Study on the osmoregulation of "Halomonas socia" NY-011 and the degradation of organic pollutants in the saline environment.

"Halomonas socia" NY-011, a new species of moderately halophilic bacteria isolated and identified in our laboratory, can grow in high concentrations of salt ranging from 0.5 to 25%. In this study, the whole genome of NY-011 was sequenced and a detailed analysis of the genomic features was provided. Especially, a series of genes related to salt tolerance and involved in xenobiotics biodegradation were annotated by COG, GO and KEGG analyses. Subsequently, RNA-Seq-based transcriptome analysis was applied to explore the osmotic regulation of NY-011 subjected to high salt stress for different times (0 h, 1 h, 3 h, 6 h, 11 h, 15 h). And we found that the genes related to osmoregulation including excluding Na+ and accumulating K+ as well as the synthesis of compatible solutes (alanine, glutamate, ectoine, hydroxyectoine and glycine betaine) were up-regulated, while the genes involved in the degradation of organic compounds were basically down-regulated during the whole process. Specifically, the expression trend of genes related to osmoregulation increased firstly then dropped, which was almost opposite to that of degrading organic pollutants genes. With the prolongation of osmotic up-shock, NY-011 survived and gradually adapted to osmotic stress, the above-mentioned two classes of genes slowly returned to normal expression level. Then, the scanning electron microscope (SEM) and transmission electron microscope (TEM) were also utilized to observe morphological properties of NY-011 under hypersaline stress, and our findings showed that the cell length of NY-011 became longer under osmotic stress, at the same time, polyhydroxyalkanoates (PHAs) were synthesized in the cells. Besides, physiological experiments confirmed that NY-011 could degrade organic compounds in a high salt environment. These data not only provide valuable insights into the mechanism of osmotic regulation of NY-011; but also make it possible for NY-011 to be exploited for biotechnological applications such as degrading organic pollutants in a hypersaline environment.

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model.

Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people's lives and property.

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios.

Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles.

Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.

Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments.

Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

Characterization of a novel mosquitocidal toxin of Cry50Ba and its potential synergism with other mosquitocidal toxins.

A putative toxin gene of cry50Ba was successfully expressed in E. coli cells and confirmed that the purified Cry50Ba toxin had very high toxic activity against Culex quinquefasciatus larvae. Furthermore, the potential synergism of Cry50Ba toxin with Cry2Aa, Cry4Aa and Cry11Aa at a ratio of 1:1 was investigated. Although no significant synergism with other toxins was observed, the Cry50Ba as a novel toxin could be used to delay rapid onset of resistance in mosquito.