Changes in Body Composition in Anorexia Nervosa: Predictors of Recovery and Treatment Outcome.

The restoration of body composition (BC) parameters is considered to be one of the most important goals in the treatment of patients with anorexia nervosa (AN). However, little is known about differences between AN diagnostic subtypes [restricting (AN-R) and binge/purging (AN-BP)] and weekly changes in BC during refeeding treatment. Therefore, the main objectives of our study were twofold: 1) to assess the changes in BC throughout nutritional treatment in an AN sample and 2) to analyze predictors of BC changes during treatment, as well as predictors of treatment outcome. The whole sample comprised 261 participants [118 adult females with AN (70 AN-R vs. 48 AN-BP), and 143 healthy controls]. BC was measured weekly during 15 weeks of day-hospital treatment using bioelectrical impedance analysis (BIA). Assessment measures also included the Eating Disorders Inventory-2, as well as a number of other clinical indices. Overall, the results showed that AN-R and AN-BP patients statistically differed in all BC measures at admission. However, no significant time×group interaction was found for almost all BC parameters. Significant time×group interactions were only found for basal metabolic rate (p = .041) and body mass index (BMI) (p = .035). Multiple regression models showed that the best predictors of pre-post changes in BC parameters (namely fat-free mass, muscular mass, total body water and BMI) were the baseline values of BC parameters. Stepwise predictive logistic regressions showed that only BMI and age were significantly associated with outcome, but not with the percentage of body fat. In conclusion, these data suggest that although AN patients tended to restore all BC parameters during nutritional treatment, only AN-BP patients obtained the same fat mass values as healthy controls. Put succinctly, the best predictors of changes in BC were baseline BC values, which did not, however, seem to influence treatment outcome.

Differential distribution of tau proteins in developing cat cerebral cortex and corpus callosum.

During the postnatal development of cat visual cortex and corpus callosum the molecular composition of tau proteins varied with age. In both structures, they changed between postnatal days 19 and 39 from a set of two juvenile forms to a set of at least two adult variants with higher molecular weights. During the first postnatal week, tau proteins were detectable with TAU-1 antibody in axons of corpus callosum and visual cortex, and in some perikarya and dendrites in the visual cortex. At later ages, tau proteins were located exclusively within axons in all cortical layers and in the corpus callosum. Dephosphorylation of postnatal day 11 cortical tissue by alkaline phosphatase strongly increased tau protein immunoreactivity on Western blots and in numerous perikarya and dendrites in all cortical layers, in sections, suggesting that some tau forms had been unmasked. During postnatal development the intensity of this phosphate-dependent somatodendritic staining decreased, but remained in a few neurons in cortical layers II and III. On blots, the immunoreactivity of adult tau to TAU-1 was only marginally increased by dephosphorylation. Other tau antibodies (TAU-2, B19 and BR133) recognized two juvenile and two adult cat tau proteins on blots, and localized tau in axons or perikarya and dendrites in tissue untreated with alkaline phosphatase. Tau proteins in mature tissue were soluble and not associated with detergent-resistant structures. Furthermore, dephosphorylation by alkaline phosphatase resulted in the appearance of more tau proteins in soluble fractions. Therefore tau proteins seem to alter their degree of phosphorylation during development. This could affect microtubule stability as well as influence axonal and dendritic differentiation.

Psychiatric Status across Body Mass Index in a Mediterranean Spanish Population.

BACKGROUND Mental and body weight disorders are among the major global health challenges, and their comorbidity may play an important role in treatment and prevention of both pathologies. A growing number of studies have examined the relationship between psychiatric status and body weight, but our knowledge is still limited. OBJECTIVE The present study aims to investigate the cross-sectional relationships of psychiatric status and body mass index (BMI) in Málaga, a Mediterranean city in the South of Spain. MATERIALS AND METHODS A total of 563 participants were recruited from those who came to his primary care physician, using a systematic random sampling, non-proportional stratified by BMI categories. Structured clinical interviews were used to assess current Axes-I and II mental disorders according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). BMI was calculated as weight (Kg) divided by square of height in meters (m2). Logistic regression was used to investigate the association between BMI and the presence of any mental disorder. BMI was introduced in the models using restricted cubic splines. RESULTS We found that high BMI values were directly associated with mood and adjustment disorders, and low BMI values were directly associated with avoidant and dependent personality disorders (PDs). We observed an inverse relationship between low BMI values and cluster A PDs. There were not significant relationships between anxiety or substance-related disorders and BMI. CONCLUSION Psychiatric status and BMI are related in a Mediterranean Spanish population. A multidisciplinary approach to both pathologies becomes increasingly more necessary.

A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC).

BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.

Adaptively weighted maximum likelihood estimation of discrete distributions

SummaryDiscrete data arise in various research fields, typically when the observations are count data.I propose a robust and efficient parametric procedure for estimation of discrete distributions. The estimation is done in two phases. First, a very robust, but possibly inefficient, estimate of the model parameters is computed and used to indentify outliers. Then the outliers are either removed from the sample or given low weights, and a weighted maximum likelihood estimate (WML) is computed.The weights are determined via an adaptive process such that if the data follow the model, then asymptotically no observation is downweighted.I prove that the final estimator inherits the breakdown point of the initial one, and that its influence function at the model is the same as the influence function of the maximum likelihood estimator, which strongly suggests that it is asymptotically fully efficient.The initial estimator is a minimum disparity estimator (MDE). MDEs can be shown to have full asymptotic efficiency, and some MDEs have very high breakdown points and very low bias under contamination. Several initial estimators are considered, and the performances of the WMLs based on each of them are studied.It results that in a great variety of situations the WML substantially improves the initial estimator, both in terms of finite sample mean square error and in terms of bias under contamination. Besides, the performances of the WML are rather stable under a change of the MDE even if the MDEs have very different behaviors.Two examples of application of the WML to real data are considered. In both of them, the necessity for a robust estimator is clear: the maximum likelihood estimator is badly corrupted by the presence of a few outliers.This procedure is particularly natural in the discrete distribution setting, but could be extended to the continuous case, for which a possible procedure is sketched.RésuméLes données discrètes sont présentes dans différents domaines de recherche, en particulier lorsque les observations sont des comptages.Je propose une méthode paramétrique robuste et efficace pour l'estimation de distributions discrètes. L'estimation est faite en deux phases. Tout d'abord, un estimateur très robuste des paramètres du modèle est calculé, et utilisé pour la détection des données aberrantes (outliers). Cet estimateur n'est pas nécessairement efficace. Ensuite, soit les outliers sont retirés de l'échantillon, soit des faibles poids leur sont attribués, et un estimateur du maximum de vraisemblance pondéré (WML) est calculé.Les poids sont déterminés via un processus adaptif, tel qu'asymptotiquement, si les données suivent le modèle, aucune observation n'est dépondérée.Je prouve que le point de rupture de l'estimateur final est au moins aussi élevé que celui de l'estimateur initial, et que sa fonction d'influence au modèle est la même que celle du maximum de vraisemblance, ce qui suggère que cet estimateur est pleinement efficace asymptotiquement.L'estimateur initial est un estimateur de disparité minimale (MDE). Les MDE sont asymptotiquement pleinement efficaces, et certains d'entre eux ont un point de rupture très élevé et un très faible biais sous contamination. J'étudie les performances du WML basé sur différents MDEs.Le résultat est que dans une grande variété de situations le WML améliore largement les performances de l'estimateur initial, autant en terme du carré moyen de l'erreur que du biais sous contamination. De plus, les performances du WML restent assez stables lorsqu'on change l'estimateur initial, même si les différents MDEs ont des comportements très différents.Je considère deux exemples d'application du WML à des données réelles, où la nécessité d'un estimateur robuste est manifeste : l'estimateur du maximum de vraisemblance est fortement corrompu par la présence de quelques outliers.La méthode proposée est particulièrement naturelle dans le cadre des distributions discrètes, mais pourrait être étendue au cas continu.

Study of chemical modification of alkaline lignin by the glyoxalation reaction

In this study, glyoxalated alkaline lignins with a non-volatile and non-toxic aldehyde, which can be obtained from several natural resources, namely glyoxal, were prepared and characterized for its use in wood adhesives. The preparation method consisted of the reaction of lignin with glyoxal under an alkaline medium. The influence of reaction conditions such as the molar ratio of sodium hydroxide-to-lignin and reaction time were studied relative to the properties of the prepared adducts. The analytical techniques used were FTIR and 1H-NMR spectroscopies, gel permeation chromatography (GPC), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). Results from both the FTIR and 1H-NMR spectroscopies showed that the amount of introduced aliphatic hydroxyl groups onto the lignin molecule increased with increasing reaction time and reached a maximum value at 10 h, and after they began to decrease. The molecular weights remained unchanged until 10 h of reaction time, and then started to increase, possibly due to the repolymerization reactions. DSC analysis showed that the glass transition temperature (Tg) decreased with the introduction of glyoxal onto the lignin molecule due to the increase in free volume of the lignin molecules. TGA analysis showed that the thermal stability of glyoxalated lignin is not influenced and remained suitable for wood adhesives. Compared to the original lignin, the improved lignin is reactive and a suitable raw material for adhesive formula

The Geographical Scope of Industrial Location Determinants: An Alternative Approach

This paper considers the estimation of the geographical scope of industrial location determinants. While previous studies impose strong assumptions on the weighting scheme of the spatial neighbour matrix, we propose a exible parametrisation that allows for di fferent (distance-based) de finitions of neighbourhood and di fferent weights to the neighbours. In particular, we estimate how far can reach indirect marginal e ffects and discuss how to report them. We also show that the use of smooth transition functions provides tools for policy analysis that are not available in the traditional threshold modelling. Keywords: count data models, industrial location, smooth transition functions, threshold models. JEL-Codes: C25, C52, R11, R30.

Feeding and the Equilibrium Feeder Animal Price-Weight Schedule, June 2005

The feeder animal price is a derivative in the sense that its value depends upon the price of animals for the consumption market. It also depends upon the biological growth technology and feed costs. Daily maintenance costs are of particular interest to the husbander because they can be avoided through accelerated feeding. In this paper, the optimal feeding path under equilibrium feeder animal prices is established. This analysis is used to gain a better understanding of feeding decisions, regulation in feedstuff markets, and the consequences of genetic innovations. It is shown that days on feed can increase or decrease with a genetic innovation or other improvement in feed conversion efficiency. The structure of comparative prices for feeder animals at different weights, the early slaughter decision, and equilibrium in feeder animal markets are also developed. Feeder animal prices can increase over a weight interval if biological feed efficiency parameters are low over the interval.

Limits on the validity of infinite length assumptions for modelling shallow landslides

The infinite slope method is widely used as the geotechnical component of geomorphic and landscape evolution models. Its assumption that shallow landslides are infinitely long (in a downslope direction) is usually considered valid for natural landslides on the basis that they are generally long relative to their depth. However, this is rarely justified, because the critical length/depth (L/H) ratio below which edge effects become important is unknown. We establish this critical L/H ratio by benchmarking infinite slope stability predictions against finite element predictions for a set of synthetic two-dimensional slopes, assuming that the difference between the predictions is due to error in the infinite slope method. We test the infinite slope method for six different L/H ratios to find the critical ratio at which its predictions fall within 5% of those from the finite element method. We repeat these tests for 5000 synthetic slopes with a range of failure plane depths, pore water pressures, friction angles, soil cohesions, soil unit weights and slope angles characteristic of natural slopes. We find that: (1) infinite slope stability predictions are consistently too conservative for small L/H ratios; (2) the predictions always converge to within 5% of the finite element benchmarks by a L/H ratio of 25 (i.e. the infinite slope assumption is reasonable for landslides 25 times longer than they are deep); but (3) they can converge at much lower ratios depending on slope properties, particularly for low cohesion soils. The implication for catchment scale stability models is that the infinite length assumption is reasonable if their grid resolution is coarse (e.g. >25?m). However, it may also be valid even at much finer grid resolutions (e.g. 1?m), because spatial organization in the predicted pore water pressure field reduces the probability of short landslides and minimizes the risk that predicted landslides will have L/H ratios less than 25. Copyright (c) 2012 John Wiley & Sons, Ltd.

Salience theory of choice under risk

We present a theory of choice among lotteries in which the decision maker's attention is drawn to (precisely defined) salient payoffs. This leads the decision maker to a context-dependent representation of lotteries in which true probabilities are replaced by decision weights distorted in favor of salient payoffs. By endogenizing decision weights as a function of payoffs, our model provides a novel and unified account of many empirical phenomena, including frequent risk-seeking behavior, invariance failures such as the Allais paradox, and preference reversals. It also yields new predictions, including some that distinguish it from Prospect Theory, which we test.

The alpha(1A/C)- and alpha(1B)-adrenergic receptors are required for physiological cardiac hypertrophy in the double-knockout mouse.

Catecholamines and alpha(1)-adrenergic receptors (alpha(1)-ARs) cause cardiac hypertrophy in cultured myocytes and transgenic mice, but heart size is normal in single KOs of the main alpha(1)-AR subtypes, alpha(1A/C) and alpha(1B). Here we tested whether alpha(1)-ARs are required for developmental cardiac hypertrophy by generating alpha(1A/C) and alpha(1B) double KO (ABKO) mice, which had no cardiac alpha(1)-AR binding. In male ABKO mice, heart growth after weaning was 40% less than in WT, and the smaller heart was due to smaller myocytes. Body and other organ weights were unchanged, indicating a specific effect on the heart. Blood pressure in ABKO mice was the same as in WT, showing that the smaller heart was not due to decreased load. Contractile function was normal by echocardiography in awake mice, but the smaller heart and a slower heart rate reduced cardiac output. alpha(1)-AR stimulation did not activate extracellular signal-regulated kinase (Erk) and downstream kinases in ABKO myocytes, and basal Erk activity was lower in the intact ABKO heart. In female ABKO mice, heart size was normal, even after ovariectomy. Male ABKO mice had reduced exercise capacity and increased mortality with pressure overload. Thus, alpha(1)-ARs in male mice are required for the physiological hypertrophy of normal postnatal cardiac development and for an adaptive response to cardiac stress.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Sistemática, filogenia e distribuição geográfica das espécies sul-americanas de Centris (Paracentris) Cameron, 1903 e de Centris (Penthemisia) Moure, 1950, incluindo uma análise filogenética do "grupo Centris" sensu Ayala, 1998 (Hymenoptera, Apoidea, Centridini)

Systematics, phylogeny and geographical distribution of the South American species of Centris (Paracentris) Cameron, 1903, and Centris (Penthemisia) Moure, 1950, including a phylogenetic analysis of the "Centris group" sensu Ayala, 1998 (Hymenoptera, Apoidea, Centridini). A cladistic analysis with the objective of testing the hypothesis of monophily of Centris (Paracentris) Cameron, 1903, and of studying its phylogenetic relationships with the other subgenera that belong to the Centris group, sensu Ayala, 1998, and the relationships among the species that occur in South America, is presented. Centris (Paracentris) is a group of New World bees of amphitropical distribution, especially diversified in the Andes and in the xeric areas of South and North America. Thirty-one species were included in the analysis, four considered as outgroup, and 49 characters, all from external morphology and genitalia of adult specimens. Parsimony analyses with equal weights for the characters and successive weighting were performed with the programs NONA and PAUP, and analyses of implied weighting with the program PeeWee. The strict consensus among the trees obtained in all the analyses indicates that C. (Paracentris), as previously recognized, is a paraphyletic group. In order to eliminate that condition, the subgenera C. (Acritocentris), C. (Exallocentris) and C. (Xerocentris), all described by SNELLING (1974) are synonymized under C. (Paracentris). The subgenus C. (Penthemisia) Moure, 1950, previously considered a synonym of C. (Paracentris), is reinstated, but in a more restricted sense than originally proposed and with the following species: Centris brethesi Schrottky, 1902; C. buchholzi Herbst, 1918; C. chilensis (Spinola, 1851), C. mixta mixta Friese, 1904, and C. mixta tamarugalis Toro & Chiappa, 1989. Centris mixta, previously recognized as the only South American species of the subgenus C. (Xerocentris), a group supposedly amphitropical, came out as the sister-species of C. buchholzi. The following South American species were recognized under Centris (Paracentris): Centris burgdorfi Friese, 1901; C. caelebs Friese, 1900; C. cordillerana Roig-Alsina, 2000; C. euphenax Cockerell, 1913; C. flavohirta Friese, 1900; C. garleppi (Schrottky, 1913); C. klugii Friese, 1900; C. lyngbyei Jensen-Haarup, 1908; C. mourei Roig-Alsina, 2000; C. neffi Moure, 2000; C. nigerrima (Spinola, 1851); C. toroi sp. nov.; C. tricolor Friese, 1900; C. unifasciata (Schrottky, 1913), and C. vogeli Roig-Alsina, 2000. The relationships among the subgenera of the "Centris group" were: (Xanthemisia (Penthemisia (Centris s. str. - Paracentris))). Centris xanthomelaena Moure & Castro 2001, an endemic species of the Caatinga and previously considered a C. (Paracentris), came out as the sister group of C. (Centris) s. str. A new species of C. (Paracentris) from Chile is described: Centris toroi sp. nov. Lectotypus designations and redescriptions are presented for Centris burgdorfi, C. caelebs, C. lyngbyei, C. tricolor, C. autrani Vachal, 1904 and C. smithii Friese, 1900. New synonyms proposed: C. buchholzi Herbst, 1918 = Centris wilmattae Cockerell, 1926 syn. nov.; C. caelebs Friese, 1900 = Paracentris fulvohirta Cameron, 1903. The female of C. vogeli Roig-Alsina, 2000 and the male of C. xanthomelaena are described.

The genetical theory of social behaviour.

We survey the population genetic basis of social evolution, using a logically consistent set of arguments to cover a wide range of biological scenarios. We start by reconsidering Hamilton's (Hamilton 1964 J. Theoret. Biol. 7, 1-16 (doi:10.1016/0022-5193(64)90038-4)) results for selection on a social trait under the assumptions of additive gene action, weak selection and constant environment and demography. This yields a prediction for the direction of allele frequency change in terms of phenotypic costs and benefits and genealogical concepts of relatedness, which holds for any frequency of the trait in the population, and provides the foundation for further developments and extensions. We then allow for any type of gene interaction within and between individuals, strong selection and fluctuating environments and demography, which may depend on the evolving trait itself. We reach three conclusions pertaining to selection on social behaviours under broad conditions. (i) Selection can be understood by focusing on a one-generation change in mean allele frequency, a computation which underpins the utility of reproductive value weights; (ii) in large populations under the assumptions of additive gene action and weak selection, this change is of constant sign for any allele frequency and is predicted by a phenotypic selection gradient; (iii) under the assumptions of trait substitution sequences, such phenotypic selection gradients suffice to characterize long-term multi-dimensional stochastic evolution, with almost no knowledge about the genetic details underlying the coevolving traits. Having such simple results about the effect of selection regardless of population structure and type of social interactions can help to delineate the common features of distinct biological processes. Finally, we clarify some persistent divergences within social evolution theory, with respect to exactness, synergies, maximization, dynamic sufficiency and the role of genetic arguments.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.