Pan-cancer analysis of TASL: a novel immune infiltration-related biomarker for tumor prognosis and immunotherapy response prediction.

BACKGROUND: New immunotherapeutic strategies based on predictors are urgently needed. Toll-like receptor adaptor interacting with SLC15A4 on the lysosome (TASL) was recently confirmed to fulfill an important role in the innate immune response. However, whether TASL is involved in tumor development and immunotherapy response prediction has not been reported. METHODS: TCGA and GTEx were used to yield transcriptional, genetic, and epigenetic levels of TASL in 33 cancer types. CIBERSORT was used to explore the correlation between TASL expression and multiple immune-related signatures and tumor-infiltrating immune cell content in different cancer types. The ability of TASL to predict tumor immunotherapy response was analyzed in seven datasets. Finally, we tested TASL expression in human glioma cell lines and tissue samples and analyzed its correlation with clinicopathological parameters. RESULTS: TASL is widely heterogeneous at the transcriptional, genetic, and epigenetic levels. High TASL expression is an independent poor prognostic factor for immune "cold" tumor Low-Grade Glioma (LGG) but an opposite factor for "hot" tumors Lung Adenocarcinoma (LUAD) and Skin Cutaneous Melanoma (SKCM). TASL may affect tumor immune infiltration by mediating tumor-infiltrating lymphocytes and tumor-associated macrophages. It may differentially affect the prognosis of the three cancers by regulating the immunosuppressive microenvironment in LGG and the immunostimulatory microenvironment in LUAD and SKCM. High TASL expression is a potential biomarker for the positive response to immunotherapy in cancers such as SKCM and was also experimentally confirmed to be positively associated with adverse clinicopathological features of gliomas. CONCLUSION: TASL expression is an independent prognostic factor for LGG, LUAD, and SKCM. High TASL expression is a potential biomarker for the positive response to immunotherapy in certain cancer types such as SKCM. Further basic studies focusing on TASL expression and tumor immunotherapy are urgently needed.

Output Consensus of Heterogeneous Linear Multiagent Systems With Directed Graphs via Adaptive Dynamic Event-Triggered Mechanism.

This article investigates the output consensus problem of heterogeneous linear multiagent systems under directed communication graphs. A novel adaptive dynamic event-triggered mechanism is proposed, intending to further save system resources consumed in communication between agents and controller update of agents themselves, and remove the assumption that the global information associated with the communication topology should be known in advance for the design of control parameters. Unlike the existing related adaptive event-triggered algorithms, the proposed algorithm could theoretically guarantee the existence of a strictly positive constant on the interevent time intervals for both communication between agents and controller update. Furthermore, it is shown by the simulation that the addition of a dynamic variable has the potential to further optimize the control cost when compared to the addition of exponential function signal or L1 signal usually adopted in existing adaptive event-triggered mechanisms. Then, the obtained results are extended from the strongly connected graph to the directed communication graph only containing a spanning tree. Finally, numerical simulation results are conducted to demonstrate the effectiveness of the proposed mechanism.

Data-Based Predictive Control via Multistep Policy Gradient Reinforcement Learning.

In this article, a model-free predictive control algorithm for the real-time system is presented. The algorithm is data driven and is able to improve system performance based on multistep policy gradient reinforcement learning. By learning from the offline dataset and real-time data, the knowledge of system dynamics is avoided in algorithm design and application. Cooperative games of the multiplayer in time horizon are presented to model the predictive control as optimization problems of multiagent and guarantee the optimality of the predictive control policy. In order to implement the algorithm, neural networks are used to approximate the action-state value function and predictive control policy, respectively. The weights are determined by using the methods of weighted residual. Numerical results show the effectiveness of the proposed algorithm.

Determination of single-kidney glomerular filtration rate (GFR) with CT urography versus renal dynamic imaging Gates method.

OBJECTIVES: To present a single-kidney CT-GFR measurement and compare it with the renal dynamic imaging Gates-GFR. MATERIALS AND METHODS: Thirty-six patients with hydronephrosis referred for CT urography and 99mTc-DTPA renal dynamic imaging were prospectively included. Informed consent was obtained from all patients. The CT urography protocol included non-contrast, nephrographic, and excretory phase imaging. The total CT-GFR was calculated by dividing the CT number increments of the total urinary system between the nephrographic and excretory phase by the products of iodine concentration in the aorta and the elapsed time, then multiplied by (1- Haematocrit). The total CT-GFR was then split into single-kidney CT-GFR by a left and right kidney proportionality factor. The results were compared with single-kidney Gates-GFR by using paired t-test, correlation analysis, and Bland-Altman plots. RESULTS: Paired difference between single-kidney CT-GFR (45.02 ± 13.91) and single-kidney Gates-GFR (51.21 ± 14.76) was 6.19 ± 5.63 ml/min, p<0.001, demonstrating 12.1% systematic underestimation with ±11.03 ml/min (±21.5%) measurement deviation. A good correlation was revealed between both measurements (r=0.87, p<0.001). CONCLUSION: The proposed single-kidney CT-GFR correlates and agrees well with the reference standard despite a systematic underestimation, therefore it could be a one-stop-shop for evaluating urinary tract morphology and split renal function. KEY POINTS: • A new CT method can assess split renal function • Only using images from CT urography and the value of haematocrit • A one-stop-shop CT technique without additional radiation dose.

Reliable Output Feedback Control for T-S Fuzzy Systems With Decentralized Event Triggering Communication and Actuator Failures.

Due to the unavailability of full state variables in many control systems, this paper is concerned with the design of reliable observer-based output feedback controller for a class of network-based Takagi-Sugeno fuzzy systems with actuator failures. In order to better allocate network resources under the case that the sensor nodes are physically distributed, the decentralized event triggering communication scheme is adopted such that each sensor node is capable to determine the transmission of its local measurement information independently. Considering that the implementation of the controller may not be synchronized with the plant trajectories due to asynchronous premise variables with such communication mechanism, a novel piecewise fuzzy observer-based output feedback controller is developed. By applying a piecewise Lyapunov function and some techniques on matrix convexification, an approach to the design of observer and controller gain is derived for the augmented closed-loop system to be asymptotically stable with a guaranteed H∞ performance and reduced transmission frequency. Finally, two examples are given to show the effectiveness of the developed method.

A new approach to guaranteed cost control of T-s fuzzy dynamic systems with interval parameter uncertainties.

This paper investigates the problem of guaranteed cost control for Takagi-Sugeno fuzzy dynamic systems with interval parameter uncertainties. The parameter uncertainty is characterized by the matrix bound, which is quite natural in real applications. Attention is focused on the fuzzy state feedback controller design via the so-called parallel distributed compensation scheme, which guarantees the closed-loop system to be robustly stable with a prescribed upper bound of the cost function. By utilizing the instrumental idea of delay dividing, a new Lyapunov-Krasovskii functional is introduced, which leads the resultant conditions to be much less conservative than most existing results in the literature. Some other new ideas such as basis dependence are also employed, which help to reduce the conservatism. All the results are formulated in the form of linear matrix inequalities (LMIs), which can readily be solved via standard numerical software. Finally, illustrative examples are given to show the less conservatism and applicability of the obtained results.