bluuebunny/crossref_metadata_2025_split huggingface dataset:
A diagnostic classification of lung nodules using multiple-scale residual network
Authors: Wang Hongfeng , Zhu Hai , Ding Lihua , Yang Kaili 
Published: July 2023

AbstractComputed tomography (CT) scans have been shown to be an effective way of improving diagnostic efficacy and reducing lung cancer mortality. However, distinguishing benign from malignant nodules in CT imaging remains challenging. This study aims to develop a multiple-scale residual network (MResNet) to automatically and precisely extract the general feature of lung nodules, and classify lung nodules based on deep learning. The MResNet aggregates the advantages of residual units and pyramid pooling module (PPM) to learn key features and extract the general feature for lung nodule classification. Specially, the MResNet uses the ResNet as a backbone network to learn contextual information and discriminate feature representation. Meanwhile, the PPM is used to fuse features under four different scales, including the coarse scale and the fine-grained scale to obtain more general lung features of the CT image. MResNet had an accuracy of 99.12%, a sensitivity of 98.64%, a specificity of 97.87%, a positive predictive value (PPV) of 99.92%, and a negative predictive value (NPV) of 97.87% in the training set. Additionally, its area under the receiver operating characteristic curve (AUC) was 0.9998 (0.99976–0.99991). MResNet's accuracy, sensitivity, specificity, PPV, NPV, and AUC in the testing set were 85.23%, 92.79%, 72.89%, 84.56%, 86.34%, and 0.9275 (0.91662–0.93833), respectively. The developed MResNet performed exceptionally well in estimating the malignancy risk of pulmonary nodules found on CT. The model has the potential to provide reliable and reproducible malignancy risk scores for clinicians and radiologists, thereby optimizing lung cancer screening management.
Comparison of in silico predicted Mycobacterium tuberculosis spoligotypes and lineages from whole genome sequencing data
Authors: Napier Gary , Couvin David , Refrégier Guislaine , Guyeux Christophe , Meehan Conor J. , Sola Christophe , Campino Susana , Phelan Jody , Clark Taane G. 
Published: July 2023

AbstractBacterial strain-types in the Mycobacterium tuberculosis complex underlie tuberculosis disease, and have been associated with drug resistance, transmissibility, virulence, and host–pathogen interactions. Spoligotyping was developed as a molecular genotyping technique used to determine strain-types, though recent advances in whole genome sequencing (WGS) technology have led to their characterization using SNP-based sub-lineage nomenclature. Notwithstanding, spoligotyping remains an important tool and there is a need to study the congruence between spoligotyping-based and SNP-based sub-lineage assignation. To achieve this, an in silico spoligotype prediction method (“Spolpred2”) was developed and integrated into TB-Profiler. Lineage and spoligotype predictions were generated for > 28 k isolates and the overlap between strain-types was characterized. Major spoligotype families detected were Beijing (25.6%), T (18.6%), LAM (13.1%), CAS (9.4%), and EAI (8.3%), and these broadly followed known geographic distributions. Most spoligotypes were perfectly correlated with the main MTBC lineages (L1-L7, plus animal). Conversely, at lower levels of the sub-lineage system, the relationship breaks down, with only 65% of spoligotypes being perfectly associated with a sub-lineage at the second or subsequent levels of the hierarchy. Our work supports the use of spoligotyping (membrane or WGS-based) for low-resolution surveillance, and WGS or SNP-based systems for higher-resolution studies.
Characterization of a novel and active temperate phage vB_AbaM_ABMM1 with antibacterial activity against Acinetobacter baumannii infection
Authors: Mardiana Meity , Teh Soon-Hian , Tsai Yun-Chan , Yang Hsueh-Hui , Lin Ling-Chun , Lin Nien-Tsung 
Published: July 2023

AbstractAcinetobacter baumannii is an opportunistic pathogen that significantly causes hospital-acquired infections. Due to its multidrug resistance, treating infections caused by this pathogen is challenging. Recently, phages have gained attention as a potential alternative to antibiotics in treating bacterial infections. While lytic phages are preferred in therapy, the use of temperate phages for this purpose has received less attention. This study characterized a novel temperate phage vB_AbaM_ABMM1 (ABMM1) with antibacterial activity toward A. baumannii. ABMM1 adsorbs quickly, has short latent periods, and is relatively stable at various temperatures and neutral pH. ABMM1 has an icosahedral head and a contractile tail. It has a 75,731 kb circular permuted dsDNA genome containing 86 gene products with 37.3% G + C content and a mosaic arrangement typical of temperate phages. Genomic analysis confirmed that ABMM1 does not have antibiotic-resistance genes or virulence-related factors. The packaging strategy was predicted in silico, suggesting that ABMM1 represents a headful phage. Only truncated ABMM1 prophage was detected and has similarity in the genome of several A. baumannii strains. Despite its ability to integrate into the host chromosome, the high MOI of ABMM1 (MOI 10) effectively killed the host bacterial cells and reduced the fatality rate of bacterial infection in the zebrafish model. These findings indicate that ABMM1 can be an alternative treatment for A. baumannii infection.
Effect of intervention based on the 5A self-management model on the improvement of behaviors related to women's reproductive health after legal abortion
Authors: Mirian Zahra , AbdiShahshahani Mahshid , Noroozi Mahnaz , Mostafavi Firozeh , Beigi Marjan 
Published: July 2023

AbstractLegal abortion has adopted many beneficial measures to prevent the birth of fetuses with congenital anomalies and maintain the health of high-risk mothers. However, abortion has its own complications and, thus, most mothers need care in this regard. The aim of this study was to determine the effect of self-management model on behaviors related to the reproductive health of mothers after legal abortion. This clinical trial study was conducted on 72 women who had legal abortion with the permission from Isfahan forensic medicine. The intervention was conducted using the 5A self-management model. The control group received the usual health system cares. Data collection tool was a researcher-made questionnaire of behaviors related to reproductive health, which was completed before and two months after the intervention by the two groups. The total mean score of the behavior (P < 0.001) and its dimensions (Prevention of post-abortion complications with P = 0.01, Prevention of sexually transmitted infections, satisfactory sexual function and family planning with P < 0.001) in the intervention group two months after the outset of the intervention was significantly higher than before the intervention. The total mean score of the behavior (P < 0.001) and its dimensions (Prevention of post-abortion complications with P = 0.002, Prevention of sexually transmitted infections, satisfactory sexual function and family planning with P < 0.001) two months after the outset of the intervention in the intervention group was significantly higher than the control group. Based on the results, this educational model can be emphasized to promote reproductive health behaviors of women after abortion.Clinical trial protocol link: https://www.irct.ir/user/trial/55184/view.
Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting
Authors: Leming Matthew J. , Bron Esther E. , Bruffaerts Rose , Ou Yangming , Iglesias Juan Eugenio , Gollub Randy L. , Im Hyungsoon 
Published: July 2023

AbstractAdvances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer’s, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.
Topology-induced chiral photon emission from a large-scale meron lattice
Authors: Wu Xuefeng , Li Xu , Kang Wenyu , Zhang Xichao , Chen Li , Zhong Zhibai , Zhou Yan , Åkerman Johan , Wu Yaping , Zhang Rong , Kang Junyong 
Published: July 2023

AbstractMerons are a class of topologically protected particle-like structures created in in-plane magnetized magnetic films. The structures can act as information carriers and could be used for magnetic storage. However, the development of such applications is hampered by limitations in the size, thermal stability and magnetic-field requirements of the systems. Here we report the construction of millimetre-scale meron lattices that are stable at room temperature and under zero magnetic field. Our system is based on a trilayer structure composed of a thin iron film sandwiched between films of palladium and magnesium oxide (Pd/Fe/MgO) on a gallium nitride wafer. It is fabricated using a molecular-beam epitaxy approach that is assisted by a high magnetic field, which leads to a strong Dzyaloshinskii–Moriya interaction. The lattices can be used for chirality transfer from merons to electrons and then to photons, and we show that the meron lattices can be used as spin injectors in nitride-based light-emitting diodes. The topology-induced spin light-emitting diode can provide 22.5% circularly polarized electroluminescence at room temperature and under zero magnetic field.
Engineered cytochrome P450 for direct arylalkene-to-ketone oxidation via highly reactive carbocation intermediates
Authors: Gergel Sebastian , Soler Jordi , Klein Alina , Schülke Kai H. , Hauer Bernhard , Garcia-Borràs Marc , Hammer Stephan C. 
Published: July 2023

AbstractKetones are crucial intermediates in synthesis and frequent moieties in many products. The direct regioselective synthesis of ketones from internal alkenes could simplify synthetic routes and solve a long-standing challenge in catalysis. Here we report the laboratory evolution of a cytochrome P450 enzyme for the direct oxidation of internal arylalkenes to ketones with several thousand turnovers. This evolved ketone synthase benefits from 15 crucial mutations, most of them distal to the active site. Computational analysis revealed that all these mutations collaborate to generate and tame a highly reactive carbocation intermediate. This is achieved through a confined, rigid, and geometrically and electrostatically preorganized active site. The engineered enzyme exploits a metal–oxo species for ketone synthesis and enables various challenging alkene functionalization reactions. This includes the catalytic, enantioselective oxidation of internal alkenes to ketones and formal asymmetric hydrofunctionalizations of internal alkenes in combination with other biocatalysts.
Disruptor: Computational identification of oncogenic mutants disrupting protein-protein and protein-DNA interactions
Authors: Kugler Valentina , Lieb Andreas , Guerin Nathan , Donald Bruce R. , Stefan Eduard , Kaserer Teresa 
Published: July 2023

AbstractWe report an Osprey-based computational protocol to prospectively identify oncogenic mutations that act via disruption of molecular interactions. It is applicable to analyse both protein-protein and protein-DNA interfaces and it is validated on a dataset of clinically relevant mutations. In addition, it is used to predict previously uncharacterised patient mutations in CDK6 and p16 genes, which are experimentally confirmed to impair complex formation.
Spin fluctuations from Bogoliubov Fermi surfaces in the superconducting state of S-substituted FeSe
Authors: Yu Zhongyu , Nakamura Koya , Inomata Kazuya , Shen Xiaoling , Mikuri Taketora , Matsuura Kohei , Mizukami Yuta , Kasahara Shigeru , Matsuda Yuji , Shibauchi Takasada , Uwatoko Yoshiya , Fujiwara Naoki 
Published: July 2023

AbstractThe study of the iron-based superconductor, FeSe, has resulted in various topics, such as the interplay among superconductivity, nematicity, and magnetism, Bardeen-Cooper-Schrieffer Bose-Einstein-condensation (BCS-BEC) crossover, and Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superconductivity. Recently, topologically protected nodal Fermi surfaces, referred to as Bogoliubov Fermi surfaces (BFSs), have garnered much attention. A theoretical model for the S-substituted FeSe system demonstrated that BFSs can manifest under the conditions of spin-orbit coupling, multi-band systems, and superconductivity with time-reversal symmetry breaking. Here we report the observation of spin fluctuations originating from BFSs in the superconducting (SC) state via 77Se-nuclear magnetic resonance measurements to 100 mK. In a heavily S-substituted FeSe, we found an anomalous enhancement of low-energy spin fluctuations deep in the SC state, which cannot be explained by an impurity effect. Such unusual behavior implies the presence of significant spin fluctuations of Bogoliubov quasiparticles, which are associated with possible nesting properties between BFSs.
Distributed consensus‐based estimation for non‐linear systems subject to missing measurements and Denial of Service attacks
Authors: Guo Yongzhen , Li Li , Xia Yuanqing , Wen Yanxin , Guo Jingjing 
Published: July 2023

AbstractThis article studies a distributed consensus‐based estimation problem for discrete time‐varying non‐linear systems with missing measurements and Denial of Service (DoS) attacks. The probability of missing measurements is independent for each sensor. The communication link between sensor nodes is unreliable and subjected to DoS attacks. To achieve accurate state estimation against missing measurements, a local estimator with compensation mechanism is designed for each sensor node. A stochastic event‐triggered mechanism is used to lessen additional information transfer. Based on this, a distributed consensus‐based estimator is constructed by continually fusing local neighbours information matrixs and vectors. Moreover, the analysis of the designed estimator boundedness is presented. Finally, the effectiveness of the proposed algorithm is verified by three numerical examples.
Position‐aware spatio‐temporal graph convolutional networks for skeleton‐based action recognition
Authors: Yang Ping , Wang Qin , Chen Hao , Wu Zizhao 
Published: July 2023

AbstractGraph Convolutional Networks (GCNs) have been widely used in skeleton‐based action recognition. Though significant performance has been achieved, it is still challenging to effectively model the complex dynamics of skeleton sequences. A novel position‐aware spatio‐temporal GCN for skeleton‐based action recognition is proposed, where the positional encoding is investigated to enhance the capacity of typical baselines for comprehending the dynamic characteristics of action sequence. Specifically, the authors’ method systematically investigates the temporal position encoding and spatial position embedding, in favour of explicitly capturing the sequence ordering information and the identity information of nodes that are used in graphs. Additionally, to alleviate the redundancy and over‐smoothing problems of typical GCNs, the authors’ method further investigates a subgraph mask, which gears to mine the prominent subgraph patterns over the underlying graph, letting the model be robust against the impaction of some irrelevant joints. Extensive experiments on three large‐scale datasets demonstrate that our model can achieve competitive results comparing to the previous state‐of‐art methods.
A novel transmission loss compensation method for radome based on non‐linear frequency modulation waveform design
Authors: Liang Da , Li Haoyang , Fu Yixiang , Pang Xiaoyu 
Published: July 2023

AbstractThe radome can protect the enclosed antenna from harsh weather and environmental conditions; however, it will cause a non‐negligible effect particularly transmission loss on the wideband signal which has been widely used in electronic systems. For this, a novel transmission loss compensation method for radome based on non‐linear frequency modulation (NLFM) waveform design is proposed. First, the transmission coefficient calculation process is given, Then, the detailed NLFM waveform design method is described in detail. Finally, the simulation experiments are demonstrated to verify the effectiveness of the proposed compensation method. Based on the designed NLFM waveform, the transmission loss can be compensated and a flat frequency response can be obtained, which can achieve excellent performance of pulse compression in the range direction. The proposed NLFM design method has great potential application value in the electronic system.
LiteDEKR: End‐to‐end lite 2D human pose estimation network
Authors: Lv Xueqiang , Hao Wei , Tian Lianghai , Han Jing , Chen Yuzhong , Cai Zangtai 
Published: July 2023

AbstractThe 2D human pose estimation plays an important role in human‐computer interaction and action recognition. Although the method based on high‐resolution network has superior performance, there is still room for improvement in terms of speed and lightweight. Here, a LiteDEKR, a 2D pose estimation method that combines lightweight and accuracy, is proposed by designing a lightweight network based on DEKR and constructing two scientifically valid loss functions. The method, constructs a multi‐instance bias regression loss that matches the true distribution of keypoint bias, improves the accuracy of bias regression, and constructs a keypoint similarity loss with the object keypoint similarity index of keypoints as the optimization objective to achieve end‐to‐end training of the network. In addition, this paper has designed a lightweight DEKR, using LitePose as the backbone network. With the optimization of the above two loss functions, LiteDEKR not only achieves lightweight but also has high accuracy. Comparative experiments on the COCO and CrowdPose datasets show that compared to the current state‐of‐the‐art Contextual Instance Decoupling, LiteDEKR achieves a similar accuracy with only 10% of its network complexity. It also shows better robustness to low‐resolution input images.
Structuring meaningful bug‐fixing patches to fix software defect
Authors: Li Hui , Liu Yong , Qi Xuexin , Yu Xi , Guo Shikai 
Published: July 2023

AbstractCurrently, software projects require a significant amount of time, effort and other resources to be invested in software testing to reduce the number of code defects. However, this process decreases the efficiency of software development and leads to a significant waste of workforce and resources. To address this challenge, researchers developed various solutions utilising deep neural networks. However, these solutions are frequently challenged by issues, such as a vast vocabulary, network training difficulties and elongated training processes resulting from the handling of redundant information. To overcome these limitations, the authors proposed a new neural network‐based model named HopFix, designed to detect software defects that may be introduced during the coding process. HopFix consists of four parts: data preprocessing, encoder, decoder and code generation components, which were used for preprocessing data, extracting information about software defects, analysing defect information, generating software patches and controlling the generation process of software patches, respectively. Experimental studies on Bug‐Fix Pairs (BFP) show that HopFix correctly fixed 47.2% (BFPsmall datasets) and 25.7% (BFPmedium datasets) of software defects.
Intérêt de la cartographie conceptuelle en formation initiale de médecine dentaire
Authors: Sadki Asmaâ , Frenay Mariane , Leloup Gaëtane , Mateu-Ramis Séverine , Vanpee Dominique , Denef Jean-François 
Published: July 2023

Contexte : L’apprentissage à l’université est une activité complexe qui implique la mise en œuvre de stratégies d’apprentissage efficaces. Pour de meilleures performances académiques (PA), l’étudiant doit construire un savoir ancré dans une compréhension approfondie. La carte conceptuelle (CC) est un outil cognitif qui favorise l’apprentissage signifiant et la compréhension approfondie. But : Examiner les effets de la participation à des ateliers visant à développer des CC, proposés à des étudiants de première année en médecine dentaire, sur la qualité d’une CC produite à l’examen ainsi que les liens avec les performances des étudiants à court et à moyen terme. Méthodes : Deux cohortes successives d’étudiants de première année en formation dentaire (N = 110) ont été suivies pour deux unités d’apprentissage (UE), UE1 en année 1 (A1) et UE2 en année 2 (A2). En A1, des ateliers CC facultatifs furent proposés. Des PA ont été mesurées au travers des questions lors de l’évaluation finale (examen). Des régressions linéaires multiples en relation avec la participation aux ateliers et la qualité des CC ont été réalisées incluant d’autres variables explicatives. Résultats : Il y a effectivement un lien statistiquement significatif et positif entre la participation aux ateliers, la qualité des CC et les PA, mais d’autres variables interviennent également comme l’historique académique, la thématique et les performances antérieures en A1. Conclusion : Les PA, à court et à moyen terme, sont globalement associées à la qualité de la CC. L’effet des ateliers sur les PA se manifeste à moyen terme.