ABSTRACT
This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete parts and regular structures into supernodes. The supernodes carry a synopsis S Q for each query class Q in use, to abstract key features of the contracted parts for answering queries of Q . Moreover, for various types of graphs, we identify regular structures to contract. The contraction scheme provides a compact graph representation and prioritizes up-to-date data. Better still, it is generic and lossless. We show that the same contracted graph is able to support multiple query classes at the same time, no matter whether their queries are label based or not, local or non-local. Moreover, existing algorithms for these queries can be readily adapted to compute exact answers by using the synopses when possible and decontracting the supernodes only when necessary. As a proof of concept, we show how to adapt existing algorithms for subgraph isomorphism, triangle counting, shortest distance, connected component and clique decision to contracted graphs. We also provide a bounded incremental contraction algorithm in response to updates, such that its cost is determined by the size of areas affected by the updates alone, not by the entire graphs. We experimentally verify that on average, the contraction scheme reduces graphs by 71.9% and improves the evaluation of these queries by 1.69, 1.44, 1.47, 2.24 and 1.37 times, respectively.
ABSTRACT
PURPOSE: Increasing evidence has demonstrated that animal models are imperative to investigate the potential molecular mechanism of metastasis and discover anti-metastasis drugs; however, efficient animal models to unveil the underlying mechanisms of metastasis in esophageal squamous cell carcinoma (ESCC) are limited. METHODS: ESCC cell EC9706 with high invasiveness was screened by repeated Transwell assays. Its biological characteristics were identified by flow cytometry as well as by the wound healing and CCK-8 assays. Besides, the levels of epithelial-mesenchymal transition-related markers were examined using Western blotting. Parental (EC9706-I0) and subpopulation (EC9706-I3) cells were employed to establish the renal capsule model. Next, the tumor growth was detected by a live animal imaging system, and hematoxylin and eosin staining was applied to evaluate the metastatic status in ESCC. RESULTS: EC9706-I3 cells showed rapid proliferation ability, S phase abundance, and high invasive ability; obvious upregulation in N-cadherin, Snail, Vimentin, and Bit1; and downregulation in E-cadherin. EC9706-I3 cells were less sensitive to the chemotherapy drug 5-fluorouracil than EC9706-I0 cells; however, both cell lines reached a tumorigenesis rate of 100% in the renal capsule model. The live animal imaging system revealed that the tumors derived from EC9706-I0 cells grew more slowly than those from EC9706-I3 cells at weeks 3-14. The EC9706-I3 xenograft model displayed a spontaneous metastatic site, including kidney, heart, liver, lung, pancreas, and spleen, with a distant metastatic rate of 80%. CONCLUSION: Our data suggested that the metastatic model was successfully established, providing a novel platform for further exploring the molecular mechanisms of metastasis in ESCC patients.
ABSTRACT
Big data analytics is often prohibitively costly and is typically conducted by parallel processing with a cluster of machines. Is big data analytics beyond the reach of small companies that can only afford limited resources? This paper tackles this question by presenting Boundedly EvAlable SQL (BEAS), a system for querying big relations with constrained resources. The idea is to make big data small. To answer a query posed on a dataset, it often suffices to access a small fraction of the data no matter how big the dataset is. In the light of this, BEAS answers queries on big data by identifying and fetching a small set of the data needed. Under available resources, it computes exact answers whenever possible and otherwise approximate answers with accuracy guarantees. Underlying BEAS are principled approaches of bounded evaluation and data-driven approximation, the focus of this paper.
ABSTRACT
BACKGROUND: Both LncRNA UCA1 and miR-495 are crucial gene regulators in various disorders. This study aims to investigate their role in epilepsy and seizure-induced brain injury. METHODS: In this research, rat model of epilepsy was established by pilocarpine induction. The RNA and protein expression in hippocampal tissues and neurons were determined by qRT-PCR and western blot, respectively. The hippocampal neurons were isolated from hippocampal tissues, and treated with magnesium-free (MGF) physiological solution for epileptiform activity induction. The endogenous expression of related genes was modulated by recombinant plasmids and cell transfection. Flow cytometry was used to analyze the cell apoptosis. Dual luciferase reporter assay was performed to determine the interaction between miR-495 and Nrf2 in HEK-293 cells. RESULTS: The lncRNA UCA1 and Nrf2 were down-regulated in epileptiform hippocampal tissues and neurons, while the miR-495 was up-regulated. Over-expression of UCA1 inhibited apoptosis of hippocampal neurons by suppressing miR-495. MiR-495 negatively regulated Nrf2. UCA1 inhibited apoptosis of hippocampal neurons through miR-495/Nrf2-ARE pathway. UCA1 suppressed pilocarpine-induced epilepsy in rat. CONCLUSION: LncRNA UCA1 suppressed pilocarpine-induced epilepsy by inhibiting apoptosis of hippocampal neurons through miR-495/Nrf2-ARE pathway, and thereby inhibiting brain injury induced by seizure.