bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of the game improvement and data analysis model for the early childhood education major via deep learning
Authors: Zhao Yu , Gao WenWen , Ku ShanShan 
Published: November 2023

AbstractAn ever-growing portion of the economy is dedicated to the field of education, intensifying the urgency of identifying strategies to secure the sector’s enduring prosperity and elevate educational standards universally. This study introduces a model for enhancing games and optimizing data analysis within the context of early childhood education (ECE) majors, hinging on deep learning (DL). This approach aims to enhance the quality of instruction provided to ECE majors and refine the effectiveness of their professional pursuits. This study commences by examining the incorporation of DL technologies within the domain of ECE and delving into their fundamental underpinnings. Subsequently, it expounds upon the design philosophy underpinning ECE games operating within the framework of DL. Finally, it outlines the game improvement and data analysis (GIADA) model tailored to ECE majors. This model is constructed upon DL technology and further refined through the integration of convolutional neural networks (CNN). Empirical findings corroborate that the DL-CNN GIADA model achieves data analysis accuracy ranging from 83 to 93% across four datasets, underscoring the pronounced optimization prowess bestowed by CNN within the DL-based GIADA model. This study stands as an invaluable reference for the application and evolution of artificial intelligence technology within the realm of education, thereby contributing substantively to the broader landscape of educational advancement.
Inhibition of ALDH2 by quercetin glucuronide suggests a new hypothesis to explain red wine headaches
Authors: Devi Apramita , Levin Morris , Waterhouse Andrew L. 
Published: November 2023

AbstractThe consumption of red wine induces headaches in some subjects who can drink other alcoholic beverages without suffering. The cause for this effect has been attributed to a number of components, often the high level of phenolics in red wine, but a mechanism has been elusive. Some alcohol consumers exhibit flushing and experience headaches, and this is attributed to a dysfunctional ALDH2 variant, the enzyme that metabolizes acetaldehyde, allowing it to accumulate. Red wine contains much higher levels of quercetin and its glycosides than white wine or other alcoholic beverages. We show that quercetin-3-glucuronide, a typical circulating quercetin metabolite, inhibits ALDH2 with an IC50 of 9.6 µM. Consumption of red wine has been reported to result in comparable levels in circulation. Thus, we propose that quercetin-3-glucoronide, derived from the various forms of quercetin in red wines inhibits ALDH2, resulting in elevated acetaldehyde levels, and the subsequent appearance of headaches in susceptible subjects. Human-subject testing is needed to test this hypothesis.
Synthesis and biochemical evaluation of 17-N-beta-aminoalkyl-4,5α-epoxynormorphinans
Authors: Ötvös Ferenc , Szűcs Edina , Urai Ákos , Köteles István , Szabó Pál T. , Varga Zsuzsanna Katalin , Gombos Dávid , Hosztafi Sándor , Benyhe Sándor 
Published: November 2023

AbstractOpiate alkaloids and their synthetic derivatives are still widely used in pain management, drug addiction, and abuse. To avoid serious side effects, compounds with properly designed pharmacological profiles at the opioid receptor subtypes are long needed. Here a series of 17-N-substituted derivatives of normorphine and noroxymorphone analogues with five- and six-membered ring substituents have been synthesized for structure–activity study. Some compounds showed nanomolar affinity to MOR, DOR and KOR in in vitro competition binding experiments with selective agonists [3H]DAMGO, [3H]Ile5,6-deltorphin II and [3H]HS665, respectively. Pharmacological characterization of the compounds in G-protein signaling was determined by [35S]GTPγS binding assays. The normorphine analogues showed higher affinity to KOR compared to MOR and DOR, while most of the noroxymorphone derivatives did not bind to KOR. The presence of 14-OH substituent resulted in a shift in the pharmacological profiles in the agonist > partial agonist > antagonist direction compared to the parent compounds. A molecular docking-based in silico method was also applied to estimate the pharmacological profile of the compounds. Docking energies and the patterns of the interacting receptor atoms, obtained with experimentally determined active and inactive states of MOR, were used to explain the observed pharmacological features of the compounds.
Factors in turnover intention of cardiothoracic surgery residents
Authors: Yu Ji Hye , Roh Hyun Woong , Song Mi Ryoung , Lee Jang Hoon , Haam Seokjin , Kim Miran 
Published: November 2023

AbstractIncreasing numbers of cardiothoracic surgery residents are resigning, without completing their training. This study analyzes how their turnover intention is related to the training environment, and individual psychological factors. Responses by 57 Korean cardiothoracic surgery residents were analyzed. Their levels of depression, anxiety, grit, and empathy, working conditions, the effect of someone’s presence to discuss their concerns with, burnout, and turnover intention were identified as the research variables. Descriptive statistical analysis, correlation analysis, and structural equation modeling were used for data analysis. Burnout has the most significant relationship with turnover intention. It has a mediating effect on the influence of depression, grit (sustained interest), and working conditions, over turnover intention. Empathy, and the presence of someone to discuss concerns with, also affect turnover intention directly. The study also confirmed that grit and work satisfaction affect turnover intention indirectly, through burnout. The study identified both individual- and systemic-level factors for an effective training environment, to reduce cardiothoracic surgery residents’ tendencies of leaving the residency program, and supporting them for greater satisfaction with their career choice. In order to resolve negative emotions such as burnout and depression, and foster empathy, a human resource development program for the residents’ psychological support must be prepared. The program director should be adequately educated to take charge of the training program, oversee the residents’ education and welfare, and perform the roles of role-model and mentor.
Time-dependent specific molecular signatures of inflammation and remodelling are associated with trimethylamine-N-oxide (TMAO)-induced endothelial cell dysfunction
Authors: Shanmugham Meyammai , Devasia Arun George , Chin Yu Ling , Cheong Kang Hao , Ong Eng Shi , Bellanger Sophie , Ramasamy Adaikalavan , Leo Chen Huei 
Published: November 2023

AbstractEndothelial dysfunction is a critical initiating factor contributing to cardiovascular diseases, involving the gut microbiome-derived metabolite trimethylamine N-oxide (TMAO). This study aims to clarify the time-dependent molecular pathways by which TMAO mediates endothelial dysfunction through transcriptomics and metabolomics analyses in human microvascular endothelial cells (HMEC-1). Cell viability and reactive oxygen species (ROS) generation were also evaluated. TMAO treatment for either 24H or 48H induces reduced cell viability and enhanced oxidative stress. Interestingly, the molecular signatures were distinct between the two time-points. Specifically, few Gene Ontology biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were modulated after a short (24H) compared to a long (48H) treatment. However, the KEGG signalling pathways namely “tumour necrosis factor (TNF)” and “cytokine-cytokine receptor interaction” were downregulated at 24H but activated at 48H. In addition, at 48H, BPs linked to inflammatory phenotypes were activated (confirming KEGG results), while BPs linked to extracellular matrix (ECM) structural organisation, endothelial cell proliferation, and collagen metabolism were repressed. Lastly, metabolic profiling showed that arachidonic acid, prostaglandins, and palmitic acid were enriched at 48H. This study demonstrates that TMAO induces distinct time-dependent molecular signatures involving inflammation and remodelling pathways, while pathways such as oxidative stress are also modulated, but in a non-time-dependent manner.
Broadband optical vortex beam generation using flat-surface nanostructured gradient index vortex phase masks
Authors: Nguyen Hue Thi , Kasztelanic Rafal , Filipkowski Adam , Pysz Dariusz , Van Le Hieu , Stepien Ryszard , Omatsu Takashige , Krolikowski Wieslaw , Buczynski Ryszard 
Published: November 2023

AbstractWe developed a new kind of compact flat-surface nanostructured gradient index vortex phase mask, for the effective generation of optical vortex beams in broadband infrared wavelength range. A low-cost nanotechnological material method was employed for this work. The binary structure component consists of 17,557 nano-sized rods made of two lead–bismuth–gallium silicate glasses which were developed in-house. Those small rods are spatially arranged in such a way that, according to effective medium theory, the refractive index of this internal structure is constant in the radial direction and linearly changes following azimuthal angle. Numerical results demonstrated that a nanostructured vortex phase mask with a thickness of 19 μm can convert Gaussian beams into fundamental optical vortices over 290 nm wavelength bandwidth from 1275 to 1565 nm. This has been confirmed in experiments using three diode laser sources operating at 1310, 1550, and 1565 nm. The generation of vortex beams is verified through their uniform doughnut-like intensity distributions, clear astigmatic transformation patterns, and spiral as well as fork-like interferograms. This new flat-surface component can be directly mounted to an optical fiber tip for simplifying vortex generator systems as well as easier manipulation of the generated OVB in three-dimensional space.
Evaluating algorithms of decision tree, support vector machine and regression for anode side catalyst data in proton exchange membrane water electrolysis
Authors: Arjmandi Mahdi , Fattahi Moslem , Motevassel Mohsen , Rezaveisi Hosna 
Published: November 2023

AbstractNowadays, due to the various type of problems stemmed from using chemical compounds and fossil fuels which have widely influence on whole environment including acid rain, polar ice melting and etc., number of researches have been leading on replacing the nonrenewable energy sources with renewable ones in order to produce clean fuels. Among these, hydrogen emerges as a quintessential clean fuel, garnering substantial attention for its potential to be synthesized from the electric power generated by renewable sources like nuclear and solar energies. This is achieved through the employment of a proton exchange membrane water electrolysis (PEMWE) system, widely recognized as one of the most proficient and economically viable technologies for effecting the separation of H2O into H+ and OH−. In this study, the important affecting parameters on the anode side of catalyst in PEMWE and analyzed them by machine-learning (ML) algorithms through developing a data science (DS) procedure were discussed. Various machine learning models were subjected to comparison, wherein the Decision Tree models, specifically those configured with maximum depths of 3 and 4, emerged as the optimal choices, attaining a perfect 100% accuracy across both Dataset 1 and Dataset 2. Moreover, notable enhancements in accuracy values were observed for the Support Vector Machine (SVM) model, registering increments from 0.79 to 0.82 for Dataset 1 and 2, respectively. In stark contrast, the remaining models experienced a decrement in their accuracy scores. This phenomenon underscores the pivotal role played by the data generation process in rendering the models more faithful to real-world scenarios.
How AlphaFold2 shaped the structural coverage of the human transmembrane proteome
Authors: Jambrich Márton A. , Tusnady Gabor E. , Dobson Laszlo 
Published: November 2023

AbstractAlphaFold2 (AF2) provides a 3D structure for every known or predicted protein, opening up new prospects for virtually every field in structural biology. However, working with transmembrane protein molecules pose a notorious challenge for scientists, resulting in a limited number of experimentally determined structures. Consequently, algorithms trained on this finite training set also face difficulties. To address this issue, we recently launched the TmAlphaFold database, where predicted AlphaFold2 structures are embedded into the membrane plane and a quality assessment (plausibility of the membrane-embedded structure) is provided for each prediction using geometrical evaluation. In this paper, we analyze how AF2 has improved the structural coverage of membrane proteins compared to earlier years when only experimental structures were available, and high-throughput structure prediction was greatly limited. We also evaluate how AF2 can be used to search for (distant) homologs in highly diverse protein families. By combining quality assessment and homology search, we can pinpoint protein families where AF2 accuracy is still limited, and experimental structure determination would be desirable.
A sequence-based evolutionary distance method for Phylogenetic analysis of highly divergent proteins
Authors: Cao Wei , Wu Lu-Yun , Xia Xia-Yu , Chen Xiang , Wang Zhi-Xin , Pan Xian-Ming 
Published: November 2023

AbstractBecause of the limited effectiveness of prevailing phylogenetic methods when applied to highly divergent protein sequences, the phylogenetic analysis problem remains challenging. Here, we propose a sequence-based evolutionary distance algorithm termed sequence distance (SD), which innovatively incorporates site-to-site correlation within protein sequences into the distance estimation. In protein superfamilies, SD can effectively distinguish evolutionary relationships both within and between protein families, producing phylogenetic trees that closely align with those based on structural information, even with sequence identity less than 20%. SD is highly correlated with the similarity of the protein structure, and can calculate evolutionary distances for thousands of protein pairs within seconds using a single CPU, which is significantly faster than most protein structure prediction methods that demand high computational resources and long run times. The development of SD will significantly advance phylogenetics, providing researchers with a more accurate and reliable tool for exploring evolutionary relationships.
Application of the HPLC-ELSD technique for the determination of major metabolites of ibuprofen and creatinine in human urine
Authors: Piechocka Justyna , Matwiej Natalia , Gaweł Marta , Matyjaszczyk Michał , Głowacki Rafał , Chwatko Grażyna 
Published: November 2023

AbstractThe report presents robust and high throughput methods, based on liquid chromatography coupled with evaporative light scattering detection (HPLC-ELSD), for the simultaneous determination of major metabolites of ibuprofen (IBU), namely 2-hydroxyibuprofen and carboxyibuprofen (method A) as well as creatinine (Crn) (method B) in human urine. The assays primarily involve straightforward sample purification. For both methods, the chromatographic separation of the analytes is achieved within 8 min at room temperature on Poroshell 120 SB-C18 (75 × 4.6 mm, 2.7 µm) column using gradient elution. The eluents consisted of 0.1% formic acid in water and acetonitrile (method A) or water and methanol (method B) delivered at a flow rate of 1 or 0.5 mL/min, respectively. In relation to metabolites of IBU, the assay linearity was observed within 0.06–0.5 g/L in urine, while the Crn assay linearity was demonstrated within 0.5–30 mmol/L in urine. The limit of quantification for IBU metabolites was determined to be 0.06 g/L, and 0.5 mmol/L for Crn. These methods were successfully applied to urine samples delivered by ten apparently healthy donors showing that the HPLC-ELSD assays are suitable for human urine screening.
Global flood extent segmentation in optical satellite images
Authors: Portalés-Julià Enrique , Mateo-García Gonzalo , Purcell Cormac , Gómez-Chova Luis 
Published: November 2023

AbstractFloods are among the most destructive extreme events that exist, being the main cause of people affected by natural disasters. In the near future, estimated flood intensity and frequency are projected to increase. In this context, automatic and accurate satellite-derived flood maps are key for fast emergency response and damage assessment. However, current approaches for operational flood mapping present limitations due to cloud coverage on acquired satellite images, the accuracy of flood detection, and the generalization of methods across different geographies. In this work, a machine learning framework for operational flood mapping from optical satellite images addressing these problems is presented. It is based on a clouds-aware segmentation model trained in an extended version of the WorldFloods dataset. The model produces accurate and fast water segmentation masks even in areas covered by semitransparent clouds, increasing the coverage for emergency response scenarios. The proposed approach can be applied to both Sentinel-2 and Landsat 8/9 data, which enables a much higher revisit of the damaged region, also key for operational purposes. Detection accuracy and generalization of proposed model is carefully evaluated in a novel global dataset composed of manually labeled flood maps. We provide evidence of better performance than current operational methods based on thresholding spectral indices. Moreover, we demonstrate the applicability of our pipeline to map recent large flood events that occurred in Pakistan, between June and September 2022, and in Australia, between February and April 2022. Finally, the high-resolution (10-30m) flood extent maps are intersected with other high-resolution layers of cropland, building delineations, and population density. Using this workflow, we estimated that approximately 10 million people were affected and 700k buildings and 25,000 km$$^2$$

2

of cropland were flooded in 2022 Pakistan floods.
Application of response surface methodology and quantitative NMR for the optimum extraction, characterization, and quantitation of Antrodia cinnamomea triterpenoids
Authors: Li Chi-Ying , Wu Yang-Chang , Chang Fang-Rong , El-Shazly Mohamed , Du Ying-Chi , Lu Chi-Yu , Duh Tsai-Hui , Wu Tung-Ying 
Published: November 2023

AbstractAntrodia cinnamomea (AC) is a treasured Asian medicinal mushroom, which has attracted attention due to recent research on its effectiveness in targeting a variety of serious ailments such as cancer and liver diseases. Among different A. cinnamomea constituents, triterpenoids are regarded as the most therapeutically attractive components because of their anti-inflammatory and cytotoxic activities. In the present study, we proposed a mathematical and statistical extraction protocol to evaluate the concentrations of total ergostane and lanostane triterpenoid derivatives from the ethanolic extract of the wild fruiting bodies of A. cinnamomea (EEAC) by utilizing response surface methodology (RSM) and quantitative NMR (qNMR) approaches. The optimum response surface model showed that the variations of the investigated response variables reached more than 90%, suggesting that the developed model is accurate in explaining response variability. Furthermore, the EEAC major characteristic triterpenoids were quantified through the comparison of the HPLC-tandem MS results with those of the qNMR results. The precision of the used techniques was also evaluated. The experimental design of the EEAC optimum extraction procedure obtained by using RSM and qNMR enabled accurate characterization and quantitation of A. cinnamomea triterpenoids.
Influence of the drying mode of support on the properties of Pd/Al2O3–ZrO2 materials used for methane combustion
Authors: Amairia C. , Fessi S. , Mhamdi M. , Ghorbel A. , Llorca J. 
Published: November 2023

AbstractThis work constitutes a new trial to enhance the properties of palladium supported on alumina modified with zirconium used as catalysts for methane combustion. The effect of the support drying mode is studied. For this aim, Al2O3-ZrO2 binary oxides with zirconium loading of 2 and 5% in weight were prepared using sol–gel process then dried under ordinary or supercritical conditions. Palladium with a loading of 0.5% was deposited on the support by wet impregnation. Several techniques have been used to investigate differences between the two types of the derived catalysts.
Experimental and digital investigations of heterogeneity in lower cretaceous carbonate reservoir using fractal and multifractal concepts
Authors: Jouini Mohamed Soufiane , Alabere Abdulquadri O. , Alsuwaidi Mohammad , Morad Sadoon , Bouchaala Fateh , Al Jallad Osama A. 
Published: November 2023

AbstractCharacterization and prediction of reservoir heterogeneity are crucial for hydrocarbon production. This study applies the multifractal theory using both numerical and experimental data to characterize quantitatively the heterogeneity of pore structures in Lower Cretaceous limestone reservoir from the United Arab Emirates. Fractal dimensions calculated from three dimensional digital images showed good correlation (R2 =  + 0.69) with experimental high-pressure mercury injection (HPMI) measurements. Moreover, both experimental and numerical fractal dimensions correlate well with experimental HPMI porosity measurements. Multifractal parameters such as the non-uniformity degree of the pore structures Δα, the asymmetry degree in the vertical axis Δf(α), the concentration of pore size distribution α0 and the asymmetry degree in the horizontal axis Rd estimated from digital and experimental data correlated well and revealed ability to quantitatively describe samples heterogeneity. The ranges of digital and experimental multifractal parameters provided the means to differentiate between homogeneous and heterogeneous samples.
Blood plasma metagenomic next-generation sequencing for identifying pathogens of febrile neutropenia in acute leukemia patients
Authors: Qi Yan , Lin Wu-Qiang , Liao Bin , Chen Jia-Wei , Chen Ze-Song 
Published: November 2023

AbstractTo investigate the value of metagenomic next-generation sequencing (mNGS) in acute leukemia (AL) patients with febrile neutropenia (FN). We retrospectively reviewed 37 AL patients with FN and compared the results of mNGS with blood culture (BC) and the clinical features of the mNGS-positive group and the mNGS-negative group. A total of 14 detected pathogens were the final clinical diagnosis, of which 9 strains were detected only by mNGS and 5 strains were detected by both mNGS and BC. The top pathogens were Klebsiella pneumoniae, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. A total of 67.57% (25/37) were bacterial infections, and 2.7% (1/37) were fungal or viral infections. The diagnostic positivity rate of mNGS (25/37, 67.6%) was significantly higher than that of BC (7/37, 18.9%), and the difference was statistically significant (p < 0.05). Then, we explored the clinical distinction between the mNGS-positive group and the mNGS-negative group, and 3 features were filtered, including lymphocyte count (LY), creatinine levels (Cr), and white blood cell count (WBC). Our study demonstrated that early implementation of mNGS can effectively improve the efficacy of pathogen detection in AL patients with FN. The higher diagnostic positivity rate and the ability to detect additional pathogens compared to BC made mNGS a valuable tool in the management of infectious complications in this patient population. Furthermore, the identified clinical features associated with mNGS results provided additional insights for the clinical indication of infection in AL patients with FN.