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Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

One-dimensional carbon materials were widely used in adsorption and purification, catalytic carriers, energy electronics and other fields because of their efficient mass transfer and diffusion efficiency, excellent mechanical properties and good chemical stability. Microwave heating met the requirements of current material processing and sustainable development by rapid treatment, improving energy efficiency and reducing equipment cost, and showed great application potential in the preparation of one-dimensional carbon materials. The development and application status of one-dimensional carbon materials and the unique advantages of microwave heating technology were introduced in this paper, the mechanism and characteristics of microwave heating were summarized, and the research and application status of microwave heating related to the preparation and performance optimization of biomass-based carbon nanotubes (CNTs), carbon nanofibers (CNFs) and polyacrylonitrile-based carbon nanofibers (PAN-based CNFs) were summarized. In addition, the action mode of microwave heating on biomass-based one-dimensional carbon materials during carbonization and the changes of structure and properties of PAN-based carbon fibers during microwave heating were emphatically analyzed. Finally, it was pointed out that accurate and reliable temperature measurement tools and corresponding microscopic characterization methods should be developed, the simulation and modeling tools should be used to innovatively design novel microwave reators, and the understanding of the interaction mechanism between materials and microwave through scientific research practice and basic research should be deepened, and to promote the extensive and in-depth application of microwave heating technology in the field of material processing.

More attention is paid on the circulating fluidized bed (CFB) combustion technology because of its advantage in low cost emission control. Recently, great achievements have been realized in China. The development history of CFB combustion technology in China for decades is reviewed in this paper, from learning to independent technological innovation. High-performance CFB boilers, energy-saving CFB boilers and ultra-low emissions CFB boilers have be successively developed, while improving steam parameters and realizing large-scale. At present, China has become the largest equipment supplier and user of CFB boilers in the world. As an important part of China's coal-fired power generation system, important contributions have been made to reliable and cheap power supply and low-quality fuel consumption. For the carbon neutralization, coal will be indispensable in the security of power system as the guaranteed energy. As the main way of efficient and clean utilization of low calorific value coal and high sulfur anthracite, greater advantages of CFB boiler in deep peak regulation and rapid load changing flexibility should be exploited. To meet the peak regulating demand of high proportion of new energy consumption, pulverized coal CFB technology can be developed, distributed small-capacity high-parameter CFBs can be explored, and the potential of 0-100% load long-term combustion break and fast restart of CFB units can be explored, so as to further improve the operational flexibility of CFB units. On the basis of operation flexibility, the advantages of CFB boiler fuel flexibility are used to realize high-sulfur anthracite ultra-supercritical high-efficiency power generation and to achieve ultra-low emissions simultaneously. Furthermore, mixed fuel CFB power generation should be developed to realize the consumption of low-carbon combustible wastes such as municipal sludge, domestic waste, biomass and other inferior fuels from of coal mining and washing. In addition, ultra-low emission control technology under flexible operation and intelligent CFB power generation technology should be developed to assist the transformation and development of China's energy structure.

Machine vision has been increasingly used for agricultural sensing tasks. The detection method based on deep learning for infield corn kernels can improve the detection accuracy. In order to obtain the number of lost corn kernels quickly and accurately after the corn harvest, and evaluate the corn harvest combine performance on grain loss, the method of directly using deep learning technology to count corn kernels in the field was developed and evaluated. Firstly, an RGB camera was used to collect image with different backgrounds and illuminations, and the datasets were generated. Secondly, different target detection networks for kernel recognition were constructed, including Mask R-CNN, EfficientDet-D5, YOLOv5-L and YOLOX-L, and the collected 420 effective images were used to train, verify and test each model. The number of images in train, verify and test datasets were 200, 40 and 180, respectively. Finally, the counting performances of different models were evaluated and compared according to the recognition results of test set images. The experimental results showed that among the four models, YOLOv5-L had overall the best performance, and could reliably identify corn kernels under different scenes and light conditions. The average precision (AP) value of the model for the image detection of the test set was 78.3%, and the size of the model was 89.3 MB. The correct rate of kernel count detection in four scenes of non-occlusion, surface mid-level-occlusion, surface severe-occlusion and aggregation were 98.2%, 95.5%, 76.1% and 83.3%, respectively, and F1 values were 94.7%, 93.8%, 82.8% and 87%, respectively. The overall detection correct rate and F1 value of the test set were 90.7% and 91.1%, respectively. The frame rate was 55.55 f/s, and the detection and counting performance were better than Mask R-CNN, EfficientDet-D5 and YOLOX-L networks. The detection accuracy was improved by about 5% compared with the second best performance of Mask R-CNN. With good precision, high throughput, and proven generalization, YOLOv5-L can realize real-time monitoring of corn harvest loss in practical operation.

Smart farming for field crops is a significant part of the smart agriculture. It aims at crop production, integrating modern sensing technology, new generation mobile communication technology, computer and network technology, Internet of Things(IoT), big data, cloud computing, blockchain and expert wisdom and knowledge. Deeply integrated application of biotechnology, engineering technology, information technology and management technology, it realizes accurate perception, quantitative decision-making, intelligent operation and intelligent service in the process of crop production, to significantly improve land output, resource utilization and labor productivity, comprehensively improves the quality, and promotes efficiency of agricultural products. In order to promote the sustainable development of the smart farming, through the analysis of the development process of smart agriculture, the overall objectives and key tasks of the development strategy were clarified, the key technologies in smart farming were condensed. Analysis and breakthrough of smart farming key technologies were crucial to the industrial development strategy. The main problems of the smart farming for field crops include: the lack of in-situ accurate measurement technology and special agricultural sensors, the large difference between crop model and actual production, the instantaneity, reliability, universality, and stability of the information transmission technologies, and the combination of intelligent agricultural equipment with agronomy. Based on the above analysis, five primary technologies and eighteen corresponding secondary technologies of smart farming for field crops were proposed, including: sensing technologies of environmental and biological information in field, agricultural IoT technologies and mobile internet, cloud computing and cloud service technologies in agriculture, big data analysis and decision-making technology in agriculture, and intelligent agricultural machinery and agricultural robots in fireld production. According to the characteristics of China's cropping region, the corresponding smart farming development strategies were proposed: large-scale smart production development zone in the Northeast region and Inner Mongolia region, smart urban agriculture and water-saving agriculture development zone in the region of Beijing, Tianjin, Hebei and Shandong, large-scale smart farming of cotton and smart dry farming green development comprehensive test zone in the Northwest arid region, smart farming of rice comprehensive development test zone in the Southeast coast region, and characteristic smart farming development zone in the Southwest mountain region. Finally, the suggestions were given from the perspective of infrastructure, key technology, talent and policy.

In this study, copper nanoparticles were synthesised according to green chemistry rules using the aqueous extract of Alhagi maurorum. The green-synthesised CuNPs were characterised using different techniques such as EDX, FE-SEM, XRD and FT-IR. The FE-SEM results confirm spherical morphology for the nanoparticles with a size of 42.68–68.87 nm. In vivo, the Mycoplasma pneumonia injection was applied for inducing pneumonia in the BALB/c mice, also the treatment was with CuNPs. The serum parameters such as inflammatory mediators (IL-6, TNF-α, IL-8, TGF and IL-1), total cell counts, and total protein content levels were evaluated. CuNPs regulated the levels of the inflammatory mediators in the infected mice. The cellular arrangements at the histopathological images were ameliorated with the administration of CuNPs. At the antioxidant test, the CuNPs and BHT removed 50% of free radicals at the concentrations of 181 and 110 µg/ml. In conclusion, the results revealed the ameliorative property of CuNPs green-formulated by A. maurorum aqueous extract on M. pneumoniae infected pneumonia mice model.