Adaptive Stabilization of Stochastic Nonlinear Systems Disturbed by Unknown Time Delay and Covariance Noise

This paper considers a more general stochastic nonlinear time-delay system driven by unknown covariance noise and investigates its adaptive state-feedback control problem. As a remarkable feature, the growth assumptions imposed on delay-dependent nonlinear terms are removed. Then, with the help of Lyapunov-Krasovskii functionals and adaptive backstepping technique, an adaptive state-feedback controller is constructed by overcoming the negative effects brought by unknown time delay and covariance noise. Based on the designed controller, the closed-loop system can be guaranteed to be globally asymptotically stable (GAS) in probability. Finally, a simulation example demonstrates the effectiveness of the proposed scheme

Data from a comparative proteomic analysis of tumor-derived lung-cancer CD105+ endothelial cells

AbstractIncreasing evidence indicates that tumor-derived endothelial cells (TECs) are more relevant for the study of tumor angiogenesis and for screening antiangiogenic drugs than normal ECs (NECs). In this data article, high-purity (>98%) primary CD105+ NECs and TECs purified from a mouse Lewis lung carcinoma model bearing 0.5cm tumors were identified using 2D-PAGE and Matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS). All the identified proteins were categorized functionally by Gene Ontology (GO) analysis, and gene-pathway annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, protein–protein interaction networks were also built. The proteomics and bioinformatics data presented here provide novel insights into the molecular characteristics and the early modulation of the TEC proteome in the tumor microenvironment

Characterization of Fungal nirK-Containing Communities and N2O Emission From Fungal Denitrification in Arable Soils

Fungal denitrifiers play important roles in soil nitrogen cycling, but we have very limited knowledge about their distribution and functions in ecosystems. In this study, three types of arable soils were collected across different climate zones in China, including quaternary red clay soils, alluvial soils, and black soils. The composition and abundance of fungal nirK-containing denitrifiers was determined by MiSeq high-throughput sequencing and qPCR, respectively. Furthermore, a substrate-induced inhibition approach was used to explore N2O emissions from fungal denitrification. The results showed that the arable soils contained a wide range of nirK-containing fungal denitrifiers, with four orders and eight genera. Additionally, approximately 57.30% of operational taxonomic unit (OTUs) belonged to unclassified nirK-containing fungi. Hypocreales was the most predominant order, with approximately 40.51% of the total number of OTUs, followed by Sordariales, Eurotiales, and Mucorales. It was further indicated that 53% of fungal nirK OTUs were shared by the three types of soils (common), and this group of fungi comprised about 98% of the total relative abundance of the nirK-containing population, indicating that the distribution of fungal nirK-containing denitrifiers was quite homogenous among the soil types. These common OTUs were determined by multiple soil characteristics, while the composition of unique OTUs was manipulated by the specific properties of each soil type. Furthermore, fungal N2O emissions were significantly and positively correlated with fungal nirK abundance in the soils, whereas it was not clearly related to fungal nirK compositions. In conclusion, although the arable soils hosted diverse nirK-containing fungal denitrifiers, fungal nirK compositions were highly homogenous among the soil types, which could be a consequence of enduring agricultural practices. The abundance of fungal nirK-containing denitrifiers, rather than their composition, may play more significant roles in relation to N2O emission from fungal denitrification

Effect of Spatial Heterogeneity on the Microbial Community of Daqu, a Fermentation Starter for Chinese Baijiu

The effect of spatial heterogeneity on the microbial community and physicochemical properties during the primary fermentation of Daqu were investigated by high-throughput sequencing technology and conventional detection methods. Nongxiangxing baijiu Daqu inoculated with the unique ripe starter obtained by gradually culturing and expanding Daqu treated by cosmic rays was used. The results showed that although the intensity of change in driving factors varied among layer, their trends were the same. The liquefying, saccharifying and esterifying power of Daqu were higher in the bottom layer than in the upper and middle layers at the same fermentation time and the fluctuation was small. The microbial community of Daqu was composed of 12 dominant bacterial genera, including Lactobacillus, Weissella, Bacillus, Kosakonia, Staphylococcu and Thermoactinomyces, and seven dominant fungal genera, such as Pichia, Thermoascus, and Rhizomucor. Principal co-ordinates analysis and hierarchical clustering analysis showed significant differences in the bacterial and fungal community structure among fermentation stages and layers. Procrustes analysis and Mantel test showed that moisture had a significant effect on the bacterial community in Daqu, and acidity had a significant effect on the bacterial community in the middle and bottom layers of Daqu. Moreover, moisture had a significant effect on the fungal community in the upper and middle layers of Daqu. Redundancy analysis showed that moisture and acidity were positively correlated with Lactobacillus and Pichia, while driving factors had different influences on the microbial communities in different layers of Daqu. Therefore, the interaction and co-occurrence patterns of microbial genera in Daqu could change due to the differences in driving factors among different layers of Daqu. These results suggested that regulating driving factors during the Daqu making process is an effective way to improve the microbial community structure and quality of Daqu

Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

OBJECTIVE: There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. METHODS: Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. RESULTS: Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient C(p) (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small‐worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. CONCLUSION: Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40035-021-00255-0

Characterizing HIV Transmission Networks Across the United States

Background. Clinically, human immunodeficiency virus type 1 (HIV-1) pol sequences are used to evaluate for drug resistance. These data can also be used to evaluate transmission networks and help describe factors associated with transmission risk

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen, China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute, Boston, USA) for their help. This work was supported by the grant of Top Ten Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu was supported by the National Natural Science Foundation of China (31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075).S