Ecological principle meets cancer treatment: treating children with acute myeloid leukemia with low-dose chemotherapy

Standard chemotherapy regimens for remission induction of pediatric acute myeloid leukemia (AML) are associated with significant morbidity and mortality. We performed a cohort study to determine the impact of reducing the intensity of remission induction chemotherapy on the outcomes of selected children with AML treated with a low-dose induction regimen plus granulocyte colony stimulating factor (G-CSF) (low-dose chemotherapy (LDC)/G-CSF). Complete response (CR) after two induction courses was attained in 87.0% (40/46) of patients receiving LDC/G-CSF. Post-remission therapy was offered to all patients, and included standard consolidation and/or stem cell transplantation. During the study period, an additional 94 consecutive children with AML treated with standard chemotherapy (SDC) for induction (80/94 (85.1%) of the patients attained CR after induction II, P = 0.953) and post-remission. In this non-randomized study, there were no significant differences in 4-year event-free (67.4 vs. 70.7%; P = 0.99) and overall (70.3 vs. 74.6%, P = 0.69) survival in the LDC/G-CSF and SDC cohorts, respectively. After the first course of induction, recovery of white blood cell (WBC) and platelet counts were significantly faster in patients receiving LDC/G-CSF than in those receiving SDC (11.5 vs. 18.5 d for WBCs (P < 0.001); 15.5 vs. 22.0 d for platelets (P < 0.001)). To examine the quality of molecular response, targeted deep sequencing was performed. Of 137 mutations detected at diagnosis in 20 children who attained hematological CR after two courses of LDC/G-CSF (n = 9) or SDC (n = 11), all of the mutations were below the reference value (variant allelic frequency <2.5%) after two courses, irrespective of the treatment group. In conclusion, children with AML receiving LDC/G-CSF appear to have similar outcomes and mutation clearance levels, but significantly lower toxicity than those receiving SDC. Thus, LDC/G-CSF should be further evaluated as an effective alternative to remission induction in pediatric AML.     

以社区为驱动力实现全民健康的研究

以社区为驱动力实现全民健康的研究是一条社区推动全面小康之路,最终目的是解决国民健康面临的现实困境,促进健康中国战略的实施。文章通过文献资料法、逻辑分析法等研究方法,从社区对全民健康计划实施的意义着手,较为全面、系统的分析了社区在全民健康目标实现过程中的作用、价值及内在联系,并提出:以卫生服务管理为突破口,构建新型卫生健康管理服务模式;以社区为单位,提升国民健康素养水平;以环境治理为基点,优化社区环境质量;以国家战略为引领,发展社区健康产业等路径,助推全民健康目标的实现进度。     

基于创新扩散理论的学术论文影响力广度研究

[目的/意义]采用被引次数衡量学术论文影响力存在诸多弊端。本文认为学术论文影响力包括其传播的深度、速度和广度3个方面，熵可用于衡量学术论文影响力传播的广度。[方法/过程]选择1901-2017年生物医学领域的诺贝尔奖论文作为最具影响力的论文纳入实验组，并根据1：1配对原则设立对照组，比较两组论文发表后5年内其施引文献所属学科数量、熵以及熵与被引次数的相关性。[结果/结论]实验组中65%以上的论文施引文献学科数量高于对照组；实验组熵的均值介于0.552-0.772，对照组介于0.251-0.481，有显著性差异（P<0.05）；两组论文的被引次数与熵的相关性较弱，均小于0.3。结果表明：①70%以上的高影响力论文在发表早期能够影响较多的学科；②采用熵对论文影响力广度进行识别具有可行性。     

基于短时交通流量预测对路网微观交通仿真的研究

针对早高峰短时交通流量预测数据少、波动大的特点,提出用灰色模型进行预测。将灰色GM(2,1)改进为灰色GM(2,1,λ,ρ)预测模型,以提高预测精度。针对粒子群算法(PSO)的早熟现象,将Logistic混沌搜索嵌入到PSO算法,应用混沌粒子群算法(CPSO)寻找灰色GM(2,1,λ,ρ)预测模型最优的参数λ和ρ。结合两者提出了基于CPSO–GM(2,1,λ,ρ)的早高峰短时交通流预测模型。利用VISSIM对研究路网进行微观交通仿真,通过VISSIM–Excel、VBA–Matlab平台实现了短时交通流量预测和路网微观交通仿真数据的交互,对集成交通控制系统的架构进行了方案设计。仿真结果表明,结合流量预测的路网优于固定信号配时下的路网仿真。     

数字经济时代的知识管理:挑战与趋势

[目的/意义]研究数字经济时代，知识作为关键战略资源和核心资产，对理论和实践提出的新挑战。[方法/过程]采用文献资料调研法和理论分析法，基于网络实体系统（CPS）对数字经济时代知识管理的挑战和趋势进行分析阐述。[结果/结论]数字经济时代，知识管理在对象上对数据的获取和整合要求更高，在增值体系上对数据到信息再到知识的精度、实时性和准确性要求更高，异构整合和数据挖掘成为知识管理的关键，人机交互和人工智作为关键技术将发挥关键作用，在知识管理的目标上，数据驱动的价值创造成为核心。未来知识管理的发展将在战略上向实时动态精准决策演化，在架构上转向全域知识管理，在组织上通过无障碍知识流打造高效创新型组织，在内容上系统整合碎片化知识，在方法上重视基于人机交互的隐性知识转化，在技术上打造基于平台的知识图谱。     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.     

高职院校《路桥CAD》课程教学改革与实践

本文分析了高职院校道桥工程技术专业《路桥CAD》课程目前所面临的问题,提出教学必须体现专业特色和职业特色,就如何提高《路桥CAD》课程的教学效果提出必须对教学模式、教学内容、实训模式、课后交流、考核方法等方面进行优化并作了详细阐述。     

新闻学与传播学期刊被引次数和被引广度分析——基于CSSCI的统计分析(2004-2006)

以2004—2006年CSSCI为数据来源,通过被引次数和被引广度两个指标分析,客观地揭示新闻学与传播学期刊的学术影响力,并针对被引次数指标数据,对新闻学与传播学期刊的层次进行划分,希望引导新闻学与传播学期刊更进一步的发展。     

信息不对称条件下图书质量的博弈分析

图书是反映、传播、积累和促进人类思想、科学文化的重要工具,提高图书质量是满足市场需求、更好地发挥图书效益的重要途径。在信息不对称的情况下,图书市场会出现失灵甚至消失,人们也会出现逆向选择行为。本文从信息不对称角度出发,通过对图书质量的博弈及其均衡结果的剖析,提出只有加强政府监管,规范出版者行为,建立信用评价体系,才能使交易双方都达到帕累托最优。