Notes

External-field-induced directional droplet carry: An overview.
The server is available at https//interactome.ku.edu.tr/sars/.
The server is available at https//interactome.ku.edu.tr/sars/.Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http//lin-group.cn/server/iRice-MS.
In fluorescence microscopy, Single Molecule Localization Microscopy (SMLM) techniques aim at localizing with high precision high density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super Resolution (SR) plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit.

In this work, we propose a deep learning-based algorithm for precise molecule localization of high density frames acquired by SMLM techniques whose ℓ2-based loss function is regularized by non-negative and ℓ0-based constraints. The ℓ0 is relaxed through its Continuous Exact ℓ0 (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data.

DeepCEL0 code is freely accessible at https//github.com/sedaboni/DeepCEL0.
DeepCEL0 code is freely accessible at https//github.com/sedaboni/DeepCEL0.Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.
Single-cell RNA sequencing allows high-resolution views of individual cells for libraries of up to millions of samples, thus motivating the use of deep learning for analysis. In this study, we introduce the use of graph neural networks for the unsupervised exploration of scRNA-seq data by developing a variational graph autoencoder architecture with graph attention layers that operates directly on the connectivity between cells, focusing on dimensionality reduction and clustering. With the help of several case studies, we show that our model, named CellVGAE, can be effectively used for exploratory analysis even on challenging datasets, by extracting meaningful features from the data and providing the means to visualise and interpret different aspects of the model.

We show that CellVGAE is more interpretable than existing scRNA-seq variational architectures by analysing the graph attention coefficients. By drawing parallels with other scRNA-seq studies on interpretability, we assess the validity of the relationships modelled by attention, and furthermore, we show that CellVGAE can intrinsically capture information such as pseudotime and NF-ĸB activation dynamics, the latter being a property that is not generally shared by existing neural alternatives. We then evaluate the dimensionality reduction and clustering performance on 9 difficult and well-annotated datasets by comparing with three leading neural and non-neural techniques, concluding that CellVGAE outperforms competing methods. Finally, we report a decrease in training times of up to × 20 on a dataset of 1.3 million cells compared to existing deep learning architectures.

The CellVGAE code is available at https//github.com/davidbuterez/CellVGAE.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
There are still insufficient data on the complexity and predictability of patient-related outcomes following trauma. The aim of this study was to assess longer-term outcomes in patients with significant injury and to develop a simple scoring method to identify patients at high risk of subsequent deficits 1-2 years after injury.

We conducted a prospective cohort study of survivors of significant injury (New Injury Severity Score, NISS greater than or equal to 8), with analysis of patients' 1- to 2-year health-related quality of life (HRQoL) and their functional outcomes based on Short Form-36 (SF-36), Trauma Outcome Profile (TOP), and Quality Of Life after Brain Injury (QOLIBRI). Documented variables suspected or known from the literature to be possible factors associated with outcome were first analysed by univariate analysis, and significant variables were entered into a stepwise logistic regression analysis. Scores predicting longer-term impaired outcome were constructed from risk factors resulting froms of significant injury, particularly in loss of cognitive function, the multiple variables analysed only led to a limited characterization of patient-related longer-term outcomes. Until more is known about additional individual influencing factors, the proposed scoring system may serve well for clinical evaluation.

NCT02165137 (http//www.clinicaltrials.gov).
NCT02165137 (http//www.clinicaltrials.gov).
We present an R-based open-source software termed ProteoDisco that allows for flexible incorporation of genomic variants, fusion-genes and (aberrant) transcriptomic variants from standardized formats into protein variant sequences. ProteoDisco allows for a flexible step-by-step workflow allowing for in-depth customization to suit a myriad of research approaches in the field of proteogenomics, on all organisms for which a reference genome and transcript annotations are available.

ProteoDisco (R package version ≥ 1.0.0) is available on Bioconductor at https//doi.org/doi10.18129/B9.bioc.ProteoDisco and from https//github.com/ErasmusMC-CCBC/ProteoDisco/.

Supplementary table, figures and data files are available at Bioinformatics online.
Supplementary table, figures and data files are available at Bioinformatics online.
Drug repositioning that aims to find new indications for existing drugs has been an efficient strategy for drug discovery. In the scenario where we only have confirmed disease-drug associations as positive pairs, a negative set of disease-drug pairs is usually constructed from the unknown disease-drug pairs in previous studies, where we do not know whether drugs and diseases can be associated, to train a model for disease-drug association prediction (drug repositioning). Drugs and diseases in these negative pairs can potentially be associated, but most studies have ignored them.

We present a method, springD2A, to capture the uncertainty in the negative pairs, and to discriminate between positive and unknown pairs because the former are more reliable. In springD2A, we introduce a spring-like penalty for the loss of negative pairs, which is strong if they are too close in a unit sphere, but mild if they are at a moderate distance. We also design a sequential sampling in which the probability of an unknown disease-drug pair sampled as negative is proportional to its score predicted as positive. Rapamycin order Multiple models are learned during sequential sampling, and we adopt parameter- and feature-based ensemble schemes to boost performance. Experiments show springspringD2A is an effective tool for drug-repositioning.

A python implementation of springD2A and datasets used in this study are available at https//github.com/wangyuanhao/springD2A.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.In natural indigo dyeing, the water-insoluble indigo included in the composted indigo leaves called sukumo is converted to water-soluble leuco-indigo through the reduction activities of microorganisms under alkaline conditions. To understand the relationship between indigo reduction and microorganisms in indigo-fermentation suspensions, we isolated and identified the microorganisms that reduce indigo and analyzed the microbiota in indigo-fermentation suspensions. Indigo-reducing microorganisms, which were not isolated by means of a conventional indigo carmine-reduction assay method, were isolated by using indigo as a direct substrate and further identified and characterized. We succeeded in isolating bacteria closely related to Corynebacterium glutamicum, Chryseomicrobium aureum, and Enterococcus sp. for the first time. Anthraquinone was found to be an effective mediator that facilitated the indigo-reduction activity of the isolated strains. On analysis of the microbiota in indigo-fermentation suspensions, the ratio of indigo-reducing bacteria and others was found to be important for maintaining the indigo-reduction activity.
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