Highlights d CD8 + T cell exhaustion is correlated with a high cholesterol level d Tumor microenvironment is enriched with cholesterol d Cholesterol in the tumor microenvironment induces CD8 + T cell exhaustion d ER stress-XBP1 pathway is required for cholesterol-induced CD8 + T cell exhaustion

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Tumor-associated macrophages (TAM) are important tumorpromoting cells. However, the mechanisms underlying how the tumor and its microenvironment reprogram these cells remain elusive. Here we report that lipids play a crucial role in generating TAMs in the tumor microenvironment (TME). Macrophages from both human and murine tumor tissues were enriched with lipids due to increased lipid uptake by macrophages. TAMs expressed elevated levels of the scavenger receptor CD36, accumulated lipids, and used fatty acid oxidation (FAO) instead of glycolysis for energy. High levels of FAO promoted mitochondrial oxidative phosphorylation, production of reactive oxygen species, phosphorylation of JAK1, and dephosphorylation of SHP1, leading to STAT6 activation and transcription of genes that regulate TAM generation and function. These processes were critical for TAM polarization and activity, both in vitro and in vivo. In summary, we highlight the importance of lipid metabolism in the differentiation and function of protumor TAMs in the TME.Significance: This study highlights the role of lipid metabolism in the differentiation and function of TAMs and suggests targeting TAM fatty acid oxidation as a potential therapeutic modality for human cancers. MiceC57BL/6J, CD36 À/À , and NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice were purchased from The Jackson Laboratory. C57BL/ KaLwRij mice were purchased from Envigo. Mice were maintained in a temperature-and humidity-controlled environment and given unrestricted access to chow diet and acidified water. Detailed methods

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CD8+ T cells can be polarized into IL-9–secreting (Tc9) cells. Ma et al. show that Tc9 cell differentiation is associated with a low cholesterol reprogramming profile, and manipulating cholesterol content in polarizing Tc9 cells significantly affects Tc9 differentiation and antitumor response.

The performance bottlenecks of graph applications depend not only on the algorithm and the underlying hardware, but also on the size and structure of the input graph. As a result, programmers must try different combinations of a large set of techniques, which make tradeoffs among locality, work-efficiency, and parallelism, to develop the best implementation for a specific algorithm and type of graph. Existing graph frameworks and domain specific languages (DSLs) lack flexibility, supporting only a limited set of optimizations.This paper introduces GraphIt, a new DSL for graph computations that generates fast implementations for algorithms with different performance characteristics running on graphs with different sizes and structures. GraphIt separates what is computed (algorithm) from how it is computed (schedule). Programmers specify the algorithm using an algorithm language, and performance optimizations are specified using a separate scheduling language. The algorithm language simplifies expressing the algorithms, while exposing opportunities for optimizations. We formulate graph optimizations, including edge traversal direction, data layout, parallelization, cache, NUMA, and kernel fusion optimizations, as tradeoffs among locality, parallelism, and work-efficiency. The scheduling language enables programmers to easily search through this complicated tradeoff space by composing together a large set of edge traversal, vertex data layout, and program structure optimizations. The separation of algorithm and schedule also enables us to build an autotuner on top of GraphIt to automatically find high-performance schedules. The compiler uses a new scheduling representation, the graph iteration space, to model, compose, and ensure the validity of the large number of optimizations. We evaluate GraphIt's performance with seven algorithms on graphs with different structures and sizes. GraphIt outperforms the next fastest of six state-of-the-art shared-memory frameworks (Ligra, Green-Marl, GraphMat, Galois, Gemini, and Grazelle) on 24 out of 32 experiments by up to 4.8×, and is never more than 43% slower than the fastest framework on the other experiments. GraphIt also reduces the lines of code by up to an order of magnitude compared to the next fastest framework.

Detection of senescence's dynamics in crop breeding is time consuming and needs considerable details regarding its rate of progression and intensity. Normalized difference red-edge index (NDREI) along with four other spectral vegetative indices (SVIs) derived from unmanned aerial vehicle (UAV) based spatial imagery, were evaluated for rapid and accurate prediction of senescence. For this, 32 selected winter wheat genotypes were planted under full and limited irrigation treatments. Significant variations for all five SVIs: green normalize difference vegetation index (GNDVI), simple ratio (SR), green chlorophyll index (GCI), red-edge chlorophyll index (RECI), and normalized difference red-edge index (NDREI) among genotypes and between treatments, were observed from heading to late grain filling stages. The SVIs showed strong relationship (R 2 = 0.69 to 0.78) with handheld measurements of chlorophyll and leaf area index (LAI), while negatively correlated (R 2 = 0.75 to 0.77) with canopy temperature (CT) across the treatments. NDREI as a new SVI showed higher correlations with ground data under both treatments, similarly as exhibited by other four SVIs. There were medium to strong correlations (r = 0.23-0.63) among SVIs, thousand grain weight (TGW) and grain yield (GY) under both treatments. Senescence rate was calculated by decreasing values of SVIs from their peak values at heading stage, while variance for senescence rate among genotypes and between treatments could be explained by SVIs variations. Under limited irrigation, 10% to 15% higher senescence rate was detected as compared with full irrigation. Principle component analysis corroborated the negative association of high senescence rate with TGW and GY. Some genotypes, such as Beijing 0045, Nongda 5181, and Zhongmai 175, were selected with low senescence rate, stable TGW and GY in both full and limited irrigation treatments, nearly in accordance with the actual performance of these cultivars in field. Thus, SVIs derived from UAV appeared as a promising tool for rapid and precise estimation of senescence rate at maturation stages.

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