基于网络药理和细胞实验探索芪玉三龙方治疗非小细胞肺癌的机制

目的 采用网络药理学和细胞实验探究芪玉三龙方治疗非小细胞肺癌的潜在作用机制。方法 利用中药系统药理学数据库与分析平台和中医药整合数据库检索芪玉三龙方成分筛选靶点,通过美国肿瘤基因组图谱数据库获得肺癌靶点基因。构建蛋白相互作用网络,并采用基因本体数据库和京都基因和基因组百科全书数据库进行富集分析。对核心靶蛋白与化合物进行分子对接分析,并以A549细胞进行实验验证。结果 芪玉三龙方对于非小细胞肺癌的潜在有效靶点162个,蛋白相互作用网络分析得到核心靶点基因20个,主要的生物学过程为对缺氧的反应、MAPK级联、炎症反应,主要的功能通路为钙信号通路、TNF信号通路、IL-17信号通路、细胞周期。分子对接结果表明,MMP9-槲皮素、PPARG-异鼠李素、PTGS2-刺芒柄花素、JUN-刺芒柄花素之间存在有效的结合亲和力。在A549细胞实验中,PCR和Western blot分析结果表明,芪玉三龙方含药血清能够促进PPAR-γ mRNA和蛋白表达（P＜0.05）,抑制c-JUN、COX-2、MMP-9 mRNA和蛋白表达（P＜0.05）。结论 芪玉三龙方通过影响A549肺癌细胞的凋亡、侵袭和肿瘤血管生成及炎症进程达到干预肺癌的作用。     

我国县城城镇化政策的适配性研究——基于政策文本分析的视角

党的十九届五中会提出"推进以县城为重要载体的城镇化建设".在国际国内发展双重压力的现实需求下,县城成为新时代中国城镇化转型升级的关键钥匙.基于政策文本分析视角,引入"适配性"概念,构建了"政策工具——政策建设场域——政策力度"三维分析框架,对60份中央和省级县城城镇化政策文本进行分析.研究发现:我国县城城镇化建设正处于...     

Probabilistic genotyping of single cell replicates from complex DNA mixtures recovers higher contributor LRs than standard analysis

DNA mixtures are a common source of crime scene evidence and are often one of the more difficult sources of biological evidence to interpret. With the implementation of probabilistic genotyping (PG), mixture analysis has been revolutionized allowing previously unresolvable mixed profiles to be analyzed and probative genotype information from contributors to be recovered. However, due to allele overlap, artifacts, or low-level minor contributors, genotype information loss inevitably occurs. In order to reduce the potential loss of significant DNA information from donors in complex mixtures, an alternative approach is to physically separate individual cells from mixtures prior to performing DNA typing thus obtaining single source profiles from contributors. In the present work, a simplified micro-manipulation technique combined with enhanced single-cell DNA typing was used to collect one or few cells, referred to as direct single-cell subsampling (DSCS). Using this approach, single and 2-cell subsamples were collected from 2 to 6 person mixtures. Single-cell subsamples resulted in single source DNA profiles while the 2-cell subsamples returned either single source DNA profiles or new mini-mixtures that are less complex than the original mixture due to the presence of fewer contributors. PG (STRmix™) was implemented, after appropriate validation, to analyze the original bulk mixtures, single source cell subsamples, and the 2-cell mini mixture subsamples from the original 2–6-person mixtures. PG further allowed replicate analysis to be employed which, in many instances, resulted in a significant gain of genotype information such that the returned donor likelihood ratios (LRs) were comparable to that seen in their single source reference profiles (i.e., the reciprocal of their random match probabilities). In every mixture, the DSCS approach gave improved results for each donor compared to standard bulk mixture analysis. With the 5- and 6- person complex mixtures, DSCS recovered highly probative LRs (≥10²⁰) from donors that had returned non-probative LRs (<10³) by standard methods.     

A social graph based text mining framework for chat log investigation

This paper presents a unified social graph based text mining framework to identify digital evidences from chat logs data. It considers both users' conversation and interaction data in group-chats to discover overlapping users' interests and their social ties. The proposed framework applies n-gram technique in association with a self-customized hyperlink-induced topic search (HITS) algorithm to identify key-terms representing users' interests, key-users, and key-sessions. We propose a social graph generation technique to model users' interactions, where ties (edges) between a pair of users (nodes) are established only if they participate in at least one common group-chat session, and weights are assigned to the ties based on the degree of overlap in users' interests and interactions. Finally, we present three possible cyber-crime investigation scenarios and a user-group identification method for each of them. We present our experimental results on a data set comprising 1100 chat logs of 11,143 chat sessions continued over a period of 29 months from January 2010 to May 2012. Experimental results suggest that the proposed framework is able to identify key-terms, key-users, key-sessions, and user-groups from chat logs data, all of which are crucial for cyber-crime investigation. Though the chat logs are recovered from a single computer, it is very likely that the logs are collected from multiple computers in real scenario. In this case, logs collected from multiple computers can be combined together to generate more enriched social graph. However, our experiments show that the objectives can be achieved even with logs recovered from a single computer by using group-chats data to draw relationships between every pair of users.     

Clustering digital forensic string search output

This research comparatively evaluates four competing clustering algorithms for thematically clustering digital forensic text string search output. It does so in a more realistic context, respecting data size and heterogeneity, than has been researched in the past. In this study, we used physical-level text string search output, consisting of over two million search hits found in nearly 50,000 allocated files and unallocated blocks. Holding the data set constant, we comparatively evaluated k-Means, Kohonen SOM, Latent Dirichlet Allocation (LDA) followed by k-Means, and LDA followed by SOM. This enables true cross-algorithm evaluation, whereas past studies evaluated singular algorithms using unique, non-reproducible datasets. Our research shows an LDA + k-Means using a linear, centroid-based user navigation procedure produces optimal results. The winning approach increased information retrieval effectiveness, from the baseline random walk absolute precision rate of 0.04, to an average precision rate of 0.67. We also explored a variety of algorithms for user navigation of search hit results, finding that the performance of k-means clustering can be greatly improved with a non-linear, non-centroid-based cluster and document navigation procedure, which has potential implications for digital forensic tools and use thereof, particularly given the popularity and speed of k-means clustering.     

Applicability of Latent Dirichlet Allocation to multi-disk search

Digital forensics practitioners face a continual increase in the volume of data they must analyze, which exacerbates the problem of finding relevant information in a noisy domain. Current technologies make use of keyword based search to isolate relevant documents and minimize false positives with respect to investigative goals. Unfortunately, selecting appropriate keywords is a complex and challenging task. Latent Dirichlet Allocation (LDA) offers a possible way to relax keyword selection by returning topically similar documents. This research compares regular expression search techniques and LDA using the Real Data Corpus (RDC). The RDC, a set of over 2400 disks from real users, is first analyzed to craft effective tests. Three tests are executed with the results indicating that, while LDA search should not be used as a replacement to regular expression search, it does offer benefits. First, it is able to locate documents when few, if any, of the keywords exist within them. Second, it improves data browsing and deals with keyword ambiguity by segmenting the documents into topics.     

试论公安文书文本的规范化

公安文书文本的规范化是公安文书规范化的两大要素之一，具有技术规范的属性。它是一种客观需要，是公安文书储存功能和传递方式的最佳形式，具有可操作性、权威性和约束力；从不同角度，可以分为模式规范、原则规范或通用规范、专用规范、借用规范、约定规范等。实现公安文书文本规范化的基本原则是效率、优化和美观等。     

马克思主义国家理论的文本解读

从马克思、恩格斯的著作文本入手，分析归纳其文本中关于国家理论的具体论述，是掌握和理解马克思主义国家理论的根本前提和基础。抛开文本或者在文本基础上对马克思主义国家理论的演绎都是精致的唯心主义的体现。本文以马克思、恩格斯的著作文本为基础，全面集中解读了马克思恩格斯关于国家理论的具体论述，主要包括国家起源、国家的本质、国家的职能、国家的消亡四个方面，进一步厘清了马克思恩格斯国家理论的本原，以期消除人们对马克思主义国家理论的神秘感或复杂感，使人们更加简单的理解和掌握马克思主义国家理论基本要义。     

开设"110"短信报警探究

随着改革的深化与进一步的对外开放,特别是加入WTO之后,国内治安形势不容乐观.为有效控制犯罪,要求"110"报警系统必须作出快速反应,尽可能缩短现场反应时间.在手机普及、短信得到普遍运用并将进一步发展的条件下,开设"110"短信报警系统具有十分重要的现实意义.     

iCOP: Live forensics to reveal previously unknown criminal media on P2P networks

The increasing levels of criminal media being shared in peer-to-peer (P2P) networks pose a significant challenge to law enforcement agencies. One of the main priorities for P2P investigators is to identify cases where a user is actively engaged in the production of child sexual abuse (CSA) media – they can be indicators of recent or on-going child abuse. Although a number of P2P monitoring tools exist to detect paedophile activity in such networks, they typically rely on hash value databases of known CSA media. As a result, these tools are not able to adequately triage the thousands of results they retrieve, nor can they identify new child abuse media that are being released on to a network. In this paper, we present a new intelligent forensics approach that incorporates the advantages of artificial intelligence and machine learning theory to automatically flag new/previously unseen CSA media to investigators. Additionally, the research was extensively discussed with law enforcement cybercrime specialists from different European countries and Interpol. The approach has been implemented into the iCOP toolkit, a software package that is designed to perform live forensic analysis on a P2P network environment. In addition, the system offers secondary features, such as showing on-line sharers of known CSA files and the ability to see other files shared by the same GUID or other IP addresses used by the same P2P client. Finally, our evaluation on real CSA case data shows high degrees of accuracy, while hands-on trials with law enforcement officers demonstrate the toolkit's complementarity to extant investigative workflows.