It型滞后随机系统的仿真

本文研究了Ｉｔｏ型滞后随机系统的数值解法与仿真,得到了Ｉｔｏ型滞后随机系统数值计算的迭代格式,分析了迭代格式的精度和用于Ｉｔｏ型滞后随机系统仿真的有效性,并用实例演示了所得方法的应用。     

随机系统利用变结构控制方法的镇定

本文研究了Ｉｔｏ型随机系统的变结构控制,构造了参数不具有噪声激励滑动流形与变结构控制律,对Ｉｔｏ型随机系统利用变结构控制方法给出镇定控制律,并用例子演示了本文结果的应用方法。     

X80级管线钢的开发研究

根据X80级管线钢技术要求,对湘潭钢铁(集团)有限公司试制X80级管线钢进行成分设计、相变规律和再结晶规律研究,开发出了1 016×22.0 mm X80级热轧钢板.检验结果表明,各项性能指标均满足技术要求.     

O2流量对O2/C4F8等离子体刻蚀SiCOH低k薄膜的影响

研究了O2/C4F8等离子体刻蚀SiCOH低k薄膜时O2流量对刻蚀率、表面结构的影响,及其放电等离子体特性的关联.发现O2流量的增大可以极大地提高多孔SiCOH薄膜的刻蚀速率,降低表面的粗糙度,减少SiCOH薄膜表面的C:F沉积.等离子体特性的光谱分析表明,O2的添加,增强了C与O之间的反应,从而在Si、F反应刻蚀Si的同时,C、O之间的反应使C消耗,实现Si、C的同步刻蚀,从而获得SiCOH低k薄膜的高刻蚀率和低粗糙度表面.     

基于烟花算法的基站群协作计算卸载模型研究

车联网、AR、AI等计算密集、时延敏感型应用迅速发展,而移动设备因自身计算能力相对不足,执行此类应用任务时会因高时延而严重影响用户体验甚至无法满足用户需求。针对此问题,提出综合考虑时延与成本的多用户、多MEC (mobile edge computing)服务器的基站群协作计算卸载模型。并提出基于凸优化的改进烟花算法（improved fireworks algorithm based on convex optimization, CVX-FWA）来对模型进行求解,对用户任务进行合理的卸载与资源分配。仿真结果表明,提出的计算卸载方案有效降低了任务总时延成本值,实现计算卸载资源的整体优化配置。     

Blockage of the NLRP3 inflammasome by MCC950 improves anti-tumor immune responses in head and neck squamous cell carcinoma

The NLRP3 inflammasome is a critical innate immune pathway responsible for producing active interleukin (IL)-1β, which is associated with tumor development and immunity. However, the mechanisms regulating the inflammatory microenvironment, tumorigenesis and tumor immunity are unclear. Herein, we show that the NLRP3 inflammasome was over-expressed in human HNSCC tissues and that the IL-1β concentration was increased in the peripheral blood of HNSCC patients. Additionally, elevated NLRP3 inflammasome levels were detected in tumor tissues of Tgfbr1/Pten 2cKO HNSCC mice, and elevated IL-1β levels were detected in the peripheral blood serum, spleen, draining lymph nodes and tumor tissues. Blocking NLRP3 inflammasome activation using MCC950 remarkably reduced IL-1β production in an HNSCC mouse model and reduced the numbers of myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs) and tumor-associated macrophages (TAMs). Moreover, inhibiting NLRP3 inflammasome activation increased the numbers of CD4⁺ and CD8⁺ T cells in HNSCC mice. Notably, the numbers of exhausted PD-1⁺ and Tim3⁺ T cells were significantly reduced. A human HNSCC tissue microarray showed that NLRP3 inflammasome expression was correlated with the expression of CD8 and CD4, the Treg marker Foxp3, the MDSC markers CD11b and CD33, and the TAM markers CD68 and CD163, PD-1 and Tim3. Overall, our results demonstrate that the NLRP3 inflammasome/IL-1β pathway promotes tumorigenesis in HNSCC and inactivation of this pathway delays tumor growth, accompanied by decreased immunosuppressive cell accumulation and an increased number of effector T cells. Thus, inhibition of the tumor microenvironment through the NLRP3 inflammasome/IL-1β pathway may provide a novel approach for HNSCC therapy.     

Genome-wide association studies of 14 agronomic traits in rice landraces

Uncovering the genetic basis of agronomic traits in crop landraces that have adapted to various agro-climatic conditions is important to world food security. Here we have identified ～ 3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method. We performed genome-wide association studies (GWAS) for 14 agronomic traits in the population of Oryza sativa indica subspecies. The loci identified through GWAS explained ～ 36% of the phenotypic variance, on average. The peak signals at six loci were tied closely to previously identified genes. This study provides a fundamental resource for rice genetics research and breeding, and demonstrates that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice.     

Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm

A high-density haplotype map recently enabled a genome-wide association study (GWAS) in a population of indica subspecies of Chinese rice landraces. Here we extend this methodology to a larger and more diverse sample of 950 worldwide rice varieties, including the Oryza sativa indica and Oryza sativa japonica subspecies, to perform an additional GWAS. We identified a total of 32 new loci associated with flowering time and with ten grain-related traits, indicating that the larger sample increased the power to detect trait-associated variants using GWAS. To characterize various alleles and complex genetic variation, we developed an analytical framework for haplotype-based de novo assembly of the low-coverage sequencing data in rice. We identified candidate genes for 18 associated loci through detailed annotation. This study shows that the integrated approach of sequence-based GWAS and functional genome annotation has the potential to match complex traits to their causal polymorphisms in rice.     

A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving

Railway transportation plays an important role in modern society. As China's massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters(e.g., loading and marshaling) of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging – REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers.