伴CIC-DUX4融合基因的高级别未分化圆细胞肉瘤的研究进展

未分化圆细胞肉瘤是一组异质性肿瘤性病变,新近的研究显示重现性CIC-DUX4融合基因是该类肿瘤的一大特征。该类肿瘤常发生于年轻人的肢体软组织,具有较强的侵袭性。细针吸取(FNA)细胞涂片技术结合免疫组织化学染色是未分化圆细胞肉瘤进一步精准基因分型的重要筛查方法。本文对该肿瘤的临床特征、FNA细胞学形态特征、病理诊断和鉴别诊断,以及CIC基因对肿瘤的调控机制作一综述

血管内皮生长因子表达与肝癌患者临床病理特征及预后的关系研究

目的:研究肝细胞肝癌(HCC)组织中血管内皮生长因子(VEGF)表达与患者临床病理特征及预后的关系。方法:采用免疫组织化学染色法检测79例在广西医科大学附属肿瘤医院行肝癌根治性切除术的HCC患者癌组织中VEGF的表达情况,分析VEGF表达与树突状细胞(DC)浸润程度、肿瘤大小、癌栓、包膜、血清AFP水平及术后肝外转移的关系,随访患者术后生存情况及肿瘤复发情况。结果:79例HCC患者中,VEGF低表达43例(54.4%),高表达36例(45.6%)。VEGF表达与DC浸润程度、肿瘤直径及血清AFP水平有关(P0.05)。VEGF高表达组2年内复发率为83.3%(30/36),明显高于VEGF低表达组的60.5%(26/43),差异有统计学意义(P0.05)。结论:VEGF与癌组织DC浸润程度、肿瘤直径及血清AFP水平有关,其在HCC组织中高表达可能增加肝癌术后短期复发风险,可作为肝癌早期复发预警指标

Self-organizing Map Algorithm Based on Intra-class Minimum Similarity Degree and Application in Reservoir Prediction

为了解决自组织映射(Self-organization map,; SOM)神经网络算法部分神经元过度利用和欠利用的问题,提出基于类内最小相似度的SOM算法(SOM based on intraclass; minimun similarity; degree,SOM-IMSD),将类内相似度这一评价指标引入SOM神经网络学习过程中,通过调整类内最小相似度来指导SOM神经网络学习,使得平均; 类内最小相似度最大,提高SOM神经网络的聚类结果质量.将SOM-IMSD算法应用于储层预测,并与基本SOM算法进行对比,实验结果表明,SOM-I; MSD算法的聚类结果更为准确.Intra-Class similarity degree is a commonly used evaluation index to; evaluate the quality of the clustering results.It can also be used to; weigh the cluster result.In order to solve the problem of excessive use; and less use of some neurons,we propose a selforganizing map algorithm; based on intra-class minimum similarity degree (SOM-IMSD),which; introduce intra-class similarity degree into the process of SOM neural; network learning.Adjust IMSD to guide SOM neural network learning,which; makes the average IMSD maximum and improves the quality of cluster; result. Apply the SOM-IMSD and basic SOM to reservoir prediction and; compare the results.The experiment shows that it has improved the; clustering results

Integrated Framework of Reliability Evaluation Method of User Behavior

用户行为可靠性评价对于网络服务的发展具有重要作用，已有研究分别从概率统计、用户意图、用户行为模式以及数据挖掘等角度对其进行广泛研究。本文在定义网络用户行为的可靠性评价基础上，梳理现有算法和模型，针对现有评价模型存在的问题，提出一种包括用户行为数据收集层、用户行为划分层、用户行为模式训练层、不可靠用户行为鉴别层以及用户行为管理与控制层的用户行为可靠性评价综合模型框架，能够在一定程度上解决用户行为可靠性评价中的恶意机器人问题。The reliability evaluation of user behavior is playing an important role on the development of network services.The current researches about reliability evaluation of user behavior include the probability statistics,user behavior intention,user behavior model and data mining.On the basis of the reliability evaluation of network user behavior,the definition of network user behavior’s trust evaluation and the summary of the existing research,the paper aims at the existing problems in the current evaluation models,and tries to propose an integrated framework of reliability evaluation method of network user behavior.There are five layers in this framework,which are data collection layer,user behavior division layer,user behavior training layer,unreliability behavior identification layer and user behavior management and control layer.This framework makes a positive effect in improving the solution to the problem of bad machine behaviors in the reliability evaluation of user behavior.本研究得到国家社会科学基金项目“面向检索的网络用户行为可靠性度量研究”(编号:13CTQ011)资助