A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy

Tumor angiogenesis is the process by which new blood vessels are formed and enhance the oxygenation and growth of tumors. As angiogenesis is recognized as being a critical event in cancer development, considerable efforts have been made to identify inhibitors of this process. Cytostatic treatments that target the molecular events of the angiogenesis process have been developed, and have met with some success. However, it is usually difficult to preclinically assess the effectiveness of targeted therapies, and apparently promising compounds sometimes fail in clinical trials.We have developed a multiscale mathematical model of angiogenesis and tumor growth. At the molecular level, the model focuses on molecular competition between pro- and anti-angiogenic substances modeled on the basis of pharmacological laws. At the tissue scale, the model uses partial differential equations to describe the spatio-temporal changes in cancer cells during three stages of the cell cycle, as well as those of the endothelial cells that constitute the blood vessel walls.This model is used to qualitatively assess how efficient endostatin gene therapy is. Endostatin is an anti-angiogenic endogenous substance. The gene therapy entails overexpressing endostatin in the tumor and in the surrounding tissue. Simulations show that there is a critical treatment dose below which increasing the duration of treatment leads to a loss of efficacy.This theoretical model may be useful to evaluate the efficacy of therapies targeting angiogenesis, and could therefore contribute to designing prospective clinical trials.     

Efficient Estimation for Rank‐Based Regression with Clustered Data

Summary Rank‐based inference is widely used because of its robustness. This article provides optimal rank‐based estimating functions in analysis of clustered data with random cluster effects. The extensive simulation studies carried out to evaluate the performance of the proposed method demonstrate that it is robust to outliers and is highly efficient given the existence of strong cluster correlations. The performance of the proposed method is satisfactory even when the correlation structure is misspecified, or when heteroscedasticity in variance is present. Finally, a real dataset is analyzed for illustration.     

广州口岸截获腹点纹牙甲Dactylosternum abdominale(Fabricius)及腐生昆虫传播探讨

报道了从菲律宾进口的废纸中截获的一种我国无分布的牙甲——腹点纹牙甲Dactylosternum abdominale（Fabricius）。讨论了腐生性昆虫世界性扩散的问题。     

Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data

The Mantel test is widely used to test the linear or monotonic independence of the elements in two distance matrices. It is one of the few appropriate tests when the hypothesis under study can only be formulated in terms of distances; this is often the case with genetic data. In particular, the Mantel test has been widely used to test for spatial relationship between genetic data and spatial layout of the sampling locations. We describe the domain of application of the Mantel test and derived forms. Formula development demonstrates that the sum-of-squares (SS) partitioned in Mantel tests and regression on distance matrices differs from the SS partitioned in linear correlation, regression and canonical analysis. Numerical simulations show that in tests of significance of the relationship between simple variables and multivariate data tables, the power of linear correlation, regression and canonical analysis is far greater than that of the Mantel test and derived forms, meaning that the former methods are much more likely than the latter to detect a relationship when one is present in the data. Examples of difference in power are given for the detection of spatial gradients. Furthermore, the Mantel test does not correctly estimate the proportion of the original data variation explained by spatial structures. The Mantel test should not be used as a general method for the investigation of linear relationships or spatial structures in univariate or multivariate data. Its use should be restricted to tests of hypotheses that can only be formulated in terms of distances.     

Modulation of mitochondrial function and morphology by interaction of Omi/HtrA2 with the mitochondrial fusion factor OPA1

Loss of Omi/HtrA2 function leads to nerve cell loss in mouse models and has been linked to neurodegeneration in Parkinson's and Huntington's disease. Omi/HtrA2 is a serine protease released as a pro-apoptotic factor from the mitochondrial intermembrane space into the cytosol. Under physiological conditions, Omi/HtrA2 is thought to be involved in protection against cellular stress, but the cytological and molecular mechanisms are not clear. Omi/HtrA2 deficiency caused an accumulation of reactive oxygen species and reduced mitochondrial membrane potential. In Omi/HtrA2 knockout mouse embryonic fibroblasts, as well as in Omi/HtrA2 silenced human HeLa cells and Drosophila S2R+ cells, we found elongated mitochondria by live cell imaging. Electron microscopy confirmed the mitochondrial morphology alterations and showed abnormal cristae structure. Examining the levels of proteins involved in mitochondrial fusion, we found a selective up-regulation of more soluble OPA1 protein. Complementation of knockout cells with wild-type Omi/HtrA2 but not with the protease mutant [S306A]Omi/HtrA2 reversed the mitochondrial elongation phenotype and OPA1 alterations. Finally, co-immunoprecipitation showed direct interaction of Omi/HtrA2 with endogenous OPA1. Thus, we show for the first time a direct effect of loss of Omi/HtrA2 on mitochondrial morphology and demonstrate a novel role of this mitochondrial serine protease in the modulation of OPA1. Our results underscore a critical role of impaired mitochondrial dynamics in neurodegenerative disorders.     

Plant Disease Severity Estimated Visually,by Digital Photography and Image Analysis,and by Hyperspectral Imaging

Reliable, precise and accurate estimates of disease severity are important for predicting yield loss, monitoring and forecasting epidemics, for assessing crop germplasm for disease resistance, and for understanding fundamental biological processes including co-evolution. Disease assessments that are inaccurate and/or imprecise might lead to faulty conclusions being drawn from the data, which in turn can lead to incorrect actions being taken in disease management decisions. Plant disease can be quantified in several different ways. This review considers plant disease severity assessment at the scale of individual plant parts or plants, and describes our current understanding of the sources and causes of assessment error, a better understanding of which is required before improvements can be targeted. The review also considers how these can be identified using various statistical tools. Indeed, great strides have been made in the last thirty years in identifying the sources of assessment error inherent to visual rating, and this review highlights ways that assessment errors can be reduced—particularly by training raters or using assessment aids. Lesion number in relation to area infected is known to influence accuracy and precision of visual estimates—the greater the number of lesions for a given area infected results in more overestimation. Furthermore, there is a widespread tendency to overestimate disease severity at low severities (<10%). Both interrater and intrarater reliability can be variable, particularly if training or rating aids are not used. During the last eighty years acceptable accuracy and precision of visual disease assessments have often been achieved using disease scales, particularly because of the time they allegedly save, and the ease with which they can be learned, but recent work suggests there can be some disadvantages to their use. This review considers new technologies that offer opportunity to assess disease with greater objectivity (reliability, precision, and accuracy). One of these, visible light photography and digital image analysis has been increasingly used over the last thirty years, as software has become more sophisticated and user-friendly. Indeed, some studies have produced very accurate estimates of disease using image analysis. In contrast, hyperspectral imagery is relatively recent and has not been widely applied in plant pathology. Nonetheless, it offers interesting and potentially discerning opportunities to assess disease. As plant disease assessment becomes better understood, it is against the backdrop of concepts of reliability, precision and accuracy (and agreement) in plant pathology and measurement science. This review briefly describes these concepts in relation to plant disease assessment. Various advantages and disadvantages of the different approaches to disease assessment are described. For each assessment method some future research priorities are identified that would be of value in better understanding the theory of disease assessment, as it applies to improving and fully realizing the potential of image analysis and hyperspectral imagery.     

A 450 million years long latitudinal gradient in age‐dependent extinction

Leigh Van Valen famously stated that under constant conditions extinction probability is independent of species age. To test this 'law of constant extinction', we developed a new method using deep learning to infer age‐dependent extinction and analysed 450 myr of marine life across 21 invertebrate clades. We show that extinction rate significantly decreases with age in > 90% of the cases, indicating that most species died out soon after their appearance while those which survived experienced ever decreasing extinction risk. This age‐dependent extinction pattern is stronger towards the Equator and holds true when the potential effects of mass extinctions and taxonomic inflation are accounted for. These results suggest that the effect of biological interactions on age‐dependent extinction rate is more intense towards the tropics. We propose that the latitudinal diversity gradient and selection at the species level account for this exceptional, yet little recognised, macroevolutionary and macroecological pattern.     

Bayesian model predicts the aboveground biomass of Caragana microphylla in sandy lands better than OLS regression models

回归模型可用于预测森林生态系统地上生物量,其中最为常用的是最小二乘回归模型。在预测灌木,尤其是多茎灌木的地上生物量 时,最小二乘法与贝叶斯方法的比较很少被研究。我们开发了小叶锦鸡儿(Caragana microphylla Lam.)生物量预测模型。小叶锦鸡儿是科尔 沁沙地广泛分布的多茎灌木,对减少风蚀、固定沙丘具有重要作用。本研究建立6种表征生物量的异速增长模型,并基于统计标准选择 在预测生物量方面表现最佳的1种,然后分别用最小二乘法与贝叶斯方法对模型中的参数进行估计。参数估计过程中用自助法考察样本量大 小的影响,同时区分测试集与训练集。最后,我们比较了最小二乘法与贝叶斯方法在小叶锦鸡儿地上生物量预测中的表现。异速增长的6个 模型均达到显著水平,其中幂指数为1的模型表现最佳。研究结果表明,采用无先验信息与有先验信息的贝叶斯方法进行估计,得到的均 方误差在测试集上低于最小二乘法。另外,基径作为预测变量在最小二乘法与贝叶斯方法中均不显著,表明在生物量预测模型中应谨慎选 择合适变量。本研究强调贝叶斯方法、自助法和异速增长模型相结合能够提升沙地灌木生物量预测模型的准确度。     

苏干湖湿地植被覆盖度时空变化格局

植被覆盖度是反映群落外貌特征和影响植被生态系统稳定性的重要因子,其时空异质性演化规律研究有助于认识湿地群落的结构功能及其环境响应机制。采用湿地群落学调查和遥感技术相结合的方法,分析了苏干湖内陆盐沼湿地近30年植被覆盖度的时空变化及其影响因素。结果表明:像元二分模型在内陆盐沼湿地植被覆盖度研究方面具有较高的模拟精度;苏干湖湿地的植被覆盖度在1987—2017年间总体呈上升趋势,年际增幅为0.162%/5a,与气温和降水呈正相关关系;在空间上植被覆盖度与地下水位埋深呈正相关,与土壤全盐量呈负相关,但不同等级植被覆盖度与地下水位埋深、土壤全盐量间的相关性各有差异。苏干湖湿地植被覆盖度受地下水位埋深、土壤全盐量空间异质性的影响,呈现出斑块状镶嵌分布。