Dead-Time Correction Applied for Extended Flux-Based Sensorless Control of Assisted PMSMs in Electric Vehicles

Sensorless control technology of PMSMs is of great importance for safety and reliability in electric vehicles. Among all existing methods, only the extended flux-based method has great performance over all speed range. However, the accuracy and reliability of the extended flux rotor position observer are greatly affected by the dead-time effect. In this paper, the extended flux-based observer is adopted to develop a sensorless control system. The influence of dead-time effect on the observer is analyzed and a dead-time correction method is specially designed to guarantee the reliability of the whole control system. A comparison of estimation precision among the extended flux-based method, the electromotive force (EMF)-based method and the high frequency signal injection method is given by simulations. The performance of the proposed sensorless control system is verified by experiments. The experimental results show that the proposed extended flux-based sensorless control system with dead-time correction has satisfactory performance over full speed range in both loaded and non-loaded situations. The estimation error of rotor speed is within 4% in all working conditions. The dead-time correction method improves the reliability of the control system effectively

Using whole-genome sequencing (WGS) to plot colorectal cancer-related gut microbiota in a population with varied geography

Abstract Background Colorectal cancer (CRC) is a multifactorial disease with genetic and environmental factors. Regional differences in risk factors are an important reason for the different incidences of CRC in different regions. Objective The goal was to clarify the intestinal microbial composition and structure of CRC patients in different regions and construct CRC risk prediction models based on regional differences. Methods A metagenomic dataset of 601 samples from 6 countries in the GMrepo and NCBI databases was collected. All whole-genome sequencing (WGS) data were annotated for species by MetaPhlAn2. We obtained the relative abundance of species composition at the species level and genus level. The MicrobiotaProcess package was used to visualize species composition and PCA. LEfSe analysis was used to analyze the differences in the datasets in each region. Spearman correlation analysis was performed for CRC differential species. Finally, the CRC risk prediction model was constructed and verified in each regional dataset. Results The composition of the intestinal bacterial community varied in different regions. Differential intestinal bacteria of CRC in different regions are inconsistent. There was a common diversity of bacteria in all six countries, such as Peptostreptococcus stomatis and Fusobacterium nucleatum at the species level. Peptostreptococcus stomatis (species level) and Peptostreptococcus (genus level) are important CRC-related bacteria that are related to other bacteria in different regions. Region has little influence on the accuracy of the CRC risk prediction model. Peptostreptococcus stomatis is an important variable in CRC risk prediction models in all regions. Conclusion Peptostreptococcus stomatis is a common high-risk pathogen of CRC worldwide, and it is an important variable in CRC risk prediction models in all regions. However, regional differences in intestinal bacteria had no significant impact on the accuracy of the CRC risk prediction model

Screening and Verification of Photosynthesis and Chloroplast-Related Genes in Mulberry by Comparative RNA-Seq and Virus-Induced Gene Silencing

Photosynthesis is one of the most important factors in mulberry growth and production. To study the photosynthetic regulatory network of mulberry we sequenced the transcriptomes of two high-yielding (E1 and E2) and one low-yielding (H32) mulberry genotypes at two-time points (10:00 and 12:00). Re-annotation of the mulberry genome based on the transcriptome sequencing data identified 22,664 high-quality protein-coding genes with a BUSCO-assessed completeness of 93.4%. A total of 6587 differentially expressed genes (DEGs) were obtained in the transcriptome analysis. Functional annotation and enrichment revealed 142 out of 6587 genes involved in the photosynthetic pathway and chloroplast development. Moreover, 3 out of 142 genes were further examined using the VIGS technique; the leaves of MaCLA1- and MaTHIC-silenced plants were markedly yellowed or even white, and the leaves of MaPKP2-silenced plants showed a wrinkled appearance. The expression levels of the ensiled plants were reduced, and the levels of chlorophyll b and total chlorophyll were lower than those of the control plants. Co-expression analysis showed that MaCLA1 was co-expressed with CHUP1 and YSL3; MaTHIC was co-expressed with MaHSP70, MaFLN1, and MaEMB2794; MaPKP2 was mainly co-expressed with GH9B7, GH3.1, and EDA9. Protein interaction network prediction revealed that MaCLA1 was associated with RPE, TRA2, GPS1, and DXR proteins; MaTHIC was associated with TH1, PUR5, BIO2, and THI1; MaPKP2 was associated with ENOC, LOS2, and PGI1. This study offers a useful resource for further investigation of the molecular mechanisms involved in mulberry photosynthesis and preliminary insight into the regulatory network of photosynthesis

Aging characteristics of colorectal cancer based on gut microbiota

Abstract Background Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). Methods A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age‐differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age‐differentiated bacteria. Results The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age‐related differential bacteria and model of CRC patients based on the 58 age‐related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease‐differentiated bacteria and the age‐differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). Conclusion Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC