Anti-ADAR monoclonal antibody, clone FQS8144 Knockout Validated

Despite progress in conventional treatment for glioblastoma (GBM), the prognosis remains poor due to high tumor recurrence. Therefore, identification of new molecular mechanisms is a pressing need for betterment of GBM patient outcomes. qRT-PCR was used to determine BDNF-AS expression in GBM cells. CCK-8, EdU incorporation, and caspase-3 activity assays were employed to analyze biological functions of BDNF-AS. RIP and RNA pull-down were conducted to detect the interactions among BDNF-AS, ADAR, and p53. Actinomycin D was utilized to examine the stability of p53 mRNA. ChIP and luciferase reporter assays were performed to detect transcriptional activation of BDNF-AS by p53. We found that BDNF-AS was significantly downregulated in GBM cell lines, and its overexpression inhibited GBM cell growth, and promoted apoptosis. Importantly, we illustrated that BDNF-AS coupled with ADAR protein to potentiate stability of p53 mRNA and thus upregulate p53. Interestingly, we further identified p53 as a transcription factor of BDNF-AS, activating transcription of BNDF-AS. This study firstly demonstrated that BDNF-AS acted as a tumor suppressor in GBM and the positive feedback circuit of BDNF-AS/ADAR/p53 served an important mechanism to control GBM proliferation. Targeting this auto-regulatory loop may provide a potential therapeutic strategy for GBM patients.

Decentralized Social Network (DSN) has attracted a lot of research and development interest in recent years. It is believed to be the solution to many problems of centralized services. Due to the data limitation imposed by common decentralized architectures, centralized algorithms that support social networking functions need to be re-designed. In this work, we tackle the problem of community detection for a given user under the constraint of limited local topology information. This naturally yields a classification formulation for community detection. As an initial study, we focus on a specific type of classifiers - classification by thresholding against a proximity measure between nodes. We investigated four proximity measures: Common Neighbours (CN), Adamic/Adar score (AA), Page Rank (PR), Personalized PageRank (PPR). Using data collected from a large-scale Online Social Network (OSN) in practice, we show that PPR can outperform the others with a few pre-known labels (37.5% to 64.97% relative improvement in terms of Area Under the ROC Curve). We further carry out extensive numerical evaluation of PPR, showing that more pre-known labels can linearly increase the capability of the single-feature classifier based on PPR. Users can thus seek for a trade-off between labeling cost and classification accuracy.