bluuebunny/crossref_metadata_2025_split huggingface dataset:
A numerical study of spatio-temporal COVID-19 vaccine model via finite-difference operator-splitting and meshless techniques
Authors: Khan Arshad A. , Ullah Saif , Altanji Mohamed , Amin Rohul , Haider Nadeem , Alshehri Ahmed , Riaz Muhammad Bilal 
Published: July 2023

AbstractIn this paper, a new spatio-temporal model is formulated to study the spread of coronavirus infection (COVID-19) in a spatially heterogeneous environment with the impact of vaccination. Initially, a detailed qualitative analysis of the spatio-temporal model is presented. The existence, uniqueness, positivity, and boundedness of the model solution are investigated. Local asymptotical stability of the diffusive COVID-19 model at steady state is carried out using well-known criteria. Moreover, a suitable nonlinear Lyapunov functional is constructed for the global asymptotical stability of the spatio-temporal model. Further, the model is solved numerically based on uniform and non-uniform initial conditions. Two different numerical schemes named: finite difference operator-splitting and mesh-free operator-splitting based on multi-quadratic radial basis functions are implemented in the numerical study. The impact of diffusion as well as some pharmaceutical and non-pharmaceutical control measures, i.e., reducing an effective contact causing infection transmission, vaccination rate and vaccine waning rate on the disease dynamics is presented in a spatially heterogeneous environment. Furthermore, the impact of the aforementioned interventions is investigated with and without diffusion on the incidence of disease. The simulation results conclude that the random motion of individuals has a significant impact on the disease dynamics and helps in setting a better control strategy for disease eradication.
Precise irrigation water and nitrogen management improve water and nitrogen use efficiencies under conservation agriculture in the maize-wheat systems
Authors: Gupta Naveen , Singh Yadvinder , Jat Hanuman S. , Singh Love K. , Choudhary Kajod M. , Sidhu Harminder S. , Gathala Mahesh K. , Jat Mangi L. 
Published: July 2023

AbstractA 3-year field experiment was setup to address the threat of underground water depletion and sustainability of agrifood systems. Subsurface drip irrigation (SDI) system combined with nitrogen management under conservation agriculture-based (CA) maize-wheat system (MWS) effects on crop yields, irrigation water productivity (WPi), nitrogen use efficiency (NUE) and profitability. Grain yields of maize, wheat, and MWS in the SDI with 100% recommended N were significantly higher by 15.8%, 5.2% and 11.2%, respectively, than conventional furrow/flood irrigation (CT-FI) system. System irrigation water savings (~ 55%) and the mean WPiwere higher in maize, wheat, and MWS under the SDI than CT-FI system. There was saving of 25% of fertilizer N in maize and MWS whereas no saving of N was observed in wheat. Net returns from MWS were significantly higher (USD 265) under SDI with 100% N (with no subsidy) than CT-FI system despite with higher cost of production. The net returns were increased by 47% when considering a subsidy of 80% on laying SDI system. Our results showed a great potential of complementing CA with SDI and N management to maximize productivity, NUE, and WPi, which may be economically beneficial and environmentally sound in MWS in Trans-IGP of South Asia.
Biomarkers of ulcerative colitis disease activity CXCL1, CYP2R1, LPCAT1, and NEU4 and their relationship to immune infiltrates
Authors: Huo Aijing , Wang Fengmei 
Published: July 2023

AbstractThe diagnosis and assessment of ulcerative colitis (UC) poses significant challenges, which may result in inadequate treatment and a poor prognosis for patients. This study aims to identify potential activity biomarkers for UC and investigate the role of infiltrating immune cells in the disease. To perform gene set enrichment analysis, we utilized the cluster profiler and ggplot2 packages. Kyoto encyclopedia of genes and genomes was used to analyze degenerate enrichment genes. Significant gene set enrichment was determined using the cluster profiler and ggplot2 packages. Additionally, quantitative PCR (qRT-PCR) was employed to validate the expression of each marker in the ulcerative colitis model. We identified 651 differentially expressed genes (DEGs) and further investigated potential UC activity biomarkers. Our analysis revealed that CXCL1 (AUC = 0.710), CYP2R1 (AUC = 0.863), LPCAT1 (AUC = 0.783), and NEU4 (AUC = 0.833) were promising activity markers for the diagnosis of UC. Using rat DSS model, we validated these markers through qRT-PCR, which showed statistically significant differences between UC and normal colon mucosa. Infiltrating immune cell analysis indicated that M1 macrophages, M2 macrophages, activated dendritic cells (DCs), and neutrophils played crucial roles in the occurrence and progression of UC. Moreover, the activity markers exhibited varying degrees of correlation with activated memory CD4 T cells, M0 macrophages, T follicular helper cells, memory B cells, and activated DCs. The potential diagnostic genes for UC activity, such as CXCL1, CYP2R1, LPCAT1, and NEU4, as well as the infiltration of immune cells, may contribute to the pathogenesis and progression of UC.
Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition
Authors: Mekruksavanich Sakorn , Jitpattanakul Anuchit 
Published: July 2023

AbstractIn the field of machine intelligence and ubiquitous computing, there has been a growing interest in human activity recognition using wearable sensors. Over the past few decades, researchers have extensively explored learning-based methods to develop effective models for identifying human behaviors. Deep learning algorithms, known for their powerful feature extraction capabilities, have played a prominent role in this area. These algorithms can conveniently extract features that enable excellent recognition performance. However, many successful deep learning approaches have been built upon complex models with multiple hyperparameters. This paper examines the current research on human activity recognition using deep learning techniques and discusses appropriate recognition strategies. Initially, we employed multiple convolutional neural networks to determine an effective architecture for human activity recognition. Subsequently, we developed a hybrid convolutional neural network that incorporates a channel attention mechanism. This mechanism enables the network to capture deep spatio-temporal characteristics in a hierarchical manner and distinguish between different human movements in everyday life. Our investigations, using the UCI-HAR, WISDM, and IM-WSHA datasets, demonstrated that our proposed model, which includes cross-channel multi-size convolution transformations, outperformed previous deep learning architectures with accuracy rates of 98.92%, 98.80%, and 98.45% respectively. These results indicate that the suggested model surpasses state-of-the-art approaches in terms of overall accuracy, as supported by the research findings.
Effect of drilling process parameters on bearing strength of glass fiber/aluminum mesh reinforced epoxy composites
Authors: Seif Amr , Fathy A. , Megahed A. A. 
Published: July 2023

AbstractThe current study attempted to evaluate the impact of drilling parameters and delamination on the bearing strength of both neat GFRP (NG) and hybrid GFRP/aluminum (Al)-wire mesh with two various configurations, first with Al-mesh in the outer surface of specimen (AG) and the other with Al-mesh in the core of specimen (GA). Drilling procedure is carried out using $$\varnothing$$
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6 mm carbide twist drill with three different tip angles (90°, 120° and 135°), as well as Three different speeds and feeds (1000, 2000, and 3000 rpm) and (20, 40, and 60 mm/min), respectively. Taguchi and ANOVA analyses are used to analyze the influence of processing parameters. The findings showed that AG specimen experienced the least delamination damage. The maximum bearing strength refers to NG specimen, which is 9.6% and 8.7% more than AG and GA specimens, respectively. Drill point angle has the major effect on bearing strength for both AG and GA specimens, while for NG feed rate is of the major effect. The developed regression model displayed a high level of fitness with an average prediction error of less than 3%.
High throughput human genotyping for variants associated with malarial disease outcomes using custom targeted amplicon sequencing
Authors: Osborne Ashley , Phelan Jody E. , Vanheer Leen N. , Manjurano Alphaxard , Gitaka Jesse , Drakeley Christopher J. , Kaneko Akira , Kita Kiyoshi , Campino Susana , Clark Taane G. 
Published: July 2023

AbstractMalaria has exhibited the strongest known selective pressure on the human genome in recent history and is the evolutionary driving force behind genetic conditions, such as sickle-cell disease, glucose-6-phosphatase deficiency, and some other erythrocyte defects. Genomic studies (e.g., The 1000 Genomes project) have provided an invaluable baseline for human genetics, but with an estimated two thousand ethno-linguistic groups thought to exist across the African continent, our understanding of the genetic differences between indigenous populations and their implications on disease is still limited. Low-cost sequencing-based approaches make it possible to target specific molecular markers and genes of interest, leading to potential insights into genetic diversity. Here we demonstrate the versatility of custom dual-indexing technology and Illumina next generation sequencing to generate a genetic profile of human polymorphisms associated with malaria pathology. For 100 individuals diagnosed with severe malaria in Northeast Tanzania, variants were successfully characterised on the haemoglobin subunit beta (HBB), glucose-6-phosphate dehydrogenase (G6PD), atypical chemokine receptor 1 (ACKR1) genes, and the intergenic Dantu genetic blood variant, then validated using pre-existing genotyping data. High sequencing coverage was observed across all amplicon targets in HBB, G6PD, ACKR1, and the Dantu blood group, with variants identified at frequencies previously observed within this region of Tanzania. Sequencing data exhibited high concordance rates to pre-existing genotyping data (> 99.5%). Our work demonstrates the potential utility of amplicon sequencing for applications in human genetics, including to personalise medicine and understand the genetic diversity of loci linked to important host phenotypes, such as malaria susceptibility.
Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts
Authors: Park Sung Yong , Ter-Saakyan Sonia , Faraci Gina , Lee Ha Youn 
Published: July 2023

AbstractSingle cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.
High-resolution three-dimensional structural determination of unstained double-gyroid block copolymers through scanning transmission electron microscopy
Authors: Chen Ying , Yang Jhih-Heng , Chang Ya-Ting , Lin I-Ming , Hsiao Chien-Nan , Chiang Yeo-Wan , Chen Chien-Chun 
Published: July 2023

AbstractBlock copolymer-based multicomponent materials have garnered considerable attention because of tunable properties due to their various constituents. The use of electron tomography through transmission electron microscopy (TEM) for the three-dimensional (3D) imaging of stained block copolymers is an established approach for investigating structure-property relationships. Recently, scanning transmission electron microscopy (STEM) with an annular dark-field (ADF) detector has emerged as a method for the 3D structural analysis of unstained block copolymers. However, because of a lack of electron contrast, only a few low-resolution 3D reconstructions were reported for light elements. Herein, we report the first 3D structural analysis of a 200-nm-thick film composed of unstained double-gyroid block copolymers-polystyrene-b-poly(2-vinylpyridine) (PS-P2VP)-at a resolution of 8.6 nm through spherical aberration Cs-corrected STEM. At this resolution, P2VP molecules can be distinguished from PS molecules in z-contrast 3D reconstructions obtained both experimentally and theoretically. The 3D reconstructions revealed structural differences between stained and unstained specimens.
Influence of extruded injector nozzle on fuel mixing and mass diffusion of multi fuel jets in the supersonic cross flow: computational study
Authors: Abdollahi Seyyed Amirreza , Rajabikhorasani Ghazal , Alizadeh As’ad 
Published: July 2023

AbstractThe efficient injection system has a great role on the overall enactment of air breathing propulsion systems at supersonic flow. In this work, the usage of extruded multi-injectors in the fuel distribution and mixing through the combustor is fully investigated. The usage of the extruded nozzles considerably intensifies the formation of the vortices nearby the injectors and this research has tried to visualize the role of these vortices on the diffusion of the fuel jet through the combustor of the scramjet. The influences of the jet space on the strength of produced circulations are fully discussed. The simulation of the high-speed air stream moving the combustion chamber with extruded nozzles is done via Computational Fluid dynamics. Based on our computational data, the use of extruded multi-jets enhances the penetration and diffusion of the hydrogen cross jet in supersonic airflow. Increasing the gap between injectors improves fuel mixing performance by up to 27% downstream of the jets, primarily by enhancing the lateral penetration of the fuel jet.
High performance microservice communication technology based on modified remote procedure call
Authors: Zhang Lei , Pang Ke , Xu Jiangtao , Niu Bingxin 
Published: July 2023

AbstractMicroservice architecture is a programming method that decomposes a single application into various smaller services and then executes them. However, this approach introduces new challenges in communication between services because of the different data structures and technology types among the multiple services. Therefore, interprocess communication (IPC) between services has become one of the important challenges facing microservice architecture. Additionally, the choice of IPC technology is an important decision that can affect the nonfunctional requirements of the entire architecture. To address this problem, this study proposes a microservice communication technology based on remote procedure calls (RPC) called RPCX to improve the communication performance between services. The RPCX communication mechanism based on RPC uses the nonblocking IO communication model and Protobuf data serialization standard method. It identifies RPC communication at the client and server ends using dynamic proxy and annotation configuration technology. We use RPCX and two traditional service communication technologies to conduct performance stress benchmarking and evaluate the performance of RPCX through the time consumed to process the requests and transactions per second (TPS) performance stress indicators. The results show that the performance of RPCX is better than that of the other two technologies under different threads and requests. In this study, we show that RPCX has overall better performance than the other two service communication techniques under different threads and requests.
Early noninvasive prenatal paternity testing by targeted fetal DNA analysis
Authors: Damour Géraldine , Baumer Karine , Legardeur Hélène , Hall Diana 
Published: July 2023

AbstractToday the challenge in paternity testing is to provide an accurate noninvasive assay that can be performed early during pregnancy. This requires the use of novel analytical methods capable of detecting the low fraction of circulating fetal DNA in maternal blood. We previously showed that forensic compound markers such as deletion/insertion polymorphisms-short tandem repeats (DIP-STR) can efficiently resolve complex mixed biological evidence including the target analysis of paternally inherited fetal alleles. In this study, we describe for the first time the validation of this type of markers in the first trimester of pregnancies, in addition to defining the statistical framework to evaluate paternity. To do so, we studied 47 DIP-STRs in 87 cases, with blood samples collected throughout gestation starting from the seven weeks of amenorrhea. Fetal DNA detection in the first trimester shows a false negative rate as low as 6%. The combined paternity index (CPI) results indicate that seven markers with fully informative genotypes are sufficient to determine the paternity. This study demonstrates that DIP-STR markers can be used from early pregnancy and that a small set of markers (about 40) is sufficient to address the question of paternity. The novel method offers substantial improvements over similar approaches in terms of reduced number of markers, lower costs and increased accuracy.
Genetic diversity in Sickleweed (Falcaria vulgaris) and using stepwise regression to identify marker associated with traits
Authors: Rahimi Mehdi , AhmadiAfzadi Masoud , Kordrostami Mojtaba 
Published: July 2023

AbstractOne of the well-known medicinal plants in the Falcaria genus is Sickleweed. Falcaria species exhibit a high degree of genetic variability, posing challenges in the examination of genetic diversity due to the significant potential for hybridization and introgression among them. Utilizing morphological traits and molecular markers may prove to be a valuable approach in evaluating and harnessing germplasm, considering the current obstacles faced in breeding this medicinal herb. In 2021, fifteen Sickleweed populations were cultivated in pots under field conditions, employing a randomized complete block design with three replications. This aimed to assess genetic diversity and conduct marker-trait association analyses utilizing morpho-physiological characteristics and SSR markers. The Sickleweed populations displayed considerable genetic diversity across all traits. Through cluster analysis of traits and the utilization of the UPGMA method based on the Gower distance matrix, the population was classified into three distinct clusters. Upon examining all genotypes, 52 polymorphic bands were detected, with an average of 8.68 bands per primer. The average expected heterozygosity across all loci was 0.864, while the average PIC was 0.855. Molecular data analysis employing the Jaccard similarity index and UPGMA method revealed the division of Sickleweed populations into two major groups. Furthermore, the results of molecular variance analysis indicated that variation within the population exceeded that between populations. Thirty-two SSR fragments were found to be significantly associated with genomic regions controlling the studied traits, determined through the application of stepwise regression. Selection based on molecular markers offers a rapid method for breeding programs, with the genetic information obtained from these markers playing a crucial role. Therefore, alongside traits, selecting superior genotypes and populations of high value in breeding programs becomes feasible. The findings highlight that certain markers are linked to multiple traits, emphasizing the critical importance of this characteristic in plant breeding for the simultaneous improvement of numerous traits. The study’s insights regarding markers hold potential for application in Sickleweed breeding programs.
5-HT2AR and NMDAR psychedelics induce similar hyper-synchronous states in the rat cognitive-limbic cortex-basal ganglia system
Authors: Brys Ivani , Barrientos Sebastian A. , Ward Jon Ezra , Wallander Jonathan , Petersson Per , Halje Pär 
Published: July 2023

AbstractThe profound changes in perception and cognition induced by psychedelic drugs are thought to act on several levels, including increased glutamatergic activity, altered functional connectivity and an aberrant increase in high-frequency oscillations. To bridge these different levels of observation, we have here performed large-scale multi-structure recordings in freely behaving rats treated with 5-HT2AR psychedelics (LSD, DOI) and NMDAR psychedelics (ketamine, PCP). While interneurons and principal cells showed disparate firing rate modulations for the two classes of psychedelics, the local field potentials revealed a shared pattern of synchronized high-frequency oscillations in the ventral striatum and several cortical areas. Remarkably, the phase differences between structures were close to zero, corresponding to <1 ms delays. Likely, this hypersynchrony has major effects on the integration of information across neuronal systems and we propose that it is a key contributor to changes in perception and cognition during psychedelic drug use. Potentially, similar mechanisms could induce hallucinations and delusions in psychotic disorders and would constitute promising targets for new antipsychotic treatments.
Evolutionary modeling suggests that addictions may be driven by competition-induced microbiome dysbiosis
Authors: Lewin-Epstein Ohad , Jaques Yanabah , Feldman Marcus W. , Kaufer Daniela , Hadany Lilach 
Published: July 2023

AbstractRecent studies revealed mechanisms by which the microbiome affects its host’s brain, behavior and wellbeing, and that dysbiosis – persistent microbiome-imbalance – is associated with the onset and progress of various chronic diseases, including addictive behaviors. Yet, understanding of the ecological and evolutionary processes that shape the host-microbiome ecosystem and affect the host state, is still limited. Here we propose that competition dynamics within the microbiome, associated with host-microbiome mutual regulation, may promote dysbiosis and aggravate addictive behaviors. We construct a mathematical framework, modeling the dynamics of the host-microbiome ecosystem in response to alterations. We find that when this ecosystem is exposed to substantial perturbations, the microbiome may shift towards a composition that reinforces the new host state. Such a positive feedback loop augments post-perturbation imbalances, hindering attempts to return to the initial equilibrium, promoting relapse episodes and prolonging addictions. We show that the initial microbiome composition is a key factor: a diverse microbiome enhances the ecosystem’s resilience, whereas lower microbiome diversity is more prone to lead to dysbiosis, exacerbating addictions. This framework provides evolutionary and ecological perspectives on host-microbiome interactions and their implications for host behavior and health, while offering verifiable predictions with potential relevance to clinical treatments.