基于RAGA的PPC模型在城市公园绿地景观生态美学评价中的应用

基于公众对生态环境的日益重视,以生态美学思想为指导,提出城市公园绿地景观生态美学评价的指标体系;同时利用基于RAGA的PPC模型,将高维数据转换到低维子空间,通过寻求最优投影方向及投影函数值来实现对城市公园绿地景观的生态美学评价。选取大庆市10个具有代表性的公园绿地为研究对象,运用该评价指标体系及模型进行景观生态美学评价。结果表明:总体景观空间多样性、植被色彩多样性、建筑与小品分布合理性、树种多样性、水体可亲近性、植被长势自然性、地形自然度、建筑与小品体量适宜度指标对大庆市公园绿地景观生态美学效果的影响较大,是今后大庆市公园绿地景观生态建设、改造与管理上应作为重点考虑的因素;在10个公园绿地中,油田乐园、城市森林公园最具生态美,而乘风广场、世纪大道(高新技术开发区段)附属带状绿地的生态美较差。应用该模型可以避免传统方法存在的一些不足,也为处理景观评价中具有模糊性、不确定性的高维数据的综合评判、排序、寻优等方面研究提供了新的思路与方法。     

基于多尺度遥感影像分割方法的湿地生态廊道设计

从生物多样性保护的角度,采用多尺度遥感影像分割方法中的人为干扰度模型,计算分割阈值确定湿地生态廊道的宽度,并结合聚类分析法分类的8种人为干扰类型,对建三江地区廊道结构设计进行了研究。结果表明,廊道分割阈值设为20%,非湿地背景噪声为9.43%,湿地生态廊道最佳宽度为1298m。8种人为干扰度中聚类c1、c2、c3和c4类型是受人为干扰较弱的区域,主要分布在浓江、乌苏里江、三江和洪河保护区原始生态环境区域,将其分别设定为核心区、实验区、边缘区、缓冲区4种类型。廊道核心区中的沼泽类型占75%,总体精度高达93.7%,实验区沼泽占72.2%,精度达到75.8%,边缘与缓冲区起到边缘护栏的作用,缓冲区宽度为945m,本研究为湿地生态廊道的建设与修复提供了可靠、科学的参考依据。     

黄河首曲玛曲县高寒湿地景观格局演变

高寒湿地是青藏高原地区最重要的生态水源涵养区之一, 也是局部气候的有效调节者, 其动态变化与成因亟待深入研究。该研究基于遥感图像分析、地理信息系统空间分析和景观生态指数分析结合的方法, 以黄河首曲玛曲县高寒湿地为研究对象, 对1995-2018年6期湿地的动态变化进行研究。结果表明, 研究区湿地在1995-2010年间不断退化, 1995-2010年湿地面积总共减少了18 680.31 hm²。在2010-2018年间黄河首曲高寒湿地面积有所增加, 但与20世纪90年代相比, 21世纪初开始湿地的面积普遍呈现下降趋势; 1995-2010年湿地斑块数不断增加, 斑块密度不断增大, 平均斑块面积下降, 景观的破碎度升高; 2010-2015年湿地斑块数和斑块密度减少, 2015-2018年湿地斑块数和斑块密度增加, 平均斑块面积先增大后减小, 景观的破碎度先降低后升高。1995-2010年研究区高寒湿地景观Shannon多样性指数和Shannon均匀度指数均呈现下降的趋势, 湿地的景观结构趋于简单, 景观类型分布更加集中。2010-2018年湿地景观Shannon多样性指数和Shannon均匀度指数均呈现上升趋势, 湿地的景观结构趋于复杂, 景观类型增加且分布更加分散。进一步的驱动力分析表明, 引起黄河首曲高寒湿地景观格局演变的主要因素是蒸发量和降水量, 其次是人口数量和大牲畜数量等人类活动影响。气候因子是影响黄河首曲高寒湿地面积变化的主要原因, 过度的人类经济活动在一定程度上加剧了湿地的变化。     

Learning Biomarkers of Pluripotent Stem Cells in Mouse

Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.     

系统发育分析中的最大简约法及其优化

随着生物技术的不断发展和系统发育学的深入研究, 在重构系统发育树时, 研究人员往往要面对更多的挑战和困难, 比如： (1)需要分析的样本数（物种数或个体数）不断增加; (2)需要分析的数据量迅速扩大。尤其在基因组测序技术的推动下, 基于分子信息的系统发育重建需要极大的计算量, 因此数学方法、 计算机技术以及其他辅助工具对于系统发育重建的效率和精确度起着至关重要的作用。最大简约法（maximum parsimony）是一种重要的系统发育重建方法, 提高其计算效率对系统发育学研究具有重要意义, 针对该算法的优化改进需要生物学家和计算机专家的共同努力。本文通过详细地阐述最大简约法的计算流程, 分析其参数选择对计算效率的影响, 帮助更多的计算机使用者, 在并不了解系统发育学基础的情况下, 更方便地针对实际的系统发育算法问题给出更好、 更快、 更精准的解决方案; 同时为系统发育研究工作者, 较为清晰地解释最大简约法的构树思想和计算逻辑, 推动针对最大简约法的不断改进与优化。     

Modelling disease spread through random and regular contacts in clustered populations

An epidemic spreading through a network of regular, repeated, contacts behaves differently from one that is spread by random interactions: regular contacts serve to reduce the speed and eventual size of an epidemic. This paper uses a mathematical model to explore the difference between regular and random contacts, considering particularly the effect of clustering within the contact network. In a clustered population random contacts have a much greater impact, allowing infection to reach parts of the network that would otherwise be inaccessible. When all contacts are regular, clustering greatly reduces the spread of infection; this effect is negated by a small number of random contacts.     

A model-free algorithm for the removal of baseline artifacts

Summary A novel algorithm for removing baseline distortions in NMR spectra is presented. The algorithm approximates the baseline as the median of the noise extrema. Consequently, the method does not require that NMR peaks be discriminated from noise peaks. In addition, no assumptions regarding the source or functional form of the distortion are made. The algorithm is shown to remove the baseline artifacts present in a particularly distorted NOESY spectrum and to reveal peaks which had been obscured by the artifacts. The parameters and spectral characteristics (signal-to-noise ratio, NMR peak density, peak linewidths) governing the resolution of the calculated baselines are also explored.