similar flow fields, evidenced by the significantly similar pressure distribution. The OME3 cases lead to a slightly higher temperature distribution within the nozzle channel, which is attributed to the lower turbulent kinetic energy and thus the slower heat transfer process.