Characterization of larval moulting cycles in Maja brachydactyla (Brachyura, Majidae) reared in the laboratory

The moulting cycles of all larval instars (zoea I, zoea II, and megalopa) of the spider crab Maja brachydactyla Balss 1922 were studied in laboratory rearing experiments. Morphological changes in the epidermis and cuticle were photographically documented in daily intervals and assigned to successive stages of the moulting cycle (based on Drach's classification system). Our moult-stage characterizations are based on microscopical examination of integumental modifications mainly in the telson, using epidermal condensation, the degree of epidermal retraction (apolysis), and morphogenesis (mainly setagenesis) as criteria. In the zoea II and megalopa, the formation of new setae was also observed in larval appendages including the antenna, maxillule, maxilla, second maxilliped, pleopods, and uropods. As principal stages within the zoea I moulting cycle, we describe postmoult (Drach's stages A–B combined), intermoult (C), and premoult (D), the latter with three substages (D0, D1, and D2). In the zoea II and megalopa, D0 and D1 had to be combined, because morphogenesis (the main characteristic of D1) was unclear in the telson and did not occur synchronically in different appendices. The knowledge of the course and time scale of successive moult-cycle events can be used as a tool for the evaluation of the developmental state within individual larval instars, providing a morphological reference system for physiological and biochemical studies related to crab aquaculture.

Estimates of patient costs related with population morbidity: Can indirect costs affect the results?

A number of health economics works require patient cost estimates as a basic information input.However the accuracy of cost estimates remains in general unspecified. We propose to investigate howthe allocation of indirect costs or overheads can affect the estimation of patient costs in order to allow forimprovements in the analysis of patient costs estimates. Instead of focusing on the costing method, thispaper proposes to highlight changes in variance explained observed when a methodology is chosen. Wecompare three overhead allocation methods for a specific Spanish population adjusted using the ClinicalRisk Groups (CRG), and we obtain different series of full-cost group estimates. As a result, there aresignificant gains in the proportion of the variance explained, depending upon the methodology used.Furthermore, we find that the global amount of variation explained by risk adjustment models dependsmainly on direct costs and is independent of the level of aggregation used in the classification system.

Eficacia de la inducción miofascial para el tratamiento de la espasticidad en pacientes con parálisis cerebral espástica: ensayo clínico aleatorizado

¿El tratamiento mediante la inducción miofascial disminuye la espasticidad en los pacientes con Parálisis Cerebral Espástica (PCE) mientras son intervenidos con un tratamiento convencional? Objetivos: Comprobar si la inducción miofascial disminuye la espasticidad en pacientes con parálisis cerebral espástica (PCE) y así mismo prevenir las complicaciones musculoesqueléticas y aumentar el rango de movilidad articular. Metodología: Ensayo controlado clínico aleatorizado que recoge un total de 96 casos de PCE con Grado l, ll y lll de afectación según la Escala de clasificación Gross Motor Function Measure (GMFM). Se asignará de forma aleatoria y equitativamente 48 sujetos al grupo control aplicándose el tratamiento convencional y 48 sujetos al grupo experimental, donde la inducción miofascial se complementará con tratamiento convencional. Durante 3 meses se llevará a cabo el plan de intervención, 2 días a la semana en ambos grupos. Los datos serán analizados a través de las siguientes escalas: el tono muscular (Escala de Ashworth Modificada, Escala de Tardieu, Test pendular Wartenberg) funcionalidad y actividad (Gross Motor Function Classification System, Gross Motor Function Mesurement, Pediatric Evaluation of Disability Inventory), valoración neurológica (National Institute of NEurological Disordes and Stroke Scale) y la satisfacción del paciente (Questionnaire on Pain Caused by Spasticity). Estos datos serán extraidos el primer día, el último y 3 meses más tarde a modo de seguimiento. Durante el plan de intervención también se realizarán valoraciones semanales y mensuales.

Differences between genders in coping: different strategies or different stressors?

These study analysed gender specificity in coping behaviours by taking into account the types of problem faced by Spanish adolescents attending school. It was focused on the ten problems most frequently reported by participants (828 adolescents, 355 boys, and 473 girls; Mage = 14.07, SD = 1.34), which were classified using a multi-axial classification system. Coping was examined as a two separate measures of approach and avoidance coping, and as a combined measure indicating the predominant use of coping, and total coping effort. A MANCOVA and subsequent univariate tests were conducted to analyse the specificity of coping according to problem and gender, controlled by age. The results showed that the percentage of types of problems reported by adolescents differed according to gender. The influence of gender on coping was scarcely relevant when the type of problem was controlled for. There were no gender differences when the predominant type of coping was considered, but when a total coping effort measure was analysed girls showed more coping efforts than boys to face interpersonal relationship problems and personal illness. Keywords: adolescence, coping, gender differences, stressors.

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.

Cartilage Appearance Using an Environmental Scanning Electron Microscope.

Because of technical principles, samples to be observed with electron microscopy need to be fixed in a chemical process and exposed to vacuum conditions that can produce some changes in the morphology of the specimen. The aim of this work was to obtain high-resolution images of the fresh articular cartilage surface with an environmental scanning electron microscope (ESEM), which is an instrument that permits examination of biological specimens without fixation methods in a 10 Torr chamber pressure, thus minimizing the risk of creating artifacts in the structure. Samples from weight-bearing areas of femoral condyles of New Zealand white rabbits were collected and photographed using an ESEM. Images were analyzed using a categorization based in the Jurvelin classification system modified by Hong and Henderson. Appearance of the observed elevations and depressions as described in the classification were observed, but no fractures or splits of cartilage surface, thought to be artifacts, were detected. The ESEM is a useful tool to obtain images of fresh articular cartilage surface appearance without either employing fixation methods or exposing the specimen to extreme vacuum conditions, reducing the risk of introducing artifacts within the specimen. For all these reasons it could become a useful tool for quality control of the preservation process of osteochondral allografting in a bank of musculoskeletal tissues.

Economic impacts of alternative water policy scenarios in the Spanish production system: an input-output analysis

The objective of this paper is to analyse the economic impacts of alternative water policies implemented in the Spanish production system. The methodology uses two versions of the input-output price model: a competitive formulation and a mark-up formulation. The input-output framework evaluates the impact of water policy measures on production prices, consumption prices, intermediate water demand and private welfare. Our results show that a tax on the water used by sectors considerably reduces the intermediate water demand, and increases the production and consumption prices. On the other hand, according to Jevons' paradox, an improvement in technical efficiency, which leads to a reduction in the water requirements of all sectors and an increase in water production, increases the amount of water consumed. The combination of a tax on water and improved technical efficiency takes the pressure off prices and significantly reduces intermediate water demand. JEL Classification: C67 ; D57 ; Q25. Keywords: Production prices; Consumption prices; Water uses; Water policy; Water taxation.

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.

Does the EU financing system contribute to shadow economic activity?

Financial contributions to the EU budget depend basically on official GDP. This means that countries with higher shadow economic activity contribute less than they should contribute in a system based on actual GDP and therefore could reduce their incentive to fight against such activities. In this paper we investigate if the EU financing system really has an influence on the intensity with which governments in EU member states fight against shadow economic activity. We find that the EU net contributors significantly fight more intensively against shadow economic activity while EU net receivers significantly fight less. As a result, shadow economic activity is higher in net receiver and lower in net contributor countries than it were in comparison with a scenario of nationally balanced EU funding. Quantitatively and averaged over the time period 2001-2007, the diagnosed effect amounts to a stimulation of hidden economic activity by almost 10% for particular economies. JEL classification: C31, D63, F33, H21, H26. Keywords: EU financing system, shadow economy, tax auditing.

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

Incorporating features of distribution and progression for automatic Makam classification

Automatic classification of makams from symbolic data is a rarely studied topic. In this paper, first a review of an n-gram based approach is presented using various representations of the symbolic data. While a high degree of precision can be obtained, confusion happens mainly for makams using (almost) the same scale and pitch hierarchy but differ in overall melodic progression, seyir. To further improve the system, first n-gram based classification is tested for various sections of the piece to take into account a feature of the seyir that melodic progression starts in a certain region of the scale. In a second test, a hierarchical classification structure is designed which uses n-grams and seyir features in different levels to further improve the system.

A system-level, molecular evolutionary analysis of mammalian phototransduction

Visual perception is initiated in the photoreceptor cells of the retina via the phototransduction system.This system has shown marked evolution during mammalian divergence in such complex attributes as activation time and recovery time. We have performed a molecular evolutionary analysis of proteins involved in mammalianphototransduction in order to unravel how the action of natural selection has been distributed throughout thesystem to evolve such traits. We found selective pressures to be non-randomly distributed according to both a simple protein classification scheme and a protein-interaction network representation of the signaling pathway. Proteins which are topologically central in the signaling pathway, such as the G proteins, as well as retinoid cycle chaperones and proteins involved in photoreceptor cell-type determination, were found to be more constrained in their evolution. Proteins peripheral to the pathway, such as ion channels and exchangers, as well as the retinoid cycle enzymes, have experienced a relaxation of selective pressures. Furthermore, signals of positive selection were detected in two genes: the short-wave (blue) opsin (OPN1SW) in hominids and the rod-specific Na+/Ca2+,K+ ion exchanger (SLC24A1) in rodents. The functions of the proteins involved in phototransduction and the topology of the interactions between them have imposed non-random constraints on their evolution. Thus, in shaping or conserving system-level phototransduction traits, natural selection has targeted the underlying proteins in a concerted manner.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables

Objective: We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection. Methods: The study population comprised 331 HIV-infected patients with available demographic, clinical, and neurocognitive data collected using a comprehensive battery of neuropsychological tests. Classification and regression trees (CART) were developed to btain detailed and reliable models to predict NCI. Following a practical clinical approach, NCI was considered the main variable for study outcomes, and analyses were performed separately in treatment-naïve and treatment-experienced patients. Results: The study sample comprised 52 treatment-naïve and 279 experienced patients. In the first group, the variables identified as better predictors of NCI were CD4 cell count and age (correct classification [CC]: 79.6%, 3 final nodes). In treatment-experienced patients, the variables most closely related to NCI were years of education, nadir CD4 cell count, central nervous system penetration-effectiveness score, age, employment status, and confounding comorbidities (CC: 82.1%, 7 final nodes). In patients with an undetectable viral load and no comorbidities, we obtained a fairly accurate model in which the main variables were nadir CD4 cell count, current CD4 cell count, time on current treatment, and past highest viral load (CC: 88%, 6 final nodes). Conclusion: Practical classification models to predict NCI in HIV infection can be obtained using demographic and clinical variables. An approach based on CART analyses may facilitate screening for HIV-associated neurocognitive disorders and complement clinical information about risk and protective factors for NCI in HIV-infected patients.