bluuebunny/crossref_metadata_2025 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

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 < 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 < 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

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

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

Motivation

The 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.

Results

In 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 > 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

Motivation

The 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.

Results

Here 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 implementation

The software, code and data for benchmarking are available at https://github.com/CostaLab/MOJITOO and https://doi.org/10.5281/zenodo.6348128.

Supplementary information

Supplementary 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

Motivation

Rooted 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.

Results

We 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 implementation

Quintet 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.

Contact

warnow@illinois.edu

Supplementary information

Supplementary 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

Motivation

Computationally 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.

Results

Extensive 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 implementation

DeepMHCII is publicly available at https://github.com/yourh/DeepMHCII.

Supplementary information

Supplementary data are available at Bioinformatics online.
Computing optimal factories in metabolic networks with negative regulation
Authors: Krieger Spencer , Kececioglu John
Published: April 2022

Motivation

A 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.

Methods

We 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.

Results

This 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 implementation

Source 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

Motivation

Genome-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.

Results

Here, 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 implementation

We provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_ISMB.

Supplementary information

Supplementary 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

Motivation

A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem.

Results

In 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 implementation

Supplemental information and software are available at the following website [http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/].

Supplementary information

Supplementary 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

Sequences 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 information

Supplementary 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

Motivation

Single-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.

Results

Our 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 implementation

Our implementation relying on PyTorch and Higra is available at github.com/hci-unihd/DTAE.

Supplementary information

Supplementary 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

Motivation

Predicting 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.

Results

We 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 implementation

Code and data can be accessed at https://github.com/anhnda/SPARSE.

Supplementary information

Supplementary data are available at Bioinformatics online.
Do-calculus enables estimation of causal effects in partially observed biomolecular pathways
Authors: Mohammad-Taheri Sara , Zucker Jeremy , Hoyt Charles Tapley , Sachs Karen , Tewari Vartika , Ness Robert , Vitek Olga
Published: April 2022

Motivation

Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways.

Results

In this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl’s do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation.

Availability and implementation

The code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus.

Supplementary information

Supplementary data are available at Bioinformatics online.
DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features
Authors: Weng Zhenyu , Yue Zongliang , Zhu Yuesheng , Chen Jake Yue
Published: April 2022

In biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene’s relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein–protein interaction weights, gene-to-gene correlations and the gene set annotations—four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer’s disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema.

Supplementary information

Supplementary data are available at Bioinformatics online.
QuCo: quartet-based co-estimation of species trees and gene trees
Authors: Rabiee Maryam , Mirarab Siavash
Published: April 2022

Motivation

Phylogenomics faces a dilemma: on the one hand, most accurate species and gene tree estimation methods are those that co-estimate them; on the other hand, these co-estimation methods do not scale to moderately large numbers of species. The summary-based methods, which first infer gene trees independently and then combine them, are much more scalable but are prone to gene tree estimation error, which is inevitable when inferring trees from limited-length data. Gene tree estimation error is not just random noise and can create biases such as long-branch attraction.

Results

We introduce a scalable likelihood-based approach to co-estimation under the multi-species coalescent model. The method, called quartet co-estimation (QuCo), takes as input independently inferred distributions over gene trees and computes the most likely species tree topology and internal branch length for each quartet, marginalizing over gene tree topologies and ignoring branch lengths by making several simplifying assumptions. It then updates the gene tree posterior probabilities based on the species tree. The focus on gene tree topologies and the heuristic division to quartets enables fast likelihood calculations. We benchmark our method with extensive simulations for quartet trees in zones known to produce biased species trees and further with larger trees. We also run QuCo on a biological dataset of bees. Our results show better accuracy than the summary-based approach ASTRAL run on estimated gene trees.

Availability and implementation

QuCo is available on https://github.com/maryamrabiee/quco.

Supplementary information

Supplementary data are available at Bioinformatics online.
A graph neural network approach for molecule carcinogenicity prediction
Authors: Fradkin Philip , Young Adamo , Atanackovic Lazar , Frey Brendan , Lee Leo J , Wang Bo
Published: April 2022

Motivation

Molecular carcinogenicity is a preventable cause of cancer, but systematically identifying carcinogenic compounds, which involves performing experiments on animal models, is expensive, time consuming and low throughput. As a result, carcinogenicity information is limited and building data-driven models with good prediction accuracy remains a major challenge.

Results

In this work, we propose CONCERTO, a deep learning model that uses a graph transformer in conjunction with a molecular fingerprint representation for carcinogenicity prediction from molecular structure. Special efforts have been made to overcome the data size constraint, such as multi-round pre-training on related but lower quality mutagenicity data, and transfer learning from a large self-supervised model. Extensive experiments demonstrate that our model performs well and can generalize to external validation sets. CONCERTO could be useful for guiding future carcinogenicity experiments and provide insight into the molecular basis of carcinogenicity.

Availability and implementation

The code and data underlying this article are available on github at https://github.com/bowang-lab/CONCERTO
teff: estimation of Treatment EFFects on transcriptomic data using causal random forest
Authors: Cáceres Alejandro , González Juan R
Published: April 2022

Motivation

Causal inference on high-dimensional feature data can be used to find a profile of patients who will benefit the most from treatment rather than no treatment. However, there is a need for usable implementations for transcriptomic data. We developed teff that applies random causal forest on gene expression data to target individuals with high expected treatment effects.

Results

We extracted a profile of high benefit of treating psoriasis with brodalumab and observed that it was associated with higher T cell abundance in non-lesional skin at baseline and a lower response for etanercept in an independent study. Individual patient targeting with causal inference profiling can inform patients on choosing between treatments before the intervention begins.

Availability and implementation

teff is an R package available at https://teff-package.github.io. The data underlying this article are available in GEO, at https://www.ncbi.nlm.nih.gov/geo/

Supplementary information

Supplementary data are available at Bioinformatics online.
Steer’n’Detect: fast 2D template detection with accurate orientation estimation
Authors: Uhlmann Virginie , Püspöki Zsuzsanna , Depeursinge Adrien , Unser Michael , Sage Daniel , Fageot Julien
Published: April 2022

Motivation

Rotated template matching is an efficient and versatile algorithm to analyze microscopy images, as it automates the detection of stereotypical structures, such as organelles that can appear at any orientation. Its performance however quickly degrades in noisy image data.

Results

We introduce Steer’n’Detect, an ImageJ plugin implementing a recently published algorithm to detect patterns of interest at any orientation with high accuracy from a single template in 2D images. Steer’n’Detect provides a faster and more robust substitute to template matching. By adapting to the statistics of the image background, it guarantees accurate results even in the presence of noise. The plugin comes with an intuitive user interface facilitating results analysis and further post-processing.

Availability and implementation

https://github.com/Biomedical-Imaging-Group/Steer-n-Detect.

Supplementary information

Supplementary data are available at Bioinformatics online.
DrugCVar: a platform for evidence-based drug annotation for genetic variants in cancer
Authors: Zhang Xiaolong , Qian Zhikai , Wang Ye , Zhang Qingfeng , Yu Kai , Zheng Yongqiang , Liu Zekun , Zhao Qi , Liu Ze-Xian
Published: April 2022

Motivation

Targeted therapy for cancer-related genetic variants is critical for precision medicine. Although several databases including The Clinical Interpretation of Variants in Cancer (CIViC), The Oncology Knowledge Base (OncoKB), The Cancer Genome Interpreter (CGI) and My Cancer Genome (MCG) provide clinical interpretations of variants in cancer, the clinical evidence was limited and miscellaneous. In this study, we developed the DrugCVar database, which integrated our manually curated cancer variant–drug targeting evidence from literature and the interpretations from the public resources.

Results

In total, 7830 clinical evidences for cancer variant–drug targeting were integrated and classified into 10 evidence tiers. Searching and browsing functions were provided for quick queries of cancer variant–drug targeting evidence. Also, batch annotation module was developed for user-provided massive genetic variants in various formats. Details, such as the mutation function, location of the variants in gene and protein structures and mutation statistics of queried genes in various tumor types, were also provided for further investigations. Thus, DrugCVar could serve as a comprehensive annotation tool to interpret potential drugs for cancer variants especially the massive ones from clinical cancer genomics studies.

Availability and implementation

The database is available at http://drugcvar.omicsbio.info.

Supplementary information

Supplementary data are available at Bioinformatics online.
Quantification of transcription noise’s impact on cell fate commitment with digital resolutions
Authors: Jiao Feng , Tang Moxun
Published: April 2022

Motivation

Gene transcription is a random and noisy process. Tremendous efforts in single-cell studies have been mapping transcription noises to phenotypic variabilities between isogenic cells. However, the exact role of the noise in cell fate commitment remains largely descriptive or even controversial.

Results

For a specified cell fate, we define the jumping digit I of a critical gene as a statistical threshold that a single cell has approximately an equal chance to commit the fate as to have at least I transcripts of the gene. When the transcription is perturbed by a noise enhancer without changing the basal transcription level E0, we find a crossing digit k such that the noise catalyzes cell fate change when I > k while stabilizes the current state when I < k; k remains stable against enormous variations of kinetic rates. We further test the reactivation of latent HIV in 22 integration sites by noise enhancers paired with transcriptional activators. Strong synergistic actions are observed when the activators increase transcription burst frequency, whereas no synergism, but antagonism, is often observed if activators increase burst size. The synergistic efficiency can be predicted accurately by the ratio I/E0. When the noise enhancers double the noise, the activators double the burst frequency, and I/E0≥7, their combination is 10 times more effective than their additive effects across all 22 sites.

Availability and implementation

The jumping digit I may provide a novel probe to explore the phenotypic consequences of transcription noise in cell functions. Code is freely available at http://cam.gzhu.edu.cn/info/1014/1223.htm. The data underlying this article are available in the article and in its online supplementary material.

Supplementary information

Supplementary data are available at Bioinformatics online.
Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution
Authors: Cai Manqi , Yue Molin , Chen Tianmeng , Liu Jinling , Forno Erick , Lu Xinghua , Billiar Timothy , Celedón Juan , McKennan Chris , Chen Wei , Wang Jiebiao
Published: April 2022

Motivation

Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods.

Results

To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data.

Availability and implementation

EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal.

Supplementary information

Supplementary data are available at Bioinformatics online.
Identification of sex chromosomes using genomic and cytogenetic methods in a range-expanding spider,Argiope bruennichi(Araneae: Araneidae)
Authors: Sheffer Monica M , Cordellier Mathilde , Forman Martin , Grewoldt Malte , Hoffmann Katharina , Jensen Corinna , Kotz Matěj , Král Jiří , Kuss Andreas W , Líznarová Eva , Uhl Gabriele
Published: April 2022

Differences between sexes in growth, ecology and behaviour strongly shape species biology. In some animal groups, such as spiders, it is difficult or impossible to identify the sex of juveniles based on external morphology. This information would be useful for field surveys, behavioural experiments and ecological studies, such as those on sex ratios and dispersal. In species with sex chromosomes, sex can be determined based on the specific sex chromosome complement. Additionally, information on the sequence of sex chromosomes provides the basis for studying sex chromosome evolution. We combined cytogenetic and genomic data to identify the sex chromosomes in the sexually dimorphic spider Argiope bruennichi and designed quantitative real-time polymerase chain reaction sex markers. We found that the genome size and GC content of this spider fall into the ranges reported for the majority of araneids. The male karyotype is formed by 24 acrocentric chromosomes with an X1X20 sex chromosome system, with little similarity between X chromosomes, suggesting an origin of these chromosomes by X chromosome fission or early duplication of an X chromosome and subsequent independent differentiation of the copies. Our data suggest X chromosomes of similar sizes in A. bruennichi. They are smaller chromosomes of the complement. Our findings open the door to new directions in spider evolutionary and ecological research.
Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping
Authors: Gao Leiwen , Banerjee Sudipto , Ritz Beate
Published: April 2022

Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, and zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To capture health disparities over regions, we seek “difference boundaries” that separate neighboring regions with significantly different spatial effects. Matters are more challenging with multiple outcomes over each unit, where we capture dependence among diseases as well as across the areal units. Here, we address multivariate difference boundary detection for correlated diseases. We formulate the problem in terms of Bayesian pairwise multiple comparisons and seek the posterior probabilities of neighboring spatial effects being different. To achieve this, we endow the spatial random effects with a discrete probability law using a class of multivariate areally referenced Dirichlet process models that accommodate spatial and interdisease dependence. We evaluate our method through simulation studies and detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.
Ultra-early initiation of postoperative rehabilitation in the post-anaesthesia care unit after major thoracic surgery: case–control study
Authors: Pastene Bruno , Labarriere Ambroise , Lopez Alexandre , Charvet Aude , Culver Aurélien , Fiocchi David , Cluzel Armand , Brioude Geoffrey , Einav Sharon , Tankel James , Hamidou Zeinab , D’Journo Xavier Benoit , Thomas Pascal , Leone Marc , Zieleskiewicz Laurent ,   the GRACE Association, Slim K. , Joris J. , Delaunay L. , Regimbeau J-M. , Ostermann S. , Beyer-Berjot L. , Lavand'homme P. , Lafortune I. , Szymkiewicz O. , Venara A. , Zieleskiewicz L. , Puppo N. , Beaupère S.
Published: April 2022

Background

Physiotherapy is a major cornerstone of enhanced rehabilitation after surgery (ERAS) and reduces the development of atelectasis after thoracic surgery. By initiating physiotherapy in the post-anaesthesia care unit (PACU), the aim was to evaluate whether the ultra-early initiation of rehabilitation (in the first hour following tracheal extubation) would improve the outcomes of patients undergoing elective thoracic surgery.

Methods

A case–control study with a before-and-after design was conducted. From a historical control group, patients were paired at a 3:1 ratio with an intervention group. This group consisted of patients treated with the ultra-early rehabilitation programme after elective thoracic surgery (clear fluids, physiotherapy, and ambulation). The primary outcome was the incidence of postoperative atelectasis and/or pneumonia during the hospital stay.

Results

After pairing, 675 patients were allocated to the historical control group and 225 patients to the intervention group. A significant decrease in the incidence of postoperative atelectasis and/or pneumonia was found in the latter (11.4 versus 6.7 per cent respectively; P = 0.042) and remained significant on multivariate analysis (OR 0.53, 95 per cent c.i. 0.26 to 0.98; P = 0.045). A subgroup analysis of the intervention group showed that early ambulation during the PACU stay was associated with a further significant decrease in the incidence of postoperative atelectasis and/or pneumonia (2.2 versus 9.5 per cent; P = 0.012).

Conclusions

Ultra-early rehabilitation in the PACU was associated with a decrease in the incidence of postoperative atelectasis and/or pneumonia after major elective thoracic surgery.
The Politics of Matching: Ethnicity, Religion and Muslim-heritage Children in Care in the UK
Authors: Cheruvallil-Contractor Sariya , Halford Alison , Boti Phiri Mphatso
Published: April 2022

In 2014, in order to improve outcomes for children from ethnic minority backgrounds and to speed up the adoption process, the UK government changed the Children and Families Act. The legal requirement on adoption agencies to consider ethnicity in the decision around ‘matching’ was removed, thus clearing the way for transracial placements. This article interrogates the impact of the change in law on social work practice around adoption, using the experiences of diverse Muslim-heritage children as a case study. Grounded in the sociology of religion, the findings presented here are based on semi-structured qualitative interviews (n = 28) with those involved in the care of Muslim-heritage children. In discussing qualitative findings, all adopters and prospective adopters interviewed in this research insisted on adopting children who ‘look like them’, and social workers continued to look for the ‘best’ possible matches. Children from minoritised backgrounds continue to wait for long periods before finding permanent homes. Our evidence raises questions about the efficacy of policy guidance. Based on this evidence we conclude that greater strategizing is needed around the recruitment of adopters from diverse backgrounds.
Implementation of the Making Safeguarding Personal Approach to Strengths-based Adult Social Care: Systematic Review of Qualitative Research Evidence
Authors: Ahuja Latika , Price Anna , Bramwell Charlotte , Briscoe Simon , Shaw Liz , Nunns Michael , O’Rourke Gareth , Baron Samantha , Anderson Rob
Published: April 2022

Since the Care Act (2014), there is fresh impetus for social workers to apply strengths-based approaches (SBAs) when working with adults. However, implementation challenges remain. This article presents our synthesis of seven studies that examined Making Safeguarding Personal (MSP). It was conducted as part of a systematic review that aimed to summarise research evidence on the implementation of different SBAs within adult social work in the UK. Qualitative studies were analysed using a framework synthesis approach. Four themes were identified: (1) MSP as an intervention: seen as initially demanding but with long-term advantages; (2) Culture and settings: required broad cultural changes; ‘outward facing’ and smaller/specialist councils tended to find this easier; (3) Individual characteristics: related to enhancing the knowledge, skills and confidence of practitioner and stakeholders in MSP; and service user willingness to engage; and (4) Embedding and sustaining MSP: depended on strong leadership and active engagement at all levels. We found a wide range of factors affecting the implementation of MSP. These may have broader relevance for other strengths-based models of social work practice.
Reunification and Re-entry to Care: An Analysis of the National Datasets for Children Looked after in England
Authors: Goldacre Allie , Hood Rick , Jones Ed , King Adam , Wang Chao
Published: April 2022

This article reports on a quantitative study of the national datasets for children in state care in England. The aim of the study was to investigate the likelihood of re-entry to care for children who returned home to their parents or main carers after an episode of care. An anonymised extract of case-level information on all children who received an episode of care between 2014 and 2020 (n = 51,238) was assembled from local authority (LA) administrative data held by the UK government. Cox regression models were used to calculate the cumulative probability of re-entry to care over time and identify the factors associated with a higher or lower probability. The findings showed that one in five children re-entered care after one year and over a third of children re-entered care after six years. A number of factors affected the probability of re-entering care, including age, ethnicity, care history, placement type and whether provision was LA, voluntary or private sector. Implications are discussed for planning and design of services to support reunification from care, and for the broader goal of reducing the number of children looked after by the state.