Many web applications adopt Transfer Control Protocol (TCP) as the underlying protocol, where congestion control (CC) plays a vital role in reliable transmission. However, some TCP mechanisms cannot cope with the requirements of new applications and ever-increasing network traffic. Therefore, people have proposed Quick UDP Internet Connection (QUIC), an excellent potential alternative based on UDP, which introduces new features to improve transmission performance and is compatible with existing CC algorithms. This paper has conducted many experiments in the testbed and actual environments to measure and compare QUIC and TCP regarding communication quality, compatibility fairness, and user experience, while considering the impacts of three typical CC algorithms: NewReno, Cubic, and BBR. QUIC outperforms TCP in most experiments for web browsing and online video, but its performance is susceptible to CC algorithms and network conditions. For example, with the Cubic algorithm, QUIC enabling the 0-RTT feature can decrease the webpages loading time by 37.11% compared with TCP. Using the BBR algorithm, both QUIC and TCP achieve high throughput, slight fluctuation, and few delayed events when playing online videos. TCP with BBR provides better fairness, while QUIC with BBR is more robust in a network with high latency or packet loss.

Website Fingerprinting (WF) enables a passive adversary to identify the website a user is visiting, even when the web access adopts security or privacy technologies. WF attacks based on deep learning are highly effective when feeding sufficient training traces, for example, hundreds of traffic traces of accessing each website. However, collecting extensive traffic consumes much time and resources, degenerating WF attacks' timeliness and invisibility. Nevertheless, decreasing training traces dramatically drops the WF accuracy. This paper proposes Tripod, a novel data augmentation method to enhance WF attacks, making them effective with a small training set. It applies three packet manipulations (Injecting, Removing, and Losing) on one collected traffic trace to generate several augmented traces. WF attacks then use the website classifier trained by the augmented set of all traces. In the closed-world scenario, the Var-CNN attack with 20 training traces per website only correctly identifies 56.1% of websites, while Tripod significantly increases this accuracy to 95.9%. Furthermore, Tripod increases the true positive rate of Var-CNN from 26.9% to 91.4% in the more realistic open-world scenario.

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.

BACKGROUND: Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development. RESULTS: Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets. CONCLUSIONS: Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.