bluuebunny/crossref_metadata_2025_split huggingface dataset:
TIAToolbox as an end-to-end library for advanced tissue image analytics
Authors: Pocock Johnathan , Graham Simon , Vu Quoc Dang , Jahanifar Mostafa , Deshpande Srijay , Hadjigeorghiou Giorgos , Shephard Adam , Bashir Raja Muhammad Saad , Bilal Mohsin , Lu Wenqi , Epstein David , Minhas Fayyaz , Rajpoot Nasir M. , Raza Shan E Ahmed 
Published: September 2022

Abstract
Background
Computational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowledge, there is no open-source software library providing a generic end-to-end API for pathology image analysis using best practices. Most researchers have designed custom pipelines from the bottom up, restricting the development of advanced algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make computational pathology accessible to computational, biomedical, and clinical researchers.

Methods
By creating modular and configurable components, we enable the implementation of computational pathology algorithms in a way that is easy to use, flexible and extensible. We consider common sub-tasks including reading whole slide image data, patch extraction, stain normalization and augmentation, model inference, and visualization. For each of these steps, we provide a user-friendly application programming interface for commonly used methods and models.

Results
We demonstrate the use of the interface to construct a full computational pathology deep-learning pipeline. We show, with the help of examples, how state-of-the-art deep-learning algorithms can be reimplemented in a streamlined manner using our library with minimal effort.

Conclusions
We provide a usable and adaptable library with efficient, cutting-edge, and unit-tested tools for data loading, pre-processing, model inference, post-processing, and visualization. This enables a range of users to easily build upon recent deep-learning developments in the computational pathology literature.
SAR‐IRGF: A novel improved rolling guidance filter for the synthetic aperture radar image
Authors: Li Jiamu , Wang Zijian , Yu Wenbo , Yu Zhongjun 
Published: September 2022

AbstractInspired by the framework of the rolling guidance filter (RGF), a novel improved RGF for synthetic aperture radar (SAR) images is proposed in this letter, named SAR‐IRGF. Based on the model of additive noise, the RGF has the complete control of detailed smoothing under a scale measure for optical images. However, the speckle is well known as a multiplicative noise, which invalidates the RGF for SAR images. In this letter, an appropriate edge guidance is utilized to optimize the RGF for SAR image denoising. Compared with the original version, better convergence is achieved. Experimental results on natural Ku‐band airborne SAR images show that the proposed SAR‐IRGF reduces more speckle while sharpening the edges effectively. Simultaneously, the SAR‐IRGF demonstrates a significant nearly 5.5‐fold improvement than RGF in the equivalent number of looks (ENL).
Protecting DFIG‐based multi‐machine power system under transient‐state by nonlinear adaptive backstepping controller‐based capacitive BFCL
Authors: Hossain Md. Arafat , Islam Md. Rashidul , Haque Md. Yah‐Ya Ul , Hasan Jakir , Roy Tushar Kanti , Sadi Mohammad Ashraf Hossain 
Published: September 2022

AbstractThe invention of doubly‐fed induction generator (DFIG) brings the wind energy one step ahead as renewable power generation. But the performance of the grid‐connected DFIGs are greatly affected by grid disturbances as their stator windings are interfaced to the grid directly. Different fault current limiters are capable of improving fault ride through capability during short circuit faults. Nonlinear controller based fault current limiters (FCLs) are superior to deal with the nonlinearity of the power systems. This paper proposes a nonlinear adaptive backstepping controller (ABSC) based capacitive bridge‐type FCL (CBFCL) to enhance the fault ride through capability of a DFIG‐based wind farm connected to a multi‐machine power system. At first, a complete modelling of the CBFCL is derived to understand its behaviour during the normal and fault period more accurately. Then, the ABSC is designed based on that dynamic model, along with the backstepping controller (BSC) and sliding mode controller (SMC) for comparison purpose. Finally, the performance of the ABSC to control the CBFCL has been analysed and verified by comparing with that of the BSC and the sliding mode controller. All the graphical and mathematical analyses favour the ABSC based CBFCL under symmetrical and asymmetrical fault (both temporary and permanent) scenarios.
LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images
Authors: Ahmed Noor , Tan Xin , Ma Lizhuang 
Published: September 2022

AbstractCoronavirus Disease 2019 (Covid‐19) overtook the worldwide in early 2020, placing the world's health in threat. Automated lung infection detection using Chest X‐ray images has a ton of potential for enhancing the traditional covid‐19 treatment strategy. However, there are several challenges to detect infected regions from Chest X‐ray images, including significant variance in infected features similar spatial characteristics, multi‐scale variations in texture shapes and sizes of infected regions. Moreover, high parameters with transfer learning are also a constraints to deploy deep convolutional neural network(CNN) models in real time environment. A novel covid‐19 lightweight CNN(LW‐CovidNet) method is proposed to automatically detect covid‐19 infected regions from Chest X‐ray images to address these challenges. In our proposed hybrid method of integrating Standard and Depth‐wise Separable convolutions are used to aggregate the high level features and also compensate the information loss by increasing the Receptive Field of the model. The detection boundaries of disease regions representations are then enhanced via an Edge‐Attention method by applying heatmaps for accurate detection of disease regions. Extensive experiments indicate that the proposed LW‐CovidNet surpasses most cutting‐edge detection methods and also contributes to the advancement of state‐of‐the‐art performance. It is envisaged that with reliable accuracy, this method can be introduced for clinical practices in the future.
On the input impedance of probe‐fed electromagnetic bandgap antennas based on lattice modes
Authors: Ceccuzzi Silvio , Baccarelli Paolo , Ponti Cristina , Tognolatti Ludovica , Schettini Giuseppe 
Published: September 2022

AbstractThe impedance of a coaxial probe, feeding an Electromagnetic Bandgap (EBG) structure conceived for radiation shaping, is studied from the theoretical and numerical viewpoints. The EBG medium is a square arrangement of dielectric cylinders placed in a parallel‐plate waveguide, where a suitable lattice mode is excited. A semi‐analytical model is developed and used to derive an approximate, closed‐form expression of the probe resistance. The model is based on a modal expansion in Floquet harmonics, on which a current distribution is projected according to the Lorentz reciprocity theorem to derive the amplitude of lattice modes propagating right above the bandgap along lattice axes. The dependence of probe impedance on lattice parameters is then investigated with the numerical simulations of a finite‐element method, which is also used to validate the developed model. A broad set of parametric analyses is presented, showing that the reactive part weakly depends on probe position, cylinder radius and permittivity, while the heights of probe and parallel‐plate waveguide play a major role in determining the resonance condition. As to the probe resistance, it decreases with cylinder radius and permittivity and decreases with probe and waveguide heights. The derived analytical formula correctly reproduces such functional dependences and its calculation is immediate, revealing its usefulness in antenna design. Matching issues are heuristically and experimentally approached by examples, demonstrating that the proposed work can be effectively employed to improve the electrical performance of EBG antennas with an embedded source.
A versatile control of solar DVR for enhanced utilization and power quality improvement in a series‐connected wind‐solar farm
Authors: Kathiresan Aravind Chellachi , Jeyaraj Pandia Rajan , Albert Brindhu Kumari , Thanikanti Sudhakar Babu , Nallapaneni Manoj Kumar , Alhelou Hassan Haes 
Published: September 2022

AbstractThis paper presents a solar dynamic voltage restorer for hybrid series‐connected solar‐wind farms to mitigate the power quality problems. The operating region of the proposed hybrid system is derived and studied through graphical analysis. Upon examination of the system's operation under various grid conditions, the feasibility of solar PV power injected into the grid is verified. It is further proved that the series injection of voltage is also capable of mitigating the effects of voltage sag/swell, and unbalance, which have adverse effects on wind‐connected induction generators. Further, the effectiveness of Solar DVR to mitigate the fault ride‐through capability of the wind farm is analyzed. The non‐requirement for an energy storage device such as a battery is also validated. A control system for the series PV inverter is proposed and the computer simulations are performed to confirm the efficacy and ride‐through capability of the system.
The emergence of multimorbidity as a matter of concern: a critical review
Authors: van Blarikom Esca , Fudge Nina , Swinglehurst Deborah 
Published: September 2022

AbstractMultimorbidity is considered one of the greatest emerging challenges for contemporary health care systems. However, the meaning of the term ‘multimorbidity’ is not straightforward. Despite many attempts to clarify the definition and its measurement, the concept remains elusive. Still, academic interest in the study of multimorbidity has grown exponentially in the past ten years. In this paper, we trace the emergence of multimorbidity as a ‘matter of concern’ within health care research, exploring what has been called ‘the multimorbidity epidemic’ in the context of changing disease categories. We analyse how multimorbidity as a concept lays bare some major unresolved challenges within contemporary care services and summons up traditional primary care ideals of holistic, person-centred care. However, we argue that the current focus on the measurement and the identification of disease clusters falls short in contributing to better care for people who live with multiple long-term conditions now. Instead, we propose a novel understanding of ‘multimorbidity’ as an experience that manifests through people’s navigations of care infrastructures. To study this experience of multimorbidity, we discuss the potential of social science approaches that focus on ‘living well’ with illness.
The importance of institutional quality: Reviewing the relevance of Indonesia’s Omnibus Law on national competitiveness
Authors: Jazuli Muhamad Rosyid , Idris Maimanah Mohammed , Yaguma Penlope 
Published: September 2022

AbstractInstitutional quality is significantly relevant in determining national competitiveness when reflected in economic growth and successful development. Quality institutions manifest in robust ‘rules of the game’ reflected by sound governance and policymaking in at least four streams: social, economic, administrative, and political. Policy reforms intended to improve national competitiveness should aim to strengthen the streams simultaneously, whereas partial reforms could instead lead to poorer outcomes. Through the lens of institutional quality analysis, this paper critically reviews the relevance of Indonesia’s Job Creation Law, colloquially known as the Omnibus Law, to improve the country’s national competitiveness as conceptually intended. Declared as an overarching regulatory framework, the Law postulates Indonesia as prospecting for increased foreign investments. However, the Law is a partial policy reform as it overlooks the pivotal aspects necessary to improve institutional quality in Indonesia, such as inter-community relations, intellectual property regime certainty, quality vocational education, and meritocratic political recruitment. Thus, regardless of the opportunities, the Law may bring about, it may weaken national competitiveness instead. The Law is currently ruled conditionally unconstitutional by Indonesia’s Constitutional Court. If it is to be defended by the government, however, further multi-sectoral collaboration is necessary for the future implementation of the Law to enhance Indonesia’s institutional quality. In a more global context, this review indicates how developing nations should be mindful of various non-economic aspects (e.g., cultural and educational levels of the population) when undertaking policy reforms especially to enhance their national competitiveness. There may be future downsides to these implications and as it is too early to critically assess them, there is an opportunity through time and future research to do so.
Key comparison BIPM.RI(I)-K1 of the air-kerma standards of the NMIJ, Japan and the BIPM in 60Co gamma radiation
Authors: Kessler C , Burns D , Kato M , Kurosawa T 
Published: September 2022

Main text
A new key comparison of the standards for air kerma of the National Metrology Institute of Japan (NMIJ), Japan, and the Bureau International des Poids et Mesures (BIPM) was carried out in the 60Co radiation beam of the BIPM in February 2022. The comparison result, based on the calibration coefficients for two transfer chambers and expressed as a ratio of the NMIJ and the BIPM standards for air kerma, is 1.0013 with a combined standard uncertainty of 2.2 parts in 103. The result agrees within the uncertainties with the direct comparison carried out in 2012. The results are analysed and presented in terms of degrees of equivalence, suitable for entry in the BIPM key comparison database.
To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.
The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Linking research of biomedical datasets
Authors: Zhao Xiu-Ju George , Cao Hui 
Published: September 2022

AbstractBiomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.
A review and performance evaluation of clustering frameworks for single-cell Hi-C data
Authors: Zhen Caiwei , Wang Yuxian , Geng Jiaquan , Han Lu , Li Jingyi , Peng Jinghao , Wang Tao , Hao Jianye , Shang Xuequn , Wei Zhongyu , Zhu Peican , Peng Jiajie 
Published: September 2022

Abstract
The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes–Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.
How does the structure of data impact cell–cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data
Authors: Watson Ebony Rose , Mora Ariane , Taherian Fard Atefeh , Mar Jessica Cara 
Published: September 2022

Abstract
Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the ‘best-performing’ metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
Authors: Bonner Stephen , Barrett Ian P , Ye Cheng , Swiers Rowan , Engkvist Ola , Bender Andreas , Hoyt Charles Tapley , Hamilton William L 
Published: September 2022

AbstractDrug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene–disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.
DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network
Authors: He Binsheng , Wang Kun , Xiang Ju , Bing Pingping , Tang Min , Tian Geng , Guo Cheng , Xu Miao , Yang Jialiang 
Published: September 2022

Abstract
The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease–disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease–gene associations to connect the disease–disease network and gene–gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease–gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease–gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson’s disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene–disease associations were highly evidenced by independent experimental studies.