State Estimation for Time-Delay Systems with Markov Jump Parameters and Missing Measurements

This paper is concerned with the state estimation problem for a class of time-delay systems with Markovian jump parameters and missing measurements, considering the fact that data missing may occur in the process of transmission and its failure rates are governed by random variables satisfying certain probabilistic distribution. By employing a new Lyapunov function and using the convexity property of the matrix inequality, a sufficient condition for the existence of the desired state estimator for Markovian jump systems with missing measurements can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Furthermore, the gain of state estimator can also be derived based on the known conditions. Finally, a numerical example is exploited to demonstrate the effectiveness of the proposed method

A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma

Background: The tricarboxylic acid cycle (TCA cycle) is an important metabolic pathway and closely related to tumor development. However, its role in the development of esophageal squamous cell carcinoma (ESCC) has not been fully investigated.Methods: The RNA expression profiles of ESCC samples were retrieved from the TCGA database, and the GSE53624 dataset was additionally downloaded from the GEO database as the validation cohort. Furthermore, the single cell sequencing dataset GSE160269 was downloaded. TCA cycle-related genes were obtained from the MSigDB database. A risk score model for ESCC based on the key genes of the TCA cycle was built, and its predictive performance was evaluated. The association of the model with immune infiltration and chemoresistance were analyzed using the TIMER database, the R package “oncoPredict” score, TIDE score and so on. Finally, the role of the key gene CTTN was validated through gene knockdown and functional assays.Results: A total of 38 clusters of 8 cell types were identified using the single-cell sequencing data. The cells were divided into two groups according to the TCA cycle score, and 617 genes were identified that were most likely to influence the TCA cycle. By intersecting 976 key genes of the TCA cycle with the results of WGCNA, 57 genes significantly associated with the TCA cycle were further identified, of which 8 were screened through Cox regression and Lasso regression to construct the risk score model. The risk score was a good predictor of prognosis across subgroups of age, N, M classification and TNM stage. Furthermore, BI-2536, camptothecin and NU7441 were identified as possible drug candidates in the high-risk group. The high-risk score was associated with decreased immune infiltration in ESCC, and the low-risk group had better immunogenicity. In addition, we also evaluated the relationship between risk scores and immunotherapy response rates. Functional assays showed that CTTN may affect the proliferation and invasion of ESCC cells through the EMT pathway.Conclusion: We constructed a predictive model for ESCC based on TCA cycle-associated genes, which achieved good prognostic stratification. The model are likely associated with the regulation of tumor immunity in ESCC

Time-Aware Service Recommendation for Mashup Creation in an Evolving Service Ecosystem

Abstract-Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques such as keyword search and semantic matching while others are based on Quality of Service (QoS) prediction. However, services and their mashups are evolving over time with publishing, perishing and changing of interfaces. Therefore, a practical service recommendation approach should take into account the evolution of a service ecosystem. In this paper, we present a method to extract service evolution patterns by exploiting Latent Dirichlet Allocation (LDA) and time series prediction. A time-aware service recommendation framework for mashup creation is presented combining service evolution, collaborative filtering and content matching. Experiments on real-world ProgrammableWeb data set show that our approach leads to a higher precision than traditional collaborative filtering and content matching methods

The mechanical behavior and collapse of graphene-assembled hollow nanospheres under compression

Recently, much interest has been attracted in the graphene-assembled hollow nanospheres (GAHNs) because of outstanding multi-functional properties. This paper systematically explores the compressive mechanical behaviors and gas bearing capability of GAHNs by a coarse-grained molecular dynamics (CGMD) simulation combining with in-situ compressive test. It was found that the GAHNs possess excellent compressive elasticity (experimentally recoverable strain can reach similar to 58%). Under large compressive strain (>90%), the GAHNs also display obvious plastic deformation owing to inter-layer slippage between graphene nanosheets. In addition, the morphology of force measurement tip (FMT) plays critical roles on the compressive failure modes of GAHNs. When FMT is sharp, it can pierce through the shell of GAHN, whereas the blunt one compels GAHN to collapse. The thermal expansion process of GAHNs was investigated by CGMD simulation. With the increase of ambient temperature, the internal pressure of GAHN increased until a crack appears. To further understand this expansion failure, an in-situ scratching experiment was designed and the tearing strength of shell of GAHN was estimated to be similar to 748 MPa. This work provides an in-depth understanding on intrinsic mechanical properties of GAHNs and broadens their potential applications. (C) 2020 Elsevier Ltd. All rights reserved