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Orsay, France

Boulkroune A.,Jijel University | Tadjine M.,LCP | M'Saad M.,University of Caen Lower Normandy | Farza M.,University of Caen Lower Normandy
Fuzzy Sets and Systems | Year: 2010

This paper investigates fuzzy adaptive control schemes for a class of multi-input multi-output (MIMO) unknown nonlinear systems with known and unknown sign of the control gain matrix. Three fuzzy adaptive control schemes are developed. In the design of the second and third controller, we will exploit a decomposition of the control gain matrix into a symmetric positive-definite matrix, a diagonal matrix with diagonal entries + 1 or - 1 and a unity upper triangular matrix. The Nussbaum-type function is used to deal with the unknown control direction (i.e. the unknown sign of the control gain matrix). For updating the parameters of the fuzzy system, an adaptation proportional-integral (PI) law is proposed. Theoretical results are illustrated through two simulation examples. Crown Copyright © 2009. Source

Oucief N.,Jijel University | Tadjine M.,LCP | Labiod S.,Jijel University
Archives of Control Sciences | Year: 2016

Fault input channels represent a major challenge for observer design for fault estimation. Most works in this field assume that faults enter in such a way that the transfer functions between these faults and a number of measured outputs are strictly positive real (SPR), that is, the observer matching condition is satisfied. This paper presents a systematic approach to adaptive observer design for joint estimation of the state and faults when the SPR requirement is not verified. The proposed method deals with a class of Lipschitz nonlinear systems subjected to piecewise constant multiplicative faults. The novelty of the proposed approach is that it uses a rank condition similar to the observer matching condition to construct the adaptation law used to obtain fault estimates. The problem of finding the adaptive observer matrices is formulated as a Linear Matrix Inequality (LMI) optimization problem. The proposed scheme is tested on the nonlinear model of a single link flexible joint robot system. © 2016 Archives of Control Sciences. Source

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Membrane proteins are an important group of proteins that are present in any living organism. They are found on the surface of cell membranes (or even penetrate through them), and perform a number of functions, mainly associated with receiving signals or transporting substances (the membrane itself is impermeable to many molecules). For example, membrane proteins participate in signal transduction through nerve cells by restoring their condition after a nerve impulse has passed through, or reacting to adrenaline, causing the cells of our bodies to work harder under stress. Why do we need their crystals? In order to gain a better understanding of how proteins function, it is very important to know their molecular structure. It determines how a protein interacts with other molecules (e.g. adrenaline). If we know the structure of a protein, computer methods can be used to accurately select molecules that will interact with the protein. This will make it cheaper and faster to develop drugs—more than 60 percent of drugs use membrane proteins as a target. In order to determine the structure of a protein, the scientists first grow crystals of its molecules, then examine them under X-ray radiation and use the diffraction pattern to restore the structure of the protein—when arranged in a regular crystal lattice, all the atoms in a molecule greatly increase the radiation in particular directions, which enables scientists to precisely determine the location of the atoms. How are the crystals obtained? Two conditions are required to obtain protein crystals. First, it must be "more favourable" for the protein molecules to be in a crystal, rather than a solution. This is relatively simple to achieve—all that is required is to select the right components of the solution and their concentrations. Second, the protein molecules must be able to move freely in the solution, so that new molecules can bind to the growing crystal. If the protein is soluble, it is relatively simple to fulfill both of these conditions. However, if the protein is not soluble in water (this applies to all membrane proteins—their native environment is not a liquid, but a lipid membrane), then this can cause problems. Even if it is possible to remove the protein from its membrane and allow it move around freely in the solution, it will simply lose its shape when it comes into contact with the different environment (it is said that the protein becomes denatured). When this happens, it will of course no longer be possible to obtain any information about its "native" structure. In order to allow a protein molecule to move freely in a solution without losing its structure, scientists use a special environment—a lipidic cubic phase. The lipidic cubic phase (LCP) is a special three-dimensional structure formed by certain lipids (molecules that make up lipid membranes) at certain temperatures and in certain concentrations. In a solution, the cubic phase forms a complex, two-dimensional surface along which membrane proteins can reach the growing crystal without leaving the comfort of their membrane. It is similar to how people walk in a park—despite the fact that one-dimensional paths don't cover the entire park, they can be used to get to almost any point in the two-dimensional park. It is the same in this case; the only difference is that the surface of the cubic phase, which represents the "paths" for the membrane proteins, is two-dimensional, and the "park" is three-dimensional (the entire space). Using this structure, proteins can "travel" through a solution and provide new molecules for a growing crystal without leaving the comfort of their environment—the lipid membrane. It should be noted that the use of such a complex technique to obtain crystals has already demonstrated positive results. Out of all of the structures of membrane proteins that are currently known, crystals for obtaining 40 percent of them were made by crystallization in the lipidic cubic phase (or, as scientists call it, in meso crystallization). What did the authors of the paper do? In their paper, the authors studied the growth of crystals of bacteriorhodopsin (which was used as a model protein of any membrane protein) using fluorescence microscopy. Over the course of a month, the scientists observed the growth of the crystals and examined how the distribution of bacteriorhodopsin in the sample changed over time. It turned out that at the beginning, the crystals formed throughout the sample fairly evenly. However, after about a week, clear depletion zones began to form around the faster growing crystals. In these zones, there were only few small crystals, and the larger crystals were found only beyond these zones. IThe larger growing crystals not only took material from the solution in order to grow, they "consumed" their smaller, nearby counterparts. In addition, the scientists discovered that crystallization does not start at random points in the sample, but along the boundaries of areas in the shape of a honeycomb. "This pattern may have something to do with the fact that the cubic phase is not uniform throughout the sample, but forms small domains with boundaries where the likelihood of crystallization is greatest. It would appear that we have been able to observe these domains that, for an unknown reason, form "honeycomb" shapes. However, what is interesting is that if we learn how to control the size of these domains, we will be able to grow larger crystals, and consequently develop better and more accurate protein structures," said Dr. Valentin Borshchevskiy, the main author of the paper. According to data from the California Biomedical Research Organisation, the development of a completely new drug takes, on average, 12 years at a cost of about $2.6 billion. In 40 percent of cases, drug targets are GPCR membrane proteins. In the future, an understanding of how drug targets are structured will make it cheaper and faster to find drug molecules that act on them. It will also make it possible to develop drugs that act strictly on one type of receptor, which will reduce a drug's side effects. Explore further: New method for determining protein structure has major implications for drug development More information: Andrey Bogorodskiy et al. Nucleation and Growth of Membrane Protein Crystals —A Fluorescence Microscopy Study , Crystal Growth & Design (2015). DOI: 10.1021/acs.cgd.5b01061

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No statistical methods were used to predetermine sample size. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. The human M4 muscarinic receptor gene (http://www.cdna.org) was cloned into a modified pFastBac1 vector to give a receptor containing an N-terminal Flag epitope tag and a carboxy (C)-terminal 8× histidine tag. Residues 226–389 of ICL3 were removed and replaced by a minimal Cys-free T4 lysozyme fusion protein26. The human M1 muscarinic receptor gene was also cloned into the modified pFastBac1 vector, and residues 219–354 of ICL3 were removed and replaced by a Cys-free T4 lysozyme fusion protein. Both fusion proteins were expressed using the Bac-to-Bac Baculovirus Expression System (Invitrogen) in Sf9 cells. Cells were infected at a density of 4.0 × 106 to 5.0 × 106 cells per millilitre, treated with 10 μM atropine, and harvested at 60 h. Receptor was solubilized and purified in the presence of tiotropium as previously described for the M3 (ref. 22) receptor using Ni-NTA chromatography, Flag affinity chromatography, and size-exclusion chromatography. The N terminus of the M4 receptor was removed by cleavage with HRV 3C protease at a concentration of 2% (w/w) during concentration of the receptor before size-exclusion chromatography (~2 h at 4 °C). After size-exclusion chromatography, purified receptor was concentrated to 85 absorbance units (~50 mg ml−1) and flash frozen in small aliquots using liquid nitrogen. Sf9 cells expressing wild-type M4 or M4-mT4L receptor, as described above, were pelleted and washed with PBS three times for 1 h each to remove any bound atropine. Cells were resuspended in binding buffer (10 mM HEPES pH 7.5, 100 mM NaCl, and 10 mM MgCl ) and flash frozen with liquid nitrogen. Saturation binding assays were performed using approximately 20,000 cells per well with 9 different concentrations of [3H]NMS in a total volume of 0.5 ml for 3 h at 37 °C. Competition binding assays with acetylcholine and tiotropium were performed in the presence of a fixed concentration of [3H]NMS over 10 different concentrations of ligand for 3 h at 37 °C. Non-specific binding was measured in the presence of 10 μM atropine, and reactions were harvested by rapid filtration through GF/B filters. Data were analysed using Prism 6.0d. Similar methods were applied for binding assays using wild type M1 and M1–T4L, except that [3H]QNB was used as the radioligand. Purified M1-T4L•tiotropium and M4-mT4L•tiotropium were crystallized using lipid cubic phase technology. Each receptor was reconstituted by mixing the protein solution into 10:1 (w/w) monoolein:cholesterol (Sigma) in 1:1.5 parts w/w protein:lipid ratio using the two-syringe method24. For the M1 receptor, samples of 50 nl (20–40 nl for M4) were spotted onto 96-well glass plates and overlaid with 800 nl (600 nl for M4) of precipitant solution for each well using a Gryphon LCP (Art Robbins Instruments). Glass plates were then sealed using a glass cover film and incubated at 20 °C. Initial crystals for the M1 receptor formed after 24 h in conditions containing 33% PEG 300, 100 mM sodium acetate, and 100 mM Bis-Tris Propane (pH 8.0). For the M4 receptor, initial crystals formed after 24 h in conditions containing 25–40% PEG 300, 50–100 mM EDTA (pH 8.0), and 100 mM MES (pH 5.5–6.5). M1 and M4 crystals were harvested using mesh grid loops (MiTeGen) and stored in liquid nitrogen before use. X-ray diffraction data were collected at the Advanced Photon Source at Argonne National Laboratories at GM/CA beamline 23ID-D. Crystals were located by initial rastering using an 80 μm by 30 μm beam with fivefold attenuation and 1 s exposure. Regions that contained strong diffraction were then sub-rastered using a 10 μm collimated beam with fivefold attenuation. Data were then collected with the 10 μm beam using no attenuation with 1–2 s exposures and 1 degree oscillations. To prevent radiation damage, data were collected in wedges of 3–10° before moving onto either a different site on the same crystal or a new crystal. Diffraction data were processed using HKL2000 (M1 receptor) or XDS46 (M4 receptor) and statistics are summarized in Extended Data Table 1. Both structures were solved by molecular replacement using Phaser47. For the M1 receptor, the inactive M3 structure22 (PDB accession number 4DAJ) was split into its receptor and T4L components and used as corresponding search models. The refinement was performed using Refmac5 (ref. 48) with manual building in Coot49. For the M4 receptor, the inactive M2 structure23 (PDB accession number 3UON) and the inactive M3-mT4L26 (PDB accession number 4U15) were used as search models for the receptor and mT4L fusion domains, respectively. The resulting model was completed by iterative refinement in Phenix50 and manual building with Coot49. MolProbity51 was used for structure validation, and figures were prepared using PyMol52. Final refinement statistics are reported in Extended Data Table 1. The inactive state structures of M1, M2, M3 (PDB 4U15, chain B), and M4 (chain A) receptors were processed by the protein preparation wizard of the Schrodinger 2014-2 suite53, after deleting the lysozyme insertion region. Missing side chains were added by Prime and hydrogens refined by minimization with the OPLS2.1 force field. Binding grids were defined using the default settings in Glide, centring the grid on the crystallized orthosteric ligand in each case. The PEG ligand in the extracellular vestibule of M3 and M4 receptors was deleted before grid generation. The ligand, pirenzepine, was treated with ligprep software to generate initial protonated 3D structures. Compound structures were docked using the induced fit docking protocol with default settings, which involves the use of the OPLS_2005 force field to refine residues around poses docked by Glide SP, followed by redocking into the generated receptor conformations, also with Glide SP. The poses with the lowest induced fit score were selected. This scoring function takes into account an estimate of the protein conformational penalty along with a protein–ligand interaction docking score. A homology model of a human active-state M4 receptor was constructed using the Prime program implemented in Maestro version 2014.1 from Schrodinger. The crystal structure of the M2 receptor with an orthosteric and allosteric agonist bound (PDB accession number 4MQT) was used as a template to build the M4 model. The M2–M4 sequence alignment generated by Prime needed no adjustment owing to the overall significant sequence homology between the two isoforms. The initial M4 receptor model was built with the allosteric ligand (LY2119620) present in the M2 crystal structure bound in the M4 allosteric site and with iperoxo bound in the orthosteric site (as also present in the M2 structure). The binding mode of LY2119620 in M4 was used as a guide to manually dock LY2033298 into the M4 allosteric binding site. In addition, iperoxo from the M4 model was manually modified into acetylcholine (ACh). The M4-ACh-LY2033298 complex was then subjected to 500 steps of energy minimization (MacroModel implemented in Maestro 2014.1 from Schrödinger53) to optimize key interactions in the binding sites. The resulting model of ACh and LY2033298 bound to M4 was used in subsequent modelling studies described in this paper. The active state of the M1 receptor was modelled on the basis of the active state structure of M2 bound to iperoxo (PDB accession number 4MQT), using the automated protein structure homology modelling web server Swiss-Model54, 55. The nanobody structure was removed and the resulting coordinates were used as a template to model the M1 primary sequence without intracellular loop 3 residues (residues 213–240). The model was built using Promod-II, minimized by steepest descent energy minimization using a GROMOS96 force field and the quality was assessed by the QMEAN scoring function. ACh and LY2033298 were docked in the M1 homology model using Swiss-Dock56, using steric and chemical considerations such as shape, charge complimentary, and keeping the protein structure constant. The top-scoring clusters were evaluated manually on the basis of chemical and steric considerations to pick the favourable pose. Owing to static docking, the top four ACh poses did not affect the docking results for LY2033298. For ACh, the selected pose is in the trans conformation similar to the M4•ACh•LY2033298 model. Finally, the structures with the ligand were energy minimized using Chimera with standard Steepest Descent and Conjugate Gradient steps. DNA encoding the human M4 mAChR with a triple HA20 or cmyc21 tag at its N terminus was subjected to QuikChange site-directed mutagenesis (Stratagene) to generate M4 mAChR sequences with the desired amino-acid substitutions. DNA constructs in pEF5/frt/V5 (Invitrogen) were stably expressed in Flp-In-CHO cells (Invitrogen), which were maintained in high-glucose Dulbecco’s modified Eagle’s medium containing 10% FBS, 16 mM HEPES, and 400 μg ml−1 hygromycin B. Mycoplasma testing was performed regularly on cell lines using the MycoAlertTM kit (Lonza); cell lines were mycoplasma-free before experiments were conducted. Cell membranes were prepared as described previously14, 57. [3H]QNB affinity (K ) at the M4 WT receptor and mutants was determined by saturation binding assays, performed by incubating varying concentrations of [3H]QNB with 10–100 μg of membranes at 37 °C for 1 h, in a final volume of 0.5–1 ml binding buffer (20 mM HEPES, 100 mM NaCl, and 10 mM MgCl at pH 7.4). Radioligand inhibition binding assays were performed by co-incubating 10–100 μg of membranes with a K concentration of [3H]QNB (determined in saturation assays, Supplementary Table 2) and varying concentrations of the non-radiolabelled test compound in 0.5–1 ml binding buffer in the presence of the guanine nucleotide, GppNHp (100 μM), which was used to promote receptor/G-protein uncoupling. These experiments determined the concentration of ACh that inhibited 20% [3H]QNB binding, defined as the 20% inhibitory concentration (IC ), which was used in subsequent interaction studies between [3H]QNB, ACh, and LY2033298. These experiments were performed by co-incubating 10–100 μg of membranes, an IC concentration of ACh, and a K concentration of [3H]QNB with increasing concentrations of LY2033298 in binding buffer containing GppNHp (100 μM). The reaction was left to reach equilibrium for 3 h at 37 °C. For all experiments, non-specific binding was defined in the presence of 10 μM atropine, total binding was determined in the absence of the test ligand, and vehicle effects were determined with 0.1% dimethylsulfoxide (DMSO). The assays were terminated by vacuum filtration through GF-B glass fibre filters, which were washed three times with ice-cold 0.9% NaCl. [3H]QNB radioactivity was measured using a Packard 1600 TR liquid scintillation beta counter. Owing to a lack [3H]QNB binding, affinity data for W164A4.57 were determined from functional pERK1/2 experiments performed as previously described20, 21. Data were analysed using Prism (GraphPad). For radioligand saturation binding, non-specific and total binding data were analysed as described previously58. Inhibition binding curves between [3H]QNB and ACh were fitted to a one-site binding model58. Interaction experiments between [3H]QNB, ACh, and LY2033298 were fitted to the following allosteric ternary complex model20, 21, 59: where Y is the specific radioligand binding, B is the total number of receptors, [A], [B], and [I] are the concentrations of radioligand, allosteric modulator, and unlabelled orthosteric ligand, respectively, K , K , and K are the equilibrium dissociation constants of the radioligand, allosteric modulator, and unlabelled orthosteric ligand, respectively, and α′ and α are the cooperativity factors between allosteric modulator and the radioligand or unlabelled orthosteric ligand, respectively. The value of α′ was taken as 1 when the binding of [3H]QNB changed by less than 10% at 10−5 M LY2033298 relative to zero LY2033298, and was fixed as such for all analyses. Otherwise, the value of α′ was determined using a global fit to the allosteric ternary complex model. Statistical differences between pharmacological parameters at wild-type versus mutant M4 receptors were determined by one-way analysis of variance with Dunnett’s post hoc test, where P < 0.01 was considered statistically significant.

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Dr. Jay Boris, Chief Scientist for Computational Physics, working in the Laboratories for Computational Physics and Fluid Dynamics at the U.S. Naval Research Laboratory (NRL), has received the Numa Manson Medal for distinguished contributions to the dynamics of explosions and reactive systems. Established in 1975 by the Institute for the Dynamics of Explosions and Reactive Systems, the award recognizes mature scientists whom are distinguished by lifelong accomplishments elucidating the prominent features of the dynamics of explosions and reactive systems. Dr. Boris was recognized for Flux-Corrected Transport (FCT) and Monotone Integrated Large Eddy Simulation (MILES), theoretical and numerical techniques that he developed at NRL. These reactive flow techniques have been instrumental in uncovering key aspects of the dynamics of explosions and reactive systems over the past three decades by scientists at NRL and around the World and have also allowed NRL to develop the instant-response CT-Analyst model for urban defense against airborne weapons mass destruction. Dr. Boris plans and leads research on advanced analytical and numerical capabilities and their engineering applications to solve problems vital to the Department of Navy (DoN), the Department of Defense (DoD), and the nation. His responsibilities include the development of advanced computing architectures for parallel processing and the applied mathematics relevant to creating unique new solution methods. A Charter Member of the Senior Executive Service (SES) since 1979, Dr. Boris has been a member of the civil service for 44 years. He was Director of the Laboratory for Computational Physics and Fluid Dynamics, from 1978 to 2011 when he converted to a Scientific and Technical (ST) position and then attached as NRL Chief Scientist, to the Laboratories for Computational Physics and Fluid Dynamics (LCP&FD). Boris joined the NRL in 1971 as a senior consultant in the Plasma Physics Division. From 1975 to 1978 he was head of NRL's Plasma Dynamics Branch. He received the bachelor's degree in physics (1964) and master's and Ph.D. degrees in astrophysical sciences (1968) from Princeton University. He then joined Princeton's Plasma Physics Laboratory before coming to NRL. About the U.S. Naval Research Laboratory The U.S. Naval Research Laboratory provides the advanced scientific capabilities required to bolster our country's position of global naval leadership. The Laboratory, with a total complement of approximately 2,500 personnel, is located in southwest Washington, D.C., with other major sites at the Stennis Space Center, Miss., and Monterey, Calif. NRL has served the Navy and the nation for over 90 years and continues to advance research further than you can imagine. For more information, visit the NRL website or join the conversation on Twitter, Facebook, and YouTube.

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