Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures

Single-cell sequencing technology can generate RNA-sequencing data at the single cell level, and one important single-cell RNA-sequencing data analysis method is to identify their cell types without supervised information. Clustering is an unsupervised approach that can help find new insights into biology especially for exploring the biological functions of specific cell type. However, it is challenging for traditional clustering methods to obtain high-quality cell type recognition results. In this research, we propose a novel Clustering method based on Matching Clusters Structures (MCSC) for identifying cell types among single-cell RNA-sequencing data. Firstly, MCSC obtains two different groups of clustering results from the same K-means algorithm because its initial centroids are randomly selected. Then, for one group, MCSC uses shared nearest neighbour information to calculate a label transition matrix, which denotes label transition probability between any two initial clusters. Each initial cluster may be reassigned if merging results after label transition satisfy a consensus function that maximizes structural matching degree of two different groups of clustering results. In essence, the MCSC may be interpreted as a label training process. We evaluate the proposed MCSC with five commonly used datasets and compare MCSC with several classical and state-of-the-art algorithms. The experimental results show that MCSC outperform other algorithms

Deep Learning Analysis and Age Prediction from Shoeprints

Human walking and gaits involve several complex body parts and are influenced by personality, mood, social and cultural traits, and aging. These factors are reflected in shoeprints, which in turn can be used to predict age, a problem not systematically addressed using any computational approach. We collected 100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age. The model integrates various convolutional neural network models together using a skip mechanism to extract age-related features, especially in pressure and abrasion regions from pair-wise shoeprints. The results show that 40.23% of the subjects had prediction errors within 5-years of age and the prediction accuracy for gender classification reached 86.07%. Interestingly, the age-related features mostly reside in the asymmetric differences between left and right shoeprints. The analysis also reveals interesting age-related and gender-related patterns in the pressure distributions on shoeprints; in particular, the pressure forces spread from the middle of the toe toward outside regions over age with gender-specific variations on heel regions. Such statistics provide insight into new methods for forensic investigations, medical studies of gait-pattern disorders, biometrics, and sport studies.Comment: 24 pages, 20 Figure

Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model

Abstract Motivation: Mislabeled samples often appear in gene expression profile because of the similarity of different sub-type of disease and the subjective misdiagnosis. The mislabeled samples deteriorate supervised learning procedures. The LOOE-sensitivity algorithm is an approach for mislabeled sample detection for microarray based on data perturbation. However, the failure of measuring the perturbing effect makes the LOOE-sensitivity algorithm a poor performance. The purpose of this article is to design a novel detection method for mislabeled samples of microarray, which could take advantage of the measuring effect of data perturbations. Results: To measure the effect of data perturbation, we define an index named perturbing influence value (PIV), based on the support vector machine (SVM) regression model. The Column Algorithm (CAPIV), Row Algorithm (RAPIV) and progressive Row Algorithm (PRAPIV) based on the PIV value are proposed to detect the mislabeled samples. Experimental results obtained by using six artificial datasets and five microarray datasets demonstrate that all proposed methods in this article are superior to LOOE-sensitivity. Moreover, compared with the simple SVM and CL-stability, the PRAPIV algorithm shows an increase in precision and high recall. Availability: The program and source code (in JAVA) are publicly available at http://ccst.jlu.edu.cn/CSBG/PIVS/index.htm Contact: [email protected]; [email protected]

Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recently, neural networks have gained popularity in this research area because they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, non-autoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP encoded information as rewards and seamlessly integrates it into reinforcement learning strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSP instances demonstrate that NAR4TSP outperforms four state-of-the-art models in terms of solution quality, inference speed, and generalization to unseen scenarios.Comment: 14 pages, 5 figure

Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems

Laser waveform control of extreme ultraviolet high harmonics from solids

Solid-state high-harmonic sources offer the possibility of compact, high-repetition-rate attosecond light emitters. However, the time structure of high harmonics must be characterized at the sub-cycle level. We use strong two-cycle laser pulses to directly control the time-dependent nonlinear current in single-crystal MgO, leading to the generation of extreme ultraviolet harmonics. We find that harmonics are delayed with respect to each other, yielding an atto-chirp, the value of which depends on the laser field strength. Our results provide the foundation for attosecond pulse metrology based on solid-state harmonics and a new approach to studying sub-cycle dynamics in solids

Exercise improves mental health status of young adults via attenuating inflammation factors but modalities matter

IntroductionThe mental health of young adults is a global public health challenge. Numerous studies have demonstrated that exercise benefits mental health. However, it is still unclear which exercise mode is optimal for protecting mental health and its association with the immune system. This study aimed to compare the intervention effect of high-intensity interval training (HIIT) and moderate-to-vigorous intensity continuous training (MVCT) on mental health and assess the underlying mechanism of exercise interventions to improve the immune system, which facilitated the mental health status.MethodsThis is a double-blinded RCT study conducted from October 13, 2020 to January 25, 2021 (ClinicalTrials.gov identifier: NCT04830059). Ninety-three participants who met the inclusion criteria were randomized into the HIIT (N = 33), MVCT (N = 32), and control groups (N = 28) with a mean age of 25.26 (SD = 2.21), and 43% of males enrolled in the study. Professional coaches guided participants in HIIT and MVCT groups to perform 40 min of exercise training three times a week for 12-week while those in the control group received 1 h of health education twice a week. Questionnaires related to mental health status and blood samples of inflammatory factors, including immunoglobulin A (IgA), immunoglobulin M (IgM), albumin (Alb), globulin (GLO), lymphocytes (LYM), and lymphocyte percentage (LYM) were assessed before and after the intervention.ResultsWe found that blood inflammation factors increased significantly in the control group during 12 weeks (ΔIgA = 0.16 g/L, ΔIgM = 0.092 g/L, ΔAlb = 2.59 g/L, ΔGlo = 3.08 g/L, ΔLYM = 0.36, and ΔLYM% = 3.72%, p < 0.05), and both MVCT and HIIT intervention could effectively defend the increased inflammatory response compared with the control group (IgA: MVCT β = −0.14, p < 0.001, HIIT β = −0.096, p < 0.05; IgM: MVCT β = −0.12, p < 0.001; HIIT β = −0.068, p < 0.05; Alb: MVCT β = −1.64, p < 0.05, HIIT β = −1.14, p > 0.05; Glo: MVCT β = −3.17, p < 0.001, HIIT β = −2.07, p < 0.01; LYM: MVCT β = −0.34, p < 0.05, HIIT β = −0.35, p < 0.05). However, the MVCT intervention modality was more conducive to enhancing positive affect (β = 0.52, p = 0.018) and well-being (β = 1.08, p = 0.035) than HIIT. Furthermore, decreased IgA, Alb, and Glo were associated with improved mental health.ConclusionBoth 12-week HIIT and MVCT are beneficial to the immune system. The MVCT intervention mode is recommended to prevent mental health problems and attenuate immune inflammation, and the immune system is a potential mechanism that exercises improving mental health.Clinical trial registration[ClinicalTrials.gov], identifier [NCT04830059]