The endothelial glycocalyx prefers albumin for evoking shear stress-induced, nitric oxide-mediated coronary dilatation

Background: Shear stress induces coronary dilatation via production of nitric oxide ( NO). This should involve the endothelial glycocalyx ( EG). A greater effect was expected of albumin versus hydroxyethyl starch ( HES) perfusion, because albumin seals coronary leaks more effectively than HES in an EG-dependent way. Methods: Isolated hearts ( guinea pigs) were perfused at constant pressure with Krebs-Henseleit buffer augmented with 1/3 volume 5% human albumin or 6% HES ( 200/0.5 or 450/0.7). Coronary flow was also determined after EG digestion ( heparinase) and with nitro-L-arginine ( NO-L-Ag). Results: Coronary flow ( 9.50 +/- 1.09, 5.10 +/- 0.49, 4.87 +/- 1.19 and 4.15 +/- 0.09 ml/ min/ g for `albumin', `HES 200', `HES 450' and `control', respectively, n = 5-6) did not correlate with perfusate viscosity ( 0.83, 1.02, 1.24 and 0.77 cP, respectively). NO-L-Ag and heparinase diminished dilatation by albumin, but not additively. Alone NO-L-Ag suppressed coronary flow during infusion of HES 450. Electron microscopy revealed a coronary EG of 300 nm, reduced to 20 nm after heparinase. Cultured endothelial cells possessed an EG of 20 nm to begin with. Conclusions: Albumin induces greater endothelial shear stress than HES, despite lower viscosity, provided the EG contains negative groups. HES 450 causes some NO-mediated dilatation via even a rudimentary EG. Cultured endothelial cells express only a rudimentary glycocalyx, limiting their usefulness as a model system. Copyright (c) 2007 S. Karger AG, Basel

Organizational Mortality of Small Firms: The Effects of Entrepreneurial Age and Human Capital

This paper addresses the issue of internal determination of organizational outcomes. It is argued that in small and simply structured organizations a considerable proportion of the variance in organizational activities and outcomes is associated with individuals. In particular, the paper uses human capital theory to derive hypotheses about individual determinants of organizational mortality. These hypotheses are tested with event-history data of firm registrations and de-registrations in a West German region. The hypotheses are corroborated by the data, but the effects may nonetheless be due to processes linking individual characteristics with organizational performance other than those suggested by the human capital approach

Higher Education as modulator of gender inequalities: Evidence of the Spanish case

Raising educational levels may help to reduce inequalities between men and women in certain social and economic aspects. Using statistics for Spain, we analyse labour market behaviours such as the rates of activity and unemployment by sex according to the educational level. The results reveal that the differences between men and women decrease as the educational level increases. In particular, the modulator effect of education is very important at the higher level, where differences in labour market behaviour between men and women with an university education almost disappear, except in terms of salaries. Nevertheless, it can be seen that the current economic crisis has reduced the modulator role of education in gender differences in Spain

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates and can be decomposed physically, but both physics and machine learning ensembles are computationally expensive. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but do not account for epistemic uncertainty.. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to those obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning

An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links

Background: Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods - which utilize a knowledge network derived from biological knowledge - have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes. Results: We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone. Conclusions: The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization. © 2013 Kimmel, Visweswaran

Mystify me: Coke, terror and the symbolic immortality boost

A panel on “Marketing as Mystification” convened at the 2011 Academy of Marketing conference in Liverpool. Ideas from the Liverpool event were supplemented by commentaries from selected other authors. Each commentary explores the aspects of “mystification” observable in marketing discourses and practices. In what follows, Laufer interprets marketing mystification as modern form of sophism, Dholakia and Firat discuss mystifying ways that inequality is marketed, Varman analyzes the perversion and mystification of “development” via neoliberal marketing of “social entrepreneurship,” Mikkonen explores mystifying marketing representations of gays and lesbians, and Freund and Jacobi present a fascinating interpretation of how Coca-Cola advertising mystically reassures us that our difficult, dangerous lifeworld is actually quite hunky-dory. </jats:p