bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive analysis of the effect of MAOA gene on inflammatory bowel disease
Authors: Yin Zhangzheng , Wu Kunfu , Peng Jie , Chen Jiayi , Wang Kaijun , Chen Wenwu 
Published: September 2024

Abstract

Mounting evidence indicates that a variety of functional genes can significantly affect the progression of inflammatory bowel disease (IBD); nevertheless, the association between monoamine oxidase A (MAOA) and the clinical features of IBD remains unclear. Here we demonstrated that MAOA was negatively correlated with the progression of IBD through GEO datasets and single-cell RNA-Seq online database. This may be that epithelial MAOA plays a major role. Then verified that MAOA was involved in the changes of drug and energy metabolism signaling pathway (MAOA high expression) and inflammatory signaling pathways (MAOA low expression) through Gene Set Enrichment Analysis (GSEA). Most importantly, we predicted the possible transcription factor of MAOA, KLF transcription factor 5 (KLF5). Subsequently, using STRING database, ten interacting proteins [e.g., dopa decarboxylase (DDC), dopamine beta-hydroxylase (DBH), and aldehyde dehydrogenase 2 family member (ALDH2)] of MAOA were found. Notably, ALDH2 is core enrichment in GSEA signaling pathway of MAOA high expression. We have also demonstrated that the MAOA’s expression is associated with therapeutic outcomes in gastrointestinal cancer. Furthermore, our findings indicate that MAOA expression is consistently downregulated in various cancers. Our research establishes the protective role of MAOA in IBD, suggesting its potential as a crucial target for addressing diseases associated with intestinal inflammation.
GFIDF:Gradual Fusion Intent Detection Framework
Authors: Yang Qimeng , Liu Yi , Lu Lanlan , Liu Lei 
Published: September 2024

Abstract

In the field of multimodal intent detection research, researchers have worked on seamlessly integrating various information types to accurately recognize user intent, which is crucial for creating efficient dialog systems in complex real-world environments. Current approaches still have significant potential for improvement in exploring deep connections between modalities and extracting key semantic features from non-textual data. In this paper, we introduce GFIDF, a step-by-step fusion intention detection framework model, which first purifies and noise reduces the video modal information and audio modal information, and then takes the textual information as the starting point to align and fuse the video and audio modal features with the textual modal information effectively, respectively. Experiments conducted on the MIntRec dataset demonstrate the effectiveness of the model in multimodal feature fusion, thus validating its research value.
Consensus Algorithm for Maintaining Large-Scale Access-Control Views of Education Data
Authors: Wu Xianglin , Meng Tianhao , Zhang Jingwei , Yang Qing , Chen Jintao 
Published: September 2024

Abstract

Exploring diverse education data applications through blockchain technology can offer innovative methods for smart education. Among them, storing multi-platform and multi-dimensional education data on the blockchain can enhance the system's ability to integrate and manage the data effectively. However, the system involves a large number of stakeholders. Furthermore, because the data are stored chronologically, relevant information may be distributed across multiple blocks. They will lead to security issues related to access and inefficient queries. We innovatively design an education data management system that combines access-control views with a consensus algorithm, effectively addressing the issue of low query performance for security data. Additionally, we propose a method that integrates main chain blocks and side blocks within a chained storage structure to recycle view blocks, effectively addressing the issue of block redundancy caused by non-periodic changes in the utilization of education data. For the proposed method, experiments are conducted on two education datasets to validate the efficiency of the proposed access control view in query performance and the effectiveness of the view maintenance consensus algorithm.
CFD Simulation of Micro-Level Water Transport of Potato Cells in Periodic Condition: Apoplastic and Symplastic Hydrodynamic
Authors: Ghorba Fatemeh Mozafari , Moghaddam Ahmad Ghazanfari , Shamsi Mohsen , Mohebbi Ali 
Published: September 2024

Abstract

The water transport mechanisms in potato microstructure consist of symplastic, apoplastic, and transcellular transport. Knowledge of the microscale behavior of water transport is important to increasing the productivity of food processing and obtaining high-grade processed food. In this research, a CFD simulation was performed in COMSOL Multiphysics for three different simplified designs of potato cell units representing a portion of microstructure, using three different equations for mass concentration in three parts, and velocity was simulated using the Brinkman equation in periodic boundary conditions during the low thermal process. The water concentration variation profile for the three designs is similar. The average velocity for all designs is the same in 0.72% cell fraction and has a difference in the highest fraction of 0.78% with 3.22×10− 9 m s− 1. From this simulation can conclude that velocity and diffusivity depend on both fraction and design, and mass concentration, and permeability depend on intercellular fraction for all designs, not the cell designs.
Exploring Submergence Impact on Flow Induced Motion Energy Harvesting with Cir-Tria Prisms
Authors: Sharifi Ali , Omidyeganeh Mohammad , Masdari Mehran 
Published: September 2024

Abstract

Recent advancements in energy harvesting have utilized Flow Induced Motion (FIM) as a renewable source, diverging from past efforts aimed at minimizing FIM's adverse effects. This study introduces kinetic energy converters using alternating lift technology (ALT), employing a prism with a Cir-tria cross-section, coupled with a spring and damper, to generate energy. Utilizing computational fluid dynamics in OpenFOAM for Reynolds numbers between 2×10³ and 13×10³ and varying submergence depth ratio from 0.98 to 5.91, the research employs a moving computational grid, two-dimensional incompressible Navier-Stokes equations, the k-omega Shear Stress Transport (SST) turbulence model, the Volume-of-Fluid, VOF, two-phase model, and the cylinder mass-spring-damper equation. Findings show that approaching the flow surface negatively impacts the FIM response due to the interaction of vortices from the flow surface and the prism’s upper shear layer. This interaction weakens and neutralizes the upper vortices, altering the flow structure around the prism and the governing FIM phenomena. Proximity to the free surface significantly affects FIM responses, with a notable decrease in vibration amplitude and energy conversion as the submergence depth ratio decreases from 5.91 to 0.98. Maximum system efficiency of 0.014 is observed in the VIV initial branch at infinite submergence (single-phase flow). Beyond a submergence depth ratio of 5.91, FIM amplitude and energy conversion plateau, indicating negligible free surface effects.
Palynotaxonomy of Leptohyptis Harley &amp; J.F.B.Pastore and Oocephalus (Benth.) Harley &amp; J.F.B.Pastore (Lamiaceae: Hyptidinae)
Authors: Carvalho Renata Jacomo Paixão , Guarino Gabriel Irene Pereira , Alves Andrielle Bezerra , Harley Raymond Mervyn , Pastore José Floriano Barêas , Mendonça Cláudia Barbieri Ferreira , Gonçalves-Esteves Vania 
Published: September 2024

Abstract

This paper aims to characterize the pollen of two genera within the subtribe Hyptidinae (Lamiaceae): Leptohyptis Harley & J.F.B. Pastore (4 species) and Oocephalus (Benth.) Harley & J.F.B. Pastore (12 species). The study evaluates whether pollen morphology corroborates the recent taxonomy of the group. Acetolysed pollen grains were measured, described, and photomicrographed. Non-acetolysed pollen grains were analyzed by scanning electron microscopy. All analyzed taxa have pollen grains shed in monads, isopolar, medium or large in size, 6-(8)-colpate, varying from suboblate to prolate, with small to very small polar area. The presence of an operculum was observed in all species of Leptohyptis. By contrast, in Oocephalus, an operculum was present only O. grazielae Harley and O. niveus (Epling) Harley & J.F.B.Pastore. The sexine is reticulate in Leptohyptis and bireticulate with ornamented lumina in Oocephalus, being densely reticulate internally. The muri are straight, with or without elevations at the intersections, forming wide and circular lumina or small lumina, as in O. halimifolius (Mart. ex Benth.) Harley & J.F.B.Pastore. The variations revealed by this study imply that pollen morphology holds significant taxonomic value for the systematics of the genera Oocephalus and Leptohyptis, particularly when morphometric and morphological characters are examined together. Assessment of quantitative values ​​by multivariate analyses (hierarchical cluster analysis and principal component analysis) was not sufficient to separate the genus Oocephalus into its four sections. Only O. niveus and O. petraeus (A.St.-Hil. ex Benth.) Harley & J.F.B.Pastore remained separated. All other species remained in the same main group in hierarchical clustering, and it was also not possible to group L. calida (Mart. ex Benth.) Harley & J.F.B.Pastore with other species of the genus. The results revealed the existence of polymorphism in pollen shape among the studied taxa.
Single-Cell Sequencing Analysis and Multiple Machine Learning Methods identified CDT1 as an oncogene in retinoblastoma
Authors: Yang Yang , Zhang Yuezhi , Xiong Weiwei , Yin Xiaolong 
Published: September 2024

Abstract

BACKGROUND
Retinoblastoma (RB) is a heterogeneous primary intraocular malignant tumor.
OBJECTIVE
This work attempted to reveal the significant gene-related to disulfidptosis in RB.
METHODS
The scRNA-seq data from RB samples and normal samples obtained from GSE159977 were analyzed to distinguish cone cells from malignant cone cells using inferCNV. Subsequently, AUCcell was used to assess the disulfidptosis levels in cone cells. Disulfidptosis-related genes in RB were analyzed through weighted gene co-expression network analysis and machine learning methods. Cell proliferation, migration, and invasion were examined through colony formation and Transwell experiments.
RESULTS
We obtained 7 annotated cell clusters. Among them, there was a significant increase in the proportion of cone cells in RB samples. Malignant cone cells exhibited a higher disulfidptosis score. Cell trajectory analysis indicated that the disulfidptosis process intensified as cone cells transition from normal cells to malignant cells. Through machine learning, disulfidptosis-related genes were screened, ultimately identifying CDT1 as a key gene. CDT1 was upregulated in WERI-Rb1 and Y79 cells. Silencing CDT1 significantly suppressed the proliferation, migration, and invasion of RB cells.
CONCLUSIONS
This work revealed that tumors contained cone cell states with distinct transcriptional programs, and provided a crucial RB-related gene CDT1 for tumor progression. Thus, this article is of great significance in formulating a molecular targeted therapy scheme to prevent the development of RB.
Broadcast Routing Algorithm Based on Combinatorial Weights in Vehicle-to-Vehicle Communication
Authors: Sun Xiao , Sun Jian , Xiao Zenghui , Gao Mingliang , Jeon Gwanggil , Mumtaz Shahid 
Published: September 2024

Abstract

With the development of the Internet of Things (IoT) and intelligent transportation systems (ITS), vehicle ad hoc networks (VANETs) are prone to network congestion and link instability caused by the high mobility of vehicles. Therefore, introducing efficient routing technologies between vehicles can reduce delay and improve reliability. The high-speed mobility of vehicle nodes leads to the rapid change of network topology. Therefore, not only location and speed, but also distance, direction and other parameters should be considered during selecting route weight factors. Given the inefficient routing selection and link instability, this paper proposes a traversal peripheral weight routing selection algorithm. According to the established paths from the source node to the destination node, the algorithm selects the optimal path according to four weight factors: distance between vehicles, the angle value of the vehicle relative to the horizontal direction, link interruption rate, and traffic density. The simulation results show that compared with traditional routing algorithms, the proposed algorithm improves the data transmission rate and link survival time by 25.12\% and 17.68\%, respectively.
Early identification of high-risk pregnancies to develop preeclampsia through non-invasive cell-free DNA methylation profiling
Authors: Machteld Baetens , Bram Van Gaever , Stephanie Deblaere , Andries De Koker , Meuris Leander , Nico Callewaert , Janssens Sandra , Kristien Roelens , Ellen Roets , Isabelle Dehaene , Björn Menten 
Published: September 2024

Abstract

Background
Aberrant embryo implantation and suboptimal placentation can lead to (severe) complications such as preeclampsia and fetal growth restriction later in pregnancy. Current identification of high-risk pregnancies relies on a combination of risk factors, biomarkers, and ultrasound examinations, a relatively inaccurate approach.
Previously, aberrant DNA methylation due to placental hypoxia has been identified as a potential marker of placental insufficiency and, hence, potential (future) pregnancy complications. The goal of the Early Prediction of prEgnancy Complications Testing, or the ExPECT study, is to validate a genome-wide, cell-free DNA (cfDNA) methylation strategy for the accurate diagnosis of preeclampsia. More importantly, the predictive potential of this strategy is also explored to reliably identify high-risk pregnancies early in gestation. Furthermore, a longitudinal study was conducted, including sequential blood samples from women experiencing both uneventful and complicated gestations, to assess the methylation dynamics of cfDNA throughout these pregnancies.
A significant strength of this study lies in its genome-wide approach, overcoming the limitations of studies with predefined target genomic regions.
Results
Investigation of methylation patterns throughout pregnancy showed different methylation trends between unaffected and affected pregnancies. We detected differentially methylated regions (DMRs) in pregnancies complicated with preeclampsia as early as 12 weeks of gestation, with distinct differences in the methylation profile between early and late pregnancy. Two classification models were developed to diagnose and predict preeclampsia, demonstrating promising results on a small set of validation samples.
Conclusions
This study offers valuable insights into methylation changes at specific genomic regions throughout pregnancy, revealing critical differences between normal and complicated pregnancies. The power of non-invasive cfDNA methylation profiling was successfully proven, suggesting the potential to integrate this non-invasive approach into routine prenatal care.
Virtual Screening, Molecular Dynamics Simulations, and Antiviral Evaluation of Ocimum basilicum Phytoconstituents Against Japanese Encephalitis Virus
Authors: Abate Selamu Kebamo , Garabadu Debapriya 
Published: September 2024

Abstract

In conventional medicinal systems, Ocimum basilicum (OB) is known to be effective against viral infections. A thorough screening of OB's phytoconstituents against the Japanese encephalitis virus (JEV) in an in-silico model has not been documented. Therefore, we used the Schrodinger software to do a virtual screening and molecular dynamics simulation (MDS) (100 ns) on 265 phytocompounds of OB against the envelope (E) protein (PDB ID: 3P54) of JEV. Chicoric acid (CA), rutin, and salvianolic acid A (SA) complex of E-protein showed outstanding docking scores (Kcal/mol) of -9.136, -9.135, and − 11.838, which were all higher than the reference mycophenolate (-4.481). The MDS analysis revealed that these hit compounds, especially CA and rutin, showed comparatively strong stability on the binding pocket of the protein. Besides this, CA and rutin exhibited lower free binding energy with this protein than the standard. Moreover, the principal component and free energy landscape analysis highlighted the antiviral potential of these hit compounds against JEV. The in vitro study further supported the antiviral potential of CA and rutin at the early stage of the virus’s lifecycle. Consequently, this study provided insight into the therapeutic potential of the topmost hit compounds, suggesting their development as novel anti-JEV agents. However, further detailed study is required to validate the mechanism of anti-JEV activity of these compounds.
Drone Rotor Performance Under Icing Conditions
Authors: Gupta Avani , Gahlot Aishwerya Singh , Sankar Lakshmi N. 
Published: September 2024

Abstract

Low- and high-fidelity physics-based models for assessing the aerodynamic performance of drone and eVTOL rotors under icing conditions are examined. The low fidelity model in this work makes use of a lifting line model of the rotor with a prescribed inflow coupled to an ice accretion solver for modeling ice growth, and an interactive boundary layer model for the generation of airfoil drag polars. The higher fidelity model employed in this study uses a 3-D unsteady Navier-Stokes analysis with a tightly coupled water droplet transport model for modeling the collection of liquid droplets over the rotor. The rotor shape is periodically updated to account for the ice formation. Low fidelity calculations for an 81% scaled-down version of the Bell APT70 drone rotor tested at Université du Québec à Montréal provide reasonable estimates of the loss in thrust and rise in power during the initial stages of ice growth. Higher fidelity models are found to be better suited for multirotor configurations with nonlinear wake interactions, while eliminating the needs for airfoil drag polars, tip loss models, and empirical compressibility corrections.
A Directional Tensile Superelasticity in Ceramic Crystal via Reversible Shuffle Twinning
Authors: Nie Anmin , Wang Chong , Li Zeya , Cheng Yingchun , Weng Xiao-ji , Zhai Kun , Xue Tianyu , Yuan Hongtao , Zhou Xiang-Feng , Wang Hongtao , Liu Zhongyuan , Tian Yongjun 
Published: September 2024

Abstract

Superelasticity, being a reversible nonlinear strain response to stress stimuli beyond the linear elastic regime, is always associated with phase transformations in its host materials, mostly metals or polymers. Theoretical rationale indicates that inorganic materials with covalent/ionic bonding normally have large energy barriers for reversible structural transitions and thus host less opportunity to achieve superelasticity. Here, we demonstrate a directional tensile superelasticity in ceramic crystal GeSe through an unconventional reversible shuffle twinning mechanism instead of martensitic phase transition. We observed, with in-situ mechanical transmission electron microscopy, an evolution in stress‒strain curve from the linear elastic behavior to a nonlinear superelastic plateau, and confirmed that such superelasticity appears simultaneously together with the generation of stripy-shaped twin domains along <110> orientation. Theoretical calculations revealed that the shuffle twinning process from “Z-shaped” to “anti-Z-shaped” bond-configuration leads to the release of elastic potential energy, being responsible for the emergence of tensile superelasticity therein. Note that such a highly-directional superelasticity prefers to emerge at angles near the zigzag direction owing to the anisotropic Young's modulus in GeSe, and has never been reported in superelastic materials. Our observation provides a novel strategy to exploit tensile superelasticity and nonlinear mechanics for advanced mechanical and flexible electronics.
Advancing Fire and Smoke Detection Reliability: Integrating Generalized ELAN and Programmable Gradient Information within YOLOv9 to Discriminate Against False Positives
Authors: Nejjar Mehdi , Amar Amine , Guennoun Mouhcine , Taha Mostafa 
Published: September 2024

Abstract

This paper presents YOLOv9, the advancement in the YOLO (You Only Look Once) series, innovating with the GELAN (Generalized Efficient Layer Aggregation Network) and PGI (Programmable Gradient Information) to enhance the precision of detection systems. We detail our empirical evaluation of YOLOv9 on robust and diverse datasets, highlighting its capability to maintain high detection accuracy while significantly reducing false positives, even with limited training data and in challenging scenarios. In this case, the approach has the potential to improve the accuracy of fire and smoke detection without being trained on pseudo-fire and pseudo-smoke scenes and without infrared lighting conditions. The findings highlight its potential as a reliable tool in this safety-critical application. Moreover, the proposed models demonstrate YOLOv9's superiority over its predecessors, with good performance, achieving high precision (P, mAP50, and mAP50-95) and a recall doing the inference in object early detection technologies.
Enhancing Laryngeal Cancer Classification Using Voice Data with Octave-band Filters: A Comparative Study of Against Mel-Frequency Filters
Authors: Song Jaemin , Kim Hyun-Bum , Lee Yong Oh 
Published: September 2024

Abstract

Laryngeal cancer is typically diagnosed through laryngoscopy, but voice-based deep learning algorithms have shown promising performance recently. Most voice-based deep learning algorithms convert voice signals into mel-frequency cepstral coefficients (MFCCs) before processing them through convolutional neural networks (CNNs). In this study, we investigated the use of octave frequency spectrum energy (OFSE), an alternative feature extraction method that improves the representation of low frequency bands in voice signals instead of MFCC. Preliminary results show that using OFSE as input to a CNN improves accuracy by up to 21.6% compared to MFCC. This suggests that OFSE enhances the CNN’s ability to recognize laryngeal cancer-specific features, leading to improved diagnostic accuracy. Additionally, we utilized Class Activation Maps (CAMs) to analyze the important regions for laryngeal cancer diagnosis on the OFSE representation, providing further insights into the effectiveness of OFSE in identifying key diagnostic areas.