Cinnamic hydroxamic acid inhibits the proliferation of gastric cancer cells via upregulation of miR 145 expression and down-regulation of P13K/Akt signaling pathway

Purpose: To investigate the anti-proliferative effect of cinnamic hydroxamic acid (CHA) on gastric cancer (GC) cells, and its mechanism of action.Methods: Two GC cell lines (SGC-7901 and MKN1) and normal human gastric epithelial cells (GES1) were used for this study. The GC cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM)supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin/streptomycin solution at 37 °C for 24 h in a humidified atmosphere of 5 % CO2 and 95 % air. GES1 cells were cultured in RPMI medium supplemented with 10 % FBS only. Cell viability and apoptosis were determined using 3 (4,5 dimethyl thiazol 2 yl) 2,5 diphenyl 2H tetrazolium bromide (MTT), and flow cytometric assays, respectively. The level of expression of microRNA-145 (miR-145) was determined using real-time quantitative polymerase chain reaction (qRT-PCR). Protein expressions of c-Myc, p-AKT, PI3K, p21, and matrix metalloproteinase (MMP)-2 and MMP-9were determined using Western blotting.Results: Treatment of GC cells with CHA for 72 h led to significant and dose-dependent reduction in their viability, and significant and dose-dependent increase in the number of apoptotic cells (p < 0.05). It also significantly arrested GC cell cycle at G1 phase (p < 0.05). The treatment significantly and dosedependently decreased SGC-7901 and MKN1 cell migration and invasion, and upregulated miR-145 mRNA expression (p < 0.05). The expression of miR-145 mRNA was significantly higher in MKN1 cells than in SGC-7901cells (p < 0.05). Treatment of SGC-7901 and MKN1 cells with CHA significantly downregulated protein expressions of c-Myc, MMP-2/9, PI3K and p-AKT, but upregulated p21 protein expression (p< 0.05).Conclusion: These results show that CHA inhibits the proliferation of GC cells via upregulation of miR-145 expression and down-regulation of  P13K/Akt signaling pathway. Therefore, CHA has a good potential as a therapeutic agent for the management of gastric cancer Keywords: Apoptosis, Cinnamic hydroxamic acid, Gastric cancer, Metastasis, Proliferatio

Simplifying Deep-Learning-Based Model for Code Search

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR) based models for code search, which match keywords in query with code text. But they fail to connect the semantic gap between query and code. To conquer this challenge, Gu et al. proposed a deep-learning-based model named DeepCS. It jointly embeds method code and natural language description into a shared vector space, where methods related to a natural language query are retrieved according to their vector similarities. However, DeepCS' working process is complicated and time-consuming. To overcome this issue, we proposed a simplified model CodeMatcher that leverages the IR technique but maintains many features in DeepCS. Generally, CodeMatcher combines query keywords with the original order, performs a fuzzy search on name and body strings of methods, and returned the best-matched methods with the longer sequence of used keywords. We verified its effectiveness on a large-scale codebase with about 41k repositories. Experimental results showed the simplified model CodeMatcher outperforms DeepCS by 97% in terms of MRR (a widely used accuracy measure for code search), and it is over 66 times faster than DeepCS. Besides, comparing with the state-of-the-art IR-based model CodeHow, CodeMatcher also improves the MRR by 73%. We also observed that: fusing the advantages of IR-based and deep-learning-based models is promising because they compensate with each other by nature; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code

Influence of different cover ratios on Gas-particle flow characteristics of a centrally-fuel-rich primary air burner: experiment and simulation

AbstractThe flow field for different cover ratios within a three-level conical ring concentrator of a centrally-fuel-rich swirl coal combustion burner has been studied both experimentally and numerically. A particle dynamics anemometer measurement system was employed in the study to measure velocity and particle volume flux after the outlet of third-level ring. And the numerical simulations were used to calculate the flow field in the conical ring region. In each cross-section, after the outlet of third-level ring, concentration ratio for each cover ratio is always larger than 2. With conical ring concentrator in the primary air tube, the coal concentration can be concentrated to a suitable range. In the cross-sections 0.5<x/D<4.0, as cover ratio increases, concentration ratio decreases and resistance coefficient increases

Where is the crowd?

Crowdfunding has received increasing attention in the financial services space in the past few years. This is because crowdfunding has become a viable alternative to traditional capital investment and thus a threat to investors in that sector. Various platforms exist which allow fundraisers to pitch an idea and spread awareness with the intention of acquiring backers. Most backers of crowdfunding campaigns come to the platform with the fundraiser rather than from the platform itself [25]. Fundraisers must find and engage a crowd and not rely on the platform for provision of the crowd. This paper sets out four action design principles for identifying and engaging a crowd. Using a boundary object theory approach, the crowdfunding campaign is broken down based on backer’s social worlds which define the crowd and their interests

LARGE CROWDS OR LARGE INVESTMENTS? HOW SOCIAL IDENTITY INFLUENCES THE COMMITMENT OF THE CROWD

Equity crowdfunding is increasing in popularity as an alternative to traditional financing for start-ups and growth companies to raise money for their business. This study discusses how equity crowdfunding is different from traditional financing, such as angel investors and venture capitalists. We argue this difference is brought further into focus when large numbers of crowd members invest small amounts, as opposed to fewer individuals making large investments. Building on existing research on Social Identity Theory, we look at why some crowdfunding campaigns are more likely to attract these contrasting types of investment (numerous small investments or fewer large investments). A model is presenting linking different characteristics of campaigns to total investment and average investment. This proposed model will be tested using public data gathered from Crowdcube, a leading UK-based equity crowdfunding platform. This study has significant implications for fundraisers who may wish to target different types of crowds according to the nature of their business, i.e. smaller numbers of passionate investors to provide informed input or larger numbers of casual investors to help create awareness and spread positive word of mouth

New kid on the block: a strategic archetypes approach to understanding the Blockchain

Emerging Blockchain technologies have received considerable attention in the financial services domain. This is due to the potential of those technologies to radically disrupt existing financial systems by introducing new types of assets and new ways of managing transactions. Yet many of these technologies are so new and seemingly complex that strategic decision-makers may not fully understand the alternative Blockchain technologies on offer, let alone the costs and benefits associated with specific instantiations. This paper reviews the Blockchain literature and identifies eight key system design characteristics. From this, four Blockchain archetypes emerge, each of which is presented using a recognizable existing system to allow tangible discussion of similarities and differences across archetypes. The identification of these archetypes provides an important foundation for future research, enabling in-depth research to be conducted that will outline the costs, benefits, risks, and issues associated with each archetype

A Novel System Anomaly Prediction System Based on Belief Markov Model and Ensemble Classification

Computer systems are becoming extremely complex, while system anomalies dramatically influence the availability and usability of systems. Online anomaly prediction is an important approach to manage imminent anomalies, and the high accuracy relies on precise system monitoring data. However, precise monitoring data is not easily achievable because of widespread noise. In this paper, we present a method which integrates an improved Evidential Markov model and ensemble classification to predict anomaly for systems with noise. Traditional Markov models use explicit state boundaries to build the Markov chain and then make prediction of different measurement metrics. A Problem arises when data comes with noise because even slight oscillation around the true value will lead to very different predictions. Evidential Markov chain method is able to deal with noisy data but is not suitable in complex data stream scenario. The Belief Markov chain that we propose has extended Evidential Markov chain and can cope with noisy data stream. This study further applies ensemble classification to identify system anomaly based on the predicted metrics. Extensive experiments on anomaly data collected from 66 metrics in PlanetLab have confirmed that our approach can achieve high prediction accuracy and time efficiency

A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms

Abstract syntax tree (AST) mapping algorithms are widely used to analyze changes in source code. Despite the foundational role of AST mapping algorithms, little effort has been made to evaluate the accuracy of AST mapping algorithms, i.e., the extent to which an algorihtm captures the evolution of code. We observe that a program element often has only one best-mapped program element. Based on this observation, we propose a hierarchical approach to automatically compare the similarity of mapped statements and tokens by different algorithms. By performing the comparison, we determine if each of the compared algorithms generates inaccurate mappings for a statement or its tokens. We invite 12 external experts to determine if three commonly used AST mapping algorithms generate accurate mappings for a statement and its tokens for 200 statements. Based on the experts' feedback,we observe that our approach achieves a precision of 0.98--1.00 and a recall of 0.65--0.75. Furthermore, we conduct a large-scale study with a dataset of ten Java projects, containing a total of 263,165 file revisions. Our approach determines that GumTree, MTDiff and IJM generate inaccurate mappings for 20%--29%, 25%--36% and 21%--30% of the file revisions, respectively. Our experimental results show that state-of-art AST mapping agorithms still need improvements