bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Success Rate Is Lower but Completion Rate of Laparoscopic Cholecystectomy Is higher in Endoscopic Transpapillary Gallbladder Drainage than Percutaneous Gallbladder Drainage for Acute Cholecystitis
Authors: Masuda Toshiro , Takamori Hiroshi , Ogata Ken-ichi , Ogawa Katsuhiro , Shimizu Kenji , Karashima Ryuichi , Nitta Hidetoshi , Matsumoto Katsutaka , Okino Tetsuya , Baba Hideo 
Published: June 2024

Background:
We investigated the success and complication rates of endoscopic transpapillary gallbladder drainage (ETGBD) and percutaneous transhepatic gallbladder drainage (PTGBD) and the outcomes of subsequent cholecystectomy for acute cholecystitis.

Methods:
Patients (N=178) who underwent cholecystectomy after ETGBD or PTGBD were retrospectively assessed.

Results:
ETGBD was successful in 47 (85.5%) of 55 procedures, whereas PTGBD was successful in 123 (100%) of 123 sessions (P<0.001). Complications related to ETGBD and PTGBD occurred in 6 (12.8%) of 47 and 16 (13.0%) of 123 patients, respectively (P=0.97). After propensity matching, 43 patients from each group were selected. Median time from drainage to cholecystectomy was 48 (14 to 560) days with ETGBD and 35 (1 to 90) days with PTGBD (P=0.004). Laparoscopy was selected more often in the ETGBD group (97.7%) than in the PTGBD group (79.1%) (P=0.007), and conversion from laparoscopy to open cholecystectomy was more common with PTGBD (41.2%) than with ETGBD (7.1%) (P<0.001). Mean operation time was significantly shorter with ETGBD (135.8±66.7 min) than with PTGBD (195.8±62.2 min) (P<0.001). The incidence of Clavien-Dindo grade ≥III postoperative complications was 9.3% with ETGBD and 11.6% with PTGBD (P=0.99).

Conclusions:
The success rate is lower but completion of laparoscopic cholecystectomy is more in endoscopic gallbladder drainage than percutaneous gallbladder drainage for acute cholecystitis.
Semantic computing for human phenotypes
Authors: Gentleman Robert , Goncalves Rafael , Carey Vincent 
Published: June 2024

AbstractIn many fields, research progress may be hindered by indefiniteness of language used to describe experimental conditions and outcomes. Harmonization of data resources generated by independent groups is important for integrative analysis. Adoption of formal ontologies and vocabularies for experiment annotation should help with harmonization tasks, but the use of ontologies also suffers from a lack of definiteness. In this study we explore how natural language characterization of human diseases coupled with ontologic mapping of study outcome terminology can be used to integrate information from multiple studies of genetic origins of disease risk. Open source tools and workflows are presented. This work exposes areas for improvement in tooling for data harmonization, which is a fundamental requirement for efficient research progress.
BEACON: Benchmark for Comprehensive RNA Tasks and Language Models
Authors: Ren Yuchen , Chen Zhiyuan , Qiao Lifeng , Jing Hongtai , Cai Yuchen , Xu Sheng , Ye Peng , Ma Xinzhu , Sun Siqi , Yan Hongliang , Yuan Dong , Ouyang Wanli , Liu Xihui 
Published: June 2024

AbstractRNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms. Despite the emergence of numerous deep learning approaches for RNA, particularly universal RNA language models, there remains a significant lack of standardized benchmarks to assess the effectiveness of these methods. In this study, we introduce the first comprehensive RNA benchmark BEACON (BEnchmArk forCOmprehensive RNA Task and Language Models). First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications, enabling a comprehensive assessment of the performance of methods on various RNA understanding tasks. Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models. Third, we investigate the vital RNA language model components from the tokenizer and positional encoding aspects. Notably, our findings emphasize the superiority of single nucleotide tokenization and the effectiveness of Attention with Linear Biases (ALiBi) over traditional positional encoding methods. Based on these insights, a simple yet strong baseline called BEACON-B is proposed, which can achieve outstanding performance with limited data and computational resources. The datasets and source code of our benchmark are available athttps://github.com/terry-r123/RNABenchmark.
Tube2FEM: a general-purpose highly-automated pipeline for flow related processes in (embedded) tubular objects
Authors: Sleimana Hani Cheikh , Moerman Kevin M. , de Oliveira Diana C. , Jacob Joseph , Mogulkoc Nesrin , Davidson Brian R. , Walker-Samuel Simon , Shipley Rebecca J. 
Published: June 2024

AbstractThis paper presents a comprehensive and highly-automated open-source pipeline for simulating flow and flow-related processes in (embedded) tubular structures. Addressing a critical gap in computational fluid dynamics (CFD) and simulation sciences, it facilitates the transition from raw three-dimensional imaging, graph networks, or CAD models of tubular objects to refined, simulation-ready meshes. This transition, traditionally labor-intensive and challenging, is streamlined and highly-automated through a series of innovative steps that include surface mesh processing, centre-line construction, anisotropic mesh generation, and volumetric meshing, leading to Finite Element Method (FEM) simulations. The pipeline leverages a range of open-source software and libraries, notablyGIBBON,FEniCS, andParaview, to provide flexibility and broad applicability across different simulation scenarios, ranging from biomedical to industrial applications. We demonstrate the versatility of our approach through five distinct applications, including the mesh generation for soil-root systems, lung airways, microcirculation networks, and portal vein networks, each originating from a different data source. Moreover, for several of these cases, we incorporate Computational Fluid Dynamics (CFD) simulations and strategies for 3D-1D coupling between the embedding domain and the embedded structures. Finally, we outline some future perspectives aimed at enhancing accuracy, reducing computational time, and incorporating advanced modeling and boundary condition strategies to further refine the framework’s capabilities.
The Negative Effects of Smoking Cigarettes on Morphological and Histological Damages to 18 and 21 Days Mouse Embryo Liver
Authors: Peyrovi Zahra , Azizi Soheil , Hosseini Seyed Mohammad 
Published: June 2024

AbstractSmoking cigarettes can lead to morphological and histological changes in several human and animal tissues. This paper describes the effects of CS exposure on the liver, weight, and length of mouse embryos. 42 NMRI adult mice were used for mating. Pregnant female mice were categorized into 3 groups: F (filtered), NF (non-filtered), and C (controlled). Each group included 14 female mice exposed to 12 cigarettes daily; 7 mice for 18 days and 7 mice for 21 days. In addition, only the group C were exposed to ambient air. Seven mouse embryos from each group were euthanized. Their livers were fixed for histological processing. The Livers of groups F & NF as compared with group C revealed changes in cellular architecture, centrilobular veins, inflammatory cells, kupffer cells, cytoplasmic vacuolation, hepatocytes necrosis, and decreased parenchymal cellularity. The average weight of embryo, liver, and CR in F & NF significantly reduced (p<0.05 and p<0.01).
Decoding dynamic visual scenes across the brain hierarchy
Authors: Chen Ye , Beech Peter , Yin Ziwei , Jia Shanshan , Zhang Jiayi , Yu Zhaofei , Liu Jian K. 
Published: June 2024

AbstractUnderstanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a paramount investigation within the domain of neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding dataset and utilize the capabilities of deep learning neural network models to study the question of neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. We find that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within both the visual cortex and subcortical nuclei, in contrast to a relatively diminished encoding activity within hippocampal neurons. Strikingly, our results reveal a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings not only corroborate existing knowledge in visual coding using artificial visual stimuli but illuminate the functional role of these deeper brain regions using dynamic natural scenes. Consequently, our results proffer a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding of dynamic natural visual scenes, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.
Compartmental structure in the secondary lymphoid tissue can slow downin vivoHIV-1 evolution in the presence of strong CTL responses
Authors: Chung Wen-Jian , Wodarz Dominik 
Published: June 2024

AbstractHuman immunodeficiency virus (HIV-1) replicates in the secondary lymphoid tissues, which are characterized by complex compartmental structures. While Cytotoxic T lymphocytes (CTL) readily access infected cells in the extrafollicular compartments, they do not home to follicular compartments, which thus represent an immune-privileged site. Using mathematical models, we have previously shown that this compartmental tissue structure can delay the emergence of CTL escape mutants. Here, we show computationally that the compartmental structure can have an impact on the evolution of advantageous mutants that are not related to CTL recognition: (i) Compartmental structure can influence the fixation probability of an advantageous mutant, with weakened selection occurring if CTL responses are of intermediate strength. (ii) Compartmental structure is predicted to reduce the rate mutant generation, which becomes more pronounced for stronger CTL responses. (iii) Compartmental structure is predicted to slow down the overall rate of mutant invasion, with the effect becoming more pronounced for stronger CTL responses. Altogether, this work shows thatin vivovirus evolution proceeds slower in models with compartmental structure compared to models that assume equivalent virus load in the absence of compartmental structure, especially for strong CTL-mediated virus control. This has implications for understanding the rate of disease progression.
EnVhog: an extended view of the viral protein families on Earth through a vast collection of HMM profiles
Authors: Rubén Pérez-Bucio , Enault François , Galiez Clovis 
Published: June 2024

AbstractOver the last twenty years, hundreds of metagenomic studies have generated millions of viral genomic sequences from a wide variety of ecosystems. Despite this, the overall genetic diversity of viruses remains elusive, both in terms of the number of protein families they encode and the diversity of these families. Indeed, even if it is recognized that the organization of the viral protein sequence space requires sensitive homology detection methods, such methods have never been applied at a large scale. To produce a more realistic and comprehensive view of the protein diversity in the viral world, we have (i) collected thousands of viromes and identified viral contigs and proteins within them, (ii) retrieved viral proteins available in different public databases, and (iii) applied sensitive similarity searches to cluster all these proteins into families and (iv) annotate the protein clusters produced. More than 46 million deduplicated proteins were clustered into less than 2.3 million protein families. After further removing genomic sequences likely of cellular origin using an iterative procedure, the remaining 2,203,457 clusters were coined enVhogs (for environmental Viral homologous groups). Their multiple sequence alignments have been transformed into HMMs to constitute the EnVhog database. Even if only a small proportion of enVhogs were annotated (15.9 %), they encompass almost half of the protein dataset (44.8 %). Applied to the annotation of four recently published viromes from diverse environments (sulfuric soil, grassland, surface seawater and human gut), enVhog HMMs doubled the number of viral sequences characterized, and increased by 54%-74% the number of proteins functionally annotated. EnVhog, the largest comprehensive compilation of viral protein information to date, is a resource that will thus further help to determine the functions of proteins encoded in newly sequenced viral genomes, and help to improve the accuracy of viral sequence detection tools.EnVhog database is available athttp://envhog.u-ga.fr/envhog.
CelEsT: a unified gene regulatory network for estimating transcription factor activities inC. elegans
Authors: Perez Marcos Francisco 
Published: June 2024

ABSTRACTTranscription factors (TFs) play a pivotal role in orchestrating the intricate patterns of gene regulation critical for development and health. Although gene expression is complex, differential expression of many genes is often due to regulation by just a handful of TFs. Despite extensive efforts to elucidate TF-target regulatory relationships inC. elegans, existing experimental datasets cover distinct subsets of TFs and leave data integration challenging.Here I introduceCelEsT, a unified gene regulatory network (GRN) designed to estimate the activity of 487 distinctC. elegansTFs - ∼58% of the total - from gene expression data. To integrate data from ChIP-seq, DNA-binding motifs, and eY1H screens, different GRNs were benchmarked against a comprehensive set of TF perturbation RNA-seq experiments and identified optimal processing of each data type. Moreover, I showcase how leveraging conservation of TF binding motifs in the promoters of candidate target orthologues across genomes of closely-related species can distil targets into a select set of highly informative interactions, a strategy which can be applied to many model organisms. Combined analyses of multiple datasets from commonly-studied conditions including heat shock, bacterial infection and male-vs-female comparison validatesCelEsT’s performance and highlights previously overlooked TFs that likely play major roles in co-ordinating the transcriptional response to these conditions.CelEsT can be used to infer TF activity on a standard laptop computer within minutes. Furthermore, anR Shinyapp is provided for the community to perform rapid analysis with minimal coding experience required. I anticipate that widespread adoption ofCelEsT will significantly enhance the interpretive power of transcriptomic experiments, both present and retrospective, thereby advancing our understanding of gene regulation inC. elegansand beyond.
A Cry For Kelp: Evidence for Polyphenolic Inhibition of Oxford Nanopore Sequencing of Brown Algae
Authors: Pearman William S. , Arranz Vanessa , Carvajal Jose I. , Whibley Annabel , Liau Yusmiati , Johnson Katherine , Gray Rachel , Treece Jackson M. , Gemmell Neil J. , Liggins Libby , Fraser Ceridwen I. , Jensen Evelyn L. , Green Nicholas J. 
Published: June 2024

AbstractGenomic resources for macroalgae are increasingly important for conservation and commercial management, however the generation of such resources continues to be hampered by difficulties in the isolation of suitable DNA. Even when DNA has been isolated that otherwise appears high quality, such samples may not perform well during the sequencing process. We here compare Oxford Nanopore long-read sequencing results for three species of macroalgae to those of non-macroalgal species and find that macroalgal samples tend to lead to rapid decline in the number of available sequencing pores resulting in reduced sequencing yield. LC-MS analysis of macroalgal DNA that would be considered suitable for sequencing reveals that DNA derived from dried macroalgae is enriched for polyphenol-DNA adducts – which may lead to sequencing inhibition. Our findings have wide-ranging implications for the generation of genomic resources from macroalgae, for example long read sequencing of dried herbarium specimens, and suggest a need to use fresh tissue wherever possible for genome sequencing.
A causal framework for the drivers of animal social network structure
Authors: Kawam Ben , Ostner Julia , McElreath Richard , Schülke Oliver , Redhead Daniel 
Published: June 2024

AbstractA major goal of behavioural ecology is to explain how phenotypic and ecological factors shape the networks of social relationships that animals form with one another. This inferential task is notoriously challenging. The social networks of interest are generally not observed, but must be approximated from behavioural samples. Moreover, these data are highly dependent: the observed network edges correlate with one another, due to biological and sampling processes. Failing to account for the resulting uncertainty and biases can lead to dysfunctional statistical procedures, and thus to incorrect results. Here, we argue that these problems should be understood—and addressed—as problems of causal inference. For this purpose, we introduce a Bayesian causal modelling framework that explicitly defines the links between the target interaction network, its causes, and the data. We illustrate the mechanics of our framework with simulation studies and an empirical example. First, we encode causal effects of individual-, dyad-, and group-level features on social interactions using Directed Acyclic Graphs and Structural Causal Models. These quantities are the objects of inquiry, ourestimands. Second, we developestimatorsfor these effects—namely, Bayesian multilevel extensions of the Social Relations Model. Third, we recover the structural parameters of interest, map statistical estimates to the underlying causal structures, and compute causalestimatesfrom the joint posterior distribution. Throughout the manuscript, we develop models layer by layer, thereby illustrating an iterative workflow for causal inference in social networks. We conclude by summarising this workflow as a set of seven steps, and provide practical recommendations.
Biofilm formation inStreptococcus suis:In vitroimpact of serovar and assessment of coinfections with other porcine respiratory disease complex bacterial pathogens
Authors: Miguélez-Pérez Rubén , Mencía-Ares Oscar , Gutiérrez-Martín César B. , González-Fernández Alba , Petrocchi-Rilo Máximo , Delgado-García Mario , Martínez-Martínez Sonia 
Published: June 2024

AbstractStreptococcus suisis a worldwide pathogen that impacts swine industry, causing severe clinical signs in postweaning piglets, including meningitis and arthritis. Biofilm formation is a major virulence mechanism inS. suis, enhancing its persistence and resistance. Here, we assessed thein vitrobiofilm formation of 240S. suisisolates from Spanish swine farms and evaluated the effects of serovars (SVs) and coinfections with other porcine respiratory disease complex (PRDC) pathogens. Our study revealed significant heterogeneity in biofilm formation amongS. suisSVs. Notably, SV2 exhibited the lowest biofilm formation, contrasting with the high biofilm-forming capacities of SV1, SV7, and SV9. Virulence factorsepf,mrp, andslywere associated (p< 0.05) with reduced biofilm formation. Other PRDC pathogens, includingActinobacillus pleuropneumoniae,Glaesserella parasuis, andPasteurella multocida, formed biofilms, though generally less robust than those ofS. suis(except for SV2), contrasting the high biofilm formation ofStaphylococcus hyicus. Coinfections demonstrated enhanced biofilm formation in mixed cultures ofS. suis, particularly withP. multocida. Other coinfections revealed variable results in pathogen interactions, suggesting the potential of biofilms for increased persistence and pathogenicity in coinfections. In conclusion, this study underscores the importance of serovar-specific differences in biofilm formation amongS. suisisolates, with significant implications for pathogenicity and persistence. The heterogeneous biofilm formation observed in coinfections with other PRDC pathogens reveals a complex interplay that could exacerbate disease severity. These findings provide a foundation for further research on biofilm mechanisms to mitigate the impact of PRDC in the swine industry.
Discovery of a Heparan Sulfate Binding Domain in Monkeypox Virus H3 as an Anti-poxviral Drug Target Combining AI and MD Simulations
Authors: Zheng Bin , Duan Meimei , Huang Yifen , Wang Shangchen , Qiu Jun , Lu Zhuojian , Liu Lichao , Tang Guojin , Cheng Lin , Zheng Peng 
Published: June 2024

AbstractViral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. This conserved domain, found acrossorthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infectionin vitroand viral replicationin vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.