Task-Similarity-Based VNF Aggregation for Air-Ground Integrated Networks.

In a harsh environment, function aggregation of air-ground integrated network service function chaining (SFC) deployment can easily cause network load imbalance, which affects the network security and reliability. In this study, a task-similarity-based virtual network function (VNF) aggregation scheme was proposed. It considered air-ground network resource consumption and load balance before SFC mapping. A model for selecting VNFs to be aggregated based on task similarity was built. The tasks were classified based on their similarity. Furthermore, the VNFs to be aggregated were selected within the class under the constraints of the underlying physical resources. Load balancing was achieved by adjusting the similarity threshold. Moreover, an SFC mapping selection scheme based on network resource awareness was used to obtain the most suitable physical nodes for single-chain and multi-chain mapping according to various attributes of physical network nodes. The simulation results indicated that the proposed scheme with a better load balance design outperformed existing works on VNF aggregation. We also demonstrated that the task-similarity-based scheme was resource-consumption efficient and effective.

The First Human Vulvar Intraepithelial Neoplasia Cell Line with Naturally Infected Episomal HPV18 Genome.

Persistent infection with high-risk HPV leads to cervical cancers and other anogenital cancers and head and neck carcinomas in both men and women. There is no effective drug fortreating HPV infection and HPV-associated carcinomas, largely due to a lack of models of natural HPV infection and the complexity of the HPV life cycle. There are no available cell lines from vulvar, anal, or penile lesions and cancers in the field. In this study, we established the first human cell line from vulvar intraepithelial neoplasia (VIN) with naturally infected HPV18 by conditional reprogramming (CR) method. Our data demonstrated that VIN cells possessed different biological characteristics and diploid karyotypes from HPV18-positive cancer cells (HeLa). Then, we determined that VIN cells contained episomal HPV18 using approaches including the ratio of HPV E2copy/E7copy, rolling cycle amplification, and sequencing. The VIN cells expressed squamous epithelium-specific markers that are different from HeLa cells, a cervical adenocarcinoma cell line. When cultured under 3D air-liquid interface (ALI) system, we observed the expression of both early and late differentiation markers involucrin and filaggrin. Most importantly, we were able to detect the expression of viral late gene L1 in the cornified layer of ALI 3D culture derived from VIN cells, suggesting quite different HPV genomic status from cancer cells. We also observed progeny viral particles under transmission electron microscopy (TEM) in ALI 3D cultures, confirming the episomal HPV18 genome and active viral life cycle in the new cell line. To our knowledge, this is the first human VIN cell line with naturally infected HPV18 genome and provides a valuable model for HPV biology studies, HPV-associated cancer initiation and progression, and drug-screening platforms.

Domain randomization-enhanced deep learning models for bird detection.

Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.