Single-cell sequencing of ascites fluid illustrates heterogeneity and therapy-induced evolution during gastric cancer peritoneal metastasis.

Peritoneal metastasis is the leading cause of death for gastrointestinal cancers. The native and therapy-induced ascites ecosystems are not fully understood. Here, we characterize single-cell transcriptomes of 191,987 ascites cancer/immune cells from 35 patients with/without gastric cancer peritoneal metastasis (GCPM). During GCPM progression, an increase is seen of monocyte-like dendritic cells (DCs) that are pro-angiogenic with reduced antigen-presenting capacity and correlate with poor gastric cancer (GC) prognosis. We also describe the evolution of monocyte-like DCs and regulatory and proliferative T cells following therapy. Moreover, we track GC evolution, identifying high-plasticity GC clusters that exhibit a propensity to shift to a high-proliferative phenotype. Transitions occur via the recently described, autophagy-dependent plasticity program, paligenosis. Two autophagy-related genes (MARCKS and TXNIP) mark high-plasticity GC with poorer prognosis, and autophagy inhibitors induce apoptosis in patient-derived organoids. Our findings provide insights into the developmental trajectories of cancer/immune cells underlying GCPM progression and therapy resistance.

Neural network-based fault detection for nonlinear networked systems with uncertain medium access constraint: Application to motor systems.

The fault detection for a class of continuous-time nonlinear networked control systems with medium access constraint is concerned in this paper, where the occurring probability of transition from one sensor to another is allowed to be partially unknown and uncertain. First of all, a Markovian system approach is adopted to describe the access process of sensors, in which only one sensor is allowed to access the communication channel. A robust filter based residual generator is proposed to generate the residual signal such that it can be used to indicate whether the fault has occurred or not. The nonlinear term is approximated by a neural network, and the Lyapunov-Krasovskii functional is introduced to analyze the fault detection system, three sufficient conditions for the stochastic stability of fault detection error system are given, and the fault detection filter gains are calculated via solving some matrix inequalities. In the simulation, a second-order DC motor system is used to validate of the main results, which shows the effectiveness of the fault detector design.