bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prognostic value of serum glycated albumin in acute coronary syndrome patients without standard modifiable cardiovascular risk factors
Authors: Zhang Xiaoming , Du Yu , Guo Qianyun , Ma Xiaoteng , Shi Dongmei , Zhou Yujie 
Published: July 2024

Abstract

Background:
Glycated albumin (GA) has been demonstrated to be associated with adverse outcomes in patients with acute coronary syndrome (ACS). However, as a specific subgroup of ACS, a significant proportion of patients with ACS without standard modifiable cardiovascular risk factors (SMuRFs) are currently being identified. The prognostic value of serum GA for adverse events in such patients remains unexplored. This study aims to evaluate the prognostic value of GA in predicting adverse outcomes in patients with ACS without SMuRFs.
Methods:
This study involved 1,140 consecutive patients who were diagnosed with ACS without SMuRFs at the Beijing Anzhen Hospital between May 2018 and December 2020 and underwent coronary angiography. Each patient was followed up for a period of 35–66 months after discharge. The primary endpoint of this study was major adverse cardiovascular and cerebrovascular events (MACCEs) that included all-cause mortality, non-fatal myocardial infarction, non-fatal ischemic stroke, and ischemia-driven revascularization.
Results:
The average age of the study participants was 59.55 ± 10.98 years, and men accounted for 61.8%. The average GA level was 14.37 ± 2.42. The median follow-up duration was 48.3 months, during which 220 cases (19.3%) experienced MACCEs. In the fully adjusted model, with GA as a continuous variable, the hazard ratio (HR) for MACCEs in the high GA group was 1.069 (95% confidence interval (CI): 1.008, 1.133), the HR for ischemia-driven revascularization was 1.095 (95% CI: 1.021, 1.175), and the HR for all-cause mortality was 1.155 (95% CI: 1.021, 1.306), all with P values less than 0.05. Similarly, when GA was considered as a categorical variable, in the fully adjusted model, GA was associated with MACCEs, ischemia-driven revascularization, and all-cause mortality, with P values all less than 0.05. The restricted cubic spline curve showed that the relationship between GA and MACCEs was linear (p for non-linear = 0.079; p for overall association = 0.026). Furthermore, GA levels were correlated with poor prognosis in the subgroups of patients.
Conclusion:
Serum GA might be an independent predictor of all-cause death, ischemia-driven revascularization, and MACCEs in patients with ACS without SMuRFs.
Characterization of 3D Printed Re-entrant Midsole Structure with Various Infill Density and Print Direction
Authors: Jung Imjoo , Lee Sunhee 
Published: July 2024

Abstract

This study focused on manufacturing 3D printed conductive RE midsoles with two slicing directions (horizontal and vertical) and three infill densities. Optimal 3D printing conditions were assessed through analyses of slicing processes, morphology, compressive and electrical properties, electromechanical property, and plantar pressure analysis. The analysis of the RE midsole was further divided into three parts: Meta (MT), Midfoot (MF), and Heel (HL). As results, horizontal direction (HD) layers were stacked horizontally, while vertical direction (VD) layers were deposited vertically, with VD being 1.5 times more rigid than HD. For VD, rigidity decreased in the order of MF > HL > MT, while for HD, it was HL > MF > MT. Both slicing directions showed similar electrical properties, with conductivity improving with higher infill density. The 50% infill density demonstrated the best electrical and electromechanical properties. Plantar pressure analysis revealed that HD provided a wider pressure area and better pressure distribution. Overall, HD midsoles with 50% infill density exhibited softer compressive property and superior electrical property during compression, offering better stability by distributing plantar pressure more effectively.
Analyzing Nonlinear Response in Reinforced Concrete Slab-on- Girder Bridges for interior girder: Influence of Intermediate Diaphragm
Authors: Shoukry Mohie E. , Ebeido Tarek , Mahmoud Zaki , Mohamed Heba 
Published: July 2024

Abstract

This study presents the outcomes of a thorough nonlinear finite element analysis conducted on slab-on-girder reinforced concrete bridges. The analysis encompasses nonlinear aspects like concrete cracking, compression behavior, and reinforcement yielding. Thirty-six bridges were examined under truck loads specified by the Egyptian Code (ECP 201–2017) [11], alongside the bridges' self-weight. Among these, nine bridges had varying spans and widths, necessitating adjustments in the number of longitudinal girders to maintain uniform girder spacing. Initially, each bridge underwent analysis without intermediate diaphragms, followed by a reanalysis with the insertion of intermediate diaphragms at the mid-span and support locations. Additionally, nine bridges without intermediate diaphragms underwent linear analysis before nonlinear analysis. Furthermore, nine bridges with varied spans and girder spacings but constant width were analyzed. The finite element model was validated using field test data from the literature. Through a parametric investigation, it was observed that the presence of intermediate diaphragms, firmly linked to the longitudinal girders, notably improves transverse load distribution, resulting in reduced loads on the longitudinal girders. This improvement escalates with increasing bridge width. Finally find the proposed equation of moment distribution factor of interior girder. This equation gives a good agreement with analysis.
An enhanced framework for local genetic correlation analysis
Authors: Shen Xia , Li Yuying , Pawitan Yudi 
Published: July 2024

Abstract

Genetic correlation is a key parameter in the joint genetic model of complex traits, but it is usually estimated on a global genomic scale. Understanding local genetic correlations provides more detailed insights into the shared genetic architecture of complex traits. However, LAVA, as the state-of-the-art tool for local genetic correlation analysis, reports biased statistics and, therefore, is prone to false inference. We extend the high-definition likelihood (HDL) method to a local version, HDL-L (HDL-Local), which divides the genetic correlation analysis into semi-independent loci. HDL-L allows for a more granular estimation of genetic variances and covariances. Simulations show that HDL-L offers more consistent heritability estimates and more efficient genetic correlation estimates compared to LAVA. Across extensive simulations under different heritability settings, HDL-L maintained robust performance. In the analysis of 30 phenotypes from the UK Biobank, HDL-L identified 889 significant local genetic correlations across 658 loci, while LAVA identified 696 significant estimates across 441 loci. Furthermore, HDL-L demonstrated a significant computational advantage, being around 50 times faster than LAVA in the simulations. HDL-L proves to be a powerful tool for uncovering the detailed genetic landscape that underlies complex human traits, offering both superior accuracy and computational efficiency.
Investigation of Amplification Effect Using Ambient Seismic Noise: Adana Basin
Authors: Bakırcı Taciser 
Published: July 2024

Abstract

The applicability of Rayleigh waves retrieved from Ambient Seismic Noise (ASN) to estimate the amplification of the Adana basin is investigated. The amplitudes of Impulse Response Functions (IRFs) obtained from pairs of stations in and around the basin are analysed. In addition, the validity of the IRFs is analysed using an earthquake that occurred near the reference station. The amplitudes of IRFs between station pairs within the basin (H1) are high, as expected from the basin structure, while those following the basin margins are low. In the D1 group, which covers the entire study area, the amplitudes in the northeast are higher than those in the H1 group. The results indicate that amplification can be successfully estimated by using ambient noise data for the region.
Fibroblasts-derived migrasomes promote skin wound healing
Authors: Sa Guoliang , Yang Xuewen , Zhou Haoyu , Zhang Zhen , Liu Zhan , Sa Guodong , Jiang Mingjing , Zou Zhongyang , Shi Yingliang , Zheng Liwu 
Published: July 2024

Abstract

Migrasomes are newly discovered organelles with demonstrated functions in organ morphogenesis and angiogenesis. However, the effect of migrasomes in tissue repair remains unreported. Our transmission electron microscopy findings revealed that migrasomes were directly connected with retraction fibers and could release their contents into the surroundings in the human and rat skins and oral mucosae. Multiplex immunofluorescence staining results revealed that these retraction fibers and migrasomes originated from fibroblasts. Live-cell imaging demonstrated that human oral mucosal fibroblast-derived migrasomes could be taken up by both fibroblasts and HaCaT cells. In addition, the injection of purified fibroblast-derived migrasomes into the edges of rat skin wounds significantly accelerated wound healing. Single-cell sequencing results suggested that the clusters of keratinocytes, fibroblasts, and endothelial cells play key roles in the wound-healing process. Moreover, the expression of Vegfa, Il-6, and Col1a1 in the fibroblast subcluster was significantly upregulated. Furthermore, these purified migrasomes increased the protein levels of VEGFA, IL-6, and COL1A1 in cultured fibroblasts in vitro. Thus, our research revealed that fibroblast-derived migrasomes are potential therapeutic vesicles for skin wound-healing repair.
Strategic Construction of Initial Datasets for Active Learning: Leveraging Self-Supervised Learning
Authors: Hwang Sekjin , Choi Jinwoo , Choi Joonsoo 
Published: July 2024

Abstract

Deep learning has demonstrated remarkable achievements across various fields. However, its success heavily relies on the availability of large-scale labeled data. Labeling data is a time-consuming and costly process, prompting numerous studies aimed at reducing these expenses. Active learning is a prominent data-efficient learning methodology that has garnered significant attention. Active learning methods iteratively select data that are most effective for training models, thereby gradually constructing a compact dataset. It typically assumes the presence of a small amount of labeled data at the start of training, and experiments generally use randomly composed initial labeled datasets. Although the importance of initial dataset construction is well recognized because of its impact on the level of model training in most active learning methods, practical research in this area remains limited. In this study, we propose a method of data initialization using self-supervised learning from an active learning perspective. This method focuses on constructing a small initial dataset that maximizes learning efficiency by utilizing an unlabeled dataset. The impact of the proposed method on active learning was evaluated using a representative image classification dataset, which demonstrated significant performance improvements.
High-Efficiency Dual-Band RF Energy Harvesting with Integrated Impedance-Matching and Rectifier Circuits
Authors: Lakade Nilesh , Nawale Shankar 
Published: July 2024

Abstract

Efficient RF energy harvesting is crucial for enabling self-powered wireless devices and Internet of Things (IoT) applications. This work presents a dual-band impedance-matching network with an integrated rectifier configuration operating at 2.45GHz (Wi-Fi and IoT) and 3.5GHz (5G mobile network), addressing the challenge of achieving simultaneous conjugate matching and maximum power transfer from the antenna to the rectifier circuit across multiple frequencies. We propose an inverted L-shaped matching network with series and shorted stubs for 2.45GHz matching, coupled with an additional series stub for 3.5GHz matching. The integrated voltage doubler rectifier, comprising Schottky diodes, filtering capacitors, and optimized load resistance, was simulated using an Advanced Design System (ADS) on an FR4 substrate with a dielectric constant of 4.3. The designed circuit exhibits a high conversion efficiency of 85.96% at -6dBm input power levels, making it suitable for low-power applications. This integrated approach facilitates efficient RF-to-DC power transfer from the antenna to the rectifier circuit across dual bands, paving the way for compact, efficient RF energy harvesting systems that contribute to the development of self-sustainable wireless sensor networks and enable a wide range of IoT applications in various domains, including environmental monitoring, healthcare, and industrial automation.
Power curve modelling for wind turbines and wind power prediction based on mixed Richards model
Authors: Wang Zhiming , Chen Xiaoguo , Wang Lingjun 
Published: July 2024

Abstract

Accurate modelling of wind speed-power curve of wind turbines plays an important role in wind power prediction, state detection and performance evaluation. While model selection is one of the keys to improve the accuracy of wind power curve (WPC) modelling. To improve the accuracy of models and accurately characterize the overall output behavior of wind turbines, a method of WPC modelling based on the mixed Richards model is proposed in this paper. By using the measured data of two wind fields, the method proposed in this paper is compared and verified with the sixth to ninth order polynomials and the four-parameter and five-parameter logistic function models based on the genetic least square method through five indicators include the root mean squared error, the coefficient of determination R2, the mean absolute percentage error, the improved Akaike information criterion and the Bayesian information criterion. Finally, based on the measured data of a wind field in Jiangsu Province, the two-fold mixed Richards model is used to predict the wind turbine power. The results show that the two-fold mixed Richards model is the optimal option with the highest fitting accuracy, effectively avoids the model’s over-fitting, and can accurately predict wind turbine output power.
Understanding regularized crack initiation through the lens of Finite Fracture Mechanics
Authors: Doitrand Aurélien , Molnár Gergely 
Published: July 2024

Abstract

The nucleation of a regularized crack at a V-notch is studied using the coupled criterion through matched asymptotic expansions. The process zone around the crack is described by crack regularization usually employed in phase-field models. The effective crack length increases with increasing regularization length so that the incremental energy release rate decreases, which in turn increases the critical generalized stress intensity factor at initiation. Decreasing incremental energy release rate is also obtained with increasing Poisson's ratio. For a given material characteristic length, it is shown that the initiation crack length only depends on the V-notch angle and Poisson's ratio. For a given geometry and Poisson's ratio, the initiation length is proportional to the regularization length. The proposed description of regularized crack initiation shows good correspondence to the generalized stress intensity factor obtained by phase-field calculation, the only difference being in the description of the process zone prior to crack initiation.
Enhancing Seismic and Resilient Performance of Resilient Walls with Concealed Bracings: Experimental and Numerical Investigation
Authors: Zhang Man , Zhang Jianwei , Sun Yuping , Dong Hongying 
Published: July 2024

Abstract

Resilient walls incorporating ultra-high-strength bars (UHSB) with weak bond performance are expected to exhibit drift-hardening properties. Thus, to improve the shear stiffness and lateral resistance of the resilient walls, the present research applied the concealed bracings composed of UHSB to the resilient walls and explored the effect on the hysteretic and resilient performance of the proposed walls. Five walls were tested to investigate the effect of relevant variables, including shear span ratio, the presence of steel fibers and recycled aggregate concrete (RAC) strength, on the hysteretic performance and resilience. Reinforced with bracings, test wall showed satisfactory hysteretic and resilient performance up to 2% drift ratio and maintained stable increase in lateral force. Concealed bracings consisted of UHSB were also conducive to reducing residual drift ratio, ranging from 8.7–31.3%. Residual drift ratios of all test walls were within the limit value of 0.5%, until a loading drift ratio of 2%. Additionally, adding steel fibers restricted the development of cracks and had a positive effect on slowing down stiffness degradation. Numerical models were established based on OpenSees platform, and more resilient-related parameters were analyzed, including reinforcement ratio and arrangement height of the concealed bracing ratio, longitudinal reinforcement ratio, axial ratio, and UHSB replacement ratio. Based on the parameter analysis results, design recommendations were given.
Presence of antibiotic resistance non-typhoidal Salmonella spp. from green leafy vegetables in Dehradun
Authors: Kumar Rahul , Kamboj Neha , Kumar Navin , Gautam Pankaj 
Published: July 2024

Abstract

Non-typhoidal Salmonella infections (NTS) are a significant public health issue in low-income countries because of inadequate hygiene and sanitation facilities. This issue is further complicated by inadequate periodic surveillance for the better management of Salmonella-related infections. Furthermore, reports of antibiotic-resistant Salmonella species pre- and post-harvest require periodic characterization of bacterial isolates to ascertain the appropriate medication for effective treatment. In the present study, 645 green leafy vegetable samples were examined for the presence of typhoidal and non-typhoidal Salmonella species. Isolate culture on differential medium and biochemical characterization confirmed the presence of Salmonella species in 61 out of 645 samples (9.45%) collected from ten sample sites in the Dehradun district of Uttarakhand state in India. The culture confirmed that 15 isolates were randomly selected, and each sample was subjected to 47 antibiotics for antimicrobial susceptibility testing. Of the bacterial isolates, 93.33% were resistant to methicillin, whereas 80% were resistant to cefpodoxime, teicoplanin, and clindamycin. In total, 55.33% of the isolates were resistant to Linezolid, Novobiocin, colistin, and nitrofurantoin. 46.66% were resistant to chloramphenicol. Salmonella sp. with Antibiotic resistance of Salmonella spp. was found in the Dehradun area, and this study may help with disease management and adequate food safety measures.
Novel Synthesis of porous BN/β-SiC/SnO 2 /In 2 O 3 Quaternary Nanocomposites and its Gas Sensing Properties under Different Working Temperatures
Authors: Otgonbayar Zambaga , Joo Young Jun , Cho Kwang Youn , Oh Won-Chun 
Published: July 2024

Abstract

The novel quaternary nanocomposite was composed of porous boron nitride, β-SiC fibers, SnO2, and In2O3, and their successful combination led to effective and highly stable gas sensing. The physical and chemical properties of the nanocomposites were analyzed using analytical techniques such as XRD, SEM, TEM-HRTEM, BET, XPS, PL, and Raman techniques. Especially, the first-principles calculations were performed to evaluate the proportion of the states occupied by the system at each energy level. The gas sensing capabilities of the quaternary nanocomposites were evaluated using CO2 and O2 gas purging at various temperatures. The usefulness of the β-SiC fiber and previously studied β-SiC/In2O3/SnO2 was altered by the outstanding electronic conductivity and chemical activity of the pBN/β-SiC/SnO2/In2O3 quaternary nanocomposites. At room temperature, the pBN-105 NCs displayed high resistivity and robust response to O2 gas but a weak response to CO2 gas. The sensor exhibited a robust sensitivity to both CO2 and O2 gases at high temperatures. In every gas-sensing test, the predicted quaternary nanocomposites displayed a strong resistivity and low current density. The effective charge transfer and separation properties, as well as the electron density of the sensor layer of the quaternary nanocomposites, allowed for better sensing under a variety of high-temperature variations, whereas the addition of MOs and porous boron nitride increased the usability of the β-SiC fiber.
Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers
Authors: Chen Yile , Lee Kyoungyeul , Woo Junwoo , Kim Dong-wook , Keum Changwon , Babbi Giulia , Casadio Rita , Martelli Pier Luigi , Savojardo Castrense , Manfredi Matteo , Shen Yang , Sun Yuanfei , Katsonis Panagiotis , Lichtarge Olivier , Pejaver Vikas , Seward David J. , Kamandula Akash , Bakolitsa Constantina , Brenner Steven E. , Radivojac Predrag , O’Donnell-Luria Anne H. , Mooney Sean , Jain Shantanu 
Published: July 2024

Abstract

Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.