Genome Characterization of the Oleaginous Fungus Mortierella alpina

Mortierella alpina is an oleaginous fungus which can produce lipids accounting for up to 50% of its dry weight in the form of triacylglycerols. It is used commercially for the production of arachidonic acid. Using a combination of high throughput sequencing and lipid profiling, we have assembled the M. alpina genome, mapped its lipogenesis pathway and determined its major lipid species. The 38.38 Mb M. alpina genome shows a high degree of gene duplications. Approximately 50% of its 12,796 gene models, and 60% of genes in the predicted lipogenesis pathway, belong to multigene families. Notably, M. alpina has 18 lipase genes, of which 11 contain the class 2 lipase domain and may share a similar function. M. alpina's fatty acid synthase is a single polypeptide containing all of the catalytic domains required for fatty acid synthesis from acetyl-CoA and malonyl-CoA, whereas in many fungi this enzyme is comprised of two polypeptides. Major lipids were profiled to confirm the products predicted in the lipogenesis pathway. M. alpina produces a complex mixture of glycerolipids, glycerophospholipids and sphingolipids. In contrast, only two major sterol lipids, desmosterol and 24(28)-methylene-cholesterol, were detected. Phylogenetic analysis based on genes involved in lipid metabolism suggests that oleaginous fungi may have acquired their lipogenic capacity during evolution after the divergence of Ascomycota, Basidiomycota, Chytridiomycota and Mucoromycota. Our study provides the first draft genome and comprehensive lipid profile for M. alpina, and lays the foundation for possible genetic engineering of M. alpina to produce higher levels and diverse contents of dietary lipids

Analyzing the Contribution of Human Mobility to Changes in Air Pollutants: Insights from the COVID-19 Lockdown in Wuhan

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest (RF) models, with the comparison of the contributions of meteorological conditions, human mobility, and emissions from industry and households between different periods. In addition, we conducted scenario analyses to suggest an appropriate limit for control of human mobility. Different mechanisms for air pollutants were shown in the pre-pandemic, pre-lockdown, lockdown, and post-pandemic periods. Wind speed and the Within-city Migration index, representing intra-city mobility intensity, were excluded from stepwise multiple linear models in the pre-lockdown and lockdown periods. The results of sensitivity experiments show that, in the COVID-19 lockdown period, 73.3% of the reduction can be attributed to decreased human mobility. In the post-pandemic period, meteorological conditions control about 42.2% of the decrease, and emissions from industry and households control 40.0%, while human mobility only contributes 17.8%. The results of the scenario analysis suggest that the priority of restriction should be given to human mobility within the city than other kinds of human mobility. The reduction in the NO2 concentration tends to be smaller when human mobility within the city decreases by more than 70%. A limit of less than 40% on the control of the human mobility can achieve a better effect, especially in cities with severe traffic pollution

Analyzing the Contribution of Human Mobility to Changes in Air Pollutants: Insights from the COVID-19 Lockdown in Wuhan

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest (RF) models, with the comparison of the contributions of meteorological conditions, human mobility, and emissions from industry and households between different periods. In addition, we conducted scenario analyses to suggest an appropriate limit for control of human mobility. Different mechanisms for air pollutants were shown in the pre-pandemic, pre-lockdown, lockdown, and post-pandemic periods. Wind speed and the Within-city Migration index, representing intra-city mobility intensity, were excluded from stepwise multiple linear models in the pre-lockdown and lockdown periods. The results of sensitivity experiments show that, in the COVID-19 lockdown period, 73.3% of the reduction can be attributed to decreased human mobility. In the post-pandemic period, meteorological conditions control about 42.2% of the decrease, and emissions from industry and households control 40.0%, while human mobility only contributes 17.8%. The results of the scenario analysis suggest that the priority of restriction should be given to human mobility within the city than other kinds of human mobility. The reduction in the NO2 concentration tends to be smaller when human mobility within the city decreases by more than 70%. A limit of less than 40% on the control of the human mobility can achieve a better effect, especially in cities with severe traffic pollution

Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things

Abstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively