bluuebunny/crossref_metadata_2025_split huggingface dataset:
Eagle-449: A volumetric, whole-brain compilation of brain atlases for vestibular functional MRI research
Authors: Smith Jeremy L. , Ahluwalia Vishwadeep , Gore Russell K. , Allen Jason W. 
Published: January 2023

AbstractHuman vestibular processing involves distributed networks of cortical and subcortical regions which perform sensory and multimodal integrative functions. These functional hubs are also interconnected with areas subserving cognitive, affective, and body-representative domains. Analysis of these diverse components of the vestibular and vestibular-associated networks, and synthesis of their holistic functioning, is therefore vital to our understanding of the genesis of vestibular dysfunctions and aid treatment development. Novel neuroimaging methodologies, including functional and structural connectivity analyses, have provided important contributions in this area, but often require the use of atlases which are comprised of well-defined a priori regions of interest. Investigating vestibular dysfunction requires a more detailed atlas that encompasses cortical, subcortical, cerebellar, and brainstem regions. The present paper represents an effort to establish a compilation of existing, peer-reviewed brain atlases which collectively afford comprehensive coverage of these regions while explicitly focusing on vestibular substrates. It is expected that this compilation will be iteratively improved with additional contributions from researchers in the field.
Antibiotic-sterol interactions provide insight into the selectivity of natural aromatic analogues of amphotericin B and their photoisomers
Authors: Borzyszkowska-Bukowska Julia , Czub Jacek , Szczeblewski Paweł , Laskowski Tomasz 
Published: January 2023

AbstractAromatic heptaene macrolides (AHMs) belong to the group of polyene macrolide antifungal antibiotics. Members of this group were the first to be used in the treatment of systemic fungal infections. Amphotericin B (AmB), a non-aromatic representative of heptaene macrolides, is of significant clinical importance in the treatment of internal mycoses. It includes the all-trans heptaene chromophore, whereas the native AHMs contain two cis-type (Z) double bonds within the chromophore system. Lately we have proven that it is possible to obtain AHMs’ stable derivatives in the form of all-trans (AmB-type) isomers by photochemical isomerization. Our further studies have shown that such alteration leads to the improvement of their selective toxicity in vitro. Computational experiments carried out so far were only an initial contribution in the investigation of the molecular basis of the mechanism of action of AHMs and did not provide explanation to observed differences in biological activity between the native (cis-trans) and isomeric (all-trans) AHMs. Herein, we presented the results of two-dimensional metadynamics studies upon AmB and its aromatic analogues (AHMs), regarding preferable binary antibiotic/sterol complexes orientation, as well as more detailed research on the behaviour of AHMs’ alkyl-aromatic side chain in cholesterol- or ergosterol-enriched lipid bilayers.
Hybrid and herd immunity 6 months after SARS-CoV-2 exposure among individuals from a community treatment program
Authors: Chevaisrakul Parawee , Lumjiaktase Putthapoom , Kietdumrongwong Pongtorn , Chuatrisorn Ittiporn , Chatsangjaroen Pongsan , Phanuphak Nittaya 
Published: January 2023

AbstractThe death rate from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in 2022 was lower than the death rate in 2021, when the infection rate increased. Hybrid immunity provided by a combination of vaccination and infection, including asymptomatic infection, may confer effective protection against death. We explored the combined effect of asymptomatic infection and hybrid immunity by studying T-cell and antibody responses against SARS-CoV-2 among individuals treated in home health care services 6 months after SARS-CoV-2 exposure. Asymptomatic SARS-CoV-2 infection was demonstrated in 24.4% of close contacts. The levels of immunity were not different between patients and close contacts. Anti-RBD IgG against SARS-CoV-2 increased in a dose-dependent manner with the number of vaccine doses. Interestingly, the T-cell response decreased soon after a booster dose of vaccine. Asymptomatic SARS-CoV-2 infection could not enhance immunity against SARS-CoV-2 among vaccinated close contacts. Full vaccination was crucial to provide hybrid immunity. However, when designing vaccine strategies, T-cell exhaustion after multiple vaccinations should be considered.
A genome-wide association study identifies distinct variants associated with pulmonary function among European and African ancestries from the UK Biobank
Authors: Sinkala Musalula , Elsheikh Samar S. M. , Mbiyavanga Mamana , Cullinan Joshua , Mulder Nicola J. 
Published: January 2023

AbstractPulmonary function is an indicator of well-being, and pulmonary pathologies are the third major cause of death worldwide. We analysed the UK Biobank genome-wide association summary statistics of pulmonary function for Europeans and individuals of recent African descent to identify variants associated with the trait in the two ancestries. Here, we show 627 variants in Europeans and 3 in Africans associated with three pulmonary function parameters. In addition to the 110 variants in Europeans previously reported to be associated with phenotypes related to pulmonary function, we identify 279 novel loci, including an ISX intergenic variant rs369476290 on chromosome 22 in Africans. Remarkably, we find no shared variants among Africans and Europeans. Furthermore, enrichment analyses of variants separately for each ancestry background reveal significant enrichment for terms related to pulmonary phenotypes in Europeans but not Africans. Further analysis of studies of pulmonary phenotypes reveals that individuals of European background are disproportionally overrepresented in datasets compared to Africans, with the gap widening over the past five years. Our findings extend our understanding of the different variants that modify the pulmonary function in Africans and Europeans, a promising finding for future GWASs and medical studies.
An ADS‐B signal poisoning method based on generative adversarial network
Authors: Wu Tianhao , Zhang Shunjie , Yang Jungang , Lei Pengfei 
Published: January 2023

AbstractAutomatic dependent surveillance‐broadcast (ADS‐B) has been widely used due to its low cost and high precision. The deep learning methods for ADS‐B signal classification have achieved a high performance. However, recent studies have shown that deep learning networks are very sensitive and vulnerable to small noise. An ADS‐B signal poisoning method based on Generative Adversarial Network is proposed. This method can generate poisoned signals. One of ADS‐B signal classification networks is assigned as the attacked network and another one as the protected network. When poisoned signals are fed into these two well‐performed classification networks, the poisoned signal will be recognized incorrectly by the attacked network while classified correctly by the protected network. An attack‐protect‐similar loss function is further proposed to achieve ‘triple‐win’ in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals. Experimental results show that the attacked network classifies poisoned signals with 1.55% classification accuracy, while the protected network classifies rate is still maintained at 99.38%.
Effective kernel‐principal component analysis based approach for wisconsin breast cancer diagnosis
Authors: Mushtaq Zohaib , Qureshi Muhammad Farrukh , Abbass Muhammad Jamshed , Al‐Fakih Sadeq Mohammed Qaid 
Published: January 2023

AbstractThis work aims to identify cancerous (malignant) and non‐malignant (non‐cancerous) cells in a breast cancer database. Wisconsin breast cancer data (WBC) was utilized and obtained from the University of California, Irvine's machine learning repository. The proposed approach involves the Naive bayes algorithm with Gaussian distribution of the function in combination with Chi‐squared‐based attributes selection approach. This experimentation has been done after reducing the dimensional space of the used data with extended Kernel Principal Component Analysis (K‐PCA). Five different kernels in K‐PCA have been tested after the implementation of necessary pre‐processing techniques. The performance assessment of the proposed system has been evaluated based on confusion matrix‐based accuracy, precision, sensitivity, and specificity. Our proposed methodology with six selected feature and sigmoid K‐PCA attained the best accuracy of 99.28%. This result outer performs many state‐of‐the‐art studies recently published on the identical dataset.
Multi‐modal machine learning based on electrocardiogram data for prediction of patients with ischemic heart disease
Authors: You Yi , Wang Wei , Li Dongze , Jia Yu , Li Dong , Zeng Rui , Zhang Lei 
Published: January 2023

AbstractElectrocardiogram is a non‐invasive method to diagnose patients with cardiovascular diseases. Electrocardiogram analysis methods using deep neural networks have been widely used in recent years and are becoming more efficient and accurate than traditional machine learning methods. Exercise electrocardiogram is useful in the diagnosis of ischemic heart disease, but the study of long‐term ischemic heart disease prediction based on exercise electrocardiogram is still limited.To investigate the early predictive value of exercise electrocardiogram in addition to basic physiological data in incident ischemic heart disease in a random general population sample followed from 40 to 69 years of age, a multi‐modal fusion model is designed to predict ischemic heart disease based on machine learning methods. With the deep feature extractor and multi‐modal feature fusion module, the proposed model performance is better than the traditional expert feature based methods and only deep neural networks. This implies that this proposed model may be improved in a contemporary era of ischemic heart disease prevention with the gradually declining incidence of ischemic heart disease.
Collaborative and adversarial network for text‐independent speaker verification in domain adaptation
Authors: Qiang Junhao , Yang Qun , Gao Jie , Liu Shaohan 
Published: January 2023

AbstractSpeaker verification models have achieved good results on the single genre data. But the performance degrades when model training and testing are not in the same domain. The adversarial training method is proposed to solve this problem by minimizing domain distribution differences. However, the adversarial training ignores domain‐specific information for the domain‐invariant speaker representations. In this paper, an improved collaborative adversarial network for domain adaptation in speaker verification is performed. Compared to the adversarial training, a collaborative discriminator is newly incorporated that learns domain‐specific information at the lower layers. Further, the projection block is added to the collaborative discriminator. It reduces the noise introduced by the collaborative discriminator. Experiments are conducted in different mismatch scenarios and using different speaker encoders. All the experimental results show that the performance of this method is better than the baseline and previous work using adversarial training.
Static sensor assisted rotor temperature estimation of permanent magnet machine for the low‐speed servo applications
Authors: Sun Le , Gan Hui , Xiao Xin 
Published: January 2023

AbstractA permanent magnet synchronous machine (PMSM) temperature estimation method fusing the classic lumped‐parameter thermal network (LPTN) and the stator sensor temperature information is investigated. With respect to the industrial servo applications, it is important to monitor the rotor magnet temperature to extend the machine thermal utilisation. A simple LPTN is developed and the quadratic programming is applied to identify the model parameters. The research work shows that the rotor temperature is sensitive to the rotor speed even at low speeds, and this simple estimation network can provide an accurate estimation. The winding sensor information can further improve the performance. A compound mechanism is built, by which the winding temperature estimation error is used as feedback to suppress the model‐based temperature estimation error accumulation. The estimation performance is compared with a regular constant LPTN‐based method. Experimental results based on a PMSM are presented to validate the proposed method.
Retracted: Cable tunnel unmanned aerial vehicle location and navigation method based on ultra wideband and depth camera fusion
Authors: He Guanghua , Huang Xueling , Xu Yahui , Qin Jun , Sun Ke 
Published: January 2023

AbstractWith the increasing demand for electric energy, more and more cities are constructing cable tunnels. In order to prevent the cable in the tunnel from being damaged, regular inspection of the cable tunnel is very important. However, due to the large scale of cable tunnel construction, the traditional manual inspection is difficult to meet the maintenance requirements. With the development of intelligent technology, the intelligent patrol mode of UAV has gradually replaced the traditional manual patrol mode and become the main patrol mode of cable tunnel. Therefore, the positioning and navigation method of cable tunnel UAV is studied in this paper. By using UWB localization algorithm and depth camera fusion, the positioning and navigation methods of UAVs are determined and compared with the positioning algorithm based on optical flow. Experimental comparison results show that the maximum and minimum yaw angles of UAV positioning based on the proposed algorithm are 2.41° and 0.16°, while the maximum and minimum yaw angles of UAV positioning based on the comparison algorithm are 3.11° and 1.51°. It can be seen that the integration of UWB and depth camera is of great significance for the research of positioning and navigation methods of cable tunnel UAVs.
Enhanced clustering based routing protocol in vehicular ad‐hoc networks
Authors: Naeem Afia , Rizwan Muhammad , Alsubai Shtwai , Almadhor Ahmad , Akhtaruzzaman Md. , Islam Shayla , Rahman Hameedur 
Published: January 2023

AbstractA vehicular ad‐hoc network (VANET) is derived from a mobile ad‐hoc network that is a part of less infrastructure network design. Vehicular communication in VANET can be achieved using vehicle‐to‐infrastructure (V2I) and vehicle‐to‐vehicle (V2V) communication. A vehicle communicates with other vehicles through onboard units while communicating with roadside units in an infrastructure mode. Secure clustering is required for the communication between nodes in the whole network. The fundamental problem with the VANET is the instability of the network that occurs due to vehicles' mobile nature, which decreases the network's efficiency. This research proposes an enhanced cluster‐based lifetime protocol ECBLTR that focuses on maximising the network's stability of routing and average throughput. The Sugeno model fuzzy inference system is used for assessing the cluster head (CH) that takes residual energy, local distance, node degree, concentration, and distance from the base station as input parameters. Our enhanced routing protocol shows that the proper channel model with an efficient routing protocol enhances the link throughput of the VANET for fixed network size. Our results show an efficient selection method of CH through the fuzzy system and a 10% increase in network lifetime. Furthermore, performance evaluation also demonstrates the impact of network sizes and routing protocols on packet delivery ratio and packet loss, average end‐to‐end delay, and overhead transmission.
Facile green synthesis inorganic cuprous oxide nanoparticles and their antibacterial properties
Authors: Binjawhar Dalal Nasser , Alsharari Salam S. , Albalawi Aisha , Abdulhasan Maryam Jawad , Khat Mehr , Ameen Fuad 
Published: January 2023

AbstractHere, the authors described a sustainable green synthesis approach to synthesize cuprous oxide nanoparticles (NPs) using Olea europaea extract. The resulting synthesized cuprous oxide NPs were characterized by the UV–Visible spectra, scanning electron microscopy (SEM)/energy‐dispersive X‐ray spectroscopy (EDS), transmission electron microscopy (TEM), and X‐ray diffraction (XRD). The average cuprous oxide NPs crystallite size of about 30 nm was calculated using the Scherrer formula. According to TEM, the resulting synthesized cuprous oxide NPs are spherical shaped. The cuprous oxide NP provides good antibacterial activity versus the two strains compared to Olea europaea aqueous extract; cuprous oxide NP has higher MIC against Staphylococcus aureus and Klebsiella pneumoniae.
Reconstructive Strategies in Pediatric Patients after Oncological Chest Wall Resection: A Systematic Review
Authors: Lonnee Pieter W. , Ovadja Zachri N. , Hulsker Caroline C.C. , van de Sande Michiel A.J. , van de Ven Cornelis P. , Paes Emma C. 
Published: January 2023

AbstractAn appropriate reconstruction strategy after surgical resection of chest wall tumors in children is important to optimize outcomes, but there is no consensus on the ideal approach. The aim of this study was to provide an up-to-date systematic review of the literature for different reconstruction strategies for chest wall defects in patients less than 18 years old. A systematic literature search of the complete available literature was performed and results were analyzed. A total of 22 articles were included in the analysis, which described a total of 130 chest wall reconstructions. All were retrospective analyses, including eight case reports. Reconstructive options were divided into primary closure (n = 21 [16.2%]), use of nonautologous materials (n = 83 [63.8%]), autologous tissue repair (n = 2 [1.5%]), or a combination of the latter two (n = 24 [18.5%]). Quality of evidence was poor, and the results mostly heterogeneous. Reconstruction of chest wall defects can be divided into four major categories, with each category including its own advantages and disadvantages. There is a need for higher quality evidence and guidelines, to be able to report uniformly on treatment outcomes and assess the appropriate reconstruction strategy.
Traumatic Spinal Cord Injury in Children: Clinical Profile and Management Outcome in a Developing Country's Rural Neurosurgery Practice
Authors: Oyemolade Toyin Ayofe , Adeleye Amos Olufemi , Ekanem Inwonoabasi Nicholas , Ogunoye Adeyemi Damilola 
Published: January 2023

Abstract
Background There is paucity of data-driven study on pediatric traumatic spinal cord injury (SCI) in the developing countries. This study aims to define the clinical profile of pediatric traumatic SCI in a rural tertiary hospital in a sub-Saharan African country.
Methods This was a prospective observational study of all children with spinal cord injury managed at our center over a 42-month period.
Results There were 20 patients, comprising 13 males and 7 females with a mean age of 11.5 years. Road traffic crash was the cause in 70% of the cases (motorcycle accident = 45%), and fall from height in 25%. Pedestrians were the victims of the road traffic crash in 42.9% (6/14) of the cases, while 21.4% (3/14) and 28.6% (4/14) were passengers on motorcycles and in motor vehicles, respectively. The cervical spine was the most common location of injury, occurring in 90% of the cases (18/20). Seventy-five percent of the patients (15/20) had transient deficits, but were grossly normal neurologically on examination (American Spinal Cord Injury Association [ASIA] grade E); 2 patients had ASIA D, while 1 patient each had ASIA C, B, and A injuries. All patients were managed nonoperatively. The patients with incomplete deficits improved, while those with complete injury did not make any motor or sensory gain.
Conclusion Road traffic accident, mostly motorcycle crash, was the most common etiology of pediatric SCI in this series, and most of the injuries were located in the cervical spine. Disabling injury constituted a small proportion of pediatric SCI in our practice.
A modified Kripkean theory of negative existentials
Authors: He Chaoan 
Published: January 2023

Abstract
In a 2019 paper, Hausmann raised a new and interesting problem for Kripke’s account of negative existentials. He argued that Kripke’s account leads to the absurd consequence that anybody who has good reasons to believe that there are no propositions also has good reasons to believe that he or she does not exist. In this paper I propose a modified Kripkean theory, which is invulnerable to a Hausmann-like argument. As will be seen, the modified theory can be squarely justified in light of the key observations behind Kripke’s original proposal.
DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal
Authors: Vilov Sergey , Heinig Matthias 
Published: January 2023

Abstract

Motivation
Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.

Results
We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.

Availability and implementation
DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.

Supplementary information
Supplementary data are available at Bioinformatics online.
MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities
Authors: Diao Yanyan , Hu Feng , Shen Zihao , Li Honglin 
Published: January 2023

Abstract

Summary
Construction of high-quality fragment libraries by segmenting organic compounds is an important part of the drug discovery paradigm. This article presents a new method, MacFrag, for efficient molecule fragmentation. MacFrag utilized a modified version of BRICS rules to break chemical bonds and introduced an efficient subgraphs extraction algorithm for rapid enumeration of the fragment space. The evaluation results with ChEMBL dataset exhibited that MacFrag was overall faster than BRICS implemented in RDKit and modified molBLOCKS. Meanwhile, the fragments acquired through MacFrag were more compliant with the ‘Rule of Three’.

Availability and implementation
https://github.com/yydiao1025/MacFrag.

Supplementary information
Supplementary data are available at Bioinformatics online.
SAMStat 2: quality control for next generation sequencing data
Authors: Lassmann Timo 
Published: January 2023

Abstract

Motivation
SAMStat is an efficient program to extract quality control metrics from fastq and SAM/BAM files. A distinguishing feature is that it displays sequence composition, base quality composition and mapping error profiles split by mapping quality. This allows users to rapidly identify reasons for poor mapping including the presence of untrimmed adapters or poor sequencing quality at individual read positions.

Results
Here, we present a major update to SAMStat. The new version now supports paired-end and long-read data. Quality control plots are drawn using the ploty javascript library.

Availability and implementation
The source code of SAMStat and code to reproduce the results are found here: https://github.com/timolassmann/samstat.