Characterization of Collapsin Response Mediator Protein 2 in Colorectal Cancer Progression in Subjects with Diabetic Comorbidity.

BACKGROUND: Common demographic risk factors are identified in colorectal cancer (CRC) and type 2 diabetes mellitus (DM), nevertheless, the molecular link and mechanism for CRC-DM comorbidity remain elusive. Dysregulated glycogen synthase kinase-3 beta under metabolic imbalance is suggested to accelerate CRC pathogenesis/progression via regulating collpasin response mediator protein-2 (CRMP2). Accordingly, roles of CRMP2 in CRC and CRC-DM patients were investigated for elucidating the molecular convergence of CRC and DM. METHODS: CRMP2 profile in tumor tissues from CRC and CRC-DM patients was investigated to explore the link between CRC and DM etiology. Meanwhile, molecular mechanism of glucose to regulate CRMP2 profile and CRC characteristics was examined in vitro and in vivo. RESULTS: CRMP2 was significantly lower in tumor lesions and associated with advanced tumor stage in CRC-DM patients. Physiological hyperglycemia suppressed CRMP2 expression/activity and augmented malignant characteristics of CRC cells. Hyperglycemia promotes actin de-polymerization, cytoskeleton flexibility and cell proliferation/metastasis by downregulating CRMP2 profile and thus contributes to CRC disease progression. CONCLUSIONS: This study uncovers molecular evidence to substantiate and elucidate the link between CRC and T2DM, as well as characterizing the roles of CRMP2 in CRC-DM. Accordingly, altered metabolic adaptations are promising targets for anti-diabetic and cancer strategies.

Mode Selection and Spectrum Allocation in Coexisting D2D and Cellular Networks with Cooperative Precoding.

In this paper, we investigate the mode selection strategies for a new device-to-device (D2D) pair becoming active in a network with a number of existing D2D sensors or users coexisting with cellular users in a D2D-enabled heterogeneous network. Specifically, we propose two selection rules, the signal-to-interference-plus-noise-ratio (SINR)-based and the capacity-based, combined with two sets of different precoding schemes and discuss their impacts on the system under a variety of scenarios. While the cooperative block diagonalization (BD) among the cellular users combined with the zero-forcing (ZF) precoding among D2D users can eliminate interference observed at the new D2D receiving sensor, the maximum signal-to-leakage-and-noise-ratio (SLNR) precoding is often a preferred option due to low-complexity implementations and comparable performance. We note that the two selection rules, the SINR-based and the capacity-based, considered in this paper impact on the system differently, with interesting tradeoff from different perspectives. Finally, we provide insights by simulations into the best selection among the three modes depending on a variety of use cases in the network.

An implementation of real-time air quality and influenza-like illness data storage and processing platform.

Recently, air pollution has become the primary concern in Taiwan as it significantly affected people's health. Some air pollution monitoring, analysis, and prediction systems were proposed to solve the problem. However, there is very little research to see whether the air quality is associated with the Influenza-Like Illness (ILI) disease or not. In this study, a system is needed, in which the air quality data and the influenza-like illness data can be analyzed together to determine their associations accurately and effectively. In this work, a novel integrated platform was implemented by building a cluster environment based on Hadoop, Spark and a visualization environment with ELK Stack as well as a backup storage system based on Ceph object storage architecture. Also, Sqoop and Alluxio were used to solve the inefficiency problem in processing vast amounts of data. The experimental results showed the visualization of air quality and influenza-like illness data collected from 2016 to 2017 in Taichung, Taiwan. Besides, the association analyses and discussion between air quality and influenza-like illness were also presented.

MGUPGMA: A Fast UPGMA Algorithm With Multiple Graphics Processing Units Using NCCL.

A phylogenetic tree is a visual diagram of the relationship between a set of biological species. The scientists usually use it to analyze many characteristics of the species. The distance-matrix methods, such as Unweighted Pair Group Method with Arithmetic Mean and Neighbor Joining, construct a phylogenetic tree by calculating pairwise genetic distances between taxa. These methods have the computational performance issue. Although several new methods with high-performance hardware and frameworks have been proposed, the issue still exists. In this work, a novel parallel Unweighted Pair Group Method with Arithmetic Mean approach on multiple Graphics Processing Units is proposed to construct a phylogenetic tree from extremely large set of sequences. The experimental results present that the proposed approach on a DGX-1 server with 8 NVIDIA P100 graphic cards achieves approximately 3-fold to 7-fold speedup over the implementation of Unweighted Pair Group Method with Arithmetic Mean on a modern CPU and a single GPU, respectively.