Supplementing a specific synbiotic suppressed the incidence of AOM/DSS-induced colorectal cancer in mice.

In this study, we evaluated the effect of a specific synbiotic on CAC (AOM/DSS-induced colitis-associated cancer). We confirmed that the synbiotic intervention was able to protect the intestinal barrier and inhibit CAC occurrence via upregulating tight junction proteins and anti-inflammatory cytokines, and downregulating pro-inflammatory cytokines. Moreover, the synbiotic significantly improved the disorder of the colonic microbiota of CAC mice, promoted the formation of SCFAs and the production of secondary bile acids, and alleviated the accumulation of primary bile acids in the CAC mice. Meanwhile, the synbiotic could significantly inhibit the abnormal activation of the intestinal Wnt/ß-catenin signaling pathway significantly related to IL-23. In a word, the synbiotic can inhibit the occurrence and development of colorectal tumors and it may be a functional food to prevent inflammation-related colon tumors, and the research also provided a theoretical basis for improving the intestinal microecological environment through diet therapy.

Fructooligosaccharide supplementation alleviated the pathological immune response and prevented the impairment of intestinal barrier in DSS-induced acute colitis mice.

The dysbiosis of gut microbiota is closely related to the occurrence and development of inflammatory bowel disease (IBD). The manipulation of intestinal flora through prebiotics or probiotics is expected to induce and maintain the remission of IBD symptoms. 6-week-old C57BL/J mice were daily gavaged with fructooligosaccharides (FOS) or the synbiotic two weeks before the administration of dextran sulfate sodium (DSS). The supplementation of FOS or synbiotic could significantly ameliorate the body weight loss and colon histological damage in DSS-induced acute colitis mice. The altered composition of gut microbiota in acute colitis mice was reversed by FOS or Synbiotic supplementation, with a characteristic of decreased abundance of Mucispirillum. Both FOS and synbiotic mitigated DSS-induced loss of mucus protein (MUC2) and epithelium tight junction proteins (ZO-1, Occluding, Claudin1) in colon mucosa. The expression of pro-inflammatory cytokines (IL-6 and TNF-α) was decreased by FOS or synbiotic treatment, while the expression of Tbx21 and IL-10 was increased. The results suggested that the modulation of gut microbiota by FOS or synbiotic supplementation could decrease the inflammation potential of colonized commensals, which prevented the impairment of the intestinal barrier and induced a regulation of immune response in DSS-induced acute colitis mice.

JRL-YOLO: A Novel Jump-Join Repetitious Learning Structure for Real-Time Dangerous Object Detection.

Campus security incidents occur from time to time, which seriously affect the public security. In recent years, the rapid development of artificial intelligence has brought technical support for campus intelligent security. In order to quickly recognize and locate dangerous targets on campus, an improved YOLOv3-Tiny model is proposed for dangerous target detection. Since the biggest advantage of this model is that it can achieve higher precision with very fewer parameters than YOLOv3-Tiny, it is one of the Tinier-YOLO models. In this paper, the dangerous targets include dangerous objects and dangerous actions. The main contributions of this work include the following: firstly, the detection of dangerous objects and dangerous actions is integrated into one model, and the model can achieve higher accuracy with fewer parameters. Secondly, to solve the problem of insufficient YOLOv3-Tiny target detection, a jump-join repetitious learning (JRL) structure is proposed, combined with the spatial pyramid pooling (SPP), which serves as the new backbone network of YOLOv3-Tiny and can accelerate the speed of feature extraction while integrating features of different scales. Finally, the soft-NMS and DIoU-NMS algorithm are combined to effectively reduce the missing detection when two targets are too close. Experimental tests on self-made datasets of dangerous targets show that the average MAP value of the JRL-YOLO algorithm is 85.03%, which increases by 3.22 percent compared with YOLOv3-Tiny. On the VOC2007 dataset, the proposed method has a 9.29 percent increase in detection accuracy compared to that using YOLOv3-Tiny and a 2.38 percent increase compared to that employing YOLOv4-Tiny, respectively. These results all evidence the great improvement in detection accuracy brought by the proposed method. Moreover, when testing the dataset of dangerous targets, the model size of JRL-YOLO is 5.84 M, which is about one-fifth of the size of YOLOv3-Tiny (33.1 M) and one-third of the size of YOLOv4-Tiny (22.4 M), separately.

Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model.

Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.

A novel abandoned object detection system based on three-dimensional image information.

A new idea of an abandoned object detection system for road traffic surveillance systems based on three-dimensional image information is proposed in this paper to prevent traffic accidents. A novel Binocular Information Reconstruction and Recognition (BIRR) algorithm is presented to implement the new idea. As initial detection, suspected abandoned objects are detected by the proposed static foreground region segmentation algorithm based on surveillance video from a monocular camera. After detection of suspected abandoned objects, three-dimensional (3D) information of the suspected abandoned object is reconstructed by the proposed theory about 3D object information reconstruction with images from a binocular camera. To determine whether the detected object is hazardous to normal road traffic, road plane equation and height of suspected-abandoned object are calculated based on the three-dimensional information. Experimental results show that this system implements fast detection of abandoned objects and this abandoned object system can be used for road traffic monitoring and public area surveillance.

A novel multisensor traffic state assessment system based on incomplete data.

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.