BigDataBench: a Big Data Benchmark Suite from Internet Services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, US

A novel FCTF evaluation and prediction model for food efficacy based on association rule mining

IntroductionFood-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field.MethodsUsing the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.ResultsThe FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene.DiscussionCenturies of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline

The molecular mechanism for inhibiting the growth of nasopharyngeal carcinoma cells using polymethoxyflavonoids purified from pericarp of Citrus reticulata ‘Chachi’ via HSCCC

Polymethoxyflavonoids (PMFs), the main bioactive compounds naturally occurring in the pericarp of Citrus reticulata ‘Chachi’ (CRCP), possess significant antitumor action. However, the action of PMFs in nasopharyngeal carcinoma (NPC) is currently unknown. The present research study was conducted to investigate the inhibitory mechanisms of PMFs from CRCP on NPC growth in vivo and in vitro. In our research, we used high-speed counter-current chromatography (HSCCC) to separate four PMFs (nobiletin (NOB), 3,5,6,7,8,3′,4′-heptamethoxyflavone (HMF), tangeretin (TGN), and 5-hydroxy-6,7,8,3′,4′-pentamethoxyflavone (5-HPMF)) from CRCP. CCK-8 assay was used to preliminarily screen cell viability following exposure to the four PMFs. Colony formation, Hoechst-33258 staining, transwell, and wound scratch assays were performed to assess the anti-proliferation, invasion, migration, and apoptosis-inducing effects of HMF on NPC cells. NPC tumors in xenograft tumor transplantation experiments were also established to explore the effect of HMF (100 and 150 mg/kg/day) on NPC. The histopathological changes in the treated rats were observed by H&E staining and Ki-67 detection by immunohistochemical techniques. The expressions of P70S6K, p-P70S6K, S6, p-S6, COX-2, p53, and p-p53 were measured by Western blot. The four PMFs were obtained with high purity (>95.0%). The results of the preliminary screening by CCK-8 assay suggested that HMF had the strongest inhibitory effect on NPC cell growth. The results of the colony formation, Hoechst-33258 staining, transwell, and wound scratch assays indicated that HMF had significant anti-proliferation, invasion, migration, and apoptosis-inducing ability in NPC cells. Moreover, HMF suppressed NPC tumor growth in xenograft tumor transplantation experiments. Further investigation suggested that HMF regulated NPC cells proliferation, apoptosis, migration, and invasion by activating AMPK-dependent signaling pathways. In conclusion, HMF-induced AMPK activation inhibited NPC cell growth, invasion, and metastatic potency by downregulating the activation of the mTOR signaling pathway and COX-2 protein levels, as well as enhancing the p53 phosphorylation level. Our study provides a crucial experimental basis for the clinical treatment of NPC, as well as the development and utilization of PMFs from CRCP