bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unveiling Hub Genes and Biological Pathways: A Bioinformatics Analysis of Trauma-Induced Coagulopathy (TIC)
Authors: Zhang Lin Gang , Liu Jing , Li Bo , Bian Yan Feng , Lin Guo Xing , Zhou Ying 
Published: August 2023

Abstract
Background
TIC is a severe condition that rapidly manifests following traumatic injury, characterized by shock, hypoperfusion, and vascular damage. This study aimed to employ bioinformatics methods to identify crucial hub genes and pathways associated with TIC.
Methods
Microarray datasets (accession number GSE223245) were obtained from the Gene Expression Omnibus (GEO) database. The data underwent analysis to identify Differentially Expressed Genes (DEGs), which were further subjected to GO and KEGG pathway analyses. Subsequently, a Protein-Protein Interaction (PPI) network was constructed, and hub DEGs closely linked to TIC were identified using CytoHubba, MCODE, and CTD scores. The diagnostic value of these hub genes was evaluated through Receiver Operating Characteristic (ROC) analysis.
Results
Among the analyzed genes, 269 were identified as DEGs, comprising 103 upregulated and 739 downregulated genes. Notably, several significant hub genes were associated with the development of TIC, as revealed by the bioinformatics analyses.
Conclusions
This study highlights the critical impact of newly discovered genes on the development and progression of TIC. Further validation through experimental research and clinical trials is warranted to confirm these findings.
Evaluating the humanistic literacy of Chinese medical students
Authors: Luo mingwei , Xie shiwei , Pang jie , Yan jing , Xu huamin 
Published: August 2023

Abstract
Background
Medical education needs to strengthen its focus on improving the humanistic quality of medical students while improving their medical knowledge and skills. This study aims to identify and integrate the current literature and research hotspots on raising medical students' humanistic literacy to provide a reference for researchers.
Methods
CiteSpace is a Java application which supports visual exploration with knowledge discovery in bibliographic databases. We searched the China Knowledge Network (CNKI) database for articles published between 1995 and 2022. "Humanistic quality of medical students" was the key search term. Titles and abstracts were screened and extracted according to the inclusion and exclusion criteria. The CiteSpace software was used to retrieve 941 humanistic quality journal articles geared toward medical students from the CNKI database up to April 2022. A comprehensive review was conducted on these articles to reveal the current situation and hotspots of research aimed at improving medical students’ humanistic quality.
Results
The study found that the number of publications on humanities literacy research for medical students has increased overall over the past 20 years, although a certain graph shows an inverted S-shaped fluctuation. Research on improving the humanistic quality of Chinese medical students is predominantly conducted by medical colleges. No single author or institutional core group dominates the field of research on medical students' humanistic qualities. The main study focuses on humanistic qualities, medical students, higher vocational students, and higher vocational colleges.
Conclusion
Based on the research trends of medical students' humanistic literacy, combined with CiteSpace’s visual knowledge maps, this study provides an in-depth understanding of the main authors, institutions, and research hotspots in humanistic literacy for medical students. The information in this study is most valuable to researchers interested in medical student education. This study summarizes the current development and ideological foundations of humanistic literacy among medical students in China. This study helps researchers to grasp the scientific development and research issues in the field of humanistic literacy of medical students in China.
Genomic Diversity of Salmonella Infection from a Tertiary Hospital in China: Insights from Phenotypic and Molecular Analyses
Authors: Ma Wanshan , Cui Xiaodi , Dong Xiutao , Li Xinpeng , Wang Yujiao , Shi Xiaohong , Chen Liang , Hao Mingju 
Published: August 2023

Abstract
Background
Salmonella enterica is a significant public health concern due to its ability to cause foodborne illnesses worldwide. To gain insights into the epidemiology and characteristics of Salmonella strains in China, a total of 20 Salmonella isolates were subjected to phenotypic and molecular analyses.
Methods
The 20 Salmonella strains were characterized through serotype identification, antimicrobial susceptibility testing (AST), biofilm formation assessment and plasmid transmission studies. Molecular characteristics and relatedness were determined using pulsed-field gel electrophoresis (PFGE) and whole genome sequencing (WGS). Additionally, SNP-based phylogenetic analysis and core genome multilocus sequence typing (cgMLST) were employed to contextualize the strains among previously sequenced S. enterica isolates in China and worldwide.
Results
The predominant serovar identified was Salmonella enterica serovar Enteritidis (S. Enteritidis), followed by S. Mbandaka, Thompson, Livingston, Alachua, and Infantis. The SNP-based phylogenetic analysis revealed substantial diversity among S. Enteritidis isolates, with a minimum of 32 SNPs between strains. Conversely, S. Mbandaka isolates were suspected to have originated from a single clonal expansion, displaying a difference within 6 SNPs. SNP-based phylogenetic analysis suggested S. Mbandaka clone in this study was likely to have been derived from a single source that persisted over 5 years in China. S. Mbandaka, Infantis, Livingston, and Alachua exhibited susceptibility to the tested antibiotics, while strains of S. Thompson and Enteritidis demonstrated resistance to ampicillin, quinolone, and sulfamethoxazole/trimethoprim (SMZ/TMP). Particularly concerning was the identification of one S. Enteritidis isolate carrying the highly transferable IncB/O/K/Z plasmid with blaCTX−M15, conferring cephalosporin resistance. Another isolate, harboring the mrkABCDF operon in an IncX1 plasmid, isolated from cutaneous lesions, demonstrated robust biofilm formation.
Conclusions
Our study emphasizes the importance of SNP-based surveillance and prompt response to Salmonella infections to protect public health. The dissemination of blaCTX−M15-harboring IncB/O/K/Z plasmid and the spread of virulent mrkABCDF operon among S. enterica in China and other global regions warrant close monitoring.
Systematic Engineering of Properties of Graphene Quantum Dots by Aryl Amines
Authors: Rani Priya , Srivast Sunita 
Published: August 2023

Abstract
Graphene quantum dots (GQDs) are becoming an efficient nanomaterial to control their optoelectronic properties by molecular engineering due to the advantages of tunability by size, shape, doping, and comparatively low degree of toxicity and a great extent of spatial confinement. Their bandgap can be tuned effectively by functionalization of their surface or edges with some specific groups. In the present study, systematic efforts have been made to tune the band gap and corresponding optical properties of the GQDs by functionalizing them with different aryl amine groups because of their potential for extremely strong and wide-ranging light absorption; these GQDs have also been demonstrated to be advantageous for photocatalysis. The absorption and fluorescence spectra have been investigated by employing density functional theory with Becke three parameters hybrid functional with Lee-Yang-Perdew (B3LYP) correlation functional as implemented in Gaussian 09 package. Functionalization with such aryl amine groups accounts for the decrement in band gap and shift of absorption spectra towards longer wavelength. Such narrow band gap GQDs are highly required for the applications such as photocatalysis and bio-imaging etc. The outcomes achieved in this way are highly consistent with other experimental findings.
Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP
Authors: Chen Jinmiao , Zhong Chengwei , Ang Kok Siong 
Published: August 2023

Abstract

Spatial transcriptomics produces high-dimensional gene expression measurements while retaining their spatial context within tissues. Obtaining a biologically meaningful low dimensional presentation of the data is a crucial step toward data interpretation and downstream analysis. Here, we present STAMP, an interpretable spatially aware dimension reduction method built on a deep generative model that returns low dimensional topics of biologically relevant spatial domains and associated gene modules. STAMP recovered the anatomical structures of the mouse hippocampus and olfactory bulb with known gene markers highly ranked in the respective gene modules. In a lung cancer sample, it delineated cell states with supporting gene markers at a higher resolution than the original annotation and uncovered a topic of cancer associated fibroblasts. Finally, we expanded STAMP to account for batch effects and identify spatiotemporal patterns across chronologically consecutive samples of mouse embryo development. STAMP is implemented in Python and downloadable at https://github.com/JinmiaoChenLab/scTM.
How structural change affects economic vulnerability: Evidence from resource-rich countries in sub-Saharan Africa.
Authors: Dimitri ENGONGA MEPOLIA Devy , Mathurin TCHAKOUNTE NJODA , Nadine EKAMENA NTSAMA Sabine 
Published: August 2023

Abstract
The goal of this essay is to assess how structural transformation indicators affect economic vulnerability of resource-rich countries in sub-Saharan Africa. Using data from 26 nations covering the period from 1996 to 2020, we estimate a linear model with the FM-OLS method. Overall, our findings indicate that while industrial employment has an amplifying effect on vulnerability, employment in the service sector, industrial value added, and value contributed in the service sector all contribute little to lessening economic vulnerability. Our findings hold up well to changes in estimating methodology (PCSE). By taking into account the distinctive features of the CFA Franc and non-CFA Franc monetary zones, sensitivity is evaluated. Therefore, we advocate for the creation of an institutional framework that places special emphasis on vocational training, so that industrial employment is no longer restricted to manoeuvring tasks, on the efficient distribution of NR rents, and on the relentless fight against corruption and embezzlement of public funds, which slow down the process of structural transformation and, consequently, of creating strong shock-resistant economies. These measures need to be stepped up in the Franc zone countries, which, according to the study, are performing less well than other monetary systems.
JEL code: F630, O110, O130, L510.
Multi-Graph Neural Network based Personalized Recommendation Model for Multimodal Data
Authors: Navarro Carlos , Brown Winona , Anderson Phineas , Hernandez Emilio 
Published: August 2023

Abstract
Collaborative filtering based recommendation methods make recommendations based on the preference information of other users who are similar to the user. The method is based on the assumption that human beings tend to form their own opinions from the reviews and opinions of similar groups. Compared with the original recommendation methods, the graph neural network recommendation method not only learns the topology of the graph network but also aggregates the various adjacencies of neighboring nodes, so that it can learn the information in the graph network more efficiently and play a key role for the subsequent recommendation. How to describe and represent the video and the user is the basis for video recommendation and determines the performance of the recommender system. Aiming at the above problems, this paper proposes a recommendation model based on a multi-graph neural network. The model utilizes the property that graph neural networks can mine the deep information of graph data more effectively and transforms the input user rating information and item side information into multiple graphs for effective feature extraction. From the perspective of multimodal semantics, a video similarity metric learning method based on semantic information is proposed. As videos contain different media types, different media types have different features. By analyzing the different media features and representing the multiple modal features in a unified way, we make a foundation for realizing the recommendation of videos.
A Novel Framework for Concept Drift Detection for Classification Problems in Data Streams
Authors: Ali Usman , Mahmood Tariq 
Published: August 2023

Abstract
In streaming data environments, data characteristics and probability distributions are likely to change over time, causing a phenomenon called concept drift, which poses challenges for machine learning models to predict accurately. In such non-stationary environments, there is a need to detect concept drift and update the model to maintain an acceptable predictive performance. Existing approaches to drift detection have inherent problems like requirements of truth labels in supervised detection methods and high false positive rate in case of unsupervised drift detection. In this paper, we propose a semi-supervised Autoencoder based Drift Detection Method (AEDDM) aimed at detecting drift without the need of truth labels, yet with a high confidence that the detected drift is real. In a binary classification setting, AEDDM uses two autoencoders in a layered architecture, trained on labelled data and uses a thresholding mechanism based on reconstruction error to signal the presence of drift. The proposed method has been evaluated on four synthetic and four real world datasets with different drifting scenarios. In case of real-world datasets, the induced and detected drifts have been evaluated from classifier’s performance viewpoint using seven mostly used batch classifiers as well as from adaptation perspective in an online learning environment using Hoeffding Tree classifier. The results show that AEDDM affectively detects the distributional changes in data which are most likely to impact the classifier’s performance (real drift) while ignoring the virtual drift thus considerably reducing the false alarms with an ability to adapt in terms of classification performance.
Numerical Estimation for the Three-Parameter Burr-XII Model Based on Picard’s Method
Authors: Maswadah M. 
Published: August 2023

Abstract
In parameter estimation techniques, there are several methods for estimating the distribution parameters in life data analysis. However, most of them are less efficient than Bayes’ method based on the informative prior. Thus, the main objective of this study is to present an optimal numerical technique, Picard’s method, for estimating Burr type-XII distribution parameters and compare them with Bayes’ method based on the informative gamma and kernel priors. A comparison between these estimators is provided using an extensive Monte Carlo simulation. The simulation results indicated that Picard’s method is highly favorable, which provides better estimates and outperforms Bayes’ method based on the generalized progressive hybrid censoring scheme. Finally, two real dataset analyses are presented to illustrate the efficiency of the proposed methods.
Developing and testing a web-based platform for visualizing flow in a watershed
Authors: Hou Jun , Zhang Shanghong , Tang Haiyun , Zhou Yang 
Published: August 2023

Abstract

Thanks to the rapid development of internet technology and computer hardware, it is now possible to use web services to provide visual simulations of flow field calculation results. Visualization technology can display complex water flow data and the laws that govern water flow through graphical means, and can be used to solve scientific and engineering problems related to water conservancy. In this study, we developed a platform for visualizing flow in a watershed based on a Cesium rendering framework with Browser/Server (B/S) architecture that used isosurface, particles, texture-based, and dynamic flow visualization techniques to visualize scalar field, vector field, and dynamic flow field data. Furthermore, our performance test results indicate that the rendering performance meets the practical application requirements for visualizing large-scale flow fields by employing frame interpolation and viewpoint-based dynamic rendering techniques. The results from testing the water flow visualization platform in the Beijiang River Basin, Guangdong Province, China, demonstrated that the platform performed well on different devices and that the running frame rate reached 50–60 fps. These findings can be used to guide further development and applications of web-side flow field visualization technology.
An Extra-articular Ganglion Cyst with Multiple Fluid-fluid Levels
Authors: Duran Semra , Kose Cem Cuneyt , Guresci Servet 
Published: August 2023

Introduction:
Ganglion cysts are the knee�s most common benign soft tissue tumors. Ganglion cysts are seen as multiloculated fluid collections on magnetic resonance imaging (MRI), and fluid-fluid levels are not an expected finding.

Case Presentation:
A 36-year-old female patient presented with swelling in her right knee. Magnetic resonance imaging revealed a multiseptated cyst with multiple fluid-fluid levels within the anterior of the right patellar tendon. Open surgical excision was performed, and the pedicle of the cyst was dissected. The histopathology revealed a ganglion cyst with hemorrhage.

Conclusion:
The ganglion cysts should be considered in the differential diagnosis of lesions with fluid-fluid levels, in addition to hemangioma, synovial sarcoma, and aneurysmal bone cysts of soft tissues.
A Case Report of Wire-localised Excision of Impalpable Recurrent Papillary
Thyroid Carcinoma and Discussion of Wire-guided Excision in the Head and
Neck Pathology
Authors: Farrell Eric , Speaker Richard , O'Driscoll Donal , Skinner Liam 
Published: August 2023

Introduction/Background:
Wire localisation techniques are used widely for precision surgery in many specialities. This convenient technique has not yet become mainstream in the field of head and neck surgery. With limited space and many vital structures coursing through the head and neck region, pathological nodes that are difficult to palpate can be a challenge for clinicians.

Case presentation:
A patient with a history of papillary thyroid cancer treated with surgery and radioactive iodine had a single pathological node detected on ultrasound surveillance. An isolated recurrence of papillary thyroid carcinoma was confirmed. An excisional biopsy was performed using ultrasound wire guidance to successfully remove the diseased node with minimal morbidity.

Conclusion:
Wire-guided lymph node excision biopsy is a safe and effective method that can be applied to multiple pathologies. As of yet, it is not routine practice to employ this technique. Larger studies would increase the generalisability and safety profile of this technique in the head and neck region.
MARCO LEGAL DO CÂMBIO E O FUTURO DA DIGITALIZAÇÃO DOS PAGAMENTOS INTERNACIONAIS
Authors: Monteiro Stelitano Medeiros Amanda 
Published: August 2023

Tendo as lacunas de eficiência presentes no modelo de operações cross-border atuais como pauta de destaque na comunidade internacional, este artigo se dedica a explorar pautas de inovação promovidas por reguladores e autoridades monetárias para possibilitar que operações transfronteiriças deixem de ser um desafio do sistema financeiro moderno. No intuito de exportar infraestruturas dos sistemas de pagamentos domésticos em tempo real para o nível transfronteiriço, a fim de solucionar barreiras de custo, segurança, lentidão e acessibilidade, o câmbio - escopo de discussão apenas em caráter internacional - demanda uma atenção na agenda dos agentes reguladores. Nessa linha, o novo marco legal do câmbio brasileiro - Lei 14.286 - abre caminhos para que o Brasil possa superar eventuais gargalos em seus projetos de internacionalização do sistema brasileiro de pagamentos instantâneos - o Pix - e, futuramente, servir como referência para demais economias globais repliquem estratégias de inovação promovidas pelo regulador brasileiro em suas infraestruturas de pagamento.
Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation
Authors: Matsuda Shinichi , Ohtomo Takumi , Okuyama Masaru , Miyake Hiraku , Aoki Kotonari 
Published: August 2023

Background
Measuring patient satisfaction is a crucial aspect of medical care. Advanced natural language processing (NLP) techniques enable the extraction and analysis of high-level insights from textual data; nonetheless, data obtained from patients are often limited.

Objective
This study aimed to create a model that quantifies patient satisfaction based on diverse patient-written textual data.

Methods
We constructed a neural network–based NLP model for this cross-sectional study using the textual content from disease blogs written in Japanese on the Internet between 1994 and 2020. We extracted approximately 20 million sentences from 56,357 patient-authored disease blogs and constructed a model to predict the patient satisfaction index (PSI) using a regression approach. After evaluating the model’s effectiveness, PSI was predicted before and after cancer notification to examine the emotional impact of cancer diagnoses on 48 patients with breast cancer.

Results
We assessed the correlation between the predicted and actual PSI values, labeled by humans, using the test set of 169 sentences. The model successfully quantified patient satisfaction by detecting nuances in sentences with excellent effectiveness (Spearman correlation coefficient [ρ]=0.832; root-mean-squared error [RMSE]=0.166; P<.001). Furthermore, the PSI was significantly lower in the cancer notification period than in the preceding control period (−0.057 and −0.012, respectively; 2-tailed t47=5.392, P<.001), indicating that the model quantifies the psychological and emotional changes associated with the cancer diagnosis notification.

Conclusions
Our model demonstrates the ability to quantify patient dissatisfaction and identify significant emotional changes during the disease course. This approach may also help detect issues in routine medical practice.