Global literature of fiddler crabs, genus Uca (Decapoda, Ocypodidae): trends and future directions

Crabs of the genus Uca Leach, 1814 are characterized by having strong sexual dimorphism and a global distribution. Currently, 97 species have been described and analyzed under several aspects, including population ecology, physiology and ethology. However, there is no general summary of the information from the various literatures. The aim of this study is to perform a scientometric analysis of fiddler crab studies. For this we searched papers available in the Thomson ISI database that contained the words "Uca" OR "fiddler* crab*" between the years 1991 and 2007. For each paper, we researched and recorded the following characteristics: publication year; journal of publication; the first author's nationality; the country where the study was conducted; study type; species studied; and the work area. Our results indicated that there was no increase in the number of articles through the years considered. The Journal of Experimental Marine Biology and Ecology published most of the articles on Uca, indicating the importance of this group as a model for testing ecological hypotheses using experimental approaches. Our results also showed that United States had the highest number of authors and published studies on Uca, following the overall trend in dominance on scientific research. Furthermore, using models with three variables (per capita income, number of species of Uca and extent of coastal countries) we observed that, according to the Akaike Information Criterion, the per capita income was the most important correlate for the number of articles per country (both the author's country and country of study). Additionally, our results show that the species U. pugilator (distributed on the East Coast of the North American continent) was the species most singularly referenced in the papers considered. Moreover, our results indicate that most studies on Uca use a descriptive and local scale. The majority of papers in our literature search reflect studies in population biology, followed by behavioral and physiological characteristics.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Inflammatory Markers and Incident Heart Failure Risk in Older Adults: The Health, Aging, and Body Composition Study

Background: Inflammation is associated with heart failure (HF) risk factors and also directly affects myocardial function. However, the association between inflammation and HF risk in older adults has not been adequately evaluated. Methods: The association of baseline serum concentrations of interleukin-6 (IL-6), tumor necrosis factor alpha (TNF- ), and C-reactive protein (CRP) with incident HF was assessed with Cox proportional hazards models among 2610 older persons without prevalent HF enrolled in the Health, Aging, and Body Composition (Health ABC) Study (age, 73.6±2.9 years; 48.3% men; 59.6% white). Results: Median (interquartile range) baseline concentrations of IL-6, TNF- , and CRP were 1.80 (1.23, 2.76) pg/mL, 3.14 (2.41, 4.06) pg/mL, and 1.64 (0.99, 3.04) µg/mL, respectively. On follow-up (median, 9.4 years), 311 participants (11.9%) developed HF. In models controlling for clinical predictors of HF and incident coronary heart disease, doubling of IL-6, TNF- , and CRP concentrations was associated with 34% (95% CI, 18 -52%; P<.001), 33% (95% CI, 9 - 63%; P=.006), and 13% (95% CI, 3-24%; P=.01) increase in HF risk, respectively. In models including all 3 markers, IL-6 and TNF- , but not CRP, remained significant. Findings were similar across sex and race. Post-HF ejection fraction (EF) was available in 239 (76.8%) cases. When only cases with preserved EF were considered (n=105), IL-6 (HR per doubling, 1.57; 95% CI, 1.28 -1.94; P<.001), TNF- (HR per doubling, 1.59; 95% CI, 1.12-2.26; P=.01), and CRP (HR per doubling, 1.23; 95% CI, 1.05-1.44; P=.01) were all associated with HF risk in adjusted models. In contrast, when only cases with reduced EF (n=134) were considered, only IL-6 attained marginal significance in adjusted models (HR per doubling, 1.20; 95% CI, 0.99 -1.46; P=.06). Participants with 2 or 3 markers above median had pronounced HF risk in adjusted models (HR, 1.66; 95% CI, 1.12-2.46; P=.01; and HR, 1.76; 95% CI, 1.16 -2.65; P=.007, respectively). Addition of IL-6 to the clinical Health ABC HF model improved discrimination (C index from 0.717 to 0.734; P=.001) and fit (decreased Bayes information criterion by 17.8; P<.001). Conclusions: Inflammatory markers are associated with HF risk among older adults and may improve HF risk stratification.

She’s a femme fatale: low-density larval development produces good disease vectors

Two hypotheses for how conditions for larval mosquitoes affect vectorial capacity make opposite predictions about the relationship of adult size and frequency of infection with vector-borne pathogens. Competition among larvae produces small adult females. The competition-susceptibility hypothesis postulates that small females are more susceptible to infection and predicts frequency of infection should decrease with size. The competition-longevity hypothesis postulates that small females have lower longevity and lower probability of becoming competent to transmit the pathogen and thus predicts frequency of infection should increase with size. We tested these hypotheses for Aedes aegypti in Rio de Janeiro, Brazil, during a dengue outbreak. In the laboratory, longevity increases with size, then decreases at the largest sizes. For field-collected females, generalised linear mixed model comparisons showed that a model with a linear increase of frequency of dengue with size produced the best Akaike’s information criterion with a correction for small sample sizes (AICc). Consensus prediction of three competing models indicated that frequency of infection increases monotonically with female size, consistent with the competition-longevity hypothesis. Site frequency of infection was not significantly related to site mean size of females. Thus, our data indicate that uncrowded, low competition conditions for larvae produce the females that are most likely to be important vectors of dengue. More generally, ecological conditions, particularly crowding and intraspecific competition among larvae, are likely to affect vector-borne pathogen transmission in nature, in this case via effects on longevity of resulting adults. Heterogeneity among individual vectors in likelihood of infection is a generally important outcome of ecological conditions impacting vectors as larvae.

Inflammatory markers and incident heart failure risk in older adults: the Health ABC (Health, Aging, and Body Composition) study.

OBJECTIVES: The purpose of this study was to evaluate the association between inflammation and heart failure (HF) risk in older adults. BACKGROUND: Inflammation is associated with HF risk factors and also directly affects myocardial function. METHODS: The association of baseline serum concentrations of interleukin (IL)-6, tumor necrosis factor-alpha, and C-reactive protein (CRP) with incident HF was assessed with Cox models among 2,610 older persons without prevalent HF enrolled in the Health ABC (Health, Aging, and Body Composition) study (age 73.6 +/- 2.9 years; 48.3% men; 59.6% white). RESULTS: During follow-up (median 9.4 years), HF developed in 311 (11.9%) participants. In models controlling for clinical characteristics, ankle-arm index, and incident coronary heart disease, doubling of IL-6, tumor necrosis factor-alpha, and CRP concentrations was associated with 29% (95% confidence interval: 13% to 47%; p < 0.001), 46% (95% confidence interval: 17% to 84%; p = 0.001), and 9% (95% confidence interval: -1% to 24%; p = 0.087) increase in HF risk, respectively. In models including all 3 markers, IL-6, and tumor necrosis factor-alpha, but not CRP, remained significant. These associations were similar across sex and race and persisted in models accounting for death as a competing event. Post-HF ejection fraction was available in 239 (76.8%) cases; inflammatory markers had stronger association with HF with preserved ejection fraction. Repeat IL-6 and CRP determinations at 1-year follow-up did not provide incremental information. Addition of IL-6 to the clinical Health ABC HF model improved model discrimination (C index from 0.717 to 0.734; p = 0.001) and fit (decreased Bayes information criterion by 17.8; p < 0.001). CONCLUSIONS: Inflammatory markers are associated with HF risk among older adults and may improve HF risk stratification.

Postglacial recolonization at a snail's pace (Trochulus villosus): confronting competing refugia hypotheses using model selection.

The localization of Last Glacial Maximum (LGM) refugia is crucial information to understand a species' history and predict its reaction to future climate changes. However, many phylogeographical studies often lack sampling designs intensive enough to precisely localize these refugia. The hairy land snail Trochulus villosus has a small range centred on Switzerland, which could be intensively covered by sampling 455 individuals from 52 populations. Based on mitochondrial DNA sequences (COI and 16S), we identified two divergent lineages with distinct geographical distributions. Bayesian skyline plots suggested that both lineages expanded at the end of the LGM. To find where the origin populations were located, we applied the principles of ancestral character reconstruction and identified a candidate refugium for each mtDNA lineage: the French Jura and Central Switzerland, both ice-free during the LGM. Additional refugia, however, could not be excluded, as suggested by the microsatellite analysis of a population subset. Modelling the LGM niche of T. villosus, we showed that suitable climatic conditions were expected in the inferred refugia, but potentially also in the nunataks of the alpine ice shield. In a model selection approach, we compared several alternative recolonization scenarios by estimating the Akaike information criterion for their respective maximum-likelihood migration rates. The 'two refugia' scenario received by far the best support given the distribution of genetic diversity in T. villosus populations. Provided that fine-scale sampling designs and various analytical approaches are combined, it is possible to refine our necessary understanding of species responses to environmental changes.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA

Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.

Accuracy of four cardiovascular risk scores in a Swiss population-based cohort

Purpose: To assess the global cardiovascular (CV) risk of an individual, several scores have been developed. However, their accuracy and comparability need to be evaluated in populations others from which they were derived. The aim of this study was to compare the predictive accuracy of 4 CV risk scores using data of a large population-based cohort. Methods: Prospective cohort study including 4980 participants (2698 women, mean age± SD: 52.7±10.8 years) in Lausanne, Switzerland followed for an average of 5.5 years (range 0.2 - 8.5). Two end points were assessed: 1) coronary heart disease (CHD), and 2) CV diseases (CVD). Four risk scores were compared: original and recalibrated Framingham coronary heart disease scores (1998 and 2001); original PROCAM score (2002) and its recalibrated version for Switzerland (IAS-AGLA); Reynolds risk score. Discrimination was assessed using Harrell's C statistics, model fitness using Akaike's information criterion (AIC) and calibration using pseudo Hosmer-Lemeshow test. The sensitivity, specificity and corresponding 95% confidence intervals were assessed for each risk score using the highest risk category ([20+ % at 10 years) as the "positive" test. Results: Recalibrated and original 1998 and original 2001 Framingham scores show better discrimination (>0.720) and model fitness (low AIC) for CHD and CVD. All 4 scores are correctly calibrated (Chi2<20). The recalibrated Framingham 1998 score has the best sensitivities, 37.8% and 40.4%, for CHD and CVD, respectively. All scores present specificities >90%. Framingham 1998, PROCAM and IAS-AGLA scores include the greatest proportion of subjects (>200) in the high risk category whereas recalibrated Framingham 2001 and Reynolds include <=44 subjects. Conclusion: In this cohort, we see variations of accuracy between risk scores, the original Framingham 2001 score demonstrating the best compromise between its accuracy and its limited selection of subjects in the highest risk category. We advocate that national guidelines, based on independently validated data, take into account calibrated CV risk scores for their respective countries.

Evolutionary footprint of coevolving positions in genes.

MOTIVATION: The analysis of molecular coevolution provides information on the potential functional and structural implication of positions along DNA sequences, and several methods are available to identify coevolving positions using probabilistic or combinatorial approaches. The specific nucleotide or amino acid profile associated with the coevolution process is, however, not estimated, but only known profiles, such as the Watson-Crick constraint, are usually considered a priori in current measures of coevolution. RESULTS: Here, we propose a new probabilistic model, Coev, to identify coevolving positions and their associated profile in DNA sequences while incorporating the underlying phylogenetic relationships. The process of coevolution is modeled by a 16 × 16 instantaneous rate matrix that includes rates of transition as well as a profile of coevolution. We used simulated, empirical and illustrative data to evaluate our model and to compare it with a model of 'independent' evolution using Akaike Information Criterion. We showed that the Coev model is able to discriminate between coevolving and non-coevolving positions and provides better specificity and specificity than other available approaches. We further demonstrate that the identification of the profile of coevolution can shed new light on the process of dependent substitution during lineage evolution.

Effect of cattle on Salmonella carriage, diversity and antimicrobial resistance in free-ranging wild boar (Sus scrofa) in northeastern Spain

Salmonella is distributed worldwide and is a pathogen of economic and public health importance. As a multi-host pathogen with a long environmental persistence, it is a suitable model for the study of wildlife-livestock interactions. In this work, we aim to explore the spill-over of Salmonella between free-ranging wild boar and livestock in a protected natural area in NE Spain and the presence of antimicrobial resistance. Salmonella prevalence, serotypes and diversity were compared between wild boars, sympatric cattle and wild boars from cattle-free areas. The effect of age, sex, cattle presence and cattle herd size on Salmonella probability of infection in wild boars was explored by means of Generalized Linear Models and a model selection based on the Akaike’s Information Criterion. Prevalence was higher in wild boars co-habiting with cattle (35.67%, CI 95% 28.19–43.70) than in wild boar from cattle-free areas (17.54%, CI 95% 8.74–29.91). Probability of a wild boar being a Salmonella carrier increased with cattle herd size but decreased with the host age. Serotypes Meleagridis, Anatum and Othmarschen were isolated concurrently from cattle and sympatric wild boars. Apart from serotypes shared with cattle, wild boars appear to have their own serotypes, which are also found in wild boars from cattle-free areas (Enteritidis, Mikawasima, 4:b:- and 35:r:z35). Serotype richness (diversity) was higher in wild boars co-habiting with cattle, but evenness was not altered by the introduction of serotypes from cattle. The finding of a S. Mbandaka strain resistant to sulfamethoxazole, streptomycin and chloramphenicol and a S. Enteritidis strain resistant to ciprofloxacin and nalidixic acid in wild boars is cause for public health concern.

Increased risk of cardiovascular disease (CVD) with age in HIV-positive men : a comparison of the D:A:D CVD risk equation and general population CVD risk equations

OBJECTIVES: The aim of the study was to statistically model the relative increased risk of cardiovascular disease (CVD) per year older in Data collection on Adverse events of anti-HIV Drugs (D:A:D) and to compare this with the relative increased risk of CVD per year older in general population risk equations. METHODS: We analysed three endpoints: myocardial infarction (MI), coronary heart disease (CHD: MI or invasive coronary procedure) and CVD (CHD or stroke). We fitted a number of parametric age effects, adjusting for known risk factors and antiretroviral therapy (ART) use. The best-fitting age effect was determined using the Akaike information criterion. We compared the ageing effect from D:A:D with that from the general population risk equations: the Framingham Heart Study, CUORE and ASSIGN risk scores. RESULTS: A total of 24 323 men were included in analyses. Crude MI, CHD and CVD event rates per 1000 person-years increased from 2.29, 3.11 and 3.65 in those aged 40-45 years to 6.53, 11.91 and 15.89 in those aged 60-65 years, respectively. The best-fitting models included inverse age for MI and age + age(2) for CHD and CVD. In D:A:D there was a slowly accelerating increased risk of CHD and CVD per year older, which appeared to be only modest yet was consistently raised compared with the risk in the general population. The relative risk of MI with age was not different between D:A:D and the general population. CONCLUSIONS: We found only limited evidence of accelerating increased risk of CVD with age in D:A:D compared with the general population. The absolute risk of CVD associated with HIV infection remains uncertain.

Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data

Maximum entropy modeling (Maxent) is a widely used algorithm for predicting species distributions across space and time. Properly assessing the uncertainty in such predictions is non-trivial and requires validation with independent datasets. Notably, model complexity (number of model parameters) remains a major concern in relation to overfitting and, hence, transferability of Maxent models. An emerging approach is to validate the cross-temporal transferability of model predictions using paleoecological data. In this study, we assess the effect of model complexity on the performance of Maxent projections across time using two European plant species (Alnus giutinosa (L.) Gaertn. and Corylus avellana L) with an extensive late Quaternary fossil record in Spain as a study case. We fit 110 models with different levels of complexity under present time and tested model performance using AUC (area under the receiver operating characteristic curve) and AlCc (corrected Akaike Information Criterion) through the standard procedure of randomly partitioning current occurrence data. We then compared these results to an independent validation by projecting the models to mid-Holocene (6000 years before present) climatic conditions in Spain to assess their ability to predict fossil pollen presence-absence and abundance. We find that calibrating Maxent models with default settings result in the generation of overly complex models. While model performance increased with model complexity when predicting current distributions, it was higher with intermediate complexity when predicting mid-Holocene distributions. Hence, models of intermediate complexity resulted in the best trade-off to predict species distributions across time. Reliable temporal model transferability is especially relevant for forecasting species distributions under future climate change. Consequently, species-specific model tuning should be used to find the best modeling settings to control for complexity, notably with paleoecological data to independently validate model projections. For cross-temporal projections of species distributions for which paleoecological data is not available, models of intermediate complexity should be selected.

Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.

PURPOSE: The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. METHODS: The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. RESULTS: The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679±0.068, Akaike's information criterion 566.7, P<0.001). CONCLUSION: A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.

Developing raster bands computed from LiDAR to improve forest

This study examines the use of di erent features derived from remotely sensed data in segmentation of forest stands. Surface interpolation methods were applied to LiDAR points in order to represent data in the form of grayscale images. Median and mean shift ltering was applied to the data for noise reduction. The ability of di erent compositions of rasters obtained from LiDAR data and an aerial image to maximize stand homogeneity in the segmentation was evaluated. The quality of forest stand delineations was assessed by the Akaike information criterion. The research was performed in co-operation with Arbonaut Ltd., Joensuu, Finland.