DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence

Recent development of spatial transcriptomics (ST) is capable of associating spatial information at different spots in the tissue section with RNA abundance of cells within each spot, which is particularly important to understand tissue cytoarchitectures and functions. However, for such ST data, since a spot is usually larger than an individual cell, gene expressions measured at each spot are from a mixture of cells with heterogenous cell types. Therefore, ST data at each spot needs to be disentangled so as to reveal the cell compositions at that spatial spot. In this study, we propose a novel method, named deconvoluting spatial transcriptomics data through graph-based convolutional networks (DSTG), to accurately deconvolute the observed gene expressions at each spot and recover its cell constitutions, thus achieving high-level segmentation and revealing spatial architecture of cellular heterogeneity within tissues. DSTG not only demonstrates superior performance on synthetic spatial data generated from different protocols, but also effectively identifies spatial compositions of cells in mouse cortex layer, hippocampus slice and pancreatic tumor tissues. In conclusion, DSTG accurately uncovers the cell states and subpopulations based on spatial localization. DSTG is available as a ready-to-use open source software (https://github.com/Su-informatics-lab/DSTG) for precise interrogation of spatial organizations and functions in tissues

A novel joint support vector machine-cubature Kalman filtering method for adaptive state of charge prediction of lithium-ion batteries.

Accurate estimation of SOC of lithium-ion batteries has always been an important work in the battery management system. However, it is often very difficult to accurately estimate the SOC of lithium-ion batteries. Therefore, a novel joint support vector machine - cubature Kalman filtering (SVM-CKF) method is proposed in this paper. SVM is used to train the output data of the CKF algorithm to obtain the model. Meanwhile, the output data of the model is used to compensate the original SOC, to obtain a more accurate estimate of SOC. After the SVM-CKF algorithm is introduced, the amount of data needed for prediction is reduced. By using Beijing Bus Dynamic Stress Test (BBDST) and the Dynamic Stress Test (DST) condition to verify the training model, the results show that the SVM-CKF algorithm can significantly improve the estimation accuracy of Lithium-ion battery SOC, and the maximum error of SOC prediction for BBDST condition is 0.800%, which is reduced by 0.500% compared with CKF algorithm. The maximum error of SOC prediction under DST condition is about 0.450%, which is 1.350% less than that of the CKF algorithm. The overall algorithm has a great improvement in generalization ability, which lays a foundation for subsequent research on SOC prediction

Associations of plasma very-long-chain SFA and the metabolic syndrome in adults

Plasma levels of very-long-chain SFA (VLCSFA) are associated with the metabolic syndrome (MetS). However, the associations may vary by different biological activities of individual VLCSFA or population characteristics. We aimed to examine the associations of VLCSFA and MetS risk in Chinese adults. Totally, 2008 Chinese population aged 35–59 years were recruited and followed up from 2010 to 2012. Baseline MetS status and plasma fatty acids data were available for 1729 individuals without serious diseases. Among 899 initially metabolically healthy individuals, we identified 212 incident MetS during the follow-up. Logistic regression analysis was used to estimate OR and 95 % CI. Cross-sectionally, each VLCSFA was inversely associated with MetS risk; comparing with the lowest quartile, the multivariate-adjusted OR for the highest quartile were 0·18 (95 % CI 0·13, 0·25) for C20 : 0, 0·26 (95 % CI 0·18, 0·35) for C22 : 0, 0·19 (95 % CI 0·13, 0·26) for C24 : 0 and 0·16 (0·11, 0·22) for total VLCSFA (all Pfor trend<0·001). The associations remained significant after further adjusting for C16 : 0, C18 : 0, C18 : 3n-3, C22 : 6n-3, n-6 PUFA and MUFA, respectively. Based on follow-up data, C20 : 0 or C22 : 0 was also inversely associated with incident MetS risk. Among the five individual MetS components, higher levels of VLCSFA were most strongly inversely associated with elevated TAG (≥1·7 mmol/l). Plasma levels of VLCSFA were significantly and inversely associated with MetS risk and individual MetS components, especially TAG. Further studies are warranted to confirm the findings and explore underlying mechanisms

scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics

Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN

Automatic Differential Analysis of ARX Block Ciphers with Application to SPECK and LEA

In this paper, we focus on the automatic differential cryptanalysis of ARX block ciphers with respect to XOR-difference, and develop Mouha et al.\u27s framework for finding differential characteristics by adding a new method to construct long characteristics from short ones. The new method reduces the searching time a lot and makes it possible to search differential characteristics for ARX block ciphers with large word sizes such as $n=48,64$. What\u27s more, we take the differential effect into consideration and find that the differential probability increases by a factor of $1.4\sim 8$ for SPECK and about $2^{10}$ for LEA when multiple characteristics are counted in. The efficiency of our method is demonstrated by improved attacks of SPECK and LEA, which attack 1, 1, 4 and 6 more rounds of SPECK48, SPECK64, SPECK96 and SPECK128, respectively, and 2 more rounds of LEA than previous works