Nonparametric estimation of ROC curves in the absence of a gold standard

In the evaluation of diagnostic accuracy of tests, a gold standard on the disease status is required. However, in many complex diseases, it is impossible or unethical to obtain such a gold standard. If an imperfect standard is used, the estimated accuracy of the tests would be biased. This type of bias is called imperfect gold standard bias. In this article we develop a nonparametric maximum likelihood method for estimating ROC curves and their areas of ordinal-scale tests in the absence of a gold standard. Our simulation study shows that the proposed estimators for the ROC curve areas have good finite-sample properties in terms of bias and mean squared error. Further simulation studies show that our nonparametric approach is comparable to the binormal parametric method, and is easier to implement. Finally, we illustrate the application of the proposed method in a real clinical study on assessing the accuracy of seven specific pathologists in detecting carcinoma in situ of the uterine cervix.     

Spatial and temporal variations in species occurrence rate affect the accuracy of occurrence models

Aim Predictive models of species occurrence have potential for prioritizing areas for competing land uses. Before widespread application, however, it is necessary to evaluate performance using independent data and effective accuracy measures. The objectives of this study were to (1) compare the effects of species occurrence rate on model accuracy, (2) assess the effects of spatial and temporal variation in occurrence rate on model accuracy, and (3) determine if the number of predictor variables affected model accuracy. Location We predicted the distributions of breeding birds in three adjacent mountain ranges in the Great Basin (Nevada, USA). Methods For each of 18 species, we developed separate models using five different data sets — one set for each of 2 years (to address the effects of temporal variation), and one set for each of three possible pairs of mountain ranges (to address the effects of spatial variation). We evaluated each model with an independent data set using four accuracy measures: discrimination ability [area under a receiver operating characteristic curve (AUC)], correct classification rate (CCR), proportion of presences correctly classified (sensitivity), and proportion of absences correctly classified (specificity). Results Discrimination ability was not affected by occurrence rate, whereas the other three accuracy measures were significantly affected. CCR, sensitivity and specificity were affected by species occurrence rate in the evaluation data sets to a greater extent than in the model‐building data sets. Discrimination ability was the only accuracy measure affected by the number of variables in a model. Main conclusions Temporal variation in species occurrence appeared to have a greater impact than did spatial variation. When temporal variation in species distributions is great, the relative costs of omission and commission errors should be assessed and long‐term census data should be examined before using predictive models of occurrence in a management setting.     

Marine, intertidal, freshwater and terrestrial biodiversity of an isolated polar archipelago

Aim We ask how biodiverse is a polar archipelago; how this faunal richness is spread across marine, intertidal, freshwater, terrestrial and parasitic realms; and how fast species are accumulated with increased sampling effort. Location The South Orkney Islands (SOI), Scotia Arc, Southern Ocean. Methods We sampled mega‐ and macro‐benthos at the SOI using scuba in the shallows (0–10 m), a rough bottom otter trawl at 150–250 m and an Agassiz trawl and epibenthic sledge, both at depths of 200, 500, 1000 and 1500 m. We also collated species occurrence at the SOI in each realm from a century of literature and modern databases to investigate patterns in species accumulation, endemism, faunistic affinities and bathymetric ranges in three model taxa. Results Our 11 benthic samples showed that point biodiversity at the SOI is high, yielding 19 classes and 158 species. Nearly a third were new to the area, whilst five species and one genus were new to science. The shallowest samples were richest but had fewest new records of species. Known richness at the SOI is dominated by marine species (1026), of which 821 (83.3%) were benthic. Across all realms, 1224 species (50 classes, 24 phyla) were recorded, of which 43 were intertidal, 64 freshwater, 100 terrestrial, 60 parasitic and 40 birds. Species accumulation curves for model taxa showed new sampling yields about 0.75% per sample of known benthic richness, so by Antarctic standards we know the SOI quite well. Most species are Southern Ocean endemics, but very few occur only at the SOI. Main conclusions This first estimate of faunal biodiversity of a polar locality demonstrates both high richness and high levels of knowledge at the SOI. As suspected but never quantified, the benthos dominates polar biodiversity, at least at the SOI. Marine species there constitute 20% of those recently listed for the entire Southern Ocean, whilst > 60% of terrestrial species are known from Antarctica. The SOI, being one of the better‐studied polar locations, of known age and with a discrete shelf, represent an important source of comparison for biodiversity studies. Our data clearly show that richness and our knowledge of the polar fauna differ across environments.     

Bayesian Hierarchical Functional Data Analysis Via Contaminated Informative Priors

Summary .  A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.     

肺磨玻璃结节中ki-67的表达及与P53,EGFR,CEA相关性研究

目的:为探究不同病理类型的肺磨玻璃结节(ground-glass opacity,GGO)中ki-67的表达情况及其与肺癌相关标志物p53、p63、EGFR等表达的相关性。方法:收集从2012年10月至2014年10月我院胸外科收治的254例GGO病人的临床病史、影像、病理及血常规等资料予以回顾性分析。结果:Ki-67表达量从良性组(n=66),不典型腺瘤样增生(atypical adenomatous hyperplasia,AAH,n=27),到原位癌(adenocarcinoma in situ,AIS,n=11),微浸润腺癌(minimally invasive adenocarcinoma,MIA,n=108),最后到浸润性腺癌(invasive adenocarcinoma,IAC,n=42)即随着早期肺癌的演进过程不断增高。Ki-67与各标志物的相关系数为0.386(p53,P0.001)、0.227(EGFR,P=0.024)、0.441(CEA,P0.001)。通过ROC曲线分析得到ki-67来判别良恶性GGO的曲线下面积和最佳阈值,也得到了早期肺癌演进过程中ki-67阈值变化。恶性GGO组全血细胞中平均单核细胞含量低于良性组GGO,且差异有统计学意义。结论:ki-67表达量在不同病理类别的GGO中有显著性差异,且在肺癌演进过程中依次增高,可作为鉴别早期肺癌不同病理类型的参考依据和预后因子;ki-67与P53、EGFR及CEA的表达具有一定的相关性。     

Co‐limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa woodlands

Photosynthetic leaf traits were determined for savanna and forest ecosystems in West Africa, spanning a large range in precipitation. Standardized major axis fits revealed important differences between our data and reported global relationships. Especially for sites in the drier areas, plants showed higher photosynthetic rates for a given N or P when compared with relationships from the global data set. The best multiple regression for the pooled data set estimated V_cmax and J_max from N_DW and S. However, the best regression for different vegetation types varied, suggesting that the scaling of photosynthesis with leaf traits changed with vegetation types. A new model is presented representing independent constraints by N and P on photosynthesis, which can be evaluated with or without interactions with S. It assumes that limitation of photosynthesis will result from the least abundant nutrient, thereby being less sensitive to the allocation of the non‐limiting nutrient to non‐photosynthetic pools. The model predicts an optimum proportionality for N and P, which is distinct for V_cmax and J_max and inversely proportional to S. Initial tests showed the model to predict V_cmax and J_max successfully for other tropical forests characterized by a range of different foliar N and P concentrations.     

Bioactive Compounds of Fruit Parts of Three Eugenia uniflora Biotypes in Four Ripening Stages

Variability of secondary metabolites in edible (peel and pulp) and inedible (seeds) parts of three pitanga varieties, red, red-orange and purple, was investigated during the maturation process. Hydrolysable tannins, anthocyanins, and flavonoids were quantified by HPLC/DAD and carotenoids by absorbance. Peel/pulp showed greater complexity of constituents (carotenoids, anthocyanins, flavonoids, and hydrolysable tannins), while only tannins were identified in seeds, but in quantities of 10 to 100 times greater. The red-orange variety showed the highest levels of phenolic compounds in seeds and peel/pulp, except anthocyanins. The analysis of the principal response curves showed that the pitanga biotype has greater influence on metabolite variation than ripening stages. During peel/pulp maturation, a reduction in the levels of flavonoids and tannins contrasted with an increase in carotenoids and cyanidin-3-O-glucoside in all varieties, whereas in the seeds oenothein B, the major tannin, increased up to 1.32 g/100 g fresh weight. Such marked differences between fruit parts demonstrate that the seeds in stages E3 and E4 are a source of hydrolysable tannins, compounds known for their antitumor activity, while peel/pulp of all varieties in the ripe stage provide natural antioxidants, such as carotenoids and flavonoids. Lastly, the purple biotype can be a rich source of the cyanidin-3-O-glucoside pigment a potent bioactive compound.     

Predicting effects of multiple interacting global change drivers across trophic levels

Global change encompasses many co-occurring anthropogenic drivers, which can act synergistically or antagonistically on ecological systems. Predicting how different global change drivers simultaneously contribute to observed biodiversity change is a key challenge for ecology and conservation. However, we lack the mechanistic understanding of how multiple global change drivers influence the vital rates of multiple interacting species. We propose that reaction norms, the relationships between a driver and vital rates like growth, mortality, and consumption, provide insights to the underlying mechanisms of community responses to multiple drivers. Understanding how multiple drivers interact to affect demographic rates using a reaction-norm perspective can improve our ability to make predictions of interactions at higher levels of organization—that is, community and food web. Building on the framework of consumer–resource interactions and widely studied thermal performance curves, we illustrate how joint driver impacts can be scaled up from the population to the community level. A simple proof-of-concept model demonstrates how reaction norms of vital rates predict the prevalence of driver interactions at the community level. A literature search suggests that our proposed approach is not yet used in multiple driver research. We outline how realistic response surfaces (i.e., multidimensional reaction norms) can be inferred by parametric and nonparametric approaches. Response surfaces have the potential to strengthen our understanding of how multiple drivers affect communities as well as improve our ability to predict when interactive effects emerge, two of the major challenges of ecology today.     

Progress Curves Analysis as an Alternative for Exploration of Activation-inhibition Phenomena in Cholinesterases

The kinetic behaviour of insect acetylcholinesterases deviates from the Michaelis-Menten pattern. These deviations are known as activation or inhibition at various substrate concentrations and can be more or less observable depending on mutations around the active site of the enzyme. Most kinetic studies on these enzymes still rely on initial rate measurements. It is demonstrated here that according to this method one of the deviations can be overlooked. We attempt to point out that in such cases a detailed step-by-step progress curves analysis is successful. The study is focused on two different methods of analysing progress curves: (i) the first one is based on an integrated initial rate equation which can sufficiently fit truncated progress curves under corresponding conditions; and (ii) the other one precludes the algebraic formulae, but uses numerical integration for searching a non analytical solution of ordinary differential equations describing a kinetic model. All methods are tested on three different acetylcholinesterase mutants from Drosophila melanogaster. The results indicate that kinetic parameters for the E107K mutant with highly expressive activation and inhibition can be well evaluated applying any analysis method. It is quite different for E107W and E107Y mutants where latent activation is present, but discovered only using one or the other progress curves analysis methods.