Ecological and physiological variance in T-cell mediated immune response in Cory's shearwaters

T-cell mediated immune response (CMI) hasbeen widely studied in relation to individual andfitness components in birds. However, few studieshave simultaneously examined individual and socialfactors and habitat-mediated variance in theimmunity of chicks and adults from the samepopulation and in the same breeding season. Weinvestigated ecological and physiological variancein CMI of male and female nestlings and adults in abreeding population of Cory's Shearwaters(Calonectrisdiomedea) in theMediterranean Sea. Explanatory variables includedindividual traits (body condition, carbon andnitrogen stable isotope ratios, plasma totalproteins, triglycerides, uric acid, osmolarity,β-hydroxy-butyrate, erythrocyte meancorpuscular diameter, hematocrit, andhemoglobin) and burrow traits(temperature, isolation, and physicalstructure). During incubation, immune responseof adult males was significantly greater than thatof females. Nestlings exhibited a lower immuneresponse than adults. Ecological and physiologicalfactors affecting immune response differed betweenadults and nestlings. General linear models showedthat immune response in adult males was positivelyassociated with burrow isolation, suggesting thatmales breeding at higher densities suffer immunesystem suppression. In contrast, immune response inchicks was positively associated with bodycondition and plasma triglyceride levels.Therefore, adult immune response appears to beassociated with social stress, whereas a trade-offbetween immune function and fasting capability mayexist for nestlings. Our results, and those fromprevious studies, provide support for anasymmetrical influence of ecological andphysiological factors on the health of differentage and sex groups within a population, and for theimportance of simultaneously considering individualand population characteristics in intraspecificstudies of immune response.

Ecological and physiological variance in T-cell mediated immune response in Cory's shearwaters

T-cell mediated immune response (CMI) hasbeen widely studied in relation to individual andfitness components in birds. However, few studieshave simultaneously examined individual and socialfactors and habitat-mediated variance in theimmunity of chicks and adults from the samepopulation and in the same breeding season. Weinvestigated ecological and physiological variancein CMI of male and female nestlings and adults in abreeding population of Cory's Shearwaters(Calonectrisdiomedea) in theMediterranean Sea. Explanatory variables includedindividual traits (body condition, carbon andnitrogen stable isotope ratios, plasma totalproteins, triglycerides, uric acid, osmolarity,β-hydroxy-butyrate, erythrocyte meancorpuscular diameter, hematocrit, andhemoglobin) and burrow traits(temperature, isolation, and physicalstructure). During incubation, immune responseof adult males was significantly greater than thatof females. Nestlings exhibited a lower immuneresponse than adults. Ecological and physiologicalfactors affecting immune response differed betweenadults and nestlings. General linear models showedthat immune response in adult males was positivelyassociated with burrow isolation, suggesting thatmales breeding at higher densities suffer immunesystem suppression. In contrast, immune response inchicks was positively associated with bodycondition and plasma triglyceride levels.Therefore, adult immune response appears to beassociated with social stress, whereas a trade-offbetween immune function and fasting capability mayexist for nestlings. Our results, and those fromprevious studies, provide support for anasymmetrical influence of ecological andphysiological factors on the health of differentage and sex groups within a population, and for theimportance of simultaneously considering individualand population characteristics in intraspecificstudies of immune response.

Social differences in avoidable mortality between small areas of 15 European cities: an ecological study

Health and inequalities in health among inhabitants of European cities are of major importance for European public health and there is great interest in how different health care systems in Europe perform in the reduction of health inequalities. However, evidence on the spatial distribution of cause-specific mortality across neighbourhoods of European cities is scarce. This study presents maps of avoidable mortality in European cities and analyses differences in avoidable mortality between neighbourhoods with different levels of deprivation. Methods: We determined the level of mortality from 14 avoidable causes of death for each neighbourhood of 15 large cities in different European regions. To address the problems associated with Standardised Mortality Ratios for small areas we smooth them using the Bayesian model proposed by Besag, York and Mollié. Ecological regression analysis was used to assess the association between social deprivation and mortality. Results: Mortality from avoidable causes of death is higher in deprived neighbourhoods and mortality rate ratios between areas with different levels of deprivation differ between gender and cities. In most cases rate ratios are lower among women. While Eastern and Southern European cities show higher levels of avoidable mortality, the association of mortality with social deprivation tends to be higher in Northern and lower in Southern Europe. Conclusions: There are marked differences in the level of avoidable mortality between neighbourhoods of European cities and the level of avoidable mortality is associated with social deprivation. There is no systematic difference in the magnitude of this association between European cities or regions. Spatial patterns of avoidable mortality across small city areas can point to possible local problems and specific strategies to reduce health inequality which is important for the development of urban areas and the well-being of their inhabitants

Feature-based matching of underwater images

This work investigates performance of recent feature-based matching techniques when applied to registration of underwater images. Matching methods are tested versus different contrast enhancing pre-processing of images. As a result of the performed experiments for various dominating in images underwater artifacts and present deformation, the outperforming preprocessing, detection and description methods are proposed

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Ecological fallacy in a time series context: evidence from the Spanish regional unemployment rates

The ecological fallacy (EF) is a common problem regional scientists have to deal with when using aggregated data in their analyses. Although there is a wide number of studies considering different aspects of this problem, little attention has been paid to the potential negative effects of the EF in a time series context. Using Spanish regional unemployment data, this paper shows that EF effects are not only observed at the cross-section level, but also in a time series framework. The empirical evidence obtained shows that analytical regional configurations are the least susceptible to time effects relative to both normative and random regional configurations, while normative configurations are an improvement over random ones.

Ecological and Epidemiological Analyses of Multiple Sclerosis Relapse Rate

The underlying cause of many human autoimmune diseases is unknown, but several environmental factors are implicated in triggering the self-destructive immune reactions. Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system, potentially leading to persistent neurological deterioration. The cause of MS is not known, and apart from immunomodulatory treatments there is no cure. In the early phase of the disease, relapsing-remitting MS (RR-MS) is characterized by unpredictable exacerbations of the neurological symptoms called relapses, which can occur at different intervals ranging from 4 weeks to several years. Microbial infections are known to be able to trigger MS relapses, and the patients are instructed to avoid all factors that might increase the risk of infections and to properly use antibiotics as well as to take care of dental hygiene. Among those environmental factors which are known to increase susceptibility to infections, high ambient air inhalable particulate matter levels affect all people within a geographical region. During the period of interest in this thesis, the occurrence of MS relapses could be effectively reduced by injections of interferon, which has immunomodulatory and antiviral properties. In this thesis, ecological and epidemiological analyses were used to study the possible connection between MS relapse occurrence, population level viral infections and air quality factors, as well as the effects of interferon medication. Hospital archive data were collected retrospectively from 1986-2001, a period in time ranging from when interferon medication first became available until just before other disease-modifying MS therapies arrived on the market. The grouped data were studied with logistic regression and intervention analysis, and individual patient data with survival analysis. Interferons proved to be effective in the treatment of MS in this observational study, as the amount of MS exacerbations was lower during interferon use as compared to the time before interferon treatment. A statistically significant temporal relationship between MS relapses and inhalable particular matter (PM₁₀) concentrations was found in this study, which implies that MS patients are affected by the exposure to PM₁₀. Interferon probably protected against the effect of PM₁₀, because a significant increase in the risk of exacerbations was only observed in MS patients without interferon medication following environmental exposure to population level specific viral infections and PM₁₀. Apart from being antiviral, interferon could thus also attenuate the enhancement of immune reactions caused by ambient air PM₁₀. The retrospective approach utilizing carefully constructed hospital records proved to be an economical and reliable source of MS disease information for statistical analyses.

Comparison of Local Feature Detectors and Descriptors for Visual Object Categorization

Local features are used in many computer vision tasks including visual object categorization, content-based image retrieval and object recognition to mention a few. Local features are points, blobs or regions in images that are extracted using a local feature detector. To make use of extracted local features the localized interest points are described using a local feature descriptor. A descriptor histogram vector is a compact representation of an image and can be used for searching and matching images in databases. In this thesis the performance of local feature detectors and descriptors is evaluated for object class detection task. Features are extracted from image samples belonging to several object classes. Matching features are then searched using random image pairs of a same class. The goal of this thesis is to find out what are the best detector and descriptor methods for such task in terms of detector repeatability and descriptor matching rate.

What can and should be legalized in ecological restoration?

After listing the five key elements of ecological restoration, ecology, economics, social values, cultural values, and politics, I celebrate the fact that in Brazil there is legislation on how to perform ecological restoration of degraded tropical forests, as well as an ongoing dialogue among legislators and scientists about this legislation, and also a lively debate among scientists as the best way forward, referring to articles by Brancalion et al. (2010) and Durigan et al. (2010) in this issue of Revista Árvore. Legislators elsewhere, especially megadiversity countries, should take note. I do not take sides in the debate; I think both groups of authors make very good points. Instead I call on the scientists and legislators concerned with restoration to ponder five strategic tools: A. Start with clear concepts. B. Decide where you want to go and why. C. Negotiate who should benefit & how, and who should pay, how, & why. D. Work out how an honest cost-benefit analysis of restoration would look, regardless of the biome in which you are working. Finally, figure out how to make the restoration immediately attractive for private landowners. Otherwise, they will not cooperate as fully as they could or should, and restoration efforts will not achieve its full potential.

Diameter structure of stratum and ecological groups of a Semideciduous Forest area in Dionísio-MG

The objective of this study was to obtain homogeneous groups of species and information on their density, dominance and volume, in terms of ecological group and diameter structure of an area of Submontane Semideciduous forest (Mata do Mumbaça) in Dionísio, MG. This work was conducted with data of the diameter distribution per species from floristic and phytosociological (Mata do Mumbaça) survey of 120 plots with 10 x 10 m each one. The 120 plots were contiguous and corresponding to a total sample area of 12,000 m² distributed over the topographic units (Low Ramp, Lower Slope, Upper Slope and Hill Top). The topographic units Low Ramp, Lower Slope and Upper Slope were in the middle stage of succession as they presented incipient stratification into two strata (canopy and understory) i.e. canopy ranging from 5 to 12 m high. However, the stratum Hill Top was classified as intermediate/advanced succession because it had a total height equal to or greater than 12 m. The distribution of individual trees of the four strata on diameter classes showed a typical J-inverted pattern that is, high concentration of individuals in smaller diameter classes and a sharp reduction towards the larger classes. In relation to absolute dominance and total volume of species, the ecological group that stood out in the four strata (Low Ramp, Lower Slope, Upper Slope and Hill Top) was the initial secondary, which were in the intermediate stage of secondary, rapidly developing into the mature phase.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task