Race, Menopause, Health-Related Quality of Life, and Psychological Well-Being in Obese Women

Race, menopause, health-related quality of life, and psychological well-being in obese women. Obes Res. 2002;10:1270 –1275. Objective: To investigate the health-related quality of life (HR-QOL) in African-American (AA) and white (W) obese women. Research Methods and Procedures: Participants were 145 obese women (80 AA and 65 W; 87 premenopausal and 58 postmenopausal) who completed the Medical Outcomes Study short form, the Brief Symptom Inventory, the Life Distress Inventory, the Satisfaction With Life Scale, and the Rosenberg Self-Esteem Scale before entering a weight-loss study. The mean age of the subjects was 46.3   11.1 years and the mean body mass index was 35.2   4.2 kg/m2. Results: Although AA women were slightly heavier (95.3   10.3 kg vs. 91.5   11.6 kg, p   0.05) and less educated (14.2   3.7 years vs. 15.7   3.7 years, p   0.05) than the W women in the sample, there was no difference between the two ethnic groups in any of the reported HR-QOL variables. Menopausal status had a significant effect on HR-QOL, with premenopausal women being more distressed (p   0.002), having more limitations in social activity (p   0.007), and having less vitality (p   0.001) than the postmenopausal women. This was especially true in the AA women. Discussion: These data show no difference in HR-QOL between AA and W obese women and suggest that menopausal status may have an impact on HR-QOL, especially in AA women

Novel colours and the content of experience

I propose a counterexample to naturalistic representational theories of phenomenal character. The counterexample is generated by experiences of novel colours reported by Crane and Piantanida. I consider various replies that a representationalist might make, including whether novel colours could be possible colours of objects and whether one can account for novel colours as one would account for binary colours or colour mixtures. I argue that none of these strategies is successful and therefore that one cannot fully explain the nature of the phenomenal character of perceptual experiences using a naturalistic conception of representation

Influence of slag composition on the stability of steel in alkali-activated cementitious materials

Among the minor elements found in metallurgical slags, sulfur and manganese can potentially influence the corrosion process of steel embedded in alkali-activated slag cements, as both are redox-sensitive. Particularly, it is possible that these could significantly influence the corrosion process of the steel. Two types of alkali-activated slag mortars were prepared in this study: 100% blast furnace slag and a modified slag blend (90% blast furnace slag? 10% silicomanganese slag), both activated with sodium silicate. These mortars were designed with the aim of determining the influence of varying the redox potential on the stability of steel passivation under exposure to alkaline and alkaline chloride-rich solutions. Both types of mortars presented highly negative corrosion potentials and high current density values in the presence of chloride. The steel bars extracted from mortar samples after exposure do not show evident pits or corrosion product layers, indicating that the presence of sulfides reduces the redox potential of the pore solution of slag mortars, but enables the steel to remain in an apparently passive state. The presence of a high amount of MnO in the slag does not significantly affect the corrosion process of steel under the conditions tested. Mass transport through the mortar to the metal is impeded with increasing exposure time; this is associated with refinement of the pore network as the slag continued to react while the samples were immersed

Numerical continuation in nonlinear experiments using local Gaussian process regression

Control-based continuation (CBC) is a general and systematic method to probe the dynamics of nonlinear experiments. In this paper, CBC is combined with a novel continuation algorithm that is robust to experimental noise and enables the tracking of geometric features of the response surface such as folds. The method uses Gaussian process regression to create a local model of the response surface on which standard numerical continuation algorithms can be applied. The local model evolves as continuation explores the experimental parameter space, exploiting previously captured data to actively select the next data points to collect such that they maximise the potential information gain about the feature of interest. The method is demonstrated experimentally on a nonlinear structure featuring harmonically coupled modes. Fold points present in the response surface of the system are followed and reveal the presence of an isola, i.e. a branch of periodic responses detached from the main resonance peak

Iron: a target for the management of Kaposi's sarcoma?

BACKGROUND: Kaposi's sarcoma (KS) is a mesenchymal tumour associated with human herpesvirus-8 infection. However, the incidence of human herpesvirus-8 infection is far higher than the prevalence of KS, suggesting that viral infection per se is not sufficient for the development of malignancy and that one or more additional cofactors are required. DISCUSSION: Epidemiological data suggest that iron may be one of the cofactors involved in the pathogenesis of KS. Iron is a well-known carcinogen and may favour KS growth through several pathways. Based on the apoptotic and antiproliferative effect of iron chelation on KS cells, it is suggested that iron withdrawal strategies could be developed for the management of KS. Studies using potent iron chelators in suitable KS animal models are critical to evaluate whether iron deprivation may be a useful anti-KS strategy. SUMMARY: It is suggested that iron may be one of non-viral co-factors involved of KS pathogenesis and that iron withdrawal strategies might interfere with tumour growth in patients with KS

NICE : A Computational solution to close the gap from colour perception to colour categorization

The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms

Robust simplifications of multiscale biochemical networks

<p>Abstract</p> <p>Background</p> <p>Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.</p> <p>Results</p> <p>We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-<it>κ</it>B pathway.</p> <p>Conclusion</p> <p>Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.</p