A method to quantitatively evaluate Hamaker constant using the jump-into-contact effect in Atomic Force microscopy

We find that the jump-into-contact of the cantilever in the atomic force microscope (AFM) is caused by an inherent instability in the motion of the AFM cantilever. The analysis is based on a simple model of the cantilever moving in a nonlinear force field. We show that the jump-into-contact distance can be used to find the interaction of the cantilever tip with the surface. In the specific context of the attractive van der Waals interaction, this method can be realized as a new method of measuring the Hamaker constant for materials. The Hamaker constant is determined from the deflection of the cantilever at the jump-into-contact using the force constant of the cantilever and the tip radius of curvature, all of which can be obtained by measurements. The results have been verified experimentally on a sample of cleaved mica, a sample of Si wafer with natural oxide and a silver film, using a number of cantilevers with different spring constants. We emphasize that the method described here is applicable only to surfaces that have van der Waals interaction as the tip-sample interaction. We also find that the tip to sample separation at the jump-into-contact is simply related to the cantilever deflection at this point, and this provides a method to exactly locate the surface.Comment: 11 pages, 4 figures, 1 tabl

Kinin B(1) receptor deficiency leads to leptin hypersensitivity and resistance to obesity

OBJECTIVE-Kinins mediate pathophysiological processes related to hypertension, pain, and inflammation through the activation of two G-protein-coupled receptors, named B(1) and B(2). Although these peptides have been related to glucose homeostasis, their effects on energy balance are still unknown.RESEARCH DESIGN and METHODS-Using genetic and pharmacological strategies to abrogate the kinin B(1) receptor in different animal models of obesity, here we present evidence of a novel role for kinins in the regulation of satiety and adiposity.RESULTS-Kinin B(1) receptor deficiency in mice (B(1)(-/-)) resulted in less fat content, hypoleptinemia, increased leptin sensitivity, and robust protection against high-fat diet-induced weight gain. Under high-fat diet, B(1)(-/-) also exhibited reduced food intake, improved lipid oxidation, and increased energy expenditure. Surprisingly, B(1) receptor deficiency was not able to decrease food intake and adiposity in obese mice lacking leptin (ob/ob-B(1)(-/-)). However, ob/ob-B(1)(-/-) mice were more responsive to the effects of exogenous leptin on body weight and food intake, suggesting that B(1) receptors may be dependent on leptin to display their metabolic roles. Finally, inhibition of weight gain and food intake by B(1) receptor ablation was pharmacologically confirmed by long-term administration of the kinin B(1) receptor antagonist SSR240612 to mice under high-fat diet.CONCLUSIONS-Our data suggest that kinin B(1) receptors participate in the regulation of the energy balance via a mechanism that could involve the modulation of leptin sensitivity.Universidade Federal de São Paulo, Dept Biophys, BR-04023062 São Paulo, BrazilUniv Mogi das Cruzes, Mogi Das Cruzes, BrazilUniversidade Federal de São Paulo, Dept Physiol, BR-04023062 São Paulo, BrazilSanofi Aventis, Montpellier, FranceUniversidade Federal de São Paulo, Dept Med, BR-04023062 São Paulo, BrazilInst Natl Sante & Rech Med, Dept Renal & Cardiac Remodeling, U858 I2MR, Toulouse, FranceUniv Toulouse 3, Inst Med Mol Rangueil, F-31062 Toulouse, FranceInst Natl Rech Agron AgroParisTech, UMR914 Nutr Physiol & Ingest Behav, Paris, FranceMax Delbruck Ctr Mol Med, Berlin, GermanyUniversidade Federal de São Paulo, Dept Biophys, BR-04023062 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Physiol, BR-04023062 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Med, BR-04023062 São Paulo, BrazilWeb of Scienc

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.Comment: 16 pages, 5 figure

OpenMM 8:Molecular Dynamics Simulation with Machine Learning Potentials

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.</p

A New Unifac Parameterization For The Prediction Of Liquid-liquid Equilibrium Of Biodiesel Systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The environmental adversities and the global concern about the conservation of non-renewable natural resources have stimulated a search for environmentally friendly energy sources. In this context, biodiesel has emerged as an important alternative to replace fossil fuels, due to its renewability, non-toxicity and biodegradability. Modeling, simulation and design of unit operations involved in the production of edible oils and biodiesel require knowledge of phase equilibrium. Several versions of the UNIFAC model are frequently used for process design when experimental determination of phase equilibrium data is difficult or time-consuming. In this work, the original UNIFAC model parameters are first checked for their predictive capability and then modified in terms of new readjusted binary interaction parameters. It was noted that the UNIFAC model without any changes in its parameters results in inadequate predictions. Thus, in order to obtain a good predictive tool, a comprehensive liquid-liquid equilibrium data bank of systems present in biodiesel production was organized and new UNIFAC interaction parameters were adjusted. At first, the molecules were divided into UNIFAC traditional structural groups. However, this first approach resulted in poor prediction, probably as a consequence of the strongly polar hydroxyl groups bonded to the consecutive carbon atoms of glycerol and acylglycerol molecules. Thus, a new group ('OHgly') was introduced and two matrices of parameters were adjusted. In general, satisfactory predictions were obtained and a significant improvement in the "performance of this group contribution model has been achieved. (C) 2016 Elsevier B.V. All rights reserved.42598107CAPESFAPESP [08/56258-8, 09/54137-1]FAEPEX/UNICAMPCNPq [483340/2012-0, 406856/2013-3, 305870/2014-9, 309780/2014-4]Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq