bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk assessment of transmission towers based on variable weight theory and matter-element extension model
Authors: Gao Haihang , Wang Minzhen , Wang Zhigang , Zhang Jiyao , Zheng Yu , Zhang Qi 
Published: October 2024

Abstract
The risk assessment of transmission towers is a critical approach to ensuring the safe and stable operation of power grids. In response to the increasingly frequent and severe impacts of seasonal permafrost on transmission towers in permafrost regions, this paper proposes a novel risk assessment method based on Variable Weight Theory and the Matter-Element Extension Model. By incorporating key influencing factors—such as the number of freeze–thaw cycles, soil moisture content, temperature fluctuations, soil dry density, melting settlement coefficient, and porosity ratio—an indicator system tailored to risk assessment in seasonal permafrost regions is established. Utilizing the Matter-Element Extension Model, this approach applies Variable Weight Theory to integrate both the best-worst method and the entropy weight method for calculating subjective and objective indicator weights. The risk level of the towers is then determined by their proximity to risk thresholds. Finally, a case study involving transmission towers in a specific region of a province in China is presented, where the assessment results are compared with the actual conditions to validate the rationality and effectiveness of the proposed method.
Cooperating with the Courts
Authors: Newbery Samantha 
Published: October 2024

Abstract
This chapter’s account of supergrasses and assisting offenders demonstrates that arrest is not always the end of a terrorist informer’s contribution to counter-terrorism. They can take on a new role after arrest by providing evidence that can help support court cases. Their provision of evidence through the supergrass system in use in Northern Ireland in the 1980s proved tricky, as many of the convictions obtained this way were later overturned. More recent attempts, in the form of ‘assisting offenders’ provided for by the Serious Organised Crime and Police Act (SOCPA) 2005, look more promising. The best-known examples of supergrasses are Raymond Gilmour of the IRA and INLA and Christopher Black of the IRA, both of whom are examined in this chapter, as is the best-known example of an assisting offender, Gary Haggarty of the UVF.
The Murder of Patrick Finucane
Authors: Newbery Samantha 
Published: October 2024

Abstract
Chapter 11 evaluates the range of types of investigation that have so far been held into the murder of solicitor Patrick Finucane and the involvement of informers and, therefore, the involvement of the security and intelligence agencies in that murder. This chapter adopts a chronological approach, analysing the role of intelligence relative to other factors that explain the decisions made regarding investigations. Although far from the only factor influencing decisions made by campaigners and government, the role of informers and the intelligence they provided runs throughout these decisions. The chapter addresses the murder investigation and the de Silva Review, as well as the views of the Finucane family, successive governments, and the courts, on whether there should be a public inquiry.
Behavioral and Psychosocial Dynamics in Diabetes Management: A Path Analysis to Examine the Influence of Acculturation in Arab Immigrant Communities
Authors: Alzubaidi Hamzah , Oliveira Vitor H , Saidawi Ward , Aljobowry Raya , Shaw Jonathan E , Samorinha Catarina 
Published: October 2024

Abstract

Background
Type 2 diabetes mellitus (T2DM) prevalence is rising globally. Arab immigrants with T2DM, one of the least studied ethnic minorities, have a higher diabetes prevalence and more prominent management-related challenges compared with mainstream host societies. Acculturation’s impact on self-care activities and diabetes distress (DD) is understudied globally.

Purpose
To examine how acculturation affects self-care practices and DD in first-generation Arab immigrants with T2DM and how health literacy, illness perceptions, and self-efficacy mediate these associations.

Methods
This multicenter cross-sectional study was conducted in Victoria, Australia. Eligible patients were invited while waiting for their appointments. The questionnaire included validated tools to assess DD, self-care activities, health literacy, self-efficacy, and acculturation. We tested mediation hypothesis using path analysis, with a maximum likelihood estimation to calculate total, direct, and indirect effects and bias-corrected accelerated 95% CI.

Results
Overall, 382 Arab immigrants with T2DM participated, with an average age of 57.9 years (SD = 8.0). Half were males, most had low education, and were married. Participants lived in Australia for a mean of 19.1 years (SD = 8.3) and had diabetes for 7.1 years (SD =4.7). Higher acculturation was directly associated with decreased DD. Illness perceptions (p = .002) and self-efficacy (p = .001) mediated the association of acculturation with self-care activities, while health literacy did not. Additionally, self-efficacy fully mediated the relationship between acculturation and DD (p = .001).

Conclusions
This research provided valuable insights into the complex interplay between acculturation, diabetes management, and psychosocial factors. Interventions targeting self-efficacy and illness perceptions may improve self-care activities and reduce DD among Arab immigrants with T2DM.
Halophytic succulence is a driver of the leaf non-structural carbohydrate contents in plants in the arid and hyper-arid deserts of northwestern China
Authors: Wang Lilong , Li Yuqiang , Wang Xuyang , Duan Yulong , Zheng Chengzhuo 
Published: October 2024

Abstract

Background and Aims
Non-structural carbohydrates (NSC), primarily sugars and starch, play a crucial role in plant metabolic processes and the ability of a plant to tolerate and recover from drought stress. Despite their importance, our understanding of NSC characteristics in the leaves of plants that thrive in hyper-arid and saline environments remains limited.

Methods
To investigate the variations in leaf NSC across different species and spatial scales and to explore their possible causes, we collected 488 leaf samples from 49 native plant species at 115 sites in the desert area of northwestern China. The contents of soluble sugars (SS), starch and total NSC were then determined.

Key Results
The average contents of SS, starch and total NSC were 26.99, 60.28 and 87.27 mg g−1, respectively, which are much lower than those reported for Chinese forest plants and global terrestrial plants. Herbaceous and woody plants had similar NSC levels. In contrast, succulent halophytes, a key component of desert flora, showed significantly lower leaf SS and total NSC contents than non-succulent plants. We observed a strong negative correlation between leaf succulence and SS content, suggesting a role of halophytic succulence in driving multispecies NSC pools. Environmental factors explained a minor portion of the spatial variation in leaf NSC, possibly owing to the narrow climatic variation in the study area, and soil properties, particularly soil salinity, emerged as more significant contributors.

Conclusions
Our findings increase the understanding of plant adaptation to drought and salt stress, emphasizing the crucial role of halophytic succulence in shaping the intricate dynamics of leaf NSC across diverse plant species in arid and hyper-arid environments.
Ecology, Pathology, and the Life of Paper
Authors: Gagné John 
Published: October 2024

Abstract
This essay charts the way that ideas of nature, life and disease generated new approaches to Europe’s documentary culture between roughly 1650 and 1950. Training its focus on the medium of paper, specifically the essay, reveals conceptual intersections between caretakers of libraries and archives and naturalists or physicians whose focus was the entomology or bacteriology. The recognition of the fundamentally organic nature of books (animal, vegetable, mineral, in a Linnaean sense) invited fresh vocabularies and practices of care and preservation. The essay first explores how book lovers—often encouraged by learned societies or state agencies in the eighteenth century—campaigned against insects whose habitat lay between the covers of precious tomes. It then traces how nineteenth-century microscopy and bacteriology informed notions of sickness or ‘pathology’ in books. It ends by describing the efforts to remediate these impairments in the twentieth century: to deploy chemistry and medicine as therapies for bringing imperilled papers back to health.
MetaDegron: multimodal feature-integrated protein language model for predicting E3 ligase targeted degrons
Authors: Zheng Mengqiu , Lin Shaofeng , Chen Kunqi , Hu Ruifeng , Wang Liming , Zhao Zhongming , Xu Haodong 
Published: October 2024

Abstract
Protein degradation through the ubiquitin proteasome system at the spatial and temporal regulation is essential for many cellular processes. E3 ligases and degradation signals (degrons), the sequences they recognize in the target proteins, are key parts of the ubiquitin-mediated proteolysis, and their interactions determine the degradation specificity and maintain cellular homeostasis. To date, only a limited number of targeted degron instances have been identified, and their properties are not yet fully characterized. To tackle on this challenge, here we develop a novel deep-learning framework, namely MetaDegron, for predicting E3 ligase targeted degron by integrating the protein language model and comprehensive featurization strategies. Through extensive evaluations using benchmark datasets and comparison with existing method, such as Degpred, we demonstrate the superior performance of MetaDegron. Among functional features, MetaDegron allows batch prediction of targeted degrons of 21 E3 ligases, and provides functional annotations and visualization of multiple degron-related structural and physicochemical features. MetaDegron is freely available at http://modinfor.com/MetaDegron/. We anticipate that MetaDegron will serve as a useful tool for the clinical and translational community to elucidate the mechanisms of regulation of protein homeostasis, cancer research, and drug development.
Biologically-informed killer cell immunoglobulin-like receptor gene annotation tool
Authors: Ford Michael K B , Hari Ananth , Zhou Qinghui , Numanagić Ibrahim , Sahinalp S Cenk 
Published: October 2024

Abstract

Summary
Natural killer (NK) cells are essential components of the innate immune system, with their activity significantly regulated by Killer cell Immunoglobulin-like Receptors (KIRs). The diversity and structural complexity of KIR genes present significant challenges for accurate genotyping, essential for understanding NK cell functions and their implications in health and disease. Traditional genotyping methods struggle with the variable nature of KIR genes, leading to inaccuracies that can impede immunogenetic research. These challenges extend to high-quality phased assemblies, which have been recently popularized by the Human Pangenome Consortium. This article introduces BAKIR (Biologically informed Annotator for KIR locus), a tailored computational tool designed to overcome the challenges of KIR genotyping and annotation on high-quality, phased genome assemblies. BAKIR aims to enhance the accuracy of KIR gene annotations by structuring its annotation pipeline around identifying key functional mutations, thereby improving the identification and subsequent relevance of gene and allele calls. It uses a multi-stage mapping, alignment, and variant calling process to ensure high-precision gene and allele identification, while also maintaining high recall for sequences that are significantly mutated or truncated relative to the known allele database. BAKIR has been evaluated on a subset of the HPRC assemblies, where BAKIR was able to improve many of the associated annotations and call novel variants. BAKIR is freely available on GitHub, offering ease of access and use through multiple installation methods, including pip, conda, and singularity container, and is equipped with a user-friendly command-line interface, thereby promoting its adoption in the scientific community.

Availability and implementation
BAKIR is available at github.com/algo-cancer/bakir
OpenDock: a pytorch-based open-source framework for protein–ligand docking and modelling
Authors: Hu Qiuyue , Wang Zechen , Meng Jintao , Li Weifeng , Guo Jingjing , Mu Yuguang , Wang Sheng , Zheng Liangzhen , Wei Yanjie 
Published: October 2024

Abstract

Motivation
Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward.

Results
To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein–ligand modelling tasks.

Availability and implementation
OpenDock is publicly available at: https://github.com/guyuehuo/opendock.
BIMSA: accelerating long sequence alignment using processing-in-memory
Authors: Alonso-Marín Alejandro , Fernandez Ivan , Aguado-Puig Quim , Gómez-Luna Juan , Marco-Sola Santiago , Mutlu Onur , Moreto Miquel 
Published: October 2024

Abstract

Motivation
Recent advances in sequencing technologies have stressed the critical role of sequence analysis algorithms and tools in genomics and healthcare research. In particular, sequence alignment is a fundamental building block in many sequence analysis pipelines and is frequently a performance bottleneck both in terms of execution time and memory usage. Classical sequence alignment algorithms are based on dynamic programming and often require quadratic time and memory with respect to the sequence length. As a result, classical sequence alignment algorithms fail to scale with increasing sequence lengths and quickly become memory-bound due to data-movement penalties.

Results
Processing-In-Memory (PIM) is an emerging architectural paradigm that seeks to accelerate memory-bound algorithms by bringing computation closer to the data to mitigate data-movement penalties. This work presents BIMSA (Bidirectional In-Memory Sequence Alignment), a PIM design and implementation for the state-of-the-art sequence alignment algorithm BiWFA (Bidirectional Wavefront Alignment), incorporating new hardware-aware optimizations for a production-ready PIM architecture (UPMEM). BIMSA supports aligning sequences up to 100K bases, exceeding the limitations of state-of-the-art PIM implementations. First, BIMSA achieves speedups up to 22.24× (11.95× on average) compared to state-of-the-art PIM-enabled implementations of sequence alignment algorithms. Second, achieves speedups up to 5.84× (2.83× on average) compared to the highest-performance multicore CPU implementation of BiWFA. Third, BIMSA exhibits linear scalability with the number of compute units in memory, enabling further performance improvements with upcoming PIM architectures equipped with more compute units and achieving speedups up to 9.56× (4.7× on average).

Availability and implementation
Code and documentation are publicly available at https://github.com/AlejandroAMarin/BIMSA.
PONYTA: prioritization of phenotype-related genes from mouse KO events using PU learning on a biological network
Authors: Kim Jun Hyeong , Koo Bonil , Kim Sun 
Published: October 2024

Abstract

Motivation
Transcriptome data from gene knock-out (KO) experiments in mice provide crucial insights into the intricate interactions between genotype and phenotype. Differentially expressed gene (DEG) analysis and network propagation (NP) are well-established methods for analysing transcriptome data. To determine genes related to phenotype changes from a KO experiment, we need to choose a cutoff value for the corresponding criterion based on the specific method. Using a rigorous cutoff value for DEG analysis and NP is likely to select mostly positive genes related to the phenotype, but many will be rejected as false negatives. On the other hand, using a loose cutoff value for either method is prone to include a number of genes that are not phenotype-related, which are false positives. Thus, the research problem at hand is how to deal with the trade-off between false negatives and false positives.

Results
We propose a novel framework called PONYTA for gene prioritization via positive-unlabeled (PU) learning on biological networks. Beginning with the selection of true phenotype-related genes using a rigorous cutoff value for DEG analysis and NP, we address the issue of handling false negatives by rescuing them through PU learning. Evaluations on transcriptome data from multiple studies show that our approach has superior gene prioritization ability compared to benchmark models. Therefore, PONYTA effectively prioritizes genes related to phenotypes derived from gene KO events and guides in vitro and in vivo gene KO experiments for increased efficiency.

Availability and implementation
The source code of PONYTA is available at https://github.com/Jun-Hyeong-Kim/PONYTA.
Heterogeneity-aware integrative regression for ancestry-specific association studies
Authors: Molstad Aaron J , Cai Yanwei , Reiner Alexander P , Kooperberg Charles , Sun Wei , Hsu Li 
Published: October 2024

ABSTRACT
Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.
Functional generalized canonical correlation analysis for studying multiple longitudinal variables
Authors: Sort Lucas , Le Brusquet Laurent , Tenenhaus Arthur 
Published: October 2024

ABSTRACT
In this paper, we introduce functional generalized canonical correlation analysis, a new framework for exploring associations between multiple random processes observed jointly. The framework is based on the multiblock regularized generalized canonical correlation analysis framework. It is robust to sparsely and irregularly observed data, making it applicable in many settings. We establish the monotonic property of the solving procedure and introduce a Bayesian approach for estimating canonical components. We propose an extension of the framework that allows the integration of a univariate or multivariate response into the analysis, paving the way for predictive applications. We evaluate the method’s efficiency in simulation studies and present a use case on a longitudinal dataset.
Case-crossover designs and overdispersion with application to air pollution epidemiology
Authors: Perreault Samuel , Dong Gracia Y , Stringer Alex , Shin Hwashin , Brown Patrick E 
Published: October 2024

ABSTRACT
Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditional logistic model for the responses, usually health outcomes, conditional on the exposures. Despite the fact that conditional logistic models have been shown equivalent, in typical air pollution epidemiology setups, to specific instances of the well-known Poisson time series model, it is often claimed that they cannot allow for overdispersion. This paper clarifies the relationship between case-crossover designs, the models that ensue from their use, and overdispersion. In particular, we propose to relax the assumption of independence between individuals traditionally made in case-crossover analyses, in order to explicitly introduce overdispersion in the conditional logistic model. As we show, the resulting overdispersed conditional logistic model coincides with the overdispersed, conditional Poisson model, in the sense that their likelihoods are simple re-expressions of one another. We further provide the technical details of a Bayesian implementation of the proposed case-crossover model, which we use to demonstrate, by means of a large simulation study, that standard case-crossover models can lead to dramatically underestimated coverage probabilities, while the proposed models do not. We also perform an illustrative analysis of the association between air pollution and morbidity in Toronto, Canada, which shows that the proposed models are more robust than standard ones to outliers such as those associated with public holidays.
Recovering nested structures in networks: an evaluation of hierarchical clustering techniques
Authors: Gera Imre , London András 
Published: October 2024

Abstract
In this article, we present various algorithms to partition the nodes of a network into groups that show the property of nestedness. Since perfect nestedness is a rare phenomenon, we consider the task from a data mining perspective, and we search for groups having high-level of nestedness. We utilize both agglomerative and divisive hierarchical clustering procedures and compare them on several benchmark and real-life networks. Furthermore, we propose different metrics derived from the results of our algorithms. We show that average-linkage and complete-linkage clustering can recover the largest fully nested clusters, and that the cluster size-weighted mean nestedness was a more stable metric for measuring clustering performance. Our proposed algorithms allow us to create multiple resolution views of nestedness-based clustering of networks, extending the field of graph-based data mining.