Measuring cell adhesion forces with the atomic force microscope at the molecular level

In the past 25 years many techniques have been developed to characterize cell adhesion and to quantify adhesion forces. Atomic force microscopy (AFM) has been used to measure forces in the pico-newton range, an experimental technique known as force spectroscopy. We modified such an AFM to measure adhesion forces between live cells or between cells and surfaces. This strategy required functionalizing the surface of the sensors for immobilizing the cell. We used Dictyostelium discoideum cells which respond to starvation by surface expression of the adhesion molecule csA and consequent aggregation to measure the adhesion force of a single csA-csA bond. Relevant experimental parameters include the duration of contact between the interacting surfaces, the force against which this contact is maintained, the number and specificity of interacting adhesion molecules and the constituents of the medium in which the interaction occurs. This technology also permits the measurement of the viscoelastic properties of single cells or cell layers. Copyright (C) 2002 S, Karger AG, Basel

Half-space theorems for minimal surfaces in Nil_3 and Sol_3

We prove some half-space theorems for minimal surfaces in the Heisenberg group Nil_3 and the Lie group Sol_3 endowed with their left-invariant Riemannian metrics. If S is a properly immersed minimal surface in Nil_3 that lies on one side of some entire minimal graph G, then S is the image of G by a vertical translation. If S is a properly immersed minimal surface in Sol_3 that lies on one side of a special plane, then S is another special plane.Comment: 19 pages, 3 figure

Firm-Level Investment in France and the United States: An Exploration of What We Have Learned in Twenty Years

Our two related goals in this paper are the following: Firstly and mainly, we want to examine the effects of major changes in modelling strategy and econometric methodology, over the past twenty years, on estimation of firm-level investment equations using panel data. Secondly, we try to assess whether the differences in the estimated investment equations, as between recent years and ten to twenty years go in the French and U.S. Manufacturing industries, are real' and economically meaningful. Thus our paper consists of a series of comparisons: a simple accelerator-profit specification versus one with error correction, traditional between- and within-firm estimation versus GMM estimation, the investment behavior of French firms versus that of U.S. firms, and investment behavior in recent years versus ten to twenty years ago. Although the important econometric advances of the past twenty years have been far from being as successful as we had hoped for, we do find some significant improvement in the specification, estimation and interpretation of firm investment equations; we also fin some real changes in the investment behavior of French and U.S. firms during these twenty years.

Assessing functional novelty of PSI structures via structure-function analysis of large and diverse superfamilies

The structural genomics initiatives have had as one of their aims to improve our understanding of protein function by providing representative structures for many structurally uncharacterised protein families. As suggested by the recent assessment of the Protein Structure Initiative (Structural Genomics Initiative, funded by the NIH), doubts have arisen as to whether Structural Genomics as initially planned were really beneficial to our understanding of biological issues, and in particular of protein function.
A few protein domain superfamilies have been shown to account for unexpectedly large numbers of proteins encoded in fully sequenced genomes. These large superfamilies are generally very diverse, spanning a wide range of functions, both in terms of molecular activities and biological processes. Some of these superfamilies, such as the Rossmann-fold P-loop nucleotide hydrolases or the TIM-barrel glycosidases, have been the subject of extensive structural studies which in turn have shed light on how evolution of the sequence and structure properties produce functional diversity amongst homologues. Recently, the Structure-Function Linkage Database (SFLD) has been setup with the aim of helping the study of structure-function correlations in such superfamilies. Since the evolutionary success of these large superfamilies suggests biological importance, several Structural Genomics Centers have focused on providing full structural coverage for representatives of all sequence families in these superfamilies.
In this work we evaluate structure/function diversity in a set of these large superfamilies and attempt to assess the quality and quantity of biological information gained from Structural Genomics.
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Firm Level Investment in France and the United States: An Exploration of What We Have Learned in Twenty Years

We review the changes in modelling strategy and econometric methodology when estimating a firm-level investment equation on panel data during the past twenty years, in order to assess which of these changes result from new estimation methods and changes in the practice of panel data econometrics, and which are "real" and due to the evolution of the economy. Thus our paper consists of a series of comparisons: a simple accelerator-profit specification versus one with error correction, traditional between- and within-firm estimation versus GMM estimation, the investment behavior of French firms versus that of U.S. firms, and investment behavior today versus ten to twenty years ago. Although the econometric advances have perhaps not been as successful as we had hoped, we do find some real change in firm behavior and some improvement in equation specification and interpretation during the past twenty years.investment, panel data, GMM, international comparisons, firm- level

Estimation of Space-Time Varying Parameters Using a Diffusion LMS Algorithm

We study the problem of distributed adaptive estimation over networks where nodes cooperate to estimate physical parameters that can vary over both space and time domains. We use a set of basis functions to characterize the space-varying nature of the parameters and propose a diffusion least mean-squares (LMS) strategy to recover these parameters from successive time measurements. We analyze the stability and convergence of the proposed algorithm, and derive closed-form expressions to predict its learning behavior and steady-state performance in terms of mean-square error. We find that in the estimation of the space-varying parameters using distributed approaches, the covariance matrix of the regression data at each node becomes rank-deficient. Our analysis reveals that the proposed algorithm can overcome this difficulty to a large extent by benefiting from the network stochastic matrices that are used to combine exchanged information between nodes. We provide computer experiments to illustrate and support the theoretical findings.Comment: IEEE Transaction on Signal Processing, Oct. 201

Validating Semi-Analytic Models of High-Redshift Galaxy Formation using Radiation Hydrodynamical Simulations

We use a cosmological hydrodynamic simulation calculated with Enzo and the semi-analytic galaxy formation model (SAM) GAMMA to address the chemical evolution of dwarf galaxies in the early universe. The long-term goal of the project is to better understand the origin of metal-poor stars and the formation of dwarf galaxies and the Milky Way halo by cross-validating these theoretical approaches. We combine GAMMA with the merger tree of the most massive galaxy found in the hydrodynamic simulation and compare the star formation rate, the metallicity distribution function (MDF), and the age-metallicity relationship predicted by the two approaches. We found that the SAM can reproduce the global trends of the hydrodynamic simulation. However, there are degeneracies between the model parameters and more constraints (e.g., star formation efficiency, gas flows) need to be extracted from the simulation to isolate the correct semi-analytic solution. Stochastic processes such as bursty star formation histories and star formation triggered by supernova explosions cannot be reproduced by the current version of GAMMA. Non-uniform mixing in the galaxy's interstellar medium, coming primarily from self-enrichment by local supernovae, causes a broadening in the MDF that can be emulated in the SAM by convolving its predicted MDF with a Gaussian function having a standard deviation of ~0.2 dex. We found that the most massive galaxy in the simulation retains nearby 100% of its baryonic mass within its virial radius, which is in agreement with what is needed in GAMMA to reproduce the global trends of the simulation.Comment: 26 pages, 13 figures, 2 tables, submitted to ApJ (version 2

Induced folding in RNA recognition by Arabidopsis thaliana DCL1

DCL1 is the ribonuclease that carries out miRNA biogenesis in plants. The enzyme has two tandem double stranded RNA binding domains (dsRBDs) in its C-terminus. Here we show that the first of these domains binds precursor RNA fragments when isolated and cooperates with the second domain in the recognition of substrate RNA. Remarkably, despite showing RNA binding activity, this domain is intrinsically disordered. We found that it acquires a folded conformation when bound to its substrate, being the first report of a complete dsRBD folding upon binding. The free unfolded form shows tendency to adopt folded conformations, and goes through an unfolded bound state prior to the folding event. The significance of these results is discussed by comparison with the behavior of other dsRBDs.Fil: Suarez, Irina Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Burdisso, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Benoit Matthieu P. M. H.. Institut de Biologie Structurale Jean Pierre Ebel; FranciaFil: Boisbouvier, Jerome. Institut de Biologie Structurale Jean Pierre Ebel; FranciaFil: Rasia, Rodolfo Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentin

Benchmarking network propagation methods for disease gene identification

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genesPeer ReviewedPostprint (published version