Biophysical characterization of the outer membrane polysaccharide export protein and the polysaccharide co-polymerase protein from Xanthomonas campestris

This study investigated the structural and biophysical characteristics of GumB and GumC, two Xanthomonas campestris membrane proteins that are involved in xanthan biosynthesis. Xanthan is an exopolysaccharide that is thought to be a virulence factor that contributes to bacterial in planta growth. It also is one of the most important industrial biopolymers. The first steps of xanthan biosynthesis are well understood, but the polymerization and export mechanisms remain unclear. For this reason, the key proteins must be characterized to better understand these processes. Here we characterized, by biochemical and biophysical techniques, GumB, the outer membrane polysaccharide export protein, and GumC, the polysaccharide co-polymerase protein of the xanthan biosynthesis system. Our results suggested that recombinant GumB is a tetrameric protein in solution. On the other hand, we observed that both native and recombinant GumC present oligomeric conformation consistent with dimers and higher-order oligomers. The transmembrane segments of GumC are required for GumC expression and/or stability. These initial results provide a starting point for additional studies that will clarify the roles of GumB and GumC in the xanthan polymerization and export processes and further elucidate their functions and mechanisms of action.Fil: Bianco, María Isabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Jacobs, Melisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Salinas, Silvina Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Salvay, Andrés Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Ielmini, M. V.. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Ielpi, Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentin

Compact Modeling and Mitigation of Parasitics in Crosspoint Accelerators of Neural Networks

In-memory computing (IMC) can accelerate data-intensive tasks, such as matrix-vector multiplication (MVM) or artificial neural networks (ANNs) inference, by means of the crosspoint memory array, allowing to reduce time and energy consumption. IMC accuracy, however, is affected by nonidealities, such as variability of the conductive weights or IR drop along wires due to parasitic resistances, whose impact steeply increases with the increase of array size. This work proposes a compact model to assess the impact of nonidealities for various circuital implementations, together with architectural schemes for their mitigation based on replicated arrays. The proposed mitigation techniques allow to restore the ANN accuracy from 72.7% to 94.9%, close to the software accuracy of 96.9%, in view of an increased area and energy consumption

Forming-Free Resistive Switching Memory Crosspoint Arrays for In-Memory Machine Learning

In-memory computing (IMC) with crosspoint arrays of resistive switching memory (RRAM) has gained wide attention for accelerating machine learning, data analysis, and deep neural networks. By IMC, matrix-vector multiplication (MVM) can be executed in the crosspoint array in just one step, thus accelerating a broad range of tasks in machine learning and data analytics. However, a key issue for RRAM crosspoint arrays is the forming operation of the memories which limits the stability and accuracy of the conductance state in the memory device. In this work, a hardware implementation of crosspoint array of forming-free devices for fast, energy-efficient accelerators of MVM is reported. RRAM devices with a 1.5 nm-thick HfO2 layer show an initial low resistance without forming and an analogue-mode programming behavior for high-accuracy IMC. Accurate hardware MVM is demonstrated by experimental eigenvalue/eigenvector calculation according to the power-iteration algorithm, with a fast convergence within about ten iterations to the correct solution. Deflation technique and principal component analysis (PCA) enable the classification of the Iris dataset with 98% accuracy compared with floating-point implementation. These results support forming-free crosspoint arrays for accelerating advanced machine learning with IMC

The North Italian innovative project for common psychiatric disorders: Evaluating the output of a treatment model of an outpatient clinic for anxiety and depression

Depressive disorders were considered the first causes of disability worldwide as early as 2018. The outpatient clinic for anxiety and depression at the University Hospital of Varese represents a service that fully responds to the growing number of requests. Approximately 1,350 medical records have been opened from 2010 to December 2021. The most frequent presenting diagnoses included anxiety disorders (36.8%), severe stress and maladaptation syndromes (35.5%), and depressive episodes (18%). The outpatient clinic has proved to be a model with great impact on users offering a range of diagnostic and therapeutic offers responding to the requests of the community

In-memory computing with emerging memory devices: Status and outlook

Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning

Biophysical characterization of the outer membrane polysaccharide export protein and the polysaccharide co-polymerase protein from <i>Xanthomonas campestris</i>

This study investigated the structural and biophysical characteristics of GumB and GumC, two Xanthomonas campestris membrane proteins that are involved in xanthan biosynthesis. Xanthan is an exopolysaccharide that is thought to be a virulence factor that contributes to bacterial in planta growth. It also is one of the most important industrial biopolymers. The first steps of xanthan biosynthesis are well understood, but the polymerization and export mechanisms remain unclear. For this reason, the key proteins must be characterized to better understand these processes. Here we characterized, by biochemical and biophysical techniques, GumB, the outer membrane polysaccharide export protein, and GumC, the polysaccharide co-polymerase protein of the xanthan biosynthesis system. Our results suggested that recombinant GumB is a tetrameric protein in solution. On the other hand, we observed that both native and recombinant GumC present oligomeric conformation consistent with dimers and higher-order oligomers. The transmembrane segments of GumC are required for GumC expression and/or stability. These initial results provide a starting point for additional studies that will clarify the roles of GumB and GumC in the xanthan polymerization and export processes and further elucidate their functions and mechanisms of action.Instituto de Física de Líquidos y Sistemas Biológico

Multilevel HfO2-based RRAM devices for low-power neuromorphic networks

Training and recognition with neural networks generally require high throughput, high energy efficiency, and scalable circuits to enable artificial intelligence tasks to be operated at the edge, i.e., in battery-powered portable devices and other limited-energy environments. In this scenario, scalable resistive memories have been proposed as artificial synapses thanks to their scalability, reconfigurability, and high-energy efficiency, and thanks to the ability to perform analog computation by physical laws in hardware. In this work, we study the material, device, and architecture aspects of resistive switching memory (RRAM) devices for implementing a 2-layer neural network for pattern recognition. First, various RRAM processes are screened in view of the device window, analog storage, and reliability. Then, synaptic weights are stored with 5-level precision in a 4 kbit array of RRAM devices to classify the Modified National Institute of Standards and Technology (MNIST) dataset. Finally, classification performance of a 2-layer neural network is tested before and after an annealing experiment by using experimental values of conductance stored into the array, and a simulation-based analysis of inference accuracy for arrays of increasing size is presented. Our work supports material-based development of RRAM synapses for novel neural networks with high accuracy and low-power consumption. (C) 2019 Author(s)