bluuebunny/crossref_metadata_2025_split huggingface dataset:
Competencies Required to Make Use of Information Science and Technology among Japanese Medical Students: A Cross-sectional Study
Authors: Ota Yuma , Asada Yoshikazu , Mieno Makiko , Matsuyama Yasushi 
Published: April 2024

Abstract

Background
Medical students have emphasized the importance of acquiring competencies that allow the use of information science and technology (IST) related to digital health. This study identified learning objectives and competencies that correspond with low self-assessment and factors that improve self-assessment among medical students.
Methods
A questionnaire was administered to sixth-year medical students across 82 medical schools in Japan between November 2022 and February 2023.
Results
Three academic goals were difficult to achieve: (1) provide an overview of the regulations, laws, and guidelines related to IST in medicine; (2) discuss ethical issues, such as social disparities caused by the digital divide that may arise in the use of IST in medicine; and (3) understand IST related to medical care. Further, problem-based learning, engaging with IST beyond class, and learning approach impacted medical students’ acquisition of competencies related to IST. However, the competencies required by medical students may change over the course of an updated medical school curriculum.
Conclusions
It is equally important for medical students to recognize the significance of learning, establish active learning methods, and gain experience in practically applying these competencies.
A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome
Authors: Heath Harrison , Peng Sichong , Szmatola Tomasz , Ryan Stephanie , Bellone Rebecca , Kalbfleisch Theodore , Petersen Jessica , Finno Carrie 
Published: April 2024

Abstract

Background: Allele-specific expression (ASE) analysis provides a nuanced view of cis-regulatory mechanisms affecting gene expression.
Results: An equine ASE analysis was performed, using integrated Iso-seq and short-read RNA sequencing data from four healthy Thoroughbreds (2 mares and 2 stallions) across 9 tissues from the Functional Annotation of Animal Genomes (FAANG) project. Allele expression was quantified by haplotypes from long-read data, with 42,900 allele expression events compared. Within these events, 635 (1.48%) demonstrated ASE, with liver tissue containing the highest proportion. Genetic variants within ASE events were in histone modified regions 64.2% of the time. Validation of allele-specific variants, using a set of 66 equine liver samples from multiple breeds, confirmed that 97% of variants demonstrated ASE.
Conclusions: This valuable publicly accessible resource is poised to facilitate investigations into regulatory variation in equine tissues. Our results highlight the tissue-specific nature of allelic imbalance in the equine genome.
Experiences of family and community care physicians and nurse residents in Madrid regarding their training during the COVID-19 pandemic: A Photovoice study
Authors: Elena Polentinos-Castro , Raquel Sanchez- Ruano , Paloma Conde-Espejo , Miguel López-MIguel , Lucia Nuevo-Coello , María Minué-Estirado , Elena Valera-Bermejo , Isabel Del Cura-González 
Published: April 2024

Abstract
Background: The COVID-19 pandemic brought about sudden changes in healthcare demands, necessitating additional healthcare professionals and altering the educational landscape for medical and nursing residents. This study aimed to explore the experiences and impact of the pandemic on the training of family and community medicine as well as family and community nursing residents.
Method: A participatory qualitative study was conducted using the Photovoice methodology. Seven residents in family and community medicine and nursing in the Madrid region were purposively sampled. Participants took photographs and engaged in weekly sessions over five weeks to discuss and analyse their photos, which were grouped into themes and further categorized. Recommendations for institutions and program leaders were developed using an adapted logical framework approach.
Results: The average age was 27 years, with 86% being female. Participants captured 96 photographs categorized into five themes (physical and emotional impact; impact on primary care system; hospital care ; social and environmental impact ; and impact on training). Through critical discussions on impact on training-related photos, seven emergent themes were identified: 1) COVID-19 monograph, buried knowledge; 2) Changing roles ; 3) Transition from face-to-face to online delivery format ; 4) Supervision; 5) Tutor as educator and caregiver ; 6) Advocacy ; 7) Family and nursing residents as agents of change. Subsequently, participants formulated seven recommendations that should be considered in similar situations.
Conclusion: The COVID-19 pandemic has posed significant challenges in implementing medical education programs. It has had both formative and professional impacts on medical and nursing residents. These findings can aid in designing coping and educational strategies to navigate exceptional situations such as the one experienced during the pandemic.
An Integrated Group Signature and Chameleon Hash Framework for Effective, Secure, and Private VANET Communication
Authors: MV Ranjith Kumar , N Bhalaji 
Published: April 2024

Abstract
VANET (Vehicular Ad hoc Networks) enable on-road wireless communication using vehicular and road-based electronic infrastructure. The services provided include routine traffic information dissemination aimed at ensuring safe driving while offering payment systems, and entertainment services are also part of VANET services. Ensuring secure, cost-effective VANET communication is a key research focus. A mechanism for unidentified message transmission, reciprocal vehicular and road base verification, as well as highly secure authentication by a trusted party, preservation of confidentiality of messages, recovery of the identity of vehicle by a trusted authority in unfortunate events, and low set up cost, is the prerequisite for trusted vehicular communication in VANET. The technique of Chameleon hashing signature provides message security with frequent changing of keys. The validation of vehicular identity in group-based communication is centralized using group signature and time stamping. This study proposes a framework combining chameleon hash signatures with group signatures to enhance VANET communication privacy, security, and cost-effectiveness.
The Effects of Environment and Loading Rate on the Mechanical Properties of Float Glass: Statistical Examination of Ring-on-Ring Testing for Air and Tin-Exposed Surface
Authors: DAIRI walid , HAMIDOUCHE mohammed 
Published: April 2024

Abstract

This study examined ring-on-ring tests on float glass samples, analyzing how two different test speeds (0.3 mm/s and 0.165 mm/s) affected the tin and air sides of the glass. The tests were conducted under both dry and wet conditions, followed by statistical analysis of various parameters for each side of the glass. The findings revealed significant differences between the Eta and Beta values, as well as on the mean stress and medians, indicating varying results depending on the parameters studied. Statistical analysis was used to assess the significance of these differences, emphasizing the importance of considering test conditions and speeds when evaluating the mechanical properties of float glass. This research sheds light on the nuanced effects of environmental conditions and test parameters on the mechanical properties of glass, including on the mean stress and medians, providing a foundation for further exploration and applications in relevant industries.
Predicting Hospitalization Costs for Pulmonary Tuberculosis Patients Based on Machine Learning
Authors: Fan Shiyu , Abulizi Abudoukeyoumujiang , You Yi , Huang Chencui , Yimit Yasen , Li Qiange , Zou Xiaoguang , Nijiati Mayidili 
Published: April 2024

Abstract

Background: Pulmonary tuberculosis is a prevalent chronic disease associated with a significant economic burden on patients. Predicting costs can help rationalize the cost structure and manage expenses efficiently. Traditional models have limited predictive performance, but machine learning and big data analysis have shown promise in predicting hospitalization costs. By utilizing accurately predicting costs, medical resources could be allocated more effectively, thereby leading to better control of patient hospitalization costs.
Methods: This study selected data from the information system of a pulmonary hospital in the Kashgar between 2020 and 2022. A total of 9570 eligible pulmonary tuberculosis patients were included in the study. Multiple regression and Multilayer Perceptron (MLP) prediction models were developed using SPSS 26.0 and Python 3.7, respectively. The training set included data from 2020 and 2021, while the test set comprised data from 2022. The models predicted seven costs related to pulmonary tuberculosis patients, including diagnostic cost, medical service cost, material cost, treatment cost, drug cost, other cost, and total hospitalization cost. The model's predictive performance was evaluated using R-square, Root Mean Squared Error, and Mean Absolute Error metrics.
Results: Among the 9570 pulmonary tuberculosis cases included in the study, the median and quartile of patient age are 67.00 (55.00, 74.00) years old; The median and quartile length of hospital stay for patients are 14.00 (11.00, 21.00) days, and the median and quartile of total hospitalization expenses are 13150.45 (9891.3419648.48) yuan. Material cost accounted for the highest proportion (30.25%) in the composition of total hospitalization cost, followed by drug cost (24.01%). Nine factors, including age, marital status, admission condition, length of hospital stay, initial treatment, presence of other diseases, transfer, drug resistance, and admission department, significantly influenced hospitalization costs for pulmonary tuberculosis patients. Excluding other cost, in both the training and test sets, the MLP model demonstrated higher R2 and lower RMSE and MAE results.
Conclusion: In conclusion, MLP can effectively leverage patient information and accurately predict various hospitalization costs, achieving a rationalized structure of hospitalization costs by adjusting higher-cost inpatient items and balancing different cost categories. The insights from this predictive model also hold relevance for research in other medical conditions.
Toward engineering lattice structures with the Material Point Method (MPM)
Authors: Giraldo-Londoño Oliver , Muñetón-López Rogelio A. , Barclay Paul L. , Zhuang Xiatian , Zhang Duan Z. , Chen Zhen 
Published: April 2024

Abstract

This study examins the potential of two variants of the material point method—the generalized interpolation material point (GIMP) and dual domain material point (DDMP) methods—in developing a robust computational framework for engineering lattice structures under different loading conditions. The study begins with assessing the ability of the two methods in predicting elastic buckling phenomena using column geometries with and without initial geometric imperfections. The results indicate that both methods effectively capture buckling phenomena when initial geometric imperfections are introduced. After this verification step, we create several models of tetrahedral lattice structures with varying strut diameter and orientation and subject them to quasi-static loading. We then validate the numerical results using laboratory test results. The results show that, while both methods accurately predict load-displacement curves in the pre-buckling regime, their predictive capabilities diminish in the post-buckling regime. Through visual comparison between the numerical and experimental deformed shapes, it appears that the discrepancies between model and experimental results are attributed to initial geometric imperfections in the lattices that occurred during 3D printing. We then establish a second set of lattice models where different types of initial geometric imperfections are considered. The results from these models show that imperfections have a negligible influence in the pre-buckling regime but affect the behavior considerably in the post-buckling regime. As a final step in this work, we subject the lattice models to impact loading and employ hyphotetical soft and stiff materials. These results show that the lattice stiffness, which depends on material stiffness, strut diameter, and orientation, significantly influences the ability of a lattice structure to resist impact. In particular, we find that a stiffer lattice (i.e., one made with a stiff material and thicker struts) is capable of absorbing more energy than a softer one during impact. Although material nonlinearities, inelasticity, and detailed contact formulations are not considered in this study, the findings obtained herein lay the groundwork for engineering lattice structures under extreme loading conditions through a simulation-driven framework based on particle-based methods.
Private Detection of Relatives in Forensic Genomics using Homomorphic Encryption
Authors: de Souza Fillipe Dias Moreira , de Lassus Hubert , Cammarota Ro 
Published: April 2024

Abstract

Background: Forensic analysis heavily relies on DNA analysis techniques, notably autosomal Single Nucleotide Polymorphisms (SNPs), to expedite the identification of unknown suspects through genomic database searches. However, the uniqueness of an individual’s genome sequence designates it as Personal Identifiable Information (PII), subjecting it to stringent privacy regulations that can impede data access and analysis, as well as restrict the parties allowed to handle the data.
Homomorphic Encryption (HE) emerges as a promising solution, enabling the execution of complex functions on encrypted data without the need for decryption. HE not only permits the processing of PII as soon as it is collected and encrypted, such as at a crime scene, but also expands the potential for data processing by multiple entities and artificial intelligence services.
Methods: This study introduces HE-based privacy-preserving methods for SNP DNA analysis, offering a means to compute kinship scores for a set of genome queries while meticulously preserving data privacy. We present three distinct approaches, including one unsupervised and two supervised methods, all of which demonstrated exceptional performance in the iDASH 2023 Track 1 competition.
Results: Our HE-based methods can rapidly predict 400 kinship scores from an encrypted database containing 2000 entries within seconds, capitalizing on advanced technologies like Intel AVX vector extensions, Intel HEXL, and Microsoft SEAL HE libraries. Crucially, all three methods achieve remarkable accuracy levels (ranging from 96% to 100%), as evaluated by the auROC score metric, while maintaining robust 128-bit security. These findings underscore the transformative potential of HE in both safeguarding genomic data privacy and streamlining precise DNA analysis.
Conclusions: Results demonstrate that HE-based solutions can be computationally practical to protect genomic privacy during screening of candidate matches for further genealogy analysis in Forensic Genetic Genealogy (FGG).
Proteome-wide Mendelian randomization identifies causal plasma proteins in Interstitial lung disease
Authors: Yu Kunrong , Li Wanying , Long Wenjie , Li Yijia , Li Yanting , Liao Huili , Liu Jianhong 
Published: April 2024

Abstract
Background
Interstitial lung disease (ILD) has exhibited limited overall treatment advancements, with scant exploration into circulating protein biomarkers causally linked to ILD and its subtypes beyond idiopathic pulmonary fibrosis (IPF). Therefore, our study aims to investigate potential drug targets and circulating protein biomarkers for ILD and its subtypes.
Methods
We utilized the most recent large-scale plasma protein quantitative trait loci (pQTL) data detected from the antibody-based method and ILD and its subtypes’ GWAS data from the updated FinnGen database for Mendelian randomization analysis. To enhance the reliability of causal associations, we conducted external validation and sensitivity analyses, including Bayesian colocalization, bidirectional Mendelian randomization analysis, and phenotype scanning.
Results
Genetic prediction levels of eight proteins were associated with the risk of ILD or its subtypes. Through a series of sensitivity analyses, three proteins were identified as priority proteins for circulating biomarkers and potential therapeutic targets. Specifically, CDH15(Cadherin-15)increased the risk of ILD༈OR = 1.32, 95%CI 1.16–1.49, P = 1.60×10− 6༉, and LTBR༈Lymphotoxin-beta receptor༉increased the risk of sarcoidosis༈OR = 1.39, 95%CI 1.20–1.61, p = 9.38×10− 6༉, while ADAM15 (A disintegrin and metalloproteinase 15) were protective proteins for ILD༈OR = 0.86, 95% CI 0.81–0.92, P = 1.59×10− 6༉and IPF༈OR = 0.81, 95% CI 0.75–0.89). Moreover, no causal proteins for other ILD subtypes were found.
Conclusion
This study identified several new circulating protein biomarkers associated with the risk of ILD and its subtypes. It offers a new perspective for future research on the diagnosis and treatment of ILD and its subtypes.
Short-term outcomes of Robotic-Assisted Sigmoidectomy using the Senhance Digital Laparoscopy System: A Comparison with Laparoscopic Surgery Using Propensity Score Matching
Authors: Akuta Sohei , Hirano Yasumitsu , Ishiyama Yasuhiro , Yamato Misuzu , Yoshizawa Masatoshi , Tanaka Hiroto , Fujii Takatsugu , Okazaki Naoto , Ishii Toshimasa , Deguchi Katsuya , Hiranuma Chikashi , Aikawa Masayasu , Sato Hiroshi , Sakuramoto Shinichi , Okamoto Kojun , Kinugasa Yusuke , Koyam Isamu 
Published: April 2024

Abstract

Background
Colorectal cancer is a common and deadly form of cancer. Sigmoid colon cancer is the most prevalent type of colon cancer. Robotic-assisted surgical systems, like the Senhance Digital Laparoscopy System, have attracted much attention in recent years. This study aimed to examine the safety of Senhance by comparing Senhance-assisted surgery with conventional laparoscopic surgery for sigmoid colon cancer.
Methods
This retrospective single-center study analyzed data from patients undergoing sigmoidectomies between March 2020 and 2023. Propensity score matching was employed to create comparable groups. Patient characteristics, short-term surgical outcomes, and pathological data were assessed. Postoperative complications were graded using the Clavien-Dindo system.
Results
Prior to propensity score matching, there were differences between groups regarding the cancer stage. After matching, two groups comprising 17 patients each were comparable across various factors. The operative time was significantly longer in the Senhance-assisted group (235 min vs. 165 min, p = 0.002), underscoring the importance of optimizing surgical techniques. However, no significant differences were observed in blood loss, complications, or pathological characteristics between the groups.
Conclusions
Robotic-assisted sigmoidectomy using Senhance appears to be as safe as conventional laparoscopic surgery.
Article Type: Apigenin alleviated peritoneal dialysis-related peritoneal fibrosis by regulating pyroptosis
Authors: Xue Dandan , Luo Nana , Li Xiang , Jiao Zhanfeng , Zhang Yiming , Ma Xiaofen 
Published: April 2024

Abstract

Prolonged exposure of the peritoneum to peritoneal dialysis fluid, peritonitis, and peritoneal blood accumulation can cause peritoneal inflammation and injury, followed by progressive fibrosis, ultimately leading to the cessation of peritoneal dialysis. Apigenin is an herbal medicine with anti-inflammatory, antioxidant, and anti-fibrotic properties. Our previous study found that apigenin could inhibit the process of peritoneal fibrosis, but whether apigenin could treat fibrosis by inhibiting pyroptosis is not known. This study aimed to investigate the protective effects of apigenin against peritoneal mesothelial cell pyroptosis and fibrosis. First, we observed changes in peritoneal mesothelial cells using VX-765, an inhibitor of cellular pyroptosis. Western blotting, immunoblotting, and electron microscopy showed that cellular pyroptosis was significantly inhibited, and peritoneal mesothelial cell fibrosis was reduced accordingly. Secondly, when the cells were co-treated with high glucose medium and apigenin, apigenin attenuated the activation of cellular pyroptosis by decreasing the levels of Gasdermin D, NOD-like receptor protein 3 inflammatory niche activation, caspase-1 and IL-1β and also inhibited the development of peritoneal fibrosis. Thus, our results suggest that apigenin plays a protective role against peritoneal fibrosis by inhibiting pyroptosis.
Industrial multivariate time-series data anomaly detection incorporating attention mechanisms and adversarial training
Authors: Yang Wenjie , Chu Wenchao , Wu Xingfu , Zhou Lianlin , Wang Jiayi , Yang Hua , Li Zirui 
Published: April 2024

Abstract
To address the challenges faced in industrial anomaly detection, including data sample imbalance, lack of anomaly labels, and complex spatiotemporal relationships in high-dimensional data, this paper proposes a novel multi-modal time-series anomaly detection model that combines attention mechanisms and adversarial training. In this model, the first step involves utilizing graph attention mechanisms to extract sequence correlation features from multi-modal time-series data, which are then summed with the original data to form a dual-feature-based data representation. Subsequently, a self-supervised learning approach is employed to input this data representation into a variational autoencoder's encoding-decoding network for reconstruction. Anomaly detection is performed by analyzing the error between the input and reconstructed data. The model also employs spatiotemporal attention mechanisms and adversarial training during reconstruction to enhance feature extraction and model generalization. By comparing our proposed model to five commonly used baseline models, we demonstrate its effectiveness in detecting anomalies in scenarios involving high-dimensional data and imbalanced abnormal samples, demonstrating superior anomaly detection performance, as well as excellent performance on real industrial production and processing datasets.
Comments on System identification of an UAS model using a subspace method
Authors: Machado Raphaela Carvalho , Zúniga David Fernando Castillo , Souza Alain , Paixão Vítor , Rosado Thiago , Balthazar José Manoel , Goés Luiz Carlos Sandoval 
Published: April 2024

Abstract
The aim of this paper is providing an experimental procedure of system identification of an Unmanned Aerial System (UAS) with fixed flexible wing using subspace identification techniques for dynamic systems. A reliable model is important for aerodynamic and flight control systems to comprehend the behavior of the system and to design a feedback loop that can be applied to minimize the effects of structural flexibility. In this scenario, this research is intended to identify a parametric model for a flexible aircraft from open-loop experimental data applying the DSR e algorithm. A flight test campaign was executed using the EOLO, a single engine aircraft with a wingspan of 4 m, weighing in total 8.87 kg. First, results related to identification using synthetic data are presented, in which preliminary estimated parameters based on Ground Vibration Test (GVT) were useful to validate the identified model. Then, the experimental results performed in open-loop operation reveal that subspace algorithms estimate a suitable state-space model covering the entire frequency range contained in the experimental data. It is important to highlight that a limiting factor for obtaining a representative model for the frequency range of interest from the collected data is having a persistently exciting condition for the input signals.