Strategies and tools for preventing neurotoxicity: to test, to predict and how to do it

A change in paradigm is needed in the prevention of toxic effects on the nervous system, moving from its present reliance solely on data from animal testing to a prediction model mostly based on in vitro toxicity testing and in silico modeling. According to the report published by the National Research Council (NRC) of the US National Academies of Science, high-throughput in vitro tests will provide evidence for alterations in"toxicity pathways" as the best possible method of large scale toxicity prediction. The challenges to implement this proposal are enormous, and provide much room for debate. While many efforts address the technical aspects of implementing the vision, many questions around it need also to be addressed. Is the overall strategy the only one to be pursued? How can we move from current to future paradigms? Will we ever be able to reliably model for chronic and developmental neurotoxicity in vitro? This paper summarizes four presentations from a symposium held at the International Neurotoxicology Conference held in Xi"an, China, in June 2011. A. Li reviewed the current guidelines for neurotoxicity and developmental neurotoxicity testing, and discussed the major challenges existing to realize the NCR vision for toxicity testing. J. Llorens reviewed the biology of mammalian toxic avoidance in view of present knowledge on the physiology and molecular biology of the chemical senses, taste and smell. This background information supports the hypothesis that relating in vivo toxicity to chemical epitope descriptors that mimic the chemical encoding performed by the olfactory system may provide a way to the long term future of complete in silico toxicity prediction. S. Ceccatelli reviewed the implementation of rodent and human neural stem cells (NSCs) as models for in vitro toxicity testing that measures parameters such as cell proliferation, differentiation and migration. These appear to be sensitive endpoints that can identify substances with developmental neurotoxic potential. C. Sun ol reviewed the use of primary neuronal cultures in testing for neurotoxicity of environmental pollutants, including the study of the effects of persistent exposures and/or in differentiating cells, which allow recording of effects that can be extrapolated to human developmental neurotoxicity.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Otolith shape analysis as a tool for population discrimination of the white grunt (Haemulon plumieri) stock in the northern coast of the Yucatan Peninsula, Mexico

In this study, the population structure of the white grunt (Haemulon plumieri) from the northern coast of the Yucatan Peninsula was determined through an otolith shape analysis based on the samples collected in three locations: Celestún (N 20°49",W 90°25"), Dzilam (N 21°23", W 88°54") and Cancún (N 21°21",W 86°52"). The otolith outline was based on the elliptic Fourier descriptors, which indicated that the H. plumieri population in the northern coast of the Yucatan Peninsula is composed of three geographically delimited units (Celestún, Dzilam, and Cancún). Significant differences were observed in mean otolith shapes among all samples (PERMANOVA; F2, 99 = 11.20, P = 0.0002), and the subsequent pairwise comparisons showed that all samples were significantly differently from each other. Samples do not belong to a unique white grunt population, and results suggest that they might represent a structured population along the northern coast of the Yucatan Peninsula

Sensory characteristics of 'Douradão' peaches submitted to modified atmosphere packaging

The sensory, physical and chemical characteristics of 'Douradão' peaches cold stored in different modified atmosphere packaging (LDPE bags of 30, 50, 60, 75µm thickness) were studied. After 14, 21 and 28 days of cold storage (1 ± 1 ºC and 90 ± 5% RH), samples were withdrawn from MAP and kept during 4 days in ambient air for ripening. Descriptive terminology and sensory profile of the peaches were developed by methodology based on the Quantitative Descriptive Analysis (QDA). The assessors consensually defined the sensory descriptors, their respective reference materials and the descriptive evaluation ballot. Fourteen individuals were selected as judges based on their discrimination capacity and reproducibility. Seven descriptors were generated showing similarities and differences among the samples. The data were analysed by ANOVA, Tukey test and Principal Component Analysis (PCA). The atmospheres that developed inside the different packaging materials during cold storage differed significantly. The PCA showed that MA50 and MA60 treatments were more characterized by the fresh peach flavour, fresh appearance, juiciness and flesh firmness, and were effective for keeping good quality of 'Douradão' peaches during 28 d of cold storage. The Control and MA30 treatments were characterized by the mealiness, the MA75 treatment showed lower intensity for all attributes evaluated and they were ineffective to maintain good quality of the fruits during cold storage. Higher correlation coefficients (positive) were found between fresh appearance and flesh firmness (0.95), fresh appearance and juiciness (0.97), ratio and intensity of fresh peach smell (0.81), as well as higher correlation coefficients (negative) between Hue angle and intensity of yellow colour (-0.91), fresh appearance and mealiness (-0.92), juiciness and mealiness (-0.95), firmness and mealiness (-0.94).

Sensory profiles of chocolates produced from cocoa cultivars resistant to Moniliophtora Perniciosa

The present study evaluated the sensory quality of chocolates obtained from two cocoa cultivars (PH16 and SR162) resistant to Moniliophtora perniciosa mould comparing to a conventional cocoa that is not resistant to the disease. The acceptability of the chocolates was assessed and the promising cultivars with relevant sensory and commercial attributes could be indicated to cocoa producers and chocolate manufacturers. The descriptive terminology and the sensory profile of chocolates were developed by Quantitative Descriptive Analysis (QDA). Ten panelists, selected on the basis of their discriminatory capacity and reproducibility, defined eleven sensory descriptors, their respective reference materials and the descriptive evaluation ballot. The data were analyzed using ANOVA, Principal Component Analysis (PCA) and Tukey's test to compare the means. The results revealed significant differences among the sensory profiles of the chocolates. Chocolates from the PH16 cultivar were characterized by a darker brown color, more intense flavor and odor of chocolate, bitterness and a firmer texture, which are important sensory and commercial attributes. Chocolates from the SR162 cultivar were characterized by a greater sweetness and melting quality and chocolates from the conventional treatment presented intermediate sensory characteristics between those of the other two chocolates. All samples indicated high acceptance, but chocolates from the PH16 and conventional cultivars obtained higher purchase intention scores.

Characteristics of 2D convective structures in Catalonia (NE Spain): an analysis using radar data and GIS

Flood simulation studies use spatial-temporal rainfall data input into distributed hydrological models. A correct description of rainfall in space and in time contributes to improvements on hydrological modelling and design. This work is focused on the analysis of 2-D convective structures (rain cells), whose contribution is especially significant in most flood events. The objective of this paper is to provide statistical descriptors and distribution functions for convective structure characteristics of precipitation systems producing floods in Catalonia (NE Spain). To achieve this purpose heavy rainfall events recorded between 1996 and 2000 have been analysed. By means of weather radar, and applying 2-D radar algorithms a distinction between convective and stratiform precipitation is made. These data are introduced and analyzed with a GIS. In a first step different groups of connected pixels with convective precipitation are identified. Only convective structures with an area greater than 32 km2 are selected. Then, geometric characteristics (area, perimeter, orientation and dimensions of the ellipse), and rainfall statistics (maximum, mean, minimum, range, standard deviation, and sum) of these structures are obtained and stored in a database. Finally, descriptive statistics for selected characteristics are calculated and statistical distributions are fitted to the observed frequency distributions. Statistical analyses reveal that the Generalized Pareto distribution for the area and the Generalized Extreme Value distribution for the perimeter, dimensions, orientation and mean areal precipitation are the statistical distributions that best fit the observed ones of these parameters. The statistical descriptors and the probability distribution functions obtained are of direct use as an input in spatial rainfall generators.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

A definition and classification of status epilepticus - Report of the ILAE Task Force on Classification of Status Epilepticus.

The Commission on Classification and Terminology and the Commission on Epidemiology of the International League Against Epilepsy (ILAE) have charged a Task Force to revise concepts, definition, and classification of status epilepticus (SE). The proposed new definition of SE is as follows: Status epilepticus is a condition resulting either from the failure of the mechanisms responsible for seizure termination or from the initiation of mechanisms, which lead to abnormally, prolonged seizures (after time point t1 ). It is a condition, which can have long-term consequences (after time point t2 ), including neuronal death, neuronal injury, and alteration of neuronal networks, depending on the type and duration of seizures. This definition is conceptual, with two operational dimensions: the first is the length of the seizure and the time point (t1 ) beyond which the seizure should be regarded as "continuous seizure activity." The second time point (t2 ) is the time of ongoing seizure activity after which there is a risk of long-term consequences. In the case of convulsive (tonic-clonic) SE, both time points (t1 at 5 min and t2 at 30 min) are based on animal experiments and clinical research. This evidence is incomplete, and there is furthermore considerable variation, so these time points should be considered as the best estimates currently available. Data are not yet available for other forms of SE, but as knowledge and understanding increase, time points can be defined for specific forms of SE based on scientific evidence and incorporated into the definition, without changing the underlying concepts. A new diagnostic classification system of SE is proposed, which will provide a framework for clinical diagnosis, investigation, and therapeutic approaches for each patient. There are four axes: (1) semiology; (2) etiology; (3) electroencephalography (EEG) correlates; and (4) age. Axis 1 (semiology) lists different forms of SE divided into those with prominent motor systems, those without prominent motor systems, and currently indeterminate conditions (such as acute confusional states with epileptiform EEG patterns). Axis 2 (etiology) is divided into subcategories of known and unknown causes. Axis 3 (EEG correlates) adopts the latest recommendations by consensus panels to use the following descriptors for the EEG: name of pattern, morphology, location, time-related features, modulation, and effect of intervention. Finally, axis 4 divides age groups into neonatal, infancy, childhood, adolescent and adulthood, and elderly.

Monitoring spatial and temporal dynamics of bird communities in Mediterranean landscapes affected by largewildfires = Seguimiento de la dinámica espacial y temporal de comunidades de aves en paisajes mediterráneos afectados por grandes fuegos

We presented a bird-monitoring database inMediterranean landscapes (Catalonia, NE Spain) affected by wildfires and we evaluated: 1) the spatial and temporal variability in the bird community composition and 2) the influence of pre-fire habitat configuration in the composition of bird communities. The DINDIS database results fromthemonitoring of bird communities occupying all areas affected by large wildfires in Catalonia since 2000.We used bird surveys conducted from 2006 to 2009 and performed a principal components analysis to describe two main gradients of variation in the composition of bird communities, which were used as descriptors of bird communities in subsequent analyses. We then analysed the relationships of these community descriptors with bioclimatic regions within Catalonia, time since fire and pre-fire vegetation (forest or shrubland).We have conducted 1,918 bird surveys in 567 transects distributed in 56 burnt areas. Eight out of the twenty most common detected species have an unfavourable conservation status, most of them being associated to open-habitats. Both bird communities’ descriptors had a strong regional component and were related to pre-fire vegetation, and to a lesser extent to the time since fire.We came to the conclusion that the responses of bird communities to wildfires are heterogeneous, complex and context dependent. Large-scale monitoring datasets, such as DINDIS, might allow identifying factors acting at different spatial and temporal scales that affect the dynamics of species and communities, giving additional information on the causes under general trends observed using other monitoring systems

A matter of scale: apparent niche differentiation of diploid and tetraploid plants may depend on extent and grain of analysis

Aim: Emerging polyploids may depend on environmental niche shifts for successful establishment. Using the alpine plant Ranunculus kuepferi as a model system, we explore the niche shift hypothesis at different spatial resolutions and in contrasting parts of the species range. Location: European Alps. Methods: We sampled 12 individuals from each of 102 populations of R. kuepferi across the Alps, determined their ploidy levels, derived coarse-grain (100x100m) environmental descriptors for all sampling sites by downscaling WorldClim maps, and calculated fine-scale environmental descriptors (2x2m) from indicator values of the vegetation accompanying the sampled individuals. Both coarse and fine-scale variables were further computed for 8239 vegetation plots from across the Alps. Subsequently, we compared niche optima and breadths of diploid and tetraploid cytotypes by combining principal components analysis and kernel smoothing procedures. Comparisons were done separately for coarse and fine-grain data sets and for sympatric, allopatric and the total set of populations. Results: All comparisons indicate that the niches of the two cytotypes differ in optima and/or breadths, but results vary in important details. The whole-range analysis suggests differentiation along the temperature gradient to be most important. However, sympatric comparisons indicate that this climatic shift was not a direct response to competition with diploid ancestors. Moreover, fine-grained analyses demonstrate niche contraction of tetraploids, especially in the sympatric range, that goes undetected with coarse-grained data. Main conclusions: Although the niche optima of the two cytotypes differ, separation along ecological gradients was probably less decisive for polyploid establishment than a shift towards facultative apomixis, a particularly effective strategy to avoid minority cytotype exclusion. In addition, our results suggest that coarse-grained analyses overestimate niche breadths of widely distributed taxa. Niche comparison analyses should hence be conducted at environmental data resolutions appropriate for the organism and question under study.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Visual SLAM for underwater vehicles using video velocity log and natural landmarks

A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal