New insights into the characteristic skin microorganisms in different grades of acne and different acne sites

BackgroundThe increasing maturity of sequencing technology provides a convenient approach to studying the role of skin microorganisms in acne pathogenesis. However, there are still too few studies about the skin microbiota of Asian acne patients, especially a lack of detailed analysis of the characteristics of the skin microbiota in the different acne sites.MethodsIn this study, a total of 34 college students were recruited and divided into the health, mild acne, and severe acne groups. The bacterial and fungal flora of samples were separately detected by 16S and 18S rRNA gene sequencing. The biomarkers of different acne grades and different acne sites [forehead, cheek, chin, torso (including chest and back)] were excavated.Results and DiscussionOur results indicated that there was no significant difference in species diversity between groups. The genera like Propionibacterium, Staphylococcus, Corynebacterium, and Malassezia, which have a relatively high abundance in the skin microbiota and were reported as the most acne-associated microbes, were no obvious differences between groups. On the contrary, the abundance of less reported Gram-negative bacteria (Pseudomonas, Ralstonia, and Pseudidiomarina) and Candida has a significant alteration. Compared with the health group and the mild group, in the severe group, the abundance of Pseudomonas and Ralstonia sharply reduced while that of Pseudidiomarina and Candida remarkably raised. Moreover, different acne sites have different numbers and types of biomarkers. Among the four acne sites, the cheek has the greatest number of biomarkers including Pseudomonas, Ralstonia, Pseudidiomarina, Malassezia, Saccharomyces, and Candida, while no biomarker was observed for the forehead. The network analysis indicated that there might be a competitive relationship between Pseudomonas and Propionibacterium. This study would provide a new insight and theoretical basis for precise and personalized acne microbial therapy

Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection

We present a generalized and scalable method, called Gen-LaneNet, to detect 3D lanes from a single image. The method, inspired by the latest state-of-the-art 3D-LaneNet, is a unified framework solving image encoding, spatial transform of features and 3D lane prediction in a single network. However, we propose unique designs for Gen-LaneNet in two folds. First, we introduce a new geometry-guided lane anchor representation in a new coordinate frame and apply a specific geometric transformation to directly calculate real 3D lane points from the network output. We demonstrate that aligning the lane points with the underlying top-view features in the new coordinate frame is critical towards a generalized method in handling unfamiliar scenes. Second, we present a scalable two-stage framework that decouples the learning of image segmentation subnetwork and geometry encoding subnetwork. Compared to 3D-LaneNet, the proposed Gen-LaneNet drastically reduces the amount of 3D lane labels required to achieve a robust solution in real-world application. Moreover, we release a new synthetic dataset and its construction strategy to encourage the development and evaluation of 3D lane detection methods. In experiments, we conduct extensive ablation study to substantiate the proposed Gen-LaneNet significantly outperforms 3D-LaneNet in average precision(AP) and F-score

ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs. However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature. In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures. Then, we propose a novel recommendation model by incorporating ID embeddings to enhance the semantic features of both content and structures. Specifically, we put forward a hierarchical attention mechanism to incorporate ID embeddings in modality fusing, coupled with contrastive learning, to enhance content representations. Meanwhile, we propose a lightweight graph convolutional network for each modality to amalgamate neighborhood and ID embeddings for improving structural representations. Finally, the content and structure representations are combined to form the ultimate item embedding for recommendation. Extensive experiments on three real-world datasets (Baby, Sports, and Clothing) demonstrate the superiority of our method over state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings

High-Fidelity Clothed Avatar Reconstruction from a Single Image

This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit model to learn the general shape in the canonical space of a person in a learning-based way, and at the second stage, we refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way. A hyper-network is utilized to generate a good initialization so that the convergence o f the optimization process is greatly accelerated. Extensive experiments on various datasets show that the proposed CAR successfully produces high-fidelity avatars for arbitrarily clothed humans in real scenes

Prevalence and risk factors of hepatitis C among former blood donors in rural China

SummaryBackgroundIllegal commercial plasma and blood donation activities in the late 1980s and early 1990s caused a large number of hepatitis C virus (HCV) infections in rural areas of China.MethodsA cross-sectional survey was carried out in 2008, in which all residents in a former blood donation village in rural Hebei Province were invited for a questionnaire interview and testing for HCV antibodies. Questionnaires were administered to collect information about their personal status and commercial blood donation history, and HCV antibodies were tested by enzyme immunoassay.ResultsOf 520 villagers who participated in the interviews, 236 (45.4%) reported a history of selling whole blood or plasma. HCV seropositivity was confirmed in 148/520 (28.5%) interviewees and 101/236 (42.8%) former commercial plasma and blood donors. Selling plasma was the strongest independent predictor of HCV seropositivity (p=0.0037). Past history of an operation was also independently associated with HCV infection (p=0.0270).ConclusionsUnsafe practices during illegal plasma donation led to a high risk of HCV seropositivity for donors during the late 1980s and early 1990s. Many infected people suffered chronic hepatitis from that time onwards and urgently needed treatment and care