Combining classification techniques to define topo-climatic landscapes

Landscape classification tackles issues related to the representation and analysis of continuous and variable ecological data. In this study, a methodology is created in order to define topo-climatic landscapes (TCL) in the north-west of Catalonia (north-east of the Iberian Peninsula). TCLs relate the ecological behaviour of a landscape in terms of topography, physiognomy and climate, which compound the main drivers of an ecosystem. Selected variables are derived from different sources such as remote sensing and climatic atlas. The proposed methodology combines unsupervised interative cluster classification with a supervised fuzzy classification. As a result, 28 TCLs have been found for the study area which may be differentiated in terms of vegetation physiognomy and vegetation altitudinal range type. Furthermore a hierarchy among TCLs is set, enabling the merging of clusters and allowing for changes of scale. Through the topo-climatic landscape map, managers may identify patches with similar environmental conditions and asses at the same time the uncertainty involved.

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

Human effector CD8+ T lymphocytes express TLR3 as a functional coreceptor.

TLR are evolutionarily conserved molecules that play a key role in the initiation of innate antimicrobial immune responses. Through their influence on dendritic cell maturation, these receptors are also thought to indirectly shape the adaptive immune response. However, no data are currently available regarding both TLR expression and function in human CD8+ T cell subsets. We report that a subpopulation of CD8+ T cells, i.e., effector, but neither naive nor central memory cells, constitutively expresses TLR3. Moreover, the ligation of the receptor by a specific agonist in TLR3-expressing CD8+ T cells increased IFN-gamma secretion induced by TCR-dependent and -independent stimulation, without affecting proliferation or specific cytolytic activity. These results thereby suggest that TLR3 ligands can not only indirectly influence the adaptive immune response through modulation of dendritic cell activation, but also directly increase IFN-gamma production by Ag-specific CD8+ T cells. Altogether, the present work might open new perspectives for the use of TLR ligands as adjuvants for immunotherapy.

The locust foraging gene.

Our knowledge of how genes act on the nervous system in response to the environment to generate behavioral plasticity is limited. A number of recent advancements in this area concern food-related behaviors and a specific gene family called foraging (for), which encodes a cGMP-dependent protein kinase (PKG). The desert locust (Schistocerca gregaria) is notorious for its destructive feeding and long-term migratory behavior. Locust phase polyphenism is an extreme example of environmentally induced behavioral plasticity. In response to changes in population density, locusts dramatically alter their behavior, from solitary and relatively sedentary behavior to active aggregation and swarming. Very little is known about the molecular and genetic basis of this striking behavioral phenomenon. Here we initiated studies into the locust for gene by identifying, cloning, and studying expression of the gene in the locust brain. We determined the phylogenetic relationships between the locust PKG and other known PKG proteins in insects. FOR expression was found to be confined to neurons of the anterior midline of the brain, the pars intercerebralis. Our results suggest that differences in PKG enzyme activity are correlated to well-established phase-related behavioral differences. These results lay the groundwork for functional studies of the locust for gene and its possible relations to locust phase polyphenism.

Environmental Kuznets Curves for Carbon Emissions: A Critical Survey

The empirical finding of an inverse U-shaped relationship between per capita income and pollution, the so-called Environmental Kuznets Curve (EKC), suggests that as countries experience economic growth, environmental deterioration decelerates and thus becomes less of an issue. Focusing on the prime example of carbon emissions, the present article provides a critical review of the new econometric techniques that have questioned the baseline polynomial specification in the EKC literature. We discuss issues related to the functional form, heterogeneity, “spurious” regressions and spatial dependence to address whether and to what extent the EKC can be observed. Despite these new approaches, there is still no clear-cut evidence supporting the existence of the EKC for carbon emissions. JEL classifications: C20; Q32; Q50; O13 Keywords: Environmental Kuznets Curve; Carbon emissions; Functional form; Heterogeneity; “Spurious” regressions; Spatial dependence.Residential satisfaction is often used as a barometer to assess the performance of public policy and programmes designed to raise individuals' well-being. However, the fact that responses elicited from residents might be biased by subjective, non-observable factors casts doubt on whether these responses can be taken as trustable indicators of the individuals' housing situation. Emotional factors such as aspirations or expectations might affect individuals' cognitions of their true residential situation. To disentangle this puzzle, we investigated whether identical residential attributes can be perceived differently depending on tenure status. Our results indicate that tenure status is crucial not only in determining the level of housing satisfaction, but also regarding how dwellers perceive their housing characteristics. Keywords: Housing satisfaction, subjective well-being, homeownership. JEL classification: D1, R2.

Improved temporal resolution for functional studies with reduced number of segments with three-dimensional echo planar imaging.

PURPOSE: To introduce a new k-space traversal strategy for segmented three-dimensional echo planar imaging (3D EPI) that encodes two partitions per radiofrequency excitation, effectively reducing the number excitations used to acquire a 3D EPI dataset by half. METHODS: The strategy was evaluated in the context of functional MRI applications for: image quality compared with segmented 3D EPI, temporal signal-to-noise ratio (tSNR) (the ability to detect resting state networks compared with multislice two-dimensional (2D) EPI and segmented 3D EPI, and temporal resolution (the ability to separate cardiac- and respiration-related fluctuations from the desired blood oxygen level-dependent signal of interest). RESULTS: Whole brain images with a nominal voxel size of 2 mm isotropic could be acquired with a temporal resolution under half a second using traditional parallel imaging acceleration up to 4× in the partition-encode direction and using novel data acquisition speed-up of 2× with a 32-channel coil. With 8× data acquisition speed-up in the partition-encode direction, 3D reduced excitations (RE)-EPI produced acceptable image quality without introduction of noticeable additional artifacts. Due to increased tSNR and better characterization of physiological fluctuations, the new strategy allowed detection of more resting state networks compared with multislice 2D-EPI and segmented 3D EPI. CONCLUSION: 3D RE-EPI resulted in significant increases in temporal resolution for whole brain acquisitions and in improved physiological noise characterization compared with 2D-EPI and segmented 3D EPI. Magn Reson Med 72:786-792, 2014. © 2013 Wiley Periodicals, Inc.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Transcriptomic and functional analysis of an autolysis-deficient, teicoplanin-resistant derivative of methicillin-resistant Staphylococcus aureus.

The molecular basis of glycopeptide-intermediate S. aureus (GISA) isolates is not well defined though frequently involves phenotypes such as thickened cell walls and decreased autolysis. We have exploited an isogenic pair of teicoplanin-susceptible (strain MRGR3) and teicoplanin-resistant (strain 14-4) methicillin-resistant S. aureus strains for detailed transcriptomic profiling and analysis of altered autolytic properties. Strain 14-4 displayed markedly deficient Triton X-100-triggered autolysis compared to its teicoplanin-susceptible parent, although microarray analysis paradoxically did not reveal significant reductions in expression levels of major autolytic genes atl, lytM, and lytN, except for sle1, which showed a slight decrease. The most important paradox was a more-than-twofold increase in expression of the cidABC operon in 14-4 compared to MRGR3, which was correlated with decreased expression of autolysis negative regulators lytSR and lrgAB. In contrast, the autolysis-deficient phenotype of 14-4 was correlated with both increased expression of negative autolysis regulators (arlRS, mgrA, and sarA) and decreased expression of positive regulators (agr RNAII and RNAIII). Quantitative bacteriolytic assays and zymographic analysis of concentrated culture supernatants showed a striking reduction in Atl-derived, extracellular bacteriolytic hydrolase activities in 14-4 compared to MRGR3. This observed difference was independent of the source of cell wall substrate (MRGR3 or 14-4) used for analysis. Collectively, our results suggest that altered autolytic properties in 14-4 are apparently not driven by significant changes in the transcription of key autolytic effectors. Instead, our analysis points to alternate regulatory mechanisms that impact autolysis effectors which may include changes in posttranscriptional processing or export.

Optimal Universal and Categorical Benefits with Classification Errors and Imperfect Enforcement

We determine he optimal combination of a universal benefit, B, and categorical benefit, C, for an economy in which individuals differ in both their ability to work - modelled as an exogenous zero quantity constraint on labour supply - and, conditional on being able to work, their productivity at work. C is targeted at those unable to work, and is conditioned in two dimensions: ex-ante an individual must be unable to work and be awarded the benefit, whilst ex-post a recipient must not subsequently work. However, the ex-ante conditionality may be imperfectly enforced due to Type I (false rejection) and Type II (false award) classification errors, whilst, in addition, the ex-post conditionality may be imperfectly enforced. If there are no classification errors - and thus no enforcement issues - it is always optimal to set C>0, whilst B=0 only if the benefit budget is sufficiently small. However, when classification errors occur, B=0 only if there are no Type I errors and the benefit budget is sufficiently small, while the conditions under which C>0 depend on the enforcement of the ex-post conditionality. We consider two discrete alternatives. Under No Enforcement C>0 only if the test administering C has some discriminatory power. In addition, social welfare is decreasing in the propensity to make each type error. However, under Full Enforcement C>0 for all levels of discriminatory power. Furthermore, whilst social welfare is decreasing in the propensity to make Type I errors, there are certain conditions under which it is increasing in the propensity to make Type II errors. This implies that there may be conditions under which it would be welfare enhancing to lower the chosen eligibility threshold - support the suggestion by Goodin (1985) to "err on the side of kindness".

The Inventor Balance and the Functional Specialization in Global Inventive Activities

We study the functional specialization whereby some countries contribute relatively more inventors vs. organizations in the production of inventions at a global scale. We propose a conceptual framework to explain this type of functional specialization, which posits the presence of feedbacks between two distinct sub-systems, each one providing inventors and organizations. We quantify the phenomenon by means of a new metric, the “inventor balance”, which we compute using patent data. We show that the observed imbalances, which are often conspicuous, are determined by several factors: the innovativeness of a country relative to its level of economic development, relative factor endowments, the degree of technological specialization and, last, cultural traits. We argue that the “inventor balance” is a useful indicator for policy makers, and its routine analysis could lead to better informed innovation policies.

Variation in habitat suitability does not always relate to variation in species' plant functional traits.

Habitat suitability models, which relate species occurrences to environmental variables, are assumed to predict suitable conditions for a given species. If these models are reliable, they should relate to change in plant growth and function. In this paper, we ask the question whether habitat suitability models are able to predict variation in plant functional traits, often assumed to be a good surrogate for a species' overall health and vigour. Using a thorough sampling design, we show a tight link between variation in plant functional traits and habitat suitability for some species, but not for others. Our contrasting results pave the way towards a better understanding of how species cope with varying habitat conditions and demonstrate that habitat suitability models can provide meaningful descriptions of the functional niche in some cases, but not in others.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.