bluuebunny/crossref_metadata_2025_split huggingface dataset:
Personality trait analysis during the COVID-19 pandemic: a comparative study on social media
Authors: Fernández-Pichel Marcos , Aragón Mario Ezra , Saborido-Patiño Julián , Losada David E. 
Published: August 2023

AbstractThe COVID-19 pandemic, a global contagion of coronavirus infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has triggered severe social and economic disruption around the world and provoked changes in people’s behavior. Given the extreme societal impact of COVID-19, it becomes crucial to understand the emotional response of the people and the impact of COVID-19 on personality traits and psychological dimensions. In this study, we contribute to this goal by thoroughly analyzing the evolution of personality and psychological aspects in a large-scale collection of tweets extracted during the COVID-19 pandemic. The objectives of this research are: i) to provide evidence that helps to understand the estimated impact of the pandemic on people’s temperament, ii) to find associations and trends between specific events (e.g., stages of harsh confinement) and people’s reactions, and iii) to study the evolution of multiple personality aspects, such as the degree of introversion or the level of neuroticism. We also examine the development of emotions, as a natural complement to the automatic analysis of the personality dimensions. To achieve our goals, we have created two large collections of tweets (geotagged in the United States and Spain, respectively), collected during the pandemic. Our work reveals interesting trends in personality dimensions, emotions, and events. For example, during the pandemic period, we found increasing traces of introversion and neuroticism. Another interesting insight from our study is that the most frequent signs of personality disorders are those related to depression, schizophrenia, and narcissism. We also found some peaks of negative/positive emotions related to specific events.
Bespoke Energetic Zeolite Imidazolate Frameworks-8 (ZIF-8)/Ammonium Perchlorate Nanocomposite: A Novel Reactive Catalyzed High Energy Dense Material with Superior Decomposition Kinetics
Authors: Attwa Mohamed , Said Ali , ElGamal M. , El-Shaer Yasser , Elbasuney Sherif 
Published: August 2023

AbstractAluminum is the universal fuel for solid propellants; however, its passive oxide layer could impede the full exploitation of its enthalpy. Meanwhile, common catalyst could not contribute to combustion enthalpy. This study shaded light on the multifunctional energetic metal-organic frameworks ZIF-8 with combustion enthalpy 21 KJ/g as high energy dense material as well as a novel catalyst for solid propellants. As-prepared ZIF-8 particles exhibited a highly crystalline structure with an average particle size of 40 nm. The performance of ZIF-8 as high-energy dense material was assessed to aluminum particles via integration into ammonium perchlorate (APC). ZIF-8/APC and Al/APC composites were prepared via the solvent–nonsolvent method; the decomposition enthalpy was investigated via DSC. ZIF-8 offered an increase in APC total decomposition enthalpy by 98.4%, to 39.11% for Al. ZIF-8 exhibited a superior catalytic behavior by lowering the APC high-temperature decomposition peak (HTD) by 81.48 °C compared to 70.3 °C for Al. The decomposition kinetics of ZIF-8/AP nanocomposite was investigated via Kissinger’s formula. The ZIF-8 offered a remarkable reduction in APC apparent activation energy at low-temperature decomposition peak and HTD peaks by peak by 16.5% and 30%, respectively. The superior catalytic performance of ZIF-8 was attributed to Zn+2 electron deficient centers with the exclusive formation of ZnO nanoparticles during combustion. ZIF-8 with gaseous decomposition products could boost specific impulses.
Polynomial Total Positivity and High Relative Accuracy Through Schur Polynomials
Authors: Díaz P. , Mainar E. , Rubio B. 
Published: August 2023

AbstractIn this paper, Schur polynomials are used to provide a bidiagonal decomposition of polynomial collocation matrices. The symmetry of Schur polynomials is exploited to analyze the total positivity on some unbounded intervals of a relevant class of polynomial bases. The proposed factorization is used to achieve relative errors of the order of the unit round-off when solving algebraic problems involving the collocation matrix of relevant polynomial bases, such as the Hermite basis. The numerical experimentation illustrates the accurate results obtained when using the findings of the paper.
Local look-up table upsampling for accelerating image processing
Authors: Tsubokawa Teppei , Tajima Hiroshi , Maeda Yoshihiro , Fukushima Norishige 
Published: August 2023

AbstractThe resolution of cameras is increasing, and speedup of various image processing is required to accompany this increase. A simple way of acceleration is processing the image at low resolution and then upsampling the result. Moreover, when we can use an additional high-resolution image as guidance formation for upsampling, we can upsample the image processing results more accurately. We propose an approach to accelerate various image processing by downsampling and joint upsampling. This paper utilizes per-pixel look-up tables (LUTs), named local LUT, which are given a low-resolution input image and output pair. Subsequently, we upsample the local LUT. We can then generate a high-resolution image only by referring to its local LUT. In our experimental results, we evaluated the proposed method on several image processing filters and applications: iterative bilateral filtering, $$\ell _0$$

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0

smoothing, local Laplacian filtering, inpainting, and haze removing. The proposed method accelerates image processing with sufficient approximation accuracy, and the proposed outperforms the conventional approaches in the trade-off between accuracy and efficiency. Our code is available at https://fukushimalab.github.io/LLF/.
Improving the generalization capability of YOLOv5 on remote sensed insect trap images with data augmentation
Authors: Suto Jozsef 
Published: August 2023

AbstractIn agricultural pest management, the traditional insect population tracking in the case of several insect types is based on outsourced sticky paper traps that are checked periodically by a human operator. However, with the aid of the Internet of Things technology and machine learning, this type of manual monitoring can be automated. Even though great progress has been made in the field of insect pest detector models, the lack of sufficient amount of remote sensed trap images prevents their practical application. Beyond the lack of sufficient data, another issue is the large discrepancy between manually taken and remote sensed trap images (different illumination, quality, background, etc.). In order to improve those problems, this paper proposes three previously unused data augmentation approaches (gamma correction, bilateral filtering, and bit-plate slicing) which artificially enrich the training data and through this increase the generalization capability of deep object detectors on remote sensed trap images. Even with the application of the widely used geometric and texture-based augmentation techniques, the proposed methods can further increase the efficiency of object detector models. To demonstrate their efficiency, we used the Faster Region-based Convolutional Neural Network (R-CNN) and the You Look Only Once version 5 (YOLOv5) object detectors which have been trained on a small set of high-resolution, manually taken trap images while the test set consists of remote sensed images. The experimental results showed that the mean average precision (mAP) of the reference models significantly improved while in some cases their counting error was reduced to a third.
Zinc oxide nanoparticles and PGPR strengthen salinity tolerance and productivity of wheat irrigated with saline water in sodic-saline soil
Authors: Alharbi Khadiga , Hafez Emad M. , Omara Alaa El-Dein , Rashwan Emadelden , Alshaal Tarek 
Published: August 2023

Abstract
Background and aims
Wheat growth and productivity need an exceptional approach to resist the deleterious effects of salt stress.

Methods
This study proposed to assess the effectiveness of the exogenous application of plant growth-promoting rhizobacteria (PGPR; i.e., Azospirillum lipoferum SP2, Bacillus coagulans NCAIM B.01123, Bacillus circulance NCAIM B.02324, and Bacillus subtilis MF497446) at a rate of 950 g ha−1 and foliar application of zinc oxide nanoparticles (ZnO-NPs; 500 mg L−1) against irrigation with saline (from a groundwater well) and fresh water (from the Nile River water) of wheat (Triticum aestivum L.) in sodic-saline soil during 2021 and 2022 growing seasons under open field conditions.

Results
The integrated application of PGPR and ZnO-NPs protected wheat plants against irrigation with saline water through increasing antioxidant enzyme activities, i.e., catalase (47%), peroxidase (102%), and superoxide dismutase (106%), and K+ uptake (27%) over control. Conversely, higher stress mitigation through the integrated application was illustrated by a considerable decline in electrolyte leakage (−62%), proline (−39%), MDA (−56%), and H2O2 levels (−60%). The N uptake by wheat grains increased by 57% upon treating plants with PGPR+ZnO-NPs, which also increased the Zn contents in grain and straw by 117% and 72%, respectively. Also, PGPR+ZnO-NPs increased the activity of soil urease and dehydrogenase by 80% and 232%, respectively, in plots irrigated with saline water.

Conclusion
The results of the present investigation suggest the use of the integrated application of PGPR and ZnO-NPs to protect wheat plants against salinity of soil and/ or irrigation water.
Estimating and Using Block Information in the Thurstonian IRT Model
Authors: Frick Susanne 
Published: August 2023

Multidimensional forced-choice (MFC) tests are increasing in popularity but their construction is complex. The Thurstonian item response model (Thurstonian IRT model) is most often used to score MFC tests that contain dominance items. Currently, in a frequentist framework, information about the latent traits in the Thurstonian IRT model is computed for binary outcomes of pairwise comparisons, but this approach neglects stochastic dependencies. In this manuscript, it is shown how to estimate Fisher information on the block level. A simulation study showed that the observed and expected standard errors based on the block information were similarly accurate. When local dependencies for block sizes >2\documentclass[12pt]{minimal}
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New trend in artificial intelligence-based assistive technology for thoracic imaging
Authors: Yanagawa Masahiro , Ito Rintaro , Nozaki Taiki , Fujioka Tomoyuki , Yamada Akira , Fujita Shohei , Kamagata Koji , Fushimi Yasutaka , Tsuboyama Takahiro , Matsui Yusuke , Tatsugami Fuminari , Kawamura Mariko , Ueda Daiju , Fujima Noriyuki , Nakaura Takeshi , Hirata Kenji , Naganawa Shinji 
Published: August 2023

AbstractAlthough there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.
Abnormal Brain Protein Abundance and Cross-tissue mRNA Expression in Amyotrophic Lateral Sclerosis
Authors: Ma Yanni , Jia Tingting , Qin Fengqin , He Yongji , Han Feng , Zhang Chengcheng 
Published: August 2023

AbstractDue to the limitations of the present risk genes in understanding the etiology of amyotrophic lateral sclerosis (ALS), it is necessary to find additional causative genes utilizing novel approaches. In this study, we conducted a two-stage proteome-wide association study (PWAS) using ALS genome-wide association study (GWAS) data (N = 152,268) and two distinct human brain protein quantitative trait loci (pQTL) datasets (ROSMAP N = 376 and Banner N = 152) to identify ALS risk genes and prioritized candidate genes with Mendelian randomization (MR) and Bayesian colocalization analysis. Next, we verified the aberrant expression of risk genes in multiple tissues, including lower motor neurons, skeletal muscle, and whole blood. Six ALS risk genes (SCFD1, SARM1, TMEM175, BCS1L, WIPI2, and DHRS11) were found during the PWAS discovery phase, and SARM1 and BCS1L were confirmed during the validation phase. The following MR (p = 2.10 × 10−7) and Bayesian colocalization analysis (ROSMAP PP4 = 0.999, Banner PP4 = 0.999) confirmed the causal association between SARM1 and ALS. Further differential expression analysis revealed that SARM1 was markedly downregulated in lower motor neurons (p = 7.64 × 10−3), skeletal muscle (p = 9.34 × 10−3), and whole blood (p = 1.94 × 10−3). Our findings identified some promising protein candidates for future investigation as therapeutic targets. The dysregulation of SARM1 in multiple tissues provides a new way to explain ALS pathology.
Digital competences of teachers in the use of YouTube as an educational resource: analysis by educational stage and gender
Authors: Basgall Lauren , Guillén-Gámez Francisco D. , Colomo-Magaña Ernesto , Cívico-Ariza Andrea 
Published: August 2023

AbstractThe purpose of this study was to analyze the digital competence of teachers on the use of YouTube as a didactic resource in the teaching and learning process of students. Specifically, this was analyzed and compared for each educational stage (Preschool Education, Primary Education, Secondary Education, Adult Education, and Vocational Education-VET) between genders of the teacher (female and male). For this, an ex post facto non-experimental design was used, with a sample of 2157 in-service teachers from all over Spain. Among the main results found in this study, teachers of all educational stages had satisfactory skills on the use of YouTube to search and communicate information, although low in relation to the creation of audio-visual material through YouTube. In addition, it was evidenced how gender was a significant factor which affected the competencies of teachers, in most of the educational stages analyzed.
RBFOX2 recognizes N6-methyladenosine to suppress transcription and block myeloid leukaemia differentiation
Authors: Dou Xiaoyang , Xiao Yu , Shen Chao , Wang Kitty , Wu Tong , Liu Chang , Li Yini , Yu Xianbin , Liu Jun , Dai Qing , Pajdzik Kinga , Ye Chang , Ge Ruiqi , Gao Boyang , Yu Jianhua , Sun Shuying , Chen Mengjie , Chen Jianjun , He Chuan 
Published: August 2023

AbstractN6-methyladenosine (m6A) methylation can be deposited on chromatin-associated RNAs (caRNAs) by the RNA methyltransferase complex (MTC) to regulate chromatin state and transcription. However, the mechanism by which MTC is recruited to distinct genomic loci remains elusive. Here we identify RBFOX2, a well-studied RNA-binding protein, as a chromatin factor that preferentially recognizes m6A on caRNAs. RBFOX2 can recruit RBM15, an MTC component, to facilitate methylation of promoter-associated RNAs. RBM15 also physically interacts with YTHDC1 and recruits polycomb repressive complex 2 (PRC2) to the RBFOX2-bound loci for chromatin silencing and transcription suppression. Furthermore, we found that this RBFOX2/m6A/RBM15/YTHDC1/PRC2 axis plays a critical role in myeloid leukaemia. Downregulation of RBFOX2 notably inhibits survival/proliferation of acute myeloid leukaemia cells and promotes their myeloid differentiation. RBFOX2 is also required for self-renewal of leukaemia stem/initiation cells and acute myeloid leukaemia maintenance. Our study presents a pathway of m6A MTC recruitment and m6A deposition on caRNAs, resulting in locus-selective chromatin regulation, which has potential therapeutic implications in leukaemia.
Natriuresis-guided diuretic therapy in acute heart failure: a pragmatic randomized trial
Authors: ter Maaten Jozine M. , Beldhuis Iris E. , van der Meer Peter , Krikken Jan A. , Postmus Douwe , Coster Jenifer E. , Nieuwland Wybe , van Veldhuisen Dirk J. , Voors Adriaan A. , Damman Kevin 
Published: August 2023

AbstractMeasurement of natriuresis has been suggested as a reliable, easily obtainable biomarker for assessment of the response to diuretic treatment in patients with acute heart failure (AHF). Here, to assess whether natriuresis-guided diuretic therapy in patients with AHF improves natriuresis and clinical outcomes, we conducted the pragmatic, open-label Pragmatic Urinary Sodium-based algoritHm in Acute Heart Failure trial, in which 310 patients (45% female) with AHF requiring treatment with intravenous loop diuretics were randomly assigned to natriuresis-guided therapy or standard of care (SOC). In the natriuresis-guided arm, natriuresis was determined at set timepoints, prompting treatment intensification if spot urinary sodium levels were <70 mmol l−1. The dual primary endpoints were 24 h urinary sodium excretion and a combined endpoint of time to all-cause mortality or adjudicated heart failure rehospitalization at 180 days. The first primary endpoint was met, as natriuresis in the natriuresis-guided and SOC arms was 409 ± 178 mmol arm versus 345 ± 202 mmol, respectively (P = 0.0061). However, there were no significant differences between the two arms for the combined endpoint of time to all-cause mortality or first heart failure rehospitalization, which occurred in 46 (31%) and 50 (31%) of patients in the natriuresis-guided and SOC arms, respectively (hazard ratio 0.92 [95% confidence interval 0.62–1.38], P = 0.6980). These findings suggest that natriuresis-guided therapy could be a first step towards personalized treatment of AHF. ClinicalTrials.gov registration: NCT04606927.
Propagation of activity through the cortical hierarchy and perception are determined by neural variability
Authors: Rowland James M. , van der Plas Thijs L. , Loidolt Matthias , Lees Robert M. , Keeling Joshua , Dehning Jonas , Akam Thomas , Priesemann Viola , Packer Adam M. 
Published: August 2023

AbstractBrains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation. Orchestration of complex behaviors requires communication between brain regions, but how neural dynamics are organized to facilitate reliable transmission is not well understood. Here we studied this process directly by generating neural activity that propagates between brain regions and drives behavior, assessing how neural populations in sensory cortex cooperate to transmit information. We achieved this by imaging two densely interconnected regions—the primary and secondary somatosensory cortex (S1 and S2)—in mice while performing two-photon photostimulation of S1 neurons and assigning behavioral salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation but also by the variability of S1 neural activity. Therefore, maximizing the signal-to-noise ratio of the stimulus representation in cortex relative to the noise or variability is critical to facilitate activity propagation and perception.
A weighted-sum chaotic sparrow search algorithm for interdisciplinary feature selection and data classification
Authors: Jia LiYun , Wang Tao , Gad Ahmed G. , Salem Ahmed 
Published: August 2023

AbstractIn today’s data-driven digital culture, there is a critical demand for optimized solutions that essentially reduce operating expenses while attempting to increase productivity. The amount of memory and processing time that can be used to process enormous volumes of data are subject to a number of limitations. This would undoubtedly be more of a problem if a dataset contained redundant and uninteresting information. For instance, many datasets contain a number of non-informative features that primarily deceive a given classification algorithm. In order to tackle this, researchers have been developing a variety of feature selection (FS) techniques that aim to eliminate unnecessary information from the raw datasets before putting them in front of a machine learning (ML) algorithm. Meta-heuristic optimization algorithms are often a solid choice to solve NP-hard problems like FS. In this study, we present a wrapper FS technique based on the sparrow search algorithm (SSA), a type of meta-heuristic. SSA is a swarm intelligence (SI) method that stands out because of its quick convergence and improved stability. SSA does have some drawbacks, like lower swarm diversity and weak exploration ability in late iterations, like the majority of SI algorithms. So, using ten chaotic maps, we try to ameliorate SSA in three ways: (i) the initial swarm generation; (ii) the substitution of two random variables in SSA; and (iii) clamping the sparrows crossing the search range. As a result, we get CSSA, a chaotic form of SSA. Extensive comparisons show CSSA to be superior in terms of swarm diversity and convergence speed in solving various representative functions from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) benchmark set. Furthermore, experimental analysis of CSSA on eighteen interdisciplinary, multi-scale ML datasets from the University of California Irvine (UCI) data repository, as well as three high-dimensional microarray datasets, demonstrates that CSSA outperforms twelve state-of-the-art algorithms in a classification task based on FS discipline. Finally, a 5%-significance-level statistical post-hoc analysis based on Wilcoxon’s signed-rank test, Friedman’s rank test, and Nemenyi’s test confirms CSSA’s significance in terms of overall fitness, classification accuracy, selected feature size, computational time, convergence trace, and stability.