Anti-DCN polyclonal antibody [Biotin]

As one of the most critical cloud services, Baremetal Servers introduce stringent performance requirements on data center networks (DCN). Stateful packet filter is an integral DCN component of ensuring connection security for bare-metal servers. However, the off-the-shelf hardware-based and software-based stateful packet filters either are prohibitively costly for cloud DCNs or introduce significant performance bottlenecks. In this paper, we present CoFilter, which employs cheap programmable switches to accelerate the stateful packet filter for bare-metal servers. CoFilter consists of two key designs. First, to support complex stateful packet filtering logic in programmability-limited switching ASICs, CoFilter partitions the stateful packet filtering logic between programmable ASICs and switch CPU. Most packets are directly processed in switching ASICs to achieve high performance, while only a small number of packets go to switch CPU for connection tracking. Second, to track massive connections with constrained hardware memory, CoFilter employs hash to compress connection states and provides an efficient settlement for hash collisions. We build a prototype of CoFilter and evaluate it on the Tofino switch under various data center traffic traces with real-world flow distribution. The evaluation shows that CoFilter largely outperforms NetFilter, i.e., forwarding packets at line rate (13x throughput of NetFilter), keeping packet delay at 1us, and freeing a significant quantity of CPU cores. Furthermore, CoFilter presents great scalability and accommodates over ten million connections with only 16MB SRAM.

Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify candidate hub genes associated with OC clinical phenotypes. The gene expression profiles were obtained from the MERAV database. Validations of candidate hub genes were performed with RNASeqV2 data and the corresponding clinical information available from The Cancer Genome Atlas (TCGA) database. In addition, we examined the candidate genes in ovarian cancer cells. Totally, 19 modules were identified and 26 hub genes were extracted from the most significant module (R-2 = .53) in clinical stages. Through the validation of TCGA data, we found that five hub genes (COL1A1, DCN, LUM, POSTN and THBS2) predicted poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that these five genes exhibited diagnostic efficiency for early-stage and advanced-stage cancer. The protein expression of these five genes in tumour tissues was significantly higher than that in normal tissues. Besides, the expression of COL1A1 was associated with the TAX resistance of tumours and could be affected by the autophagy level in OC cell line. In conclusion, our findings identified five genes could serve as biomarkers related to the prognosis of OC and may be helpful for revealing pathogenic mechanism and developing further research.