Xuechen Zhang

Motivation: The three-dimensional protein tertiary structure alignment is a fundamental problem that seeks insights into functions and evolution. Previous structure alignment algorithms have adopted the sequential assumption and used dynamic programming solvers. However, many distantly related structures exhibit non-sequential similarities, and non-sequential alignment tools are less efficient and accurate than sequential ones. In this paper, we formulate the non-sequential alignment as the Entropy-regularized Partial Linear Sum Assignment Problem (epLSAP) and propose a solver based on Sinkhorn algorithms, referred to as epLSAP-Align. Results: Compared with existing non-sequential alignment solvers, our epLSAP-Align can explicitly model the gap penalty, efficiently achieve global optimality and balance coverage and fidelity. We show that epLSAP-Align can be easily integrated into the existing frameworks, such as TM-align and MICAN, resulting in the non-sequential alignment tool epLSAP-TM and epLSAP-MICAN, respectively. Both epLSAP-TM and epLSAP-MICAN achieve better performance than the existing non-sequential alignment tools in terms of biologically meaningful structure overlaps on two sequential alignment test sets MALIDUP and MALISAM, and four non-sequential alignment test sets MALIDUP-ns, MALISAM-ns, 64-difficult-case and RIPC datasets. Also, compared with the most recent non-sequential alignment tool USalign2, our epLSAP-TM is at least 22% faster under the same setting. Availability and implementation: Our source code is available at https://github.com/xzhangem/epLSAP-align.

In this study, we investigate the performance of nine protein structure alignment tools by analyzing the influence of alignment results in accomplishing three downstream biological tasks: homology detection, phylogeny reconstruction, and function inference. These tools include (1) traditional sequential methods using both 3D and 2D structure representations, (2) non-sequential methods, (3) flexible methods, and (4) deep-learning methods. Canonical sequence alignment methods Needleman-Wunsch algorithm and BLASTp are used as baseline methods. We show that accuracies in downstream tasks can be uncorrelated with alignment quality metrics such as TM-score and RMSD, highlighting the discrepancy between the alignment results and the purposes of using them. We identify scenarios where structure alignment results outperform sequence alignment results. In homology detection, structure-based methods are much better than sequence alignment. In phylogeny reconstruction, structure-based methods generally outperform sequence-based methods on the filtered dataset with proteins sharing low sequence similarity. Moreover, we show that structure information improves the overall performance of these tools when used together with sequence information in phylogeny reconstruction and function inference. We also test the running time and CPU/GPU memory consumption of these tools for a large number of queries. Our study suggests that biological problems that were previously addressed with sequence-based methods using only sequence information could be further improved by using structure information alone or using both sequence and structure information. The trade-off between task accuracy and speed is the major consideration in developing new alignment tools for downstream tasks. We recommend both TMalign and KPAX for these tasks because of their good balance between running time and memory consumption, and relatively good and stable accuracy performance in downstream tasks. In tasks that require a large number of pairwise comparisons, such as homology detection and function inference, traditional methods outperform DL methods at the cost of long running time, and Foldseek is the best choice to achieve relatively high accuracy in a reasonable time. 1 Graphical Fig. 1: Graphical

In this paper, we propose a 3D fully automatic joint erosion algorithms for hand HR-pQCT, and its performance can reach consensus with human specialist.

Single image super-resolution (SISR) is a notori- ously challenging ill-posed problem that aims to obtain a high- resolution (HR) output from one of its low-resolution (LR) versions. Recently, powerful deep learning algorithms have been applied to SISR and have achieved state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods and group them into two categories according to their contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is first established, and several critical limitations of the baseline are summarized. Then, representative works on overcoming these limitations are presented based on their original content, as well as our critical exposition and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally, we conclude this review with some current challenges and future trends in SISR that leverage deep learning algorithms.