bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Role of Verteporfin in Prevention of Periprosthetic Capsular Fibrosis: An Experimental Study
Authors: Yi Zhen , Zeng Jinshi , Chen Zixiang , Chen Lulu , Lu Hai-bin , Zhang Qi , Yang Xiaonan , Qi Zuoliang 
Published: April 2022

Abstract

Background
Capsular contracture (CC) characterized by excessive fibrosis is one of the most common complications after silicone implant surgery. Verteporfin (VP), an inhibitor of Yes-associated protein 1 (YAP1), has recently been found to reduce the fibrotic process.

Objectives
The aim of this study was to use an in vivo rabbit model to evaluate the efficacy of VP for the prevention of CC.

Methods
Twenty-four New Zealand rabbits received 10-cc smooth saline silicone implants inserted in the dorsal skin and were randomly divided into 2 groups to receive 2 mL VP (1.5 mg/mL) or 2 mL phosphate-buffered saline solution instillation in the implant pocket. When the animals were killed on Day 60, capsule formation was observed both macroscopically and microscopically. Histologic evaluation included capsule thickness, fibrosis degree, and myofibroblast (α smooth muscle actin positive) content. In addition, the YAP1 expression level was examined by immunofluorescence staining. Transforming growth factor β1, collagen I, and connective tissue growth factor expression were measured by real-time quantitative polymerase chain reaction.

Results
The VP-treated group exhibited thinner, more transparent capsules and less fibrosis than the control group at 60 days postsurgery (P &lt; 0.05). Moreover, the VP treatment significantly reduced α smooth muscle actin, YAP1, transforming growth factor β1, collagen I, and connective tissue growth factor expression levels in the capsular tissues (P &lt; 0.05).

Conclusions
VP reduced capsule formation after silicone implantation by inhibiting YAP1-mediated mechanical signaling, thereby attenuating excessive collagen deposition in the rabbit model. This preclinical study may provide a feasible strategy to prevent periprosthetic capsular fibrosis in clinical application.
The role of behavioral type composition on resource use and growth of a juvenile predator
Authors: Nannini Michael A , Parkos Joseph J , Wahl David 
Published: April 2022

Abstract
Juvenile largemouth bass have distinct behavioral types that separate along the exploring behavioral axis and differ in diet. We used a mesocosm experiment to test the hypothesis that groups composed of mixed behavioral types would have more efficient use of prey resources and reduced competition between individuals than experimental populations composed of similar behavioral types. Fish growth, diets, and prey composition were quantified over a 4-week period in mesocosms containing prey communities that were mixtures of zooplankton from the pond water used to fill them and natural colonization by terrestrial insects. Mesocosms contained juvenile largemouth bass of either all fast exploratory-type, all slow exploratory-type, or a 50/50 mix of the two behavioral types. Treatments with similar behavioral types had lower growth than treatments with mixed behavioral types. While evidence showed that slow explorers in homogeneous groups consumed fewer macroinvertebrates outside of refuge habitat, the same was not true of fast explorers. Results suggest that populations composed of different compositions of behavioral types may also differ in their food web interactions. Potential alternative explanations for the difference in growth between mixed versus homogeneous communities include higher activity, increased antagonistic interactions and the role of social cues that might signal when it is safe to begin foraging after exposure to a predator. The differences in growth suggest that individual performance can be higher in populations with a balanced mixture of behavioral types compared to more homogenous populations and adds to the growing knowledge that individual behavioral traits can have emergent population-level effects.
A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data
Authors: Huang Le , Yang Yuchen , Li Gang , Jiang Minzhi , Wen Jia , Abnousi Armen , Rosen Jonathan D , Hu Ming , Li Yun 
Published: April 2022

Abstract
The three-dimensional organization of chromatin plays a critical role in gene regulation. Recently developed technologies, such as HiChIP and proximity ligation-assisted ChIP-Seq (PLAC-seq) (hereafter referred to as HP for brevity), can measure chromosome spatial organization by interrogating chromatin interactions mediated by a protein of interest. While offering cost-efficiency over genome-wide unbiased high-throughput chromosome conformation capture (Hi-C) data, HP data remain sparse at kilobase (Kb) resolution with the current sequencing depth in the order of 108 reads per sample. Deep learning models, including HiCPlus, HiCNN, HiCNN2, DeepHiC and Variationally Encoded Hi-C Loss Enhancer (VEHiCLE), have been developed to enhance the sequencing depth of Hi-C data, but their performance on HP data has not been benchmarked. Here, we performed a comprehensive evaluation of HP data sequencing depth enhancement using models developed for Hi-C data. Specifically, we analyzed various HP data, including Smc1a HiChIP data of the human lymphoblastoid cell line GM12878, H3K4me3 PLAC-seq data of four human neural cell types as well as of mouse embryonic stem cells (mESC), and mESC CCCTC-binding factor (CTCF) PLAC-seq data. Our evaluations lead to the following three findings: (i) most models developed for Hi-C data achieve reasonable performance when applied to HP data (e.g. with Pearson correlation ranging 0.76–0.95 for pairs of loci within 300 Kb), and the enhanced datasets lead to improved statistical power for detecting long-range chromatin interactions, (ii) models trained on HP data outperform those trained on Hi-C data and (iii) most models are transferable across cell types. Our results provide a general guideline for HP data enhancement using existing methods designed for Hi-C data.
Synthetic-to-real: instance segmentation of clinical cluster cells with unlabeled synthetic training
Authors: Zhao Meng , Wang Siyu , Shi Fan , Jia Chen , Sun Xuguo , Chen Shengyong 
Published: April 2022

AbstractMotivationThe presence of tumor cell clusters in pleural effusion may be a signal of cancer metastasis. The instance segmentation of single cell from cell clusters plays a pivotal role in cluster cell analysis. However, current cell segmentation methods perform poorly for cluster cells due to the overlapping/touching characters of clusters, multiple instance properties of cells, and the poor generalization ability of the models.ResultsIn this article, we propose a contour constraint instance segmentation framework (CC framework) for cluster cells based on a cluster cell combination enhancement module. The framework can accurately locate each instance from cluster cells and realize high-precision contour segmentation under a few samples. Specifically, we propose the contour attention constraint module to alleviate over- and under-segmentation among individual cell-instance boundaries. In addition, to evaluate the framework, we construct a pleural effusion cluster cell dataset including 197 high-quality samples. The quantitative results show that the numeric result of APmask is &gt; 90%, a more than 10% increase compared with state-of-the-art semantic segmentation algorithms. From the qualitative results, we can observe that our method rarely has segmentation errors.
MOJITOO: a fast and universal method for integration of multimodal single-cell data
Authors: Cheng Mingbo , Li Zhijian , Costa Ivan G 
Published: April 2022

AbstractMotivationThe advent of multi-modal single-cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single-cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities.ResultsHere we present a single-cell multi-modal integration method, named Multi-mOdal Joint IntegraTion of cOmpOnents (MOJITOO). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single-cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality-specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single-cell datasets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering.Availability and implementationThe software, code and data for benchmarking are available at https://github.com/CostaLab/MOJITOO and https://doi.org/10.5281/zenodo.6348128.Supplementary informationSupplementary data are available at Bioinformatics online.
Quintet Rooting: rooting species trees under the multi-species coalescent model
Authors: Tabatabaee Yasamin , Sarker Kowshika , Warnow Tandy 
Published: April 2022

AbstractMotivationRooted species trees are a basic model with multiple applications throughout biology, including understanding adaptation, biodiversity, phylogeography and co-evolution. Because most species tree estimation methods produce unrooted trees, methods for rooting these trees have been developed. However, most rooting methods either rely on prior biological knowledge or assume that evolution is close to clock-like, which is not usually the case. Furthermore, most prior rooting methods do not account for biological processes that create discordance between gene trees and species trees.ResultsWe present Quintet Rooting (QR), a method for rooting species trees based on a proof of identifiability of the rooted species tree under the multi-species coalescent model established by Allman, Degnan and Rhodes (J. Math. Biol., 2011). We show that QR is generally more accurate than other rooting methods, except under extreme levels of gene tree estimation error.Availability and implementationQuintet Rooting is available in open source form at https://github.com/ytabatabaee/Quintet-Rooting. The simulated datasets used in this study are from a prior study and are available at https://www.ideals.illinois.edu/handle/2142/55319. The biological dataset used in this study is also from a prior study and is available at http://gigadb.org/dataset/101041.Contactwarnow@illinois.eduSupplementary informationSupplementary data are available at Bioinformatics online.
DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction
Authors: You Ronghui , Qu Wei , Mamitsuka Hiroshi , Zhu Shanfeng 
Published: April 2022

AbstractMotivationComputationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with a binding interaction convolution layer, which allows to integrate all potential binding cores (in a given peptide) with the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels.ResultsExtensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as 5-fold cross-validation, leave one molecule out, validation with independent testing sets and binding core prediction. All these results and visualization of the predicted binding cores indicate the effectiveness of our model, DeepMHCII, and the importance of properly modeling biological facts in deep learning for high predictive performance and efficient knowledge discovery.Availability and implementationDeepMHCII is publicly available at https://github.com/yourh/DeepMHCII.Supplementary informationSupplementary data are available at Bioinformatics online.
Computing optimal factories in metabolic networks with negative regulation
Authors: Krieger Spencer , Kececioglu John 
Published: April 2022

AbstractMotivationA factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation.MethodsWe introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation. We model this problem with directed hypergraphs, prove it is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negative regulation by an iterative approach that generates next-best factories.ResultsThis optimization-based approach is remarkably fast in practice, typically finding optimal factories in a few seconds, even for metabolic networks involving tens of thousands of reactions and metabolites, as demonstrated through comprehensive experiments across all instances from standard reaction databases.Availability and implementationSource code for an implementation of our new method for optimal factories with negative regulation in a new tool called Odinn, together with all datasets, is available free for non-commercial use at http://odinn.cs.arizona.edu.
CALDERA: finding all significant de Bruijn subgraphs for bacterial GWAS
Authors: Roux de Bézieux Hector , Lima Leandro , Perraudeau Fanny , Mary Arnaud , Dudoit Sandrine , Jacob Laurent 
Published: April 2022

AbstractMotivationGenome-wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single-nucleotide polymorphisms to mobile genetic elements. This approach does not require a reference genome, making it easier to account for accessory genes. However, a same gene can exist in slightly different versions across different strains, leading to diluted effects.ResultsHere, we overcome this issue by testing covariates built from closed connected subgraphs (CCSs) of the de Bruijn graph defined over genomic k-mers. These covariates capture polymorphic genes as a single entity, improving k-mer-based GWAS both in terms of power and interpretability. However, a method naively testing all possible subgraphs would be powerless due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all CCSs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. Our method integrates with existing visual tools to facilitate interpretation.Availability and implementationWe provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_ISMB.Supplementary informationSupplementary data are available at Bioinformatics online.
From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures
Authors: Namba Satoko , Iwata Michio , Yamanishi Yoshihiro 
Published: April 2022

AbstractMotivationA critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem.ResultsIn this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases. Predictions were performed by a trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown of 4345 genes and overexpression of 3114 genes) and disease-specific gene transcriptomic signatures of 79 diseases. The trans-disease method, which takes into account similarities among diseases, enabled us to distinguish the inhibitory from activatory targets and to predict the therapeutic targetability of not only proteins with known target–disease associations but also orphan proteins without known associations. Our proposed method is expected to be useful for understanding the commonality of mechanisms among diseases and for therapeutic target identification in drug discovery.Availability and implementationSupplemental information and software are available at the following website [http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/].Supplementary informationSupplementary data are available at Bioinformatics online.
Bridging the gaps in statistical models of protein alignment
Authors: Sumanaweera Dinithi , Allison Lloyd , Konagurthu Arun S 
Published: April 2022

AbstractSummarySequences of proteins evolve by accumulating substitutions together with insertions and deletions (indels) of amino acids. However, it remains a common practice to disconnect substitutions and indels, and infer approximate models for each of them separately, to quantify sequence relationships. Although this approach brings with it computational convenience (which remains its primary motivation), there is a dearth of attempts to unify and model them systematically and together. To overcome this gap, this article demonstrates how a complete statistical model quantifying the evolution of pairs of aligned proteins can be constructed using a time-parameterized substitution matrix and a time-parameterized alignment state machine. Methods to derive all parameters of such a model from any benchmark collection of aligned protein sequences are described here. This has not only allowed us to generate a unified statistical model for each of the nine widely used substitution matrices (PAM, JTT, BLOSUM, JO, WAG, VTML, LG, MIQS and PFASUM), but also resulted in a new unified model, MMLSUM. Our underlying methodology measures the Shannon information content using each model to explain losslessly any given collection of alignments, which has allowed us to quantify the performance of all the above models on six comprehensive alignment benchmarks. Our results show that MMLSUM results in a new and clear overall best performance, followed by PFASUM, VTML, BLOSUM and MIQS, respectively, amongst the top five. We further analyze the statistical properties of MMLSUM model and contrast it with others.Supplementary informationSupplementary data are available at Bioinformatics online.
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder
Authors: Garrido Quentin , Damrich Sebastian , Jäger Alexander , Cerletti Dario , Claassen Manfred , Najman Laurent , Hamprecht Fred A 
Published: April 2022

AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) allows studying the development of cells in unprecedented detail. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data are expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree structure in two dimensions is highly desirable for biological interpretation and exploratory analysis.ResultsOur two contributions are an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data, and a visualization method respecting the tree structure. We extract the tree structure by means of a density-based maximum spanning tree on a vector quantization of the data and show that it captures biological information well. We then introduce density-tree biased autoencoder (DTAE), a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space. We compare to other dimension reduction methods and demonstrate the success of our method both qualitatively and quantitatively on real and toy data.Availability and implementationOur implementation relying on PyTorch and Higra is available at github.com/hci-unihd/DTAE.Supplementary informationSupplementary data are available at Bioinformatics online.
SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug–drug interactions
Authors: Nguyen Duc Anh , Nguyen Canh Hao , Petschner Peter , Mamitsuka Hiroshi 
Published: April 2022

AbstractMotivationPredicting side effects of drug–drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to express high-order relationships among two interacting drugs and a side effect. The idea of these methods is that each side effect is caused by a unique combination of latent features of the corresponding interacting drugs. However, in reality, a side effect might have multiple, different mechanisms that cannot be represented by a single combination of latent features of drugs. Moreover, DDI data are sparse, suggesting that using a sparsity regularization would help to learn better latent representations to improve prediction performances.ResultsWe propose SPARSE, which encodes the DDI hypergraph and drug features to latent spaces to learn multiple types of combinations of latent features of drugs and side effects, controlling the model sparsity by a sparse prior. Our extensive experiments using both synthetic and three real-world DDI datasets showed the clear predictive performance advantage of SPARSE over cutting-edge competing methods. Also, latent feature analysis over unknown top predictions by SPARSE demonstrated the interpretability advantage contributed by the model sparsity.Availability and implementationCode and data can be accessed at https://github.com/anhnda/SPARSE.Supplementary informationSupplementary data are available at Bioinformatics online.