bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compact Archimedean spiral antenna with high gain for electrostatic discharge detection
Authors: Wang Tong , Chen Dongwei , Li Mingjie , Liu Weidong , Fang Qingyuan 
Published: January 2024

AbstractA compact Archimedean spiral antenna with high gain for electrostatic discharge (ESD) detection is investigated. A frustum‐shaped cavity proposed in this work is formed to optimise the maximum antenna gain. Additionally, a helix arm is loaded to enhance antenna gain in lower band. The measured results demonstrate that the proposed antenna, which employs a unidirectional radiation mode, exhibits a maximum antenna gain ranging from −0.74 dBi to 9.46 dBi and a voltage standing wave ratio less than 3 across a frequency range of 0.65–5 GHz. A test system consisted of proposed antenna and a commercial ESD detection antenna is established. The verified results indicate that the proposed antenna has a performance close to the commercial antenna, so that proposed antenna with compact size is preferred to be utilised for ESD detection.
Comparative analysis of three‐phase dual active bridge converter with different transformer topology and modern universal control for DC microgrids
Authors: Bachman Serafin , Turzyński Marek , Jasiński Marek 
Published: January 2024

AbstractThe presented work discusses issues related to the use of modern multiphase topologies of Dual Active Bridge (DAB)‐type converters. Converters of this type are widely used in most DC microgrid applications. The introduction emphasizes a comparative analysis between single‐phase and multi‐phase DAB topologies within high‐power DC microgrids, delving into their respective advantages, drawbacks, design procedures, and considerations based on the latest knowledge. The publication explores the comparison and selection of viable topologies for deployment in high‐power and high‐efficiency DC microgrids. The unified method of controlling 1‐phase and multi‐phase DAB converters was proposed in this design, simplifying the issues of DC microgrid control. All topologies were tested on the same controller concept. The study performs laboratory investigation of DAB 1‐phase and 3‐phase: Star–Star, and Star–Delta topologies. Attention was paid to maintaining uniform operating conditions of the system, contrary to studies known from the literature, all tests were carried out on the same laboratory stand and the same magnetic components in different configurations. Analytical and laboratory analyses of the Zero Voltage Switching (ZVS) region were performed, accounting for non‐linear phenomena. Based on these findings, an assessment of the system's performance in soft switching was carried out. The presented results were implemented in a simulation model and subsequently validated through tests on a constructed laboratory setup to ensure the proper operation of the system. This work meticulously presents and discusses variations in efficiency, dynamic response, phase current harmonic distribution, phase shift distribution, ZVS switching region, and more among the examined topologies. To ensure a fair comparison, the converter configuration for both simulation and laboratory models utilized identical components across all configurations.
Graph‐based spectrum sensing algorithm via nonlinear function regulation
Authors: Wu Shanshan , Hu Guobing 
Published: January 2024

AbstractTo solve the difficulties in threshold selection and poor performance under low signal‐to‐noise ratio (SNR) conditions in existing spectrum sensing algorithms, a graph‐based spectrum sensing algorithm using nonlinear function regulation was proposed. The idea was to add a specific nonlinear transformation between the normalisation and quantization steps of the existing signal‐to‐graph converter (SGC). If the autocorrelation function of the observed signal selected as the input fed to SGC, the nonlinear function has the ability to adjust the uniformity of its probability distribution, increasing the probability of the observed signal being transformed into a complete graph under the alternative hypothesis, whereas remaining a noncomplete graph under the null hypothesis. Thus transformed the graph‐based spectrum sensing into a complete graph‐detection problem. Based on the theory of dispersive ordering, a theoretical analysis of the mechanism by which nonlinear transformations affect graph connectivity was conducted. The simulation results showed that the detection performance of the proposed algorithm was superior to that of existing graph‐based spectrum sensing algorithms. When SNR was −7 dB, the detection probability of the proposed algorithm exceeded 95%. Moreover, among the existing graph‐based spectrum sensing algorithms, the proposed algorithm exhibited the lowest computational complexity apart from the block range‐based method.
An unmanned aerial vehicle light detection and ranging Simultaneous Localisation And Mapping algorithm based on factor graph optimisation for tunnel 3D mapping
Authors: Xie Jian , Wu Zhuoping , Wang Bing , Xu Aoshu , Chen Yunfei , Li Jing 
Published: January 2024

AbstractThe current mature Simultaneous Localisation And Mapping (SLAM) algorithms, when applied to tunnel scenarios with point cloud degradation and poor lighting conditions, often lead to a sharp increase in the estimated attitude error of the unmanned aerial vehicle (UAV), or even prevent the UAV from moving autonomously due to severe feature degradation. To address the above problems, the authors propose a SLAM algorithm based on factor graph optimisation, Iterative Closest Point and Normal Distributions Transform algorithms. A front‐end point cloud registration module and a back‐end construction algorithm based on filtering and graph optimisation are designed. To verify the effectiveness of the proposed algorithm, experiments are conducted on KITTI dataset and real tunnel scenes, and compared with LiDAR Odometry and Mapping (LOAM) and lightweight and ground optimised (LeGO)‐LOAM algorithms. The results show that the average processing time of the proposed method is about 75 ms, which can meet the real‐time requirements of autonomous aerial vehicles. Compared with LOAM and LeGO‐LOAM in the real tunnel experiment, the proposed method shows the tunnel 3D map construction.
Demystifying the Radiography of Age Estimation in Criminal Jurisprudence: A Pictorial Review
Authors: Bhardwaj Vritika , Kumar Ishan , Aggarwal Priyanka , Singh Pramod Kumar , Shukla Ram C. , Verma Ashish 
Published: January 2024

AbstractSkeletal radiographs along with dental examination are frequently used for age estimation in medicolegal cases where documentary evidence pertaining to age is not available. Wrist and hand radiographs are the most common skeletal radiograph considered for age estimation. Other parts imaged are elbow, shoulder, knee, and hip according to suspected age categories. Age estimation by wrist radiographs is usually done by the Tanner-Whitehouse method where the maturity level of each bone is categorized into stages and a final total score is calculated that is then transformed into the bone age. Careful assessment and interpretation at multiple joints are needed to minimize the error and categorize into age-group. In this article, we aimed to summarize a suitable radiographic examination and interpretation for bone age estimation in living children, adolescents, young adults, and adults for medicolegal purposes.
Shall brands create their own virtual influencers? A comprehensive study of 33 virtual influencers on Instagram
Authors: Shen Zheng 
Published: January 2024

AbstractIncreasing customer-brand engagement on social media has been a focus of brand success for many years. Recently, virtual influencers have gained popularity as a new way for brands to increase customer engagement, but there has been limited analysis of this new phenomenon. As such, by investigating 33 virtual influencers on Instagram, this study explored whether brands should create or collaborate with virtual influencers and how they can increase customer-brand engagement. The findings reveal that non-branded virtual influencers are more engaged than branded virtual influencers. Also, virtual influencers’ communication strategies to increase customer-brand engagement were further discussed in the study to develop a typology of virtual influencers. Thus, this study fills a theoretical gap in the limited analysis of virtual influencers in customer-brand engagement, and suggests that brands collaborate with virtual influencers rather than creating their own virtual influencers in practice.
The Irony of British Human Rights Exceptionalism, 1948–1998
Authors: Kahn Jeffrey 
Published: January 2024

Abstract
Assessments of London’s relationship with Strasbourg tend to highlight recent discontent about perceived infringements of parliamentary sovereignty by the European Court of Human Rights. The British criticism is, in short, that this is not what we signed up for. But this complaint is not new. This Article argues against understanding this story as one in which this relationship only recently soured. A recurrent theme in the United Kingdom’s involvement with the institutions of the Council of Europe, especially its human rights commission and court, has been the dawning realization, again and again, that the treaties and institutions that the United Kingdom’s own lawyers and diplomats were so instrumental in devising for an emergent Council of Europe could and would be applied to its own foreign and domestic interests.
It is ironic that the United Kingdom should have been so central to crafting human rights instruments from which it later sought to exempt itself. The irony is heightened by the frequency with which this founding member state realized, opposed, and then accepted the implications of its actions. An unexpected consequence of this ironic refrain of exceptionalism may be to reinforce the objective legitimacy of the system’s most challenged institutions. After all, it is hard to allege an infringement of parliamentary sovereignty if the state’s ministers repeatedly choose to abide by the (sometimes changing) treaty obligations they helped establish. On the other hand, when ministers in high dudgeon declare their outrage at the latest Strasbourg ruling, the erosion they facilitate to popular legitimacy may ultimately reduce overall respect for the European Convention. Ironically, we become the masks we wear.
The landscape of the methodology in drug repurposing using human genomic data: a systematic review
Authors: Wang Lijuan , Lu Ying , Li Doudou , Zhou Yajing , Yu Lili , Mesa Eguiagaray Ines , Campbell Harry , Li Xue , Theodoratou Evropi 
Published: January 2024

Abstract
The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.
Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework
Authors: Zeng Junxiang , Gao Xiupan , Gao Limei , Yu Youyou , Shen Lisong , Pan Xiujun 
Published: January 2024

Abstract
Rare antinuclear antibody (ANA) pattern recognition has been a widely applied technology for routine ANA screening in clinical laboratories. In recent years, the application of deep learning methods in recognizing ANA patterns has witnessed remarkable advancements. However, the majority of studies in this field have primarily focused on the classification of the most common ANA patterns, while another subset has concentrated on the detection of mitotic metaphase cells. To date, no prior research has been specifically dedicated to the identification of rare ANA patterns. In the present paper, we introduce a novel attention-based enhancement framework, which was designed for the recognition of rare ANA patterns in ANA-indirect immunofluorescence images. More specifically, we selected the algorithm with the best performance as our target detection network by conducting comparative experiments. We then further developed and enhanced the chosen algorithm through a series of optimizations. Then, attention mechanism was introduced to facilitate neural networks in expediting the learning process, extracting more essential and distinctive features for the target features that belong to the specific patterns. The proposed approach has helped to obtained high precision rate of 86.40%, 82.75% recall, 84.24% F1 score and 84.64% mean average precision for a 9-category rare ANA pattern detection task on our dataset. Finally, we evaluated the potential of the model as medical technologist assistant and observed that the technologist’s performance improved after referring to the results of the model prediction. These promising results highlighted its potential as an efficient and reliable tool to assist medical technologists in their clinical practice.
Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction
Authors: Luo Zeyu , Wang Rui , Sun Yawen , Liu Junhao , Chen Zongqing , Zhang Yu-Juan 
Published: January 2024

Abstract
As the application of large language models (LLMs) has broadened into the realm of biological predictions, leveraging their capacity for self-supervised learning to create feature representations of amino acid sequences, these models have set a new benchmark in tackling downstream challenges, such as subcellular localization. However, previous studies have primarily focused on either the structural design of models or differing strategies for fine-tuning, largely overlooking investigations into the nature of the features derived from LLMs. In this research, we propose different ESM2 representation extraction strategies, considering both the character type and position within the ESM2 input sequence. Using model dimensionality reduction, predictive analysis and interpretability techniques, we have illuminated potential associations between diverse feature types and specific subcellular localizations. Particularly, the prediction of Mitochondrion and Golgi apparatus prefer segments feature closer to the N-terminal, and phosphorylation site-based features could mirror phosphorylation properties. We also evaluate the prediction performance and interpretability robustness of Random Forest and Deep Neural Networks with varied feature inputs. This work offers novel insights into maximizing LLMs’ utility, understanding their mechanisms, and extracting biological domain knowledge. Furthermore, we have made the code, feature extraction API, and all relevant materials available at https://github.com/yujuan-zhang/feature-representation-for-LLMs.
CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters
Authors: Vural-Ozdeniz Merve , Calisir Kubra , Acar Rana , Yavuz Aysenur , Ozgur Mustafa M , Dalgıc Ertugrul , Konu Ozlen 
Published: January 2024

Abstract
Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.
The CUT&RUN greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping
Authors: de Mello Fabio N , Tahira Ana C , Berzoti-Coelho Maria Gabriela , Verjovski-Almeida Sergio 
Published: January 2024

Abstract
Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a recent development for epigenome mapping, but its unique methodology can hamper proper quantitative analyses. As traditional normalization approaches have been shown to be inaccurate, we sought to determine endogenous normalization factors based on the human genome regions of constant nonspecific signal. This constancy was determined by applying Shannon’s information entropy, and the set of normalizer regions, which we named the ‘Greenlist’, was extensively validated using publicly available datasets. We demonstrate here that the greenlist normalization outperforms the current top standards, and remains consistent across different experimental setups, cell lines and antibodies; the approach can even be applied to different species or to CUT&Tag. Requiring no additional experimental steps and no added cost, this approach can be universally applied to CUT&RUN experiments to greatly minimize the interference of technical variation over the biological epigenome changes of interest.
Multi-modal features-based human-herpesvirus protein–protein interaction prediction by using LightGBM
Authors: Yang Xiaodi , Wuchty Stefan , Liang Zeyin , Ji Li , Wang Bingjie , Zhu Jialin , Zhang Ziding , Dong Yujun 
Published: January 2024

Abstract
The identification of human-herpesvirus protein–protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.
Sex and reproduction: a theoretical analysis
Authors: Xu Zhongneng 
Published: January 2024

Abstract
Sex in some organisms shows a continuous spectrum. Sometimes sexual differences in an individual’s organs are contradictory and determined by physiological, pathological and environmental factors, leading to debate over the definitions of sex in individuals. This study indicates that the essence of sex during reproduction distinguishes between reproductive subjects and that the unit of reproduction is not the individual but the reproductive cell. To properly supplement the existing definitions of sex, a definition of sex in reproduction in which only reproductive cells have sexes was herein addressed, focusing on material contributions to reproduction and the formation of zygotes by reproductive cells. Equations to describe sexes are proposed, as well as equations to make this only reproductive-cell sex definition compatible with the individual sex definition based on the quantitative proportions of different reproductive cells. The explanation of sex determination, sex differences and sex competition based on the definition of only reproductive-cell sex in reproduction is discussed.
The basal forebrain cholinergic system as target for cell replacement therapy in Parkinson’s disease
Authors: Björklund Anders , Barker Roger A 
Published: January 2024

Abstract
In recent years there has been a renewed interest in the basal forebrain cholinergic system as a target for the treatment of cognitive impairments in patients with Parkinson’s disease, due in part to the need to explore novel approaches to treat the cognitive symptoms of the disease and in part to the development of more refined imaging tools that have made it possible to monitor the progressive changes in the structure and function of the basal forebrain system as they evolve over time. In parallel, emerging technologies allowing the derivation of authentic basal forebrain cholinergic neurons from human pluripotent stem cells are providing new powerful tools for the exploration of cholinergic neuron replacement in animal models of Parkinson’s disease-like cognitive decline.
In this review, we discuss the rationale for cholinergic cell replacement as a potential therapeutic strategy in Parkinson’s disease and how this approach can be explored in rodent models of Parkinson’s disease-like cognitive decline, building on insights gained from the extensive animal experimental work that was performed in rodent and primate models in the 1980s and 90s. Although therapies targeting the cholinergic system have so far been focused mainly on patients with Alzheimer’s disease, Parkinson’s disease with dementia may be a more relevant condition. In Parkinson’s disease with dementia, the basal forebrain system undergoes progressive degeneration and the magnitude of cholinergic cell loss has been shown to correlate with the level of cognitive impairment. Thus, cell therapy aimed to replace the lost basal forebrain cholinergic neurons represents an interesting strategy to combat some of the major cognitive impairments in patients with Parkinson’s disease dementia.