bluuebunny/crossref_metadata_2025_split huggingface dataset:
Empathetic communication of accessibility barriers on the dynamic web to understand the accessible user experience (AUX)
Authors: Afra Pascual Almenara , Toni Granollers Saltiveri 
Published: October 2023

Abstract

A prototype is presented with accessible and non-accessible web elements, formalized in a dynamic web context (VUE Framework). The result is a system that empathetically informs about the accessibility barriers that impact users with disabilities. The aim of the system is to display messages through the perspective of people with disabilities so that prosumer users – those who produce and consume web content – understand the need to incorporate accessibility features in their web content. It is important to consider that making a website accessible is not only about complying with the WCAG guidelines, but also about thinking about what aspects should be considered so that people with disabilities have an accessible user experience on the web (AUX).
Biomolecule Identification Using Superlattice AlGaN/GaN High-K MOSHEMT: A Cutting-Edge Biosensing Technique
Authors: Saha Tulip Kumar , Mukherjee Moumita , Dhar Rudra Sankar 
Published: October 2023

Abstract

This paper presents biomolecule identification process using a novel biosensing technique with high-K metal-oxide-semiconductor high electron mobility transistor (MOSHEMT). The authors have simulated a MOSHEMT device with high-K dielectric material to improve the sensitivity of biosensors. High-K dielectric material is utilized to examine the electrical efficacy of MOSHEMT-based biosensors. When high-K materials are used, Two-Dimensional Electron Gas (2DEG) benefits from carrier confinement and leakage current reduction. Therefore, the on-current of the device has been increased. For numerical modeling, TCAD Silvaco Atlas is used. For label-free identification of biomolecules, simulator is used to investigate and compare various performance parameters with SiO2 MOSHEMT. Experimental evidence verifies the accuracy of the model. According to the authors' knowledge, this is the first investigation on high-K dielectric AlGaN/GaN MOSHEMT biosensors for efficient label-free biomolecule detection. AlGaN/GaN MOSHEMTs, which use a high-K material, are found to be promising for use in biosensors.
CRISPR/Cas9-mediated knockout of NYC1 gene for the development of stay-green variety in Zoysia matrella (L.) Merrill
Authors: Ng Hwan May , Takahiro Gondo , Tanaka Hidenori , Akashi Ryo 
Published: October 2023

Abstract
Zoysia matrella is a widely used C4 warm-season turfgrass for landscaping, golf courses, and sports fields. Here, we used the CRISPR/Cas9 system to targetthe Non-Yellow Coloring1 (ZmNYC1) gene in the highly heterozygous allotetraploid Z. matrella 'Wakaba', aiming to generate a novel stay-green variety. Of 441 Agrobacterium-infected calli, 22 (5.0%) were transformed, and 14 of these (63.6%) showed targeted mutations through cleaved amplified polymorphic sequences analysis. Sequencing analysis revealed mutations mostly consisting of 1 or 2 bp indels, occurring 2 to 4 bp upstream of the PAM sequence. Regenerated plants exhibited five ZmNYC1 target locus genotypes, including homozygous mutants with a complete knockout of all four alleles in the T0 generation. Under dark treatment, ZmNYC1-mutated plants displayed suppressed chlorophyll b (Chl b) degradation, leading to higher chlorophyll content and Chl b, with a lower chlorophyll a/chlorophyll b ratio compared to the wild type (WT). Additionally, during winter simulation, mutant with a complete knockout retained greenness longer than the WT. This is the first successful use of CRISPR/Cas9 genome editing in zoysiagrass. The knockout mutants of the ZmNYC1 gene would serve as valuable breeding material for developing improved zoysiagrass varieties that can maintain their green color for longer periods, even during winter dormancy.
Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning
Authors: Yu Jingzhi , Yang Xiaoyun , Deng Yu , Krefman Amy E. , Pool Lindsay R. , Zhao Lihui , Mi Xinlei , Ning Hongyan , Wilkins John , Lloyd-Jones Donald M. , Petito Lucia C. , Allen Norrina B. 
Published: October 2023

Abstract
Background
It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown.
Objective
To develop a ASCVD risk prediction model that incorporates longitudinal risk factors using deep learning.
Methods
Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset.
Results
Of the 15,565 participants in our dataset, 2,170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI: 0.782-0.844) vs 0.792 (CI: 0.760-0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE.
Conclusion
Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.
Dysfunctional dorsal raphe dopaminergic neurons and locus coeruleus noradrenergic
neurons cause anxiety and depression in a Parkinson’s disease mouse model
Authors: Moratalla Rosario , Sanz-Magro Adrian , Granado Noelia , García-Verdugo Mario , Salas Carlos , Alberquilla Samuel , Vegas-Suárez Sergio , Deisseroth Karl , Spillantini Maria Grazia , Jercog Pablo , Tonini Raffaella 
Published: October 2023

Abstract
Parkinson's disease is a progressive neurodegenerative disorder whose symptoms appear in a longitudinal temporal pattern along the neuropathological burden. Before motor impairment, most patients suffer anxiety/depression, the most common and disabling emotional comorbidities. The anatomical and functional bases of these comorbidities are not well established, though some studies find that the dorsal raphe (DRN) and locus coeruleus (LC) nuclei are affected by Lewy pathology at early stages of the disease when affective symptoms appear. To establish the involvement of the DRN and LC in anxiety/depression, we use a progressive mouse model that accumulates pathological human alpha-synuclein under the TH promoter in dopaminergic and noradrenergic neurons. Molecular, neurophysiological, and behavioral investigations show that such accumulation in DRN dopaminergic neurons and LC noradrenergic neurons progressively alters neuronal integrity and catecholamine signaling in the target areas, the bed nucleus of stria terminalis (BNST) and central amygdala (CeA). The onset of these neuronal and circuit dysfunctions is directly linked to the appearance of anxiety and depression-like behaviors in this model that recapitulate the emotional symptoms present at the early stages of Parkinson’s disease.
The association between dietary intake of branched-chain amino acids and odds and severity of rheumatoid arthritis
Authors: Soleimani-Damaneh¹ Mohadeseh , Aryaeian Naheed , Khajoenia Shole , Azadbakht Leila , Hosseini-Baharanchi Fatemeh Sadat 
Published: October 2023

Abstract
This study examined the relationship between dietary branched‑chain amino acids (BCAAs) and the risk of rheumatoid arthritis (RA) and disease severity. This case-control study was conducted on RA patients and age- and sex-matched healthy controls. Dietary intake of BCAAs was assessed using a food frequency questionnaire, and the amounts of valine, leucine, and isoleucine consumed were summed to calculate total BCAA intake. We assessed the disease severity using the disease activity score 28 (DAS-28), ESR, VAS, morning stiffness, and tender and swollen joints. 95 RA patients and 190 healthy participants participated in the study. After multivariate adjustment, the odds of RA were 2.57 times higher for participants in the highest tertile of BCAAs intake than for those in the lowest tertile (95% confidence interval (CI): 1.14–5.75; P for trend = 0.02); in addition, the highest compared to the lowest tertile of valine was associated with increased risk of RA (OR: 2.31; 95% CI: 1.06–5.02; P for trend = 0.04). However, dietary BCAA intake was not significantly associated with disease severity in either crude or multivariate models. Higher dietary intakes of BCAAs, particularly valine, may contribute to the development of RA.
Determination of uranium concentration in blood samples of women with breast cancer in Babylon Province of Iraq using CR-39 nuclear track detector
Authors: Essa Haider O. , Al-Attiyah Khalid H. H. , Al-Hamzawi Anees A. 
Published: October 2023

Abstract

Breast cancer is the most common cancer among women in Iraq, accounting for approximately 30% of all cancers diagnosed in women. It is also the leading cause of cancer death among women in Iraq. This scientific paper aims to determine the uranium levels in the blood samples collected from breast cancer patients and analyzed using the CR-39 track detector to measure uranium concentrations. Fission track etching technique with CR-39 detector is a non-destructive technique used to detect and measure the spatial distribution of neutron-induced fission events in solid materials. It is a powerful tool for various applications in nuclear physics, radiation dosimetry, environmental monitoring, and geochronology. The results show that the uranium concentration among the patient women varied from 3.259 ppb to 1.918 ppb, while the uranium concentration among the healthy women varied from 2.15 ppb to 0.59 ppb. Increased uranium concentration in patient groups compared to healthy groups is well-documented. This is thought to be due to exposure to uranium through the environment, workplace, or medical procedures. Uranium is a heavy metal that can accumulate in the body over time and cause several health problems, including cancer, kidney damage, and congenital disabilities.
Shifts in the habitat suitability for large hammerhead sharks under climate change
Authors: Santos Catarina Pereira , Borges Francisco Oliveira , Guerreiro Miguel , Pissarra Vasco , Varela Jaquelino , Frazão-Santos Catarina , Rosa Rui 
Published: October 2023

Abstract

One of the chief consequences of climate change is the worldwide redistribution of species in the pursue of physiologically and ecologically favourable conditions. As part of one of the most threatened groups of vertebrates, assessing how climate change may affect the distribution patterns of key shark species is essential for their long-term conservation and management. The present study aims to assess how climate change projections (based on the representative concentration pathways 2.6, 4.5, 6.0, and 8.5) for the middle and end of the century may affect the global patterns of habitat suitability (HS) for large hammerhead sharks – a unique and particularly threatened group of elasmobranchs. Here, a decline in the average HS is projected for both Sphyrna lewini and Sphyrna zygaena, with the scale of HS decline generally increasing with time and RCP severity, while an increase in HS is generally projected for Sphyrna mokarran, albeit reduced over the long-term and overturned under long-term RCP8.5. Meanwhile, poleward shifts are projected for all three species, with considerable tropical declines for both S. lewini and S. zygaena. Additionally, regional shifts in suitability have the potential to affect population connectivity, the access to key habitats, and levels of exposure to other anthropogenic pressures, along with the effectiveness of conservation and management efforts. Taken together, these results showcase the importance of climate change for species’ extinction risk assessment and the urgency of its consideration for the design and implementation of conservation measures tailored to the oceans of tomorrow.
WITHDRAWN: Prediction of Vibration Waveform and Division of Influenced Partitions under the Action of Blasting
Authors: Zhao Yan , Wang Hailong , Li Yunhe , Yan Jialei 
Published: October 2023

Abstract
To study the impact of cutting blasting on the surface of the tunnel, a vibration waveform prediction function was constructed, and a method of dividing the affected area based on the vibration waveform theory was proposed. Based on the equivalent spherical charge theory, it is possible to establish a connection between the fitting coefficient and the engineering parameters in the equivalent source intensity function. Furthermore, a blasting vibration waveform function suitable for practical engineering can be constructed. Secondly, the reliability of the method introduced in this paper is verified based on the blasting vibration data monitored on site. Finally, the impact areas of blasting vibration are divided based on the peak particle velocity and vibration displacement as standards. The research results show that the vibration waveform prediction system introduced can better restore the vibration waveform corresponding to cutting blasting. In addition, the zoning method introduced in this article can reasonably divide the scope of the affected area. Moreover, the calculation process is simple and practicable. It has certain reference significance for similar blasting project.
Hydrogeochemistry and Groundwater Quality Assessment in Gujarat
Authors: Dalsania Kelvy , Pandey Vaishnavi , Sircar Anirbid , Chaudhary Dipti , Yadav Kriti 
Published: October 2023

Abstract

Assessing water quality is crucial for ensuring the availability of safe drinking water, sustainable agricultural practices, and safe industrial utilization. This paper presents a comprehensive study conducted over a five-year period in Lasundra, Savarkundla, Saputara, and Junagadh to determine whether the water is suitable for drinking, irrigation, and industrial use. The objective is to offer insightful information on the long-term fluctuations and trends in water quality in these areas. The collected data were utilized to calculate water quality indices specifically tailored for drinking, irrigation, and industrial purposes. The essential Water Quality Indices such as Ryznar Stability Index (RSI) and Langelier Saturation Index (LSI) have been examined for industrial application and primary indices like Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Kelly’s Ratio (KR), Potential Salinity (PS) and Permeability Index (PI) have been examined for drinking and irrigational applications of groundwater. These indices provided a quantitative assessment of overall water quality, enabling comparative analysis across the four study areas. Furthermore, advanced visualization techniques such as Piper, Wilcox, and Schoeller plots were illustrated to understand the changes in physiochemical characteristics over a five-year period. The results can help policymakers, water resource managers, and local communities in implementing appropriate measures to safeguard water resources, improve treatment processes, and enhance the water's quality for use in industrial, agricultural, and drinking purposes.
Experimental study on dredged slurry improved by vacuum preloading with top and bottom drainage
Authors: Fu Hongtao , Lin Jiahui , Wang Jun , Ni Junfeng , Gao Ziyang 
Published: October 2023

Abstract
The conventional vacuum preloading method is widely used to treat dredged slurry. However, this method cannot fully meet engineering requirements due to the loss of vacuum pressure. To overcome this issue, a method of vacuum preloading with top and bottom drainage is proposed, and the consolidation behavior of this method and the influence of the time ratio (a ratio of bottom vacuum application time to top vacuum application time) on this method are studied through laboratory model experiments. The results demonstrated that compared with the conventional vacuum preloading method, the average vane shear strength of the dredged slurry treated by vacuum preloading with top and bottom drainage increased by 20.6%, and the water content reduced by 6.0%. The time ratio has a significant effect on the consolidation of the dredged slurry, and the optimum time ratio is 1:1. Thus, vacuum preloading with top and bottom drainage can better complete the consolidation of the dredged slurry. In addition, this method can be used to develop a design guide for engineering practice.
Community Composition of Black Flies During and After the 2020 Vesicular Stomatitis Virus Outbreak In Southern New Mexico, USA
Authors: Whelpley Madelin J. , Zhou Lawrence H. , Rascon Jeremy , Payne Bailey , Moehn Brett , Young Katherine I. , Mire Chad E. , Peters Debra P. C. , Rodriguez Luis L. , Hanley Kathryn A. 
Published: October 2023

Abstract
Background:
Vesicular stomatitis virus (VSV), a vector-borne pathogen of livestock, emerges periodically in the western United States. In New Mexico (NM), USA, most cases occur close to the Rio Grande, implicating black flies (Simulium spp.) as a vector. In 2020, VS cases were reported in NM from April to May, although total black fly abundance remained high until September. We investigated the hypothesis that transience of local VSV transmission results from transient abundance of key, competent black fly species. Additionally, we investigated whether irrigation canals in southern NM support a different community of black flies than the main river. Lastly, to gain insight into the source of local black flies, in 2023 we collected black fly larvae prior to the release of the Rio Grande.
Methods:
We randomly sub-sampled adult black flies collected along the Rio Grande during and after the 2020 VSV outbreak. Further, we collected black fly adults along the Rio Grande in 2021 and 2022 and at southern NM farms and irrigation canals in 2022, and we collected black fly larvae from dams in 2023. All collections were counted, and individual specimens were subjected to molecular barcoding for species identification.
Results:
DNA barcoding of adult black flies detected four species in 2020: Simulium meridionale (N = 158), S. mediovittatum (N = 83), S. robynae (N = 26), and S. griseum/notatum (N = 1). Simulium robynae was only detected during the VSV outbreak period, S. meridionale showed higher relative abundance, but lower absolute abundance, during the outbreak than post-outbreak period, and S. mediovittatum was rare during the outbreak period but predominated later in the summer. In 2022, relative abundance of black fly species did not differ significantly between the Rio Grande sites and farm and irrigation canals. Intriguingly, 63 larval black flies comprised 56% S. vittatum, 43% S. argus, and 1% S. encisoi, species that were either extremely rare or not detected in previous adult collections.
Conclusions:
Our results suggest that S. robynae and S. meridionale could be shaping patterns of VSV transmission in southern NM. Thus, field studies of the source of these species as well as vector competence studies are warranted.
Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy
Authors: Zhang Tianwei , Zhu Ling , Wang Xinning , Zhang Xiaofei , Wang Zijie , Xu Shang , Jiao Wei 
Published: October 2023

Abstract

Objective The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).
Methods We retrospectively reviewed the clinical data of 337 patients who received PCNL between May 2020 and June 2022. In our study, eighty percent of the data were used as the training set, and the remaining data were used as the testing set. Separate prediction models based on the six machine learning algorithms were created using the training set. The predictive performance of each machine learning model was determined by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity using the testing set. We used coefficients to interpret the contribution of each variable to the predictive performance.
Results Among the six machine learning algorithms, the support vector machine (SVM) delivered the best performance with accuracy of 0.868, AUC of 0.942 (95% CI 0.890-0.994) in the testing set. Further analysis using the SVM model showed that prealbumin contributed the most to the prediction of the outcome, followed by preoperative urine culture, systemic immune-inflammation (SII), neutrophil to lymphocyte ratio (NLR), staghorn stones, fibrinogen, operation time, preoperative urine white blood cell (WBC), preoperative urea nitrogen, hydronephrosis, stone burden, sex and preoperative lymphocyte count.
Conclusion Machine learning-based prediction models can accurately predict the possibility of SIRS after PCNL in advance by learning patient clinical data, and should be used to guide surgeons in clinical decision-making.
Optimizing Decision-Making with Aggregation Operators for Generalized Intuitionistic Fuzzy Sets and Their Applications in the Tech Industry
Authors: Wasim Muhammad , Yousaf Awais , Alolaiyan Hanan , Akbar Muhammad Azeem , Alburaikan Alhanouf , Khalifa Hamiden Abd El-Wahed 
Published: October 2023

Abstract

Intuitionistic fuzzy sets (IFSs) extend the principles of fuzzy set (FS) theory by incorporating dual-degree attributes, encompassing both membership and non-membership degrees constrained within unity. IFSs find versatile applications across various domains, effectively addressing complex decision-making challenges. This study advances IFS theory to Generalized Intuitionistic Fuzzy Sets (GIFSBs) and introduces novel operators GIFWAA, GIFWGA, GIFOWAA, and GIFOWGA, tailored for GIFSBs. The primary aim is to enhance decision-making capabilities by introducing aggregation operators within the GIFSB framework that align with preferences for optimal outcomes. The article introduces new operators for GIFSBs characterized by attributes like Idempotency, Boundedness, Monotonicity and Commutativity, resulting in aggregated values aligned with GIFNs. A comprehensive analysis of the relationships among these operations is conducted, offering a thorough understanding of their applicability. These operators are practically demonstrated in a multiple-criteria decision-making process for evaluating startup success in the Tech Industry, broadening their utility for decision-makers, and aligning with existing ranking formulas for IFSs and Pythagorean fuzzy sets under specific conditions.