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Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level. [ABSTRACT FROM AUTHOR]

Motivation Phosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but most of them are based on convolutional neural network that may not capture enough information about long-range dependencies between residues in a protein sequence. In addition, existing deep learning methods only make use of sequence information for predicting phosphorylation sites, and it is highly desirable to develop a deep learning architecture that can combine heterogeneous sequence and protein–protein interaction (PPI) information for more accurate phosphorylation site prediction. Results We present a novel integrated deep neural network named PhosIDN, for phosphorylation site prediction by extracting and combining sequence and PPI information. In PhosIDN, a sequence feature encoding sub-network is proposed to capture not only local patterns but also long-range dependencies from protein sequences. Meanwhile, useful PPI features are also extracted in PhosIDN by a PPI feature encoding sub-network adopting a multi-layer deep neural network. Moreover, to effectively combine sequence and PPI information, a heterogeneous feature combination sub-network is introduced to fully exploit the complex associations between sequence and PPI features, and their combined features are used for final prediction. Comprehensive experiment results demonstrate that the proposed PhosIDN significantly improves the prediction performance of phosphorylation sites and compares favorably with existing general and kinase-specific phosphorylation site prediction methods. Availability and implementation PhosIDN is freely available at https://github.com/ustchangyuanyang/PhosIDN. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

• A novel method (IdeSelf-Deep) is used to predict anti-coronavirus peptides. • The AAC, DC, CTD, PseAAC, CT, and AD methods are fused to extract antimicrobial peptides sequence features information. • K-means SMOTE algorithm is used to process the category imbalance data for the first time. • We firstly build a new deep learning framework which combines the BiLSTM, self- attention and fully connected neural network to predict the anti-coronavirus peptides. • IdeSelf-Deep increases the prediction performance compared with other methods. The disease caused by infection with the SARS-CoV-2 virus is threatening people's health and lives. Antimicrobial peptides (AMPs) with anti-coronavirus (anti-CoV) functions may be potentially effective therapeutics for the treatment of infected coronaviruses. Due to the complexity of experimental methods, it is particularly important to identify anti-CoV peptides by computational techniques. In this paper, we propose a new predictive model, called AntiCVP-Deep. First, six feature extraction methods are used to obtain the original feature vector of antimicrobial peptides, and K-means SMOTE is used to handle imbalance data. Then, the processed balanced sample data is passed through the input gate, forget gate and output gate of a bidirectional long short-term memory network (BILSTM) to select optimal features. Next, the important antimicrobial peptides information is given a higher weight through the self-attention mechanism, which enhances the model's ability to learn features. Finally, the data from self-attention layer are input into fully connected neural network (FCN) to predict anti-CoV peptides. The AUC values of the four datasets all reach above 98%. The geometric mean (GMean) of antivirus, non-AVP, non-AMP and all-Neg recognition on the independent test sets reach 90.05%, 92.63%, 99.46% and 91.24%, respectively. This experimental result shows that the AntiCVP-Deep method is helpful for identifying anti-CoV peptides and has better predictive performance compared to other existing predictive models. [ABSTRACT FROM AUTHOR]

This paper presents a novel digital video stabilization approach that provides both efficiency and robustness. In this approach, features of each frame are first detected by the oriented features from accelerated segment test (FAST) method, and then the corresponding features between consecutive frames are matched by a very fast binary descriptor which is based on the rotated binary robust independent elementary features (BRIEF). The oriented FAST combined with the rotated BRIEF, which is called ORB, is very efficient in feature detection and matching, and can be used to speed up the motion estimation without any hardware acceleration. In addition, an improved motion smoothing method is proposed to smooth affine model based motion parameters without accumulative global motion estimation. Unlike the conventional method, the proposed method uses unstable input frames and stabilized output frames instead of original input frames to estimate motion parameters directly, allowing for more desirable motion parameters. Experiments with a variety of videos demonstrate that the proposed approach is both efficient and robust. [ABSTRACT FROM PUBLISHER]