Caveolar disruption causes contraction of rat femoral arteries via reduced basal NO release and subsequent closure of BKCa channels

Background and Purpose. Caveolae act as signalling hubs in endothelial and smooth muscle cells. Caveolar disruption by the membrane cholesterol depleting agent methyl-β-cyclodextrin (M-β-CD) has various functional effects on arteries including (i) impairment of endothelium-dependent relaxation, and (ii) alteration of smooth muscle cell (SMC) contraction independently of the endothelium. The aim of this study was to explore the effects of M-β-CD on rat femoral arteries.Methods. Isometric force was measured in rat femoral arteries stimulated to contract with a solution containing 20 mM K+ and 200 nM Bay K 8644 (20 K/Bay K) or with one containing 80 mM K+(80 K).Results. Incubation of arteries with M-β-CD (5 mM, 60 min) increased force in response to 20 K/Bay K but not that induced by 80 K. Application of cholesterol saturated M-β-CD (Ch-MCD, 5 mM, 50 min) reversed the effects of M-β-CD. After mechanical removal of endothelial cells M-β-CD caused only a small enhancement of contractions to 20 K/Bay K. This result suggests M-β-CD acts via altering release of an endothelial-derived vasodilator or vasoconstrictor. When nitric oxide synthase was blocked by pre-incubation of arteries with L-NAME (250 µM) the contraction of arteries to 20 K/Bay K was enhanced, and this effect was abolished by pre-treatment with M-β-CD. This suggests M-β-CD is inhibiting endothelial NO release. Inhibition of large conductance voltage- and Ca2+-activated (BKCa) channels with 2 mM TEA+ or 100 nM Iberiotoxin (IbTX) enhanced 20 K/Bay K contractions. L-NAME attenuated the contractile effect of IbTX, as did endothelial removal.Conclusions. Our results suggest caveolar disruption results in decreased release of endothelial-derived nitric oxide in rat femoral artery, resulting in a reduced contribution of BKCa channels to the smooth muscle cell membrane potential, causing depolarisation and contraction

Fairway: A Way to Build Fair ML Software

Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a biased manner that gives undue advantages to a specific group of people (where those groups are determined by sex, race, etc.). This "algorithmic discrimination" in the AI software systems has become a matter of serious concern in the machine learning and software engineering community. There have been works done to find "algorithmic bias" or "ethical bias" in the software system. Once the bias is detected in the AI software system, the mitigation of bias is extremely important. In this work, we a)explain how ground-truth bias in training data affects machine learning model fairness and how to find that bias in AI software,b)propose a methodFairwaywhich combines pre-processing and in-processing approach to remove ethical bias from training data and trained model. Our results show that we can find bias and mitigate bias in a learned model, without much damaging the predictive performance of that model. We propose that (1) test-ing for bias and (2) bias mitigation should be a routine part of the machine learning software development life cycle. Fairway offers much support for these two purposes.Comment: ESEC/FSE'20: The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineerin

Estimation of Phonon Dispersion Relations Using Correlation Effects Among Thermal Displacements of Atoms

Neutron diffraction measurement of powder α-Fe sample at 295 K was carried out at the high resolution powder diffractometer installed at Japan Proton Accelerator Research Complex (J-PARC). Crystal parameters were determined from Rietveld analysis. The correlation effects among thermal displacements of atoms were estimated from a generalized equation based on the results of fomer diffuse scattering analysis. The force constants among atoms were obtained using an equation for transforming of the correlation effects to force constants. The force constants and the crystal structure of α-Fe were used to estimate the phonon dispersion relations, phonon density of states, and specific heat by computer simulation. The obtained force constants among first-nearest-neighboring atoms is 2.3 eV/Å2 at 295 K and the specific heat is 185 meV/K at 150 K. The calculated phonon dispersion relations and specific heat of α-Fe are similar to those obtained from inelastic neutron scattering and specific heat measurements, respectively. Received: 04 October 2014; Revised: 22 January 2015; Accepted: 30 March 201

Estimation of Phonon Dispersion Relations Using Correlation Effects Among Thermal Displacements of Atoms

Neutron diffraction measurement of powder α-Fe sample at 295 K was carried out at the high resolution powder diffractometer installed at Japan Proton Accelerator Research Complex (J-PARC). Crystal parameters were determined from Rietveld analysis. The correlation effects among thermal displacements of atoms were estimated from a generalized equation based on the results of fomer diffuse scattering analysis. The force constants among atoms were obtained using an equation for transforming of the correlation effects to force constants. The force constants and the crystal structure of α-Fe were used to estimate the phonon dispersion relations, phonon density of states, and specific heat by computer simulation. The obtained force constants among first-nearest-neighboring atoms is 2.3 eV/Å2 at 295 K and the specific heat is 185 meV/K at 150 K. The calculated phonon dispersion relations and specific heat of α-Fe are similar to those obtained from inelastic neutron scattering and specific heat measurements, respectively. Received: 04 October 2014; Revised: 22 January 2015; Accepted: 30 March 201

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web Conference (WWW), 2017. Code available at: https://github.com/mbilalzafar/fair-classificatio

Estimation of Phonon Dispersion Relations Using Correlation Effects Among Thermal Displacements of Atoms

Neutron diffraction measurement of powder α-Fe sample at 295 K was carried out at the high resolution powder diffractometer installed at Japan Proton Accelerator Research Complex (J-PARC). Crystal parameters were determined from Rietveld analysis. The correlation effects among thermal displacements of atoms were estimated from a generalized equation based on the results of fomer diffuse scattering analysis. The force constants among atoms were obtained using an equation for transforming of the correlation effects to force constants. The force constants and the crystal structure of α-Fe were used to estimate the phonon dispersion relations, phonon density of states, and specific heat by computer simulation. The obtained force constants among first-nearest-neighboring atoms is 2.3 eV/Å2 at 295 K and the specific heat is 185 meV/K at 150 K. The calculated phonon dispersion relations and specific heat of α-Fe are similar to those obtained from inelastic neutron scattering and specific heat measurements, respectivel

Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality

As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.Comment: 7 pages, 2 figures, 2 tables.To appear in Proceedings of the International Conference on World Wide Web (WWW), 201

Bioenergetic profile of human coronary artery smooth muscle cells and effect of metabolic intervention

Bioenergetics of artery smooth muscle cells is critical in cardiovascular health and disease. An acute rise in metabolic demand causes vasodilation in systemic circulation while a chronic shift in bioenergetic profile may lead to vascular diseases. A decrease in intracellular ATP level may trigger physiological responses while dedifferentiation of contractile smooth muscle cells to a proliferative and migratory phenotype is often observed during pathological processes. Although it is now possible to dissect multiple building blocks of bioenergetic components quantitatively, detailed cellular bioenergetics of artery smooth muscle cells is still largely unknown. Thus, we profiled cellular bioenergetics of human coronary artery smooth muscle cells and effects of metabolic intervention. Mitochondria and glycolysis stress tests utilizing Seahorse technology revealed that mitochondrial oxidative phosphorylation accounted for 54.5% of ATP production at rest with the remaining 45.5% due to glycolysis. Stress tests also showed that oxidative phosphorylation and glycolysis can increase to a maximum of 3.5 fold and 1.25 fold, respectively, indicating that the former has a high reserve capacity. Analysis of bioenergetic profile indicated that aging cells have lower resting oxidative phosphorylation and reduced reserve capacity. Intracellular ATP level of a single cell was estimated to be over 1.1 mM. Application of metabolic modulators caused significant changes in mitochondria membrane potential, intracellular ATP level and ATP:ADP ratio. The detailed breakdown of cellular bioenergetics showed that proliferating human coronary artery smooth muscle cells rely more or less equally on oxidative phosphorylation and glycolysis at rest. These cells have high respiratory reserve capacity and low glycolysis reserve capacity. Metabolic intervention influences both intracellular ATP concentration and ATP:ADP ratio, where subtler changes may be detected by the latter

Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models

International audienceWe present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through user actions or behaviors. Our task is to infer the groups of users as well as their latent behavioral functions. We also propose a non-parametric version based on a Dirichlet Process to automatically infer the number of clusters. We test the properties and performance of the model on a synthetic dataset that represents the participation of users in the threads of an online forum. Experiments show that dual-view models outperform single-view ones when one of the views lacks information