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Cryptocurrency is designed for anonymous financial transactions to avoid centralized control, censorship, and regulations. To protect anonymity in the underlying P2P networking, Bitcoin adopts and supports anonymous routing of Tor, I2P, and CJDNS. We analyze the networking performances of these anonymous routing with the focus on their impacts on the blockchain consensus protocol. Compared to non-anonymous routing, anonymous routing adds inherent-by-design latency performance costs due to the additions of the artificial P2P relays. However, we discover that the lack of ecosystem plays an even bigger factor in the performances of the anonymous routing for cryptocurrency blockchain. I2P and CJDNS, both advancing the anonymous routing beyond Tor, in particular lack the ecosystem of sizable networking-peer participation. I2P and CJDNS thus result in the Bitcoin experiencing networking partitioning, which has traditionally been researched and studied in cryptocurrency/blockchain security. We focus on I2P and Tor and compare them with the non-anonymous routing because CJDNS has no active public peers resulting in no connectivity. Tor results in slow propagation while I2P yields soft partition, which is a partition effect long enough to have a substantial impact in the PoW mining. To better study and identify the latency and the ecosystem factors of the cryptocurrency networking and consensus costs, we study the behaviors both in the connection manager (directly involved in the P2P networking) and the address manager (informing the connection manager of the peer selections on the backend). This paper presents our analyses results to inform the state of cryptocurrency blockchain with anonymous routing and discusses future work directions and recommendations to resolve the performance and partition issues. 

With the proliferation of Internet of Things (IoT) devices, real-time stream processing at the edge of the network has gained significant attention. However, edge stream processing systems face substantial challenges due to the heterogeneity and constraints of computational and network resources and the intricacies of multi-tenant application hosting. An optimized placement strategy for edge application topology becomes crucial to leverage the advantages offered by Edge computing and enhance the throughput and end-to-end latency of data streams. This paper presents Beaver, a resource scheduling framework designed to efficiently deploy stream processing topologies across distributed edge nodes. Its core is a novel scheduler that employs a synergistic integration of graph partitioning within application topologies and a two-sided matching technique to optimize the strategic placement of stream operators. Beaver aims to achieve optimal performance by minimizing bottlenecks in the network, memory, and CPU resources at the edge. We implemented a prototype of Beaver using Apache Storm and Kubernetes orchestration engine and evaluated its performance using an open-source real-time IoT benchmark (RIoTBench). Compared to state-of-the-art techniques, experimental evaluations demonstrate at least 1.6× improvement in the number of tuples processed within a one-second deadline under varying network delay and bandwidth scenarios. 

This is a mini-review capturing the views and opinions of selected participants at the 2021 IEEE BIBM 3rd Annual LncRNA Workshop, held in Dubai, UAE. The views and opinions are expressed on five broad themes related to problems in lncRNA, namely, challenges in the computational analysis of lncRNAs, lncRNAs and cancer, lncRNAs in sports, lncRNAs and COVID-19, and lncRNAs in human brain activity. 

Abstract Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial–mesenchymal transition, cell–cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug’s Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user’s exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.