Anti cancer molecular mechanism of Actinidia chinensis Planch in gastric cancer based on network pharmacology and molecular docking

Purpose: To determine the anti-tumor effects of Actinidia chinensis Planch (ACP) root extract as well as its mechanism of action against gastric cancer (GC) using network pharmacology.Methods: The bioactive compounds and targets of ACP, as well as GC-related genes were identified from a series of public databases. Functional enrichment analysis was conducted to find relevant biological processes and pathways. The survival analysis was conducted using GEPIA tool. Autodock was used to carry out molecular docking between the ingredients and their targets.Results: A total of 20 bioactive compounds with 209 corresponding targets were identified for ACP, and a total of 871 GC-related genes were obtained. Forty-nine (49) targets of ACP were identified as candidate genes for the prevention of GC, and the PPI network with 584 interactions among these genes was constructed. The data demonstrated that the candidate targets were involved in multiple biological processes such as oxidative stress response, apoptosis, and proliferation. Moreover, these candidate targets were significantly associated with cancer-related pathways and signal transduction pathways. The compound-target-pathway network containing 16 bioactive compounds, 49 targets and 10 pathways was constructed and visualized, and the top 3 targets with a higher degree value were AKT1, MYC, and JUN, respectively. Survival analysis revealed significant associations between GC prognosis and several targets (PREP, PTGS1, AR, and PTGS2). Molecular docking further revealed good binding affinities between bioactive compounds and the prognosis-related targets, indicating the potential roles of these ingredient-target interactions in GC protection.Conclusion: Taken together, this study has provided novel clues for the determination of the antigastric cancer mechanism of ACP

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA, all software processes in the system start execution subjected to a conventional ML detector for fast classification. If a piece of software receives a borderline classification, it is subjected to further analysis via more performance expensive and more accurate DL methods, via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays to the execution of software subjected to deep learning analysis as a way to "buy time" for DL analysis and to rate-limit the impact of possible malware in the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and 877 commonly used benign software samples from various categories for the Windows OS. Our results show that the false positive rate for conventional ML methods can reach 20%, and for modern DL methods it is usually below 6%. However, the classification time for DL can be 100X longer than conventional ML methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the percentage of software subjected to DL analysis was approximately 40% on average. Further, the application of delays in software subjected to ML reduced the detection time by approximately 10%. Finally, we found and discussed a discrepancy between the detection accuracy offline (analysis after all traces are collected) and on-the-fly (analysis in tandem with trace collection). Our insights show that conventional ML and modern DL-based malware detectors in isolation cannot meet the needs of efficient and effective malware detection: high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure

Effectively Grouping Named Entities From Click- Through Data Into Clusters Of Generated Keywords1

Many studies show that named entities are closely related to users\u27 search behaviors, which brings increasing interest in studying named entities in search logs recently. This paper addresses the problem of forming fine grained semantic clusters of named entities within a broad domain such as “company”, and generating keywords for each cluster, which help users to interpret the embedded semantic information in the cluster. By exploring contexts, URLs and session IDs as features of named entities, a three-phase approach proposed in this paper first disambiguates named entities according to the features. Then it properly weights the features with a novel measurement, calculates the semantic similarity between named entities with the weighted feature space, and clusters named entities accordingly. After that, keywords for the clusters are generated using a text-oriented graph ranking algorithm. Each phase of the proposed approach solves problems that are not addressed in existing works, and experimental results obtained from a real click through data demonstrate the effectiveness of the proposed approach

Structure, expression differentiation and evolution of duplicated fiber developmental genes in Gossypium barbadense and G. hirsutum

<p>Abstract</p> <p>Background</p> <p>Both <it>Gossypium hirsutum </it>and <it>G. barbadense </it>probably originated from a common ancestor, but they have very different agronomic and fiber quality characters. Here we selected 17 fiber development-related genes to study their structures, tree topologies, chromosomal location and expression patterns to better understand the interspecific divergence of fiber development genes in the two cultivated tetraploid species.</p> <p>Results</p> <p>The sequence and structure of 70.59% genes were conserved with the same exon length and numbers in different species, while 29.41% genes showed diversity. There were 15 genes showing independent evolution between the A- and D-subgenomes after polyploid formation, while two evolved via different degrees of colonization. Chromosomal location showed that 22 duplicate genes were located in which at least one fiber quality QTL was detected. The molecular evolutionary rates suggested that the D-subgenome of the allotetraploid underwent rapid evolutionary differentiation, and selection had acted at the tetraploid level. Expression profiles at fiber initiation and early elongation showed that the transcripts levels of most genes were higher in Hai7124 than in TM-1. During the primary-secondary transition period, expression of most genes peaked earlier in TM-1 than in Hai7124. Homeolog expression profile showed that A-subgenome, or the combination of A- and D-subgenomes, played critical roles in fiber quality divergence of <it>G. hirsutum </it>and <it>G. barbadense</it>. However, the expression of D-subgenome alone also played an important role.</p> <p>Conclusion</p> <p>Integrating analysis of the structure and expression to fiber development genes, suggests selective breeding for certain desirable fiber qualities played an important role in divergence of <it>G. hirsutum </it>and <it>G. barbadense</it>.</p

Polar phase transitions in heteroepitaxial stabilized La0.5Y0.5AlO3 thin films

PAPER Polar phase transitions in heteroepitaxial stabilized La0.5Y0.5AlO3 thin films Shenghua Liu1, Chunfeng Zhang1, Mengya Zhu1, Qian He2, Jak Chakhalian3, Xiaoran Liu3,4, Albina Borisevich2, Xiaoyong Wang1 and Min Xiao1,4 Published 1 September 2017 • © 2017 IOP Publishing Ltd Journal of Physics: Condensed Matter, Volume 29, Number 40 Article PDF Figures References PDF 18 Total downloads Turn on MathJax Get permission to re-use this article Share this article Article information Abstract We report on the fabrication of epitaxial La0.5Y0.5AlO3 ultrathin films on (001) LaAlO3 substrates. Structural characterizations by scanning transmission electron microscopy and x-ray diffraction confirm the high quality of the film with a − b + c − AlO6 octahedral tilt pattern. Unlike either of the nonpolar parent compound, LaAlO3 and YAlO3, second harmonic generation measurements on the thin films suggest a nonpolar–polar phase transition at T c near 500 K, and a polar–polar phase transition at T a near 160 K. By fitting the angular dependence of the second harmonic intensities, we further propose that the two polar structures can be assigned to the Pmc2 1 and Pmn2 1 space group, while the high temperature nonpolar structure belongs to the Pbnm space group