Wenxi Zhang

Abstract A major limitation of chimeric antigen receptor (CAR) T cell therapies is the poor persistence of these cells in vivo 1 . The expression of memory-associated genes in CAR T cells is linked to their long-term persistence in patients and clinical efficacy 2–6 , suggesting that memory programs may underpin durable CAR T cell function. Here we show that the transcription factor FOXO1 is responsible for promoting memory and restraining exhaustion in human CAR T cells. Pharmacological inhibition or gene editing of endogenous FOXO1 diminished the expression of memory-associated genes, promoted an exhaustion-like phenotype and impaired the antitumour activity of CAR T cells. Overexpression of FOXO1 induced a gene-expression program consistent with T cell memory and increased chromatin accessibility at FOXO1-binding motifs. CAR T cells that overexpressed FOXO1 retained their function, memory potential and metabolic fitness in settings of chronic stimulation, and exhibited enhanced persistence and tumour control in vivo. By contrast, overexpression of TCF1 (encoded by TCF7 ) did not enforce canonical memory programs or enhance the potency of CAR T cells. Notably, FOXO1 activity correlated with positive clinical outcomes of patients treated with CAR T cells or tumour-infiltrating lymphocytes, underscoring the clinical relevance of FOXO1 in cancer immunotherapy. Our results show that overexpressing FOXO1 can increase the antitumour activity of human CAR T cells, and highlight memory reprogramming as a broadly applicable approach for optimizing therapeutic T cell states.

Shadow detection and compensation on high-resolution orthophoto is one of the most important tasks for ensuring high-quality of the radiometric balance of digital orthophoto map (DOM). This paper proposes a novel shadow detection method through semantic matching between the artificial “shadow” polygons (ASPs) and the real shadowed polygons (RSPs). The ASPs are created by digital building model (DBM), solar zenith and solar azimuth. A group of polygons semantic features, such as position similarity, area similarity, direction similarity and shape similarity are described and used as matching parameters, and then the real shadow regions (polygons) (RSPs) are detected using two-level matchings between the ASPs and the RSPs. The initial of matching aims at obtaining the initial probability and the candidates of the matching pair through determining the initial search circle cantered at the ASPs. The final of matching aims at finding the final match pairs through iteration of semantic matching, of which the maximum probability, which is calculated by the support coefficient of the adjacent match pair, is adopted as the criterion of the iteration. The experimental area located in Denver, Colorado, USA is used to validate the proposed algorithm. When compared with the dual-threshold, the Least-Squares Support Vector Machine (LS-SVM), and the UNet shadow detection method, the method proposed in this paper is able to increase the rate of shadow detection by 38.98%, 17.33%, and 13.14%, and decrease the fault detection rate by 19.43%, 10.01%, and 4.57%.

High-temperature thermal insulation and microwave absorption performance of PI/MXene composite aerogels.