Progress in autoantibodies to platelet and idiopathic thrombocytopenic purpura

The onset of idiopathic thrombocytopenic purpura (ITP) is related to excessive destruction of platelet resulting from antiplatelet autoantibody. The immunity with ITP is imbalance, so the hypofunction of suppressor T cell (Ts) can't restrain B lymphocytes producing antibody, as a result, autoant ibody is produced, the clearance of platelet in blood circulation is accelerated. The different antibodies have different functions on ITP. The measuring of platelet antibody has its significance in diagnosis, therapeutic evaluat ion and expectation of prognosis.     

Modelling Plant Disease Epidemics

An epidemic is the progress of disease in time and space. Each epidemic has a structure whose temporal dynamics and spatial patterns are jointly determined by the pathosystem characteristics and environmental conditions. One of the important objectives in epidemiology is to understand such spatio-temporal dynamics via mathematical and statistical modelling. In this paper, we outline common methodologies that are used to quantify and model spatio-temporal dynamics of plant diseases, with emphasis on developing temporal forecast models and on quantifying spatial patterns. Several examples of epidemiological models in cereal crops are described, including one for Fusarium head blight.     

Geostatistical interpolation and aggregation of crop growth model outputs

Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country’s crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation.     

K步稳定的鞋印花纹图像自动聚类

目的 鞋印是刑事侦查的重要物证之一,如何对积累的大量鞋印花纹图像进行自动归类管理是刑事技术迫切需要解决的问题之一。与其他类图像不同,鞋印花纹图像具有种类多但数目未知、同类花纹分布不均匀且同类花纹数目少的特点。基于鞋印花纹图像的这些特点,用目前典型的聚类算法对鞋印花纹图像集进行聚类,并不能取得很好的效果。在对鞋印花纹图像进行分析的基础上,提出一种K步稳定的鞋印花纹图像自动聚类算法。方法 对已标记的鞋印花纹图像进行统计发现,各类鞋印花纹之间在特征空间上存在互不相交的区域(本文称为隔离带)。算法的核心思想是寻找各类鞋印花纹之间的隔离带,来将各类分开。过程为:以单调递增或递减的方式调整特征空间中判定两点为一类的阈值,得到数据集的多次划分;若在连续K次划分的过程中,某一类的成员不发生变化,则说明这K次调整是在隔离带中进行的,即聚出一类,并从数据集中删除已标记的数据;选择下一个阈值对剩余的数据集进行划分,输出K步不变的类;依此类推,直到剩余数据集为空,聚类完成。结果 在两类公开测试数据集和实际鞋印花纹数据集上进行实验,本文算法的主要性能指标都超过典型算法,其中在包含5792枚实际鞋印花纹数据集上的聚类准确率和F-Measure值分别达到了99.68%和95.99%。结论 针对鞋印花纹图像特点,提出了一种通过寻找各类之间的隔离带进行自动聚类的算法,并在实际应用中取得了很好的效果。且算法性能受参数的变化以及类的形状影响较小。本文算法同样适用于具有类似特点的其他数据集的自动聚类。     

纳米ZnO对浮萍（Lemna minor L.）的生态毒性及其聚集性和溶解性分析

纳米颗粒的大量生产和应用增加了其环境释放风险,为此以不同浓度的纳米ZnO（0、1、10mg·L-1和50mg·L-1）处理浮萍（LemnaminorL.）7d,分析了纳米颗粒对植物D665/D665a值（叶绿素与脱镁叶绿素的比率）以及超氧化物歧化酶（SOD）、过氧化氢酶（CAT）、过氧化物酶（POD）和Na＋K＋-ATP酶（Na＋K＋-ATPase）活性的影响,并对纳米ZnO的聚集性与溶解性进行了测试。研究结果显示,浓度为50mg·L-1的纳米颗粒显著抑制D665/D665a值和Na＋K＋-ATPase活性,而抗氧化酶活性则显著升高;纳米颗粒在培养液中易发生聚集作用而沉积,12h后基本上完全沉积到底部。结果说明,50mg·L-1的纳米ZnO对浮萍产生了显著的胁迫作用,其对浮萍的毒性作用主要来源于其溶出的Zn2＋。     

An aggregation index (AI) to quantify spatial patterns of landscapes

There is often need to measure aggregation levels of spatial patterns within a single map class in landscape ecological studies. The contagion index (CI), shape index (SI), and probability of adjacency of the same class (Qi), all have certain limits when measuring aggregation of spatial patterns. We have developed an aggregation index (AI) that is class specific and independent of landscape composition. AI assumes that a class with the highest level of aggregation (AI =1) is comprised of pixels sharing the most possible edges. A class whose pixels share no edges (completely disaggregated) has the lowest level of aggregation (AI =0). AI is similar to SI and Qi, but it calculates aggregation more precisely than the latter two. We have evaluated the performance of AI under varied levels of (1) aggregation, (2) number of patches, (3) spatial resolutions, and (4) real species distribution maps at various spatial scales. AI was able to produce reasonable results under all these circumstances. Since it is class specific, it is more precise than CI, which measures overall landscape aggregation. Thus, AI provides a quantitative basis to correlate the spatial pattern of a class with a specific process. Since AI is a ratio variable, map units do not affect the calculation. It can be compared between classes from the same or different landscapes, or even the same classes from the same landscape under different resolutions.