Likelihood of changes in forest species suitability,distribution, and diversity under future climate: The case of Southern Europe

Forest conservation strategies and plans can be unsuccessful if the new habitat conditions determined by climate change are not considered. Our work aims at investigating the likelihood of future suitability, distribution and diversity for some common European forest species under the projected changes in climate, focusing on Southern Europe. We combine an Ensemble Platform for Species Distribution Models (SDMs) to five Global Circulation Models (GCMs) driven by two Representative Concentration Pathways (RCPs), to produce maps of future climate‐driven habitat suitability for ten categories of forest species and two time horizons. For each forest category and time horizon, ten maps of future distribution (5 GCMs by 2 RCPs) are thus combined in a single suitability map supplied with information about the “likelihood” adopting the IPCC terminology based on consensus among projections. Then, the statistical significance of spatially aggregated changes in forest composition at local and regional level is analyzed. Finally, we discuss the importance, among SDMs, that environmental predictors seem to have in influencing forest distribution. Future impacts of climate change appear to be diversified across forest categories. A strong change in forest regional distribution and local diversity is projected to take place, as some forest categories will find more suitable conditions in previously unsuitable locations, while for other categories the same new conditions will become less suited. A decrease in species diversity is projected in most of the area, with Alpine region showing the potentiality to become a refuge for species migration.     

Significance analysis of functional categories in gene expression studies: a structured permutation approach

MOTIVATION: In high-throughput genomic and proteomic experiments, investigators monitor expression across a set of experimental conditions. To gain an understanding of broader biological phenomena, researchers have until recently been limited to post hoc analyses of significant gene lists.Method: We describe a general framework, significance analysis of function and expression (SAFE), for conducting valid tests of gene categories ab initio. SAFE is a two-stage, permutation-based method that can be applied to various experimental designs, accounts for the unknown correlation among genes and enables permutation-based estimation of error rates. RESULTS: The utility and flexibility of SAFE is illustrated with a microarray dataset of human lung carcinomas and gene categories based on Gene Ontology and the Protein Family database. Significant gene categories were observed in comparisons of (1) tumor versus normal tissue, (2) multiple tumor subtypes and (3) survival times. AVAILABILITY: Code to implement SAFE in the statistical package R is available from the authors. SUPPLEMENTARY INFORMATION: http://www.bios.unc.edu/~fwright/SAFE.     

Discriminating between homodimeric and monomeric proteins in the crystalline state

Scores calculated from intermolecular contacts of proteins in the crystalline state are used to differentiate monomeric and homodimeric proteins, by classification into two categories separated by a cut-off score value. The generalized classification error is estimated by using bootstrap re-sampling on a nonredundant set of 172 water-soluble proteins whose prevalent quaternary state in solution is known to be either monomeric or homodimeric. A statistical potential, based on atom-pair frequencies across interfaces observed with homodimers, is found to yield an error rate of 12.5%. This indicates a small but significant improvement over the measure of solvent accessible surface area buried in the contact interface, which achieves an error rate of 15.4%. A further modification of the latter parameter relating the two most extensive contacts of the crystal results in an even lower error rate of 11.1%.     

Applications of proportional odds ordinal logistic regression models and continuation ratio models in examining the association of physical inactivity with erectile dysfunction among type 2 diabetic patients

[Purpose]Many studies have observed a high prevalence of erectile dysfunction among individuals performing physical activity in less leisure-time. However, this relationship in patients with type 2 diabetic patients is not well studied. In exposure outcome studies with ordinal outcome variables, investigators often try to make the outcome variable dichotomous and lose information by collapsing categories. Several statistical models have been developed to make full use of all information in ordinal response data, but they have not been widely used in public health research. In this paper, we discuss the application of two statistical models to determine the association of physical inactivity with erectile dysfunction among patients with type 2 diabetes.[Methods]A total of 204 married men aged 20-60 years with a diagnosis of type 2 diabetes at the outpatient unit of the Department of Endocrinology at PSG hospitals during the months of May and June 2019 were studied. We examined the association between physical inactivity and erectile dysfunction using proportional odds ordinal logistic regression models and continuation ratio models.[Results]The proportional odds model revealed that patients with diabetes who perform leisure time physical activity for over 40 minutes per day have reduced odds of erectile dysfunction (odds ratio=0.38) across the severity categories of erectile dysfunction after adjusting for age and duration of diabetes.[Conclusion]The present study suggests that physical inactivity has a negative impact on erectile function. We observed that the simple logistic regression model had only 75% efficiency compared to the proportional odds model used here; hence, more valid estimates were obtained here.     

The method of pattern combination analysis in dermatoglyphics: I. Diversity and sex differences among the regional populations of the Yanadi

A method of pattern combination analysis (PCA) is described. It consists of three categories: Pattern combination types (PCT), Triradial pattern combination types (PCT-tr) and Pattern combination ratios (PCR) with specific reference to ten digits and its several indices. The method has been used to investigate its validity in discriminating subdivided populations among the Yanadi tribe. The results show significant differences and a few unique pattern combinations in monomorphic and trimorphic frequencies and also in triradial pattern combination types, especially observed in IY population. Between the five Yanadi populations, the results obtained in all the categories of pattern combinations, in pattern combination indices, were in agreement with respect to the population structure variables of the five populations. The results also show clear sex differences especially observed in triradial pattern combination types (PCT-tr) and absence of tetramorphic patterns and ratios among females. This method can be further used for studying inheritance of the PCA, inbreeding effects and other issues of statistical distributional pattern and other theoretical and empirical aspects of anthropological genetics.     

Verification of protein structures: patterns of nonbonded atomic interactions.

A novel method for differentiating between correctly and incorrectly determined regions of protein structures based on characteristic atomic interaction is described. Different types of atoms are distributed nonrandomly with respect to each other in proteins. Errors in model building lead to more randomized distributions of the different atom types, which can be distinguished from correct distributions by statistical methods. Atoms are classified in one of three categories: carbon (C), nitrogen (N), and oxygen (O). This leads to six different combinations of pairwise noncovalently bonded interactions (CC, CN, CO, NN, NO, and OO). A quadratic error function is used to characterize the set of pairwise interactions from nine-residue sliding windows in a database of 96 reliable protein structures. Regions of candidate protein structures that are mistraced or misregistered can then be identified by analysis of the pattern of nonbonded interactions from each window.     

Measurement error: Inter-and Intraobserver Variability. An Empiric Study

The intra- and inter-observer measurement error variability was studied using univariate and multivariate statistical tests. Eleven skeletal variables of four individuals each in four Primate species were measured ten times by three different researchers, using six different tools. An average measurement error of 0.52 mm. was obtained. Univariate statistics showed significant differences among reseachers. A multivariate discriminant analysis could also discriminate them. The measurement error may be either systematic or random, and depends not only on the researcher, but also on the tool used, the variable measured, and on the magnitude of the variable. The technique of Measurement Replication is proposed in order to reduce the measurement error, specially when compairing small samples or when trying to find small average differences between populations. The replication technique also reduces the standard deviation of the population sample.     

Evidential statistics as a statistical modern synthesis to support 21st century science

During the 20th century, population ecology and science in general relied on two very different statistical paradigms to solve its inferential problems: error statistics (also referred to as classical statistics and frequentist statistics) and Bayesian statistics. A great deal of good science was done using these tools, but both schools suffer from technical and philosophical difficulties. At the turning of the 21st century (Royall in Statistical evidence: a likelihood paradigm. Chapman & Hall, London, 1997 ; Lele in The nature of scientific evidence: statistical, philosophical and empirical considerations. The University of Chicago Press, Chicago, pp 191–216, 2004a ), evidential statistics emerged as a seriously contending paradigm. Drawing on and refining elements from error statistics, likelihoodism, Bayesian statistics, information criteria, and robust methods, evidential statistics is a statistical modern synthesis that smoothly incorporates model identification, model uncertainty, model comparison, parameter estimation, parameter uncertainty, pre-data control of error, and post-data strength of evidence into a single coherent framework. We argue that evidential statistics is currently the most effective statistical paradigm to support 21st century science. Despite the power of the evidential paradigm, we think that there is no substitute for learning how to clarify scientific arguments with statistical arguments. In this paper we sketch and relate the conceptual bases of error statistics, Bayesian statistics and evidential statistics. We also discuss a number of misconceptions about the paradigms that have hindered practitioners, as well as some real problems with the error and Bayesian statistical paradigms solved by evidential statistics.     

Histopathologic observations on thyroid carcinoma with amyloid stroma.

The authors describe histopathologic aspects in ten cases of thyroid carcinoma with amyloid stroma. This form of cancer represents 3.4% of all the thyroid cancer cases in the last five years in the Institute of Endocrinology. Microscopic examination reveals a polymorphous picture alongside with amyloid deposits. The authors attribute to the tumor cells an important role in producing thyroid amyloidosis.     

Validation of plantar pressure simulations using finite and discrete element modelling in healthy and diabetic subjects

Plantar pressure simulation driven by integrated 3D motion capture data, using both a finite element and a discrete element model, is compared for ten healthy and ten diabetic neuropathic subjects. The simulated peak pressure deviated on average between 16.7 and 34.2% from the measured peak pressure. The error in the position of the peak pressure was on average smaller than 4.2 cm. No method was more accurate than the other although statistical differences were found between them. Both techniques are thus complementary and useful tools to better understand the alteration of diabetic foot biomechanics during gait.     

黄土区农业景观空间格局分析

景观空间格局分析是景观生态学研究的核心问题之一。本文用地理信息系统,分维分析和统计分析相结合,以１∶１万土地利用现状图为基础,选取斑块大小、分维数、斑块伸长指数、多样性、优势度、相对丰富度、破碎度等指标,陕北米脂县泉家沟流域农业景观的空间格局。结果显示,乔木林地、坡耕地、果园、草地和梯田农地的分维数较低,坝地、水库、灌溉农田、居信用地和灌木林地的人维数较高,斑块形状较为复杂。随着斑块面积的增加,农     

False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review

PurposeA number of recent publications have proposed that a family of image-derived indices, called texture features, can predict clinical outcome in patients with cancer. However, the investigation of multiple indices on a single data set can lead to significant inflation of type-I errors. We report a systematic review of the type-I error inflation in such studies and review the evidence regarding associations between patient outcome and texture features derived from positron emission tomography (PET) or computed tomography (CT) images.MethodsFor study identification PubMed and Scopus were searched (1/2000–9/2013) using combinations of the keywords texture, prognostic, predictive and cancer. Studies were divided into three categories according to the sources of the type-I error inflation and the use or not of an independent validation dataset. For each study, the true type-I error probability and the adjusted level of significance were estimated using the optimum cut-off approach correction, and the Benjamini-Hochberg method. To demonstrate explicitly the variable selection bias in these studies, we re-analyzed data from one of the published studies, but using 100 random variables substituted for the original image-derived indices. The significance of the random variables as potential predictors of outcome was examined using the analysis methods used in the identified studies.ResultsFifteen studies were identified. After applying appropriate statistical corrections, an average type-I error probability of 76% (range: 34–99%) was estimated with the majority of published results not reaching statistical significance. Only 3/15 studies used a validation dataset. For the 100 random variables examined, 10% proved to be significant predictors of survival when subjected to ROC and multiple hypothesis testing analysis.ConclusionsWe found insufficient evidence to support a relationship between PET or CT texture features and patient survival. Further fit for purpose validation of these image-derived biomarkers should be supported by appropriate biological and statistical evidence before their association with patient outcome is investigated in prospective studies.     

Within-species variation and measurement error in phylogenetic comparative methods

Most phylogenetically based statistical methods for the analysis of quantitative or continuously varying phenotypic traits assume that variation within species is absent or at least negligible, which is unrealistic for many traits. Within-species variation has several components. Differences among populations of the same species may represent either phylogenetic divergence or direct effects of environmental factors that differ among populations (phenotypic plasticity). Within-population variation also contributes to within-species variation and includes sampling variation, instrument-related error, low repeatability caused by fluctuations in behavioral or physiological state, variation related to age, sex, season, or time of day, and individual variation within such categories. Here we develop techniques for analyzing phylogenetically correlated data to include within-species variation, or "measurement error" as it is often termed in the statistical literature. We derive methods for (i) univariate analyses, including measurement of "phylogenetic signal," (ii) correlation and principal components analysis for multiple traits, (iii) multiple regression, and (iv) inference of "functional relations," such as reduced major axis (RMA) regression. The methods are capable of incorporating measurement error that differs for each data point (mean value for a species or population), but they can be modified for special cases in which less is known about measurement error (e.g., when one is willing to assume something about the ratio of measurement error in two traits). We show that failure to incorporate measurement error can lead to both biased and imprecise (unduly uncertain) parameter estimates. Even previous methods that are thought to account for measurement error, such as conventional RMA regression, can be improved by explicitly incorporating measurement error and phylogenetic correlation. We illustrate these methods with examples and simulations and provide Matlab programs.     

Modelling errors in scoring the darkness of staining

OBJECTIVE: To construct a statistical model for the agreements and disagreements between two observers on darkness of staining. STUDY DESIGN: Data from an earlier observer-agreement study by van Diest et al were reanalyzed. RESULTS: A model in which the random variation in error is permitted to depend upon the true darkness of staining wasfound tofit the data much better than does one in which the random variation is constant. CONCLUSION: For the dataset analyzed, error tends to be greater (that is, correlation between observers tends to be less) when staining is darker.     

Bayesian integrated functional analysis of microarray data

MOTIVATION: The statistical analysis of microarray data usually proceeds in a sequential manner, with the output of the previous step always serving as the input of the next one. However, the methods currently used in such analyses do not properly account for the fact that the intermediate results may not always be correct, then leading to cumulating error in the inferences drawn based on such steps. RESULTS: Here we show that, by an application of hierarchical Bayesian methodology, this sequential procedure can be replaced by a single joint analysis, while systematically accounting for the uncertainties in this process. Moreover, we can also integrate relevant functional information available from databases into such an analysis, thereby increasing the reliability of the biological conclusions that are drawn. We illustrate these points by analysing real data and by showing that the genes can be divided into categories of interest, with the defining characteristic depending on the biological question that is considered. We contend that the proposed method has advantages at two levels. First, there are gains in the statistical and biological results from the analysis of this particular dataset. Second, it opens up new possibilities in analysing microarray data in general.     

Rosetta error model for gene expression analysis

MOTIVATION: In microarray gene expression studies, the number of replicated microarrays is usually small because of cost and sample availability, resulting in unreliable variance estimation and thus unreliable statistical hypothesis tests. The unreliable variance estimation is further complicated by the fact that the technology-specific variance is intrinsically intensity-dependent. RESULTS: The Rosetta error model captures the variance-intensity relationship for various types of microarray technologies, such as single-color arrays and two-color arrays. This error model conservatively estimates intensity error and uses this value to stabilize the variance estimation. We present two commonly used error models: the intensity error-model for single-color microarrays and the ratio error model for two-color microarrays or ratios built from two single-color arrays. We present examples to demonstrate the strength of our error models in improving statistical power of microarray data analysis, particularly, in increasing expression detection sensitivity and specificity when the number of replicates is limited.     

Developments in the identification of human skeletal material (1968-1978).

A review of publications over the last ten years of methods in the field of physical anthropology for determining age, sex, race and stature of human skeletal material is presented. Comparisons are made with the types of papers published in the previous ten years (1958-1968) in six categories: (1) Visual examination of bones (2) Anthropometric measurements of bones (3) Anthropometric measurements with subsequent use of statistics in the form of discriminate function analyses (4) Time and sequence of eruption of the teeth (5) X-ray examination of the internal structure of bone sections (6) Microscopic examination of the internal structure of bone.     

Application of adaptive designs--a review

A literature search has been performed to review applications of the adaptive design methodology based on the combination test or conditional error function approach. Some features of the 60 papers identified are summarized, e.g., the specific methodology used, calendar year, country, impact factor of the journal, number of planned and performed stages respectively, stopping for futility boundaries, type of adaptations and others. A selection of the ten recent publications in journals with the highest impact factors is discussed in more detail. Most applications up to now aim at sample size reassessment, the majority of papers is coming from Germany. Although we found that renowned journals allow for sufficient space to present the new statistical methodology in all its necessary details, the general impression is that the presentation of the adaptive designs methodology in applied papers has to be improved. Education and development of standards could help to achieve this.     

Many‐to‐one Comparisons in Stratified Designs

Cheung and Holland (1992) extended Dunnett's procedure for comparing all active treatments with a control simultaneously within each of r groups while maintaining the Type I error rate at some designated level α allowing different sample sizes for each of the group‐treatment categories. This paper shows that exact percentage points can be easily calculated with current available statistical software (SAS). This procedure is compared to resampling techniques and a Bonferroni corrected Dunnett‐within‐group procedure by means of a simulation study.     

Interobserver error in macroscopic methods of estimating age at death from the human skeleton

This research evaluates the interobserver error when the macroscopic methods recommended by American and European anthropologists to estimate age at death of a skeleton, were applied to a sample of the Terry Collection (Smithsonian Institution, Washington D.C.). Although no statistical differences among observers were found for any of these methods, small dissimilarities suggest that techniques using a narrower scale of categories produce greater agreement among researchers. The present study is within a wider research project designed to evaluate the accuracy of these methods, when applied to an identified (age known) sample of 963 skeleton from the Terry Collection.