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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]

Protein cysteine S-sulfenylation is an essential and reversible post-translational modification that plays a crucial role in transcriptional regulation, stress response, cell signaling and protein function. Studies have shown that S-sulfenylation is involved in many human diseases such as cancer, diabetes and arteriosclerosis. However, experimental identification of protein S-sulfenylation sites is generally expensive and time-consuming. In this study, we proposed a new protein S-sulfenylation sites prediction method SulSite-GTB. First, fusion of amino acid composition, dipeptide composition, encoding based on grouped weight, K nearest neighbors, position-specific amino acid propensity, position-weighted amino acid composition and pseudo-position specific score matrix feature extraction to obtain the initial feature space. Secondly, we use the synthetic minority oversampling technique (SMOTE) algorithm to process the class imbalance data, and the least absolute shrinkage and selection operator (LASSO) are employed to remove the redundant and irrelevant features. Finally, the optimal feature subset is input into the gradient tree boosting classifier to predict the S-sulfenylation sites, and the five-fold cross-validation and independent test set method are used to evaluate the prediction performance of the model. Experimental results showed the overall prediction accuracy is 92.86% and 88.53%, respectively, and the AUC values are 0.9706 and 0.9425, respectively, on the training set and the independent test set. Compared with other prediction methods, the results show that the proposed method SulSite-GTB is significantly superior to other state-of-the-art methods and provides a new idea for the prediction of post-translational modification sites of other proteins. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/SulSite-GTB/. [ABSTRACT FROM AUTHOR]

Light-field images captured by light-field cameras usually suffer from low spatial resolution due to the inherent limited sensor resolution. Light-field spatial super-resolution thus becomes increasingly desirable for subsequent applications. Although continuous progress has been achieved, the existing methods still failed to thoroughly explore the coherence among light-field views. To address this issue, we propose an efficient neural network for light-field spatial super-resolution, in which the spatial and angular information can be fully exploited by repeatedly alternating spatial and angular domain. Specifically, an enhanced spatial-angular separable convolution block is proposed to efficiently exploit the correlation information between sub-aperture images. Moreover, a multi-scale feature extraction block is introduced to extract feature representations at different scales and capture rich texture and semantic information. Experimental results on both synthetic and real-world light-field datasets demonstrate that the proposed method outperforms other state-of-the-art methods with higher peak signal-to-noise ratio (PSNR)/structural similarity (SSIM) values and fewer parameters. [ABSTRACT FROM AUTHOR]

Malonylation is a new protein post-translational modification and regulates a variety of cellular physiological processes. However, it is costly and time-consuming to identify malonylation sites through traditional experiments. Therefore, the prediction of malonylation sites by computational methods plays an important role in experimental design. In this paper, a new prediction model of malonylation sites, Malsite-Deep, is proposed. First, the seven feature extraction methods are used to extract feature information of protein sequences. Then, the under-sampling NearMiss-2 method is applied to handle imbalance data, and the update gate and reset gate of gated recurrent units (GRU) are used to select the optimal feature subset. Finally, the data from GRU layer is input into deep neural networks (DNN) to predict the malonylation sites, and the model performance is evaluated by 10-fold cross-validation and independent test sets. The 10-fold cross-validation shows that the AUC value on the training dataset reaches 0.99. The AUC values on the four independent test datasets all reach above 0.95. Results suggest that Malsite-Deep presented here facilitates the identification of protein malonylation sites. • A novel method (Malsite-Deep) is used to predict malonylation sites. • The BE, PWAA, BPB, EAAC, DC, EBGW and BLOSUM62 methods are fused to extract protein sequence features information. • NearMiss-2 algorithm is used to process the category imbalance data for the first time. • We firstly build a new deep learning framework which combines the GRU and DNN to predict the malonylation sites. [ABSTRACT FROM AUTHOR]

Carbonylation is an irreversible post-translational modification of proteins and regulates various cellular physiological processes. Due to the limitations of experimental methods, it is necessary to predict carbonylation sites by computational methods. In this paper, a new prediction model of carbonylation, Precar_Deep, is proposed. First, six feature extraction methods are used to obtain the original feature space from the protein sequences. Then, the Group LASSO method is used to remove redundant information and the oversampling Borderline-SMOTE method is employed to balance the data to obtain a new feature space. Finally, the processed data is input into the deep learning framework constructed in this paper to predict the carbonylation sites, and the performance of the model is evaluated by using 10-fold cross-validation and independent test datasets. The AUC values of the four datasets are all more than 90%. The experimental results show that PreCar_Deep is superior to other existing models and is helpful to identify protein carbonylation sites. The source code and all datasets are available at https://github.com/QUST-SHULI/PreCar_Deep/. • A novel method (PreCar_Deep) is used to predict carbonylation sites. • The AAC, DC, AAindex, EGAAC, CT and KNN methods are fused to extract protein sequence features information. • Borderline-SMOTE algorithm is utilized to process the category imbalance data for the first time. • We firstly build a new deep learning framework which combines the CNN and BiLSTM to predict the carbonylation sites. [ABSTRACT FROM AUTHOR]

Mitosis counting of biopsies is an important biomarker for breast cancer patients, which supports disease prognostication and treatment planning. Developing a robust mitotic cell detection model is highly challenging due to its complex growth pattern and high similarities with non-mitotic cells. Most mitosis detection algorithms have poor generalizability across image domains and lack reproducibility and validation in multicenter settings. To overcome these issues, we propose a generalizable and robust mitosis detection algorithm (called FMDet), which is independently tested on multicenter breast histopathological images. To capture more refined morphological features of cells, we convert the object detection task as a semantic segmentation problem. The pixel-level annotations for mitotic nuclei are obtained by taking the intersection of the masks generated from a well-trained nuclear segmentation model and the bounding boxes provided by the MIDOG 2021 challenge. In our segmentation framework, a robust feature extractor is developed to capture the appearance variations of mitotic cells, which is constructed by integrating a channel-wise multi-scale attention mechanism into a fully convolutional network structure. Benefiting from the fact that the changes in the low-level spectrum do not affect the high-level semantic perception, we employ a Fourier-based data augmentation method to reduce domain discrepancies by exchanging the low-frequency spectrum between two domains. Our FMDet algorithm has been tested in the MIDOG 2021 challenge and ranked first place. Further, our algorithm is also externally validated on four independent datasets for mitosis detection, which exhibits state-of-the-art performance in comparison with previously published results. These results demonstrate that our algorithm has the potential to be deployed as an assistant decision support tool in clinical practice. Our code has been released at https://github.com/Xiyue-Wang/1st-in-MICCAI-MIDOG-2021-challenge. [Display omitted] • A generalizable and effective mitosis detection algorithm to fully consider the mismatch across domains. • We transform the mitosis detection task into a semantic segmentation one to achieve precise mitotic figure identification. • An attention mechanism is utilized to generate channel-wise multi-scale features, which helps the network to extract more robust features. • An unsupervised Fourier-based data augmentation is used to align the domain discrepancies. • Our method won first place in the 2021 MIDOG challenge and showed successful generalization to unseen independent multicenter datasets. [ABSTRACT FROM AUTHOR]

Benefiting from the large-scale archiving of digitized whole-slide images (WSIs), computer-aided diagnosis has been well developed to assist pathologists in decision-making. Content-based WSI retrieval can be a new approach to find highly correlated WSIs in a historically diagnosed WSI archive, which has the potential usages for assisted clinical diagnosis, medical research, and trainee education. During WSI retrieval, it is particularly challenging to encode the semantic content of histopathological images and to measure the similarity between images for interpretable results due to the gigapixel size of WSIs. In this work, we propose a Retrieval with Clustering-guided Contrastive Learning (RetCCL) framework for robust and accurate WSI-level image retrieval, which integrates a novel self-supervised feature learning method and a global ranking and aggregation algorithm for much improved performance. The proposed feature learning method makes use of existing large-scale unlabeled histopathological image data, which helps learn universal features that could be used directly for subsequent WSI retrieval tasks without extra fine-tuning. The proposed WSI retrieval method not only returns a set of WSIs similar to a query WSI, but also highlights patches or sub-regions of each WSI that share high similarity with patches of the query WSI, which helps pathologists interpret the searching results. Our WSI retrieval framework has been evaluated on the tasks of anatomical site retrieval and cancer subtype retrieval using over 22,000 slides, and the performance exceeds other state-of-the-art methods significantly (around 10% for the anatomic site retrieval in terms of average m M V @ 10). Besides, the patch retrieval using our learned feature representation offers a performance improvement of 24% on the TissueNet dataset in terms of m M V @ 5 compared with using ImageNet pre-trained features, which further demonstrates the effectiveness of the proposed CCL feature learning method. [Display omitted] • A novel WSI retrieval algorithm called RetCCL is proposed. • RetCCL provides interpretable results by highlighting relevant sub-regions. • Our feature extractor (CCL) contains a weighted InfoNCE and a group-level InfoNCE. • CCL is pretrained using a large and diverse database of histopathological images. • CCL outperforms ImageNet-pretrained features or other SSL-based features. • RetCCL shows state-of-the-art WSI retrieval results. [ABSTRACT FROM AUTHOR]

A large-scale and well-annotated dataset is a key factor for the success of deep learning in medical image analysis. However, assembling such large annotations is very challenging, especially for histopathological images with unique characteristics (e.g., gigapixel image size, multiple cancer types, and wide staining variations). To alleviate this issue, self-supervised learning (SSL) could be a promising solution that relies only on unlabeled data to generate informative representations and generalizes well to various downstream tasks even with limited annotations. In this work, we propose a novel SSL strategy called semantically-relevant contrastive learning (SRCL), which compares relevance between instances to mine more positive pairs. Compared to the two views from an instance in traditional contrastive learning, our SRCL aligns multiple positive instances with similar visual concepts, which increases the diversity of positives and then results in more informative representations. We employ a hybrid model (CTransPath) as the backbone, which is designed by integrating a convolutional neural network (CNN) and a multi-scale Swin Transformer architecture. The CTransPath is pretrained on massively unlabeled histopathological images that could serve as a collaborative local–global feature extractor to learn universal feature representations more suitable for tasks in the histopathology image domain. The effectiveness of our SRCL-pretrained CTransPath is investigated on five types of downstream tasks (patch retrieval, patch classification, weakly-supervised whole-slide image classification, mitosis detection, and colorectal adenocarcinoma gland segmentation), covering nine public datasets. The results show that our SRCL-based visual representations not only achieve state-of-the-art performance in each dataset, but are also more robust and transferable than other SSL methods and ImageNet pretraining (both supervised and self-supervised methods). Our code and pretrained model are available at https://github.com/Xiyue-Wang/TransPath. [Display omitted] • The first Transformer-based unsupervised feature extractor for histopathological images. • A semantically-relevant contrastive learning approach is proposed. • A hybrid backbone (CTransPath) is constructed by combining CNN and Transformer. • CTransPath could be a general-purpose feature extractor for histopathological images. [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]