Report of Lepocreadium Bimarium manter, 1940, Vitellibaculum Spinosa (Siddioi) & Cable, 1960) and Hirudinella Ventricosa (Pallas, 1774), parasites of marine fishes in Brazil

Lepocreadium bimarinum and Vitellibaculum spinosa are referred for the first time in Stephanolepis hispidus and in Brazil, and Hirudinella ventricosa is reported from Scomberomorus cavalla. Measurements, figures and comments are given.

Generic object classification for autonomous robots

Un dels principals problemes de la interacció dels robots autònoms és el coneixement de l'escena. El reconeixement és fonamental per a solucionar aquest problema i permetre als robots interactuar en un escenari no controlat. En aquest document presentem una aplicació pràctica de la captura d'objectes, de la normalització i de la classificació de senyals triangulars i circulars. El sistema s'introdueix en el robot Aibo de Sony per a millorar-ne la interacció. La metodologia presentada s'ha comprobat en simulacions i problemes de categorització reals, com ara la classificació de senyals de trànsit, amb resultats molt prometedors.

A classification, up to hyperbolicity, of groups given by 2 generators and one relator of length 8

"Vegeu el resum a l'inici del document del fitxer adjunt."

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

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.

Dinosoma clupeola sp. n. (Hemiuridae) and Pseudoacanthostomum floridensis Nahhas & Short, 1965 (Acanthostomidae), digenetic trematodes in Brazilian Marine fishes

Dinosoma clupeola sp. n. is described from Harengula clupeola, and resembles D. hawaiiense Yamaguti, 1970, from which it differs in the entire, elongate-saccular seminal vesicle, tegument weakly plicated, smaller size of body and internal organs, and slightly larger and narrower eggs. Pseudoacanthostomum floridensis Nahhas & Short, 1965 is referred to Netuma barba, which represents a new host record.

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.

An ATP-binding cassette subfamily G full transporter is essential for the retention of leaf water in both wild barley and rice.

Land plants have developed a cuticle preventing uncontrolled water loss. Here we report that an ATP-binding cassette (ABC) subfamily G (ABCG) full transporter is required for leaf water conservation in both wild barley and rice. A spontaneous mutation, eibi1.b, in wild barley has a low capacity to retain leaf water, a phenotype associated with reduced cutin deposition and a thin cuticle. Map-based cloning revealed that Eibi1 encodes an HvABCG31 full transporter. The gene was highly expressed in the elongation zone of a growing leaf (the site of cutin synthesis), and its gene product also was localized in developing, but not in mature tissue. A de novo wild barley mutant named "eibi1.c," along with two transposon insertion lines of rice mutated in the ortholog of HvABCG31 also were unable to restrict water loss from detached leaves. HvABCG31 is hypothesized to function as a transporter involved in cutin formation. Homologs of HvABCG31 were found in green algae, moss, and lycopods, indicating that this full transporter is highly conserved in the evolution of land plants.

Schikhobalotrema solitaria sp. n. and S. acanthuri Yamaguti, 1970 (Haplosplanchnidae: Digenea) in Brazilian marine fishes

Schikhobalotrema solitaria sp. n. is described from Stephanolepis hispidus (Linnaeus, 1758), and is characterized by the relation between oral sucker and pharynx 1:1,1. This Relation in the other species ranges between 1:0.3-0.6. It is more similar to S. manteri Siddiqi & Cable, 1960 in the distribution of vitellaria, differing from it in the size of vitelline follicles, extension of uterus and in the size of the eggs. S. acanthuri Yamaguti, 1970 is referred for the first time in South America in Mugil liza Valenciennes, 1836 representing a new host record.

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".

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.