NMR and molecular modelling studies on elastase inhibitor-peptides for wound management

Proteases play an important and critical role in the physiological process of wound repair. However, excessive and unregulated release of proteolytic enzymes (e.g., elastase) mediates abnormal degradation of healthy tissues, which leads to inflammatory disorders such as chronic wounds. Thus, it is of therapeutic interest to develop novel synthetic inhibitor-peptides of elastase, which can restore the balance between the free enzyme and the endogenous inhibitors in chronic wounds. In previous works, we have reported two different drug delivery systems to release novel elastase inhibitors to the wound site. In both systems synthetic peptides (KRCCPDTCGIKCL-Pep4 and KRMMPDTMGIKML-Pep4M) based on the primary structure of the endogenous elastase inhibitor, secretory leucocyte protease inhibitor, were used as active material. Phosphorylation of the reported peptides prompts significant structural differences, which reflects in distinct inhibitory capacity towards elastase. These structural modifications were prompted by electrostatic interactions and hydrogen bonds established from the peptide phosphoresidue. The current study was also extended to another synthetic peptide (WCTASVPPQCY-PepBBI) that is based on the reactive loop of another elastase inhibitor, the Bowmen-Birk inhibitor. PepBBI, phosphorylated and non-phosphorylated, displays similar behaviour to Pep4 and Pep4M. The structural modifications reported herein were evaluated by two-dimensional nuclear magnetic resonance and molecular modelling approaches.     

Two-stage simulation optimization for agile manufacturing capacity planning

Capacity planning involves the selection of manufacturing technologies and the allocation of budget to specific equipment acquisitions. In today's highly volatile manufacturing world, an agile capacity-planning tool is required. This tool must provide the mechanism for a company to thrive in an environment of uncertainty. Uncertain future demands make capacity planning and technology selection difficult tasks, whether they are caused by variations in forecasts of direct demand or by upstream variability in a supply chain. In this paper, a practical modelling technique for minimizing the required investment in capacity planning for discrete manufacturing sites under an uncertain demand stream is presented. The method consists of a two-stage stochastic integer program. The first stage characterizes the optimal response of the system under uncertainty. The second stage selects a tool set based on the characterization from the first stage, with the addition of budget constraints. The model is scalable, allowing for multiple products, multiple operations, multiple flow paths including re-entrant flow, and multiple tool types. A simple example is introduced to explain the methodology, followed by the results of a large-scale real-world application in the semiconductor industry.     

Caffe CNN-based classification of hyperspectral images on GPU

Deep learning techniques based on Convolutional Neural Networks (CNNs) are extensively used for the classification of hyperspectral images. These techniques present high computational cost. In this paper, a GPU (Graphics Processing Unit) implementation of a spatial-spectral supervised classification scheme based on CNNs and applied to remote sensing datasets is presented. In particular, two deep learning libraries, Caffe and CuDNN, are used and compared. In order to achieve an efficient GPU projection, different techniques and optimizations have been applied. The implemented scheme comprises Principal Component Analysis (PCA) to extract the main features, a patch extraction around each pixel to take the spatial information into account, one convolutional layer for processing the spectral information, and fully connected layers to perform the classification. To improve the initial GPU implementation accuracy, a second convolutional layer has been added. High speedups are obtained together with competitive classification accuracies.

     

Energy-aware robust model predictive control based on noisy wireless sensors

Wireless sensor networks (WSNs) are becoming fundamental components of modern control systems due to their flexibility, ease of deployment and low cost. However, the energy-constrained nature of WSNs poses new issues in control design; in particular the discharge of batteries of sensor nodes, which is mainly due to radio communications, must be taken into account. In this paper we present a novel transmission strategy for communication between controller and sensors which is intended to minimize the data exchange over the wireless channel. Moreover, we propose an energy-aware control technique for constrained linear systems based on explicit model predictive control (MPC), providing closed-loop stability in the presence of disturbances. The presented control schemes are compared to traditional MPC techniques. The results show the effectiveness of the proposed energy-aware approach, which achieves a profitable trade-off between energy savings and closed-loop performance.     

Oxidative stabilization of ultra-high omega-3 concentrates as ethyl esters or triacylglycerols

A comparative study of the oxidative stability of “ultra-high” ω3 concentrates (80%) from fish oils as triacylglycerols (TAG) or ethyl esters (EE), as well as their stabilization by supercritical extracts of rosemary (SER), α-tocopherol or their mixture, by Rancimat method and throughout storage time, was carried out. No significant differences were found between EE and TAG on oxidative stability when measured by Rancimat conditions in the range of 50?90 °C. However, storage experiments revealed a poor stability of the EE concentrate form, measured by the monitorization of peroxide and p-anisidine values. Concerning the stabilization by antioxidants, there was a lack of antioxidant effect of evaluated compounds when Rancimat assays were performed at 70 °C, whereas an antioxidant effect began to evidence at 60 °C, and a clearest antioxidant protection was measured at 50 °C for the mixture of the SER plus α-tocopherol in both EE and TAG forms. This selected binary mixture efficiently stabilized the ω3-TAG concentrate throughout 50 days of storage conditions, whereas the stabilization of ω3-EE concentrate was worse. Therefore, the combination of SER and α-tocopherol seemed to be a good antioxidant mixture for the efficient stabilization of extremely labile ultra-high ω3 concentrates, especially in the form of ω3 TAG oils.     

Experimental and numerical investigation of shear strain along an elasto-plastic bonded lap joint

This study investigates the use of an elasto-plastic adhesive in a bonded double lap joint to increase its ultimate capacity. The first section describes the experimental work we performed to characterize the materials and measure the shear strain along the joint. A comparison between an elastic adhesive and a highly plastic adhesive is made and the effect of the type of behaviour on both the capacity and the characteristic anchorage length is studied. The second section then compares the experimental results with a classical 3D finite element model. Good correlation was found between numerical expectations and experimental measurements, and the modelling allowed us to highlight typical phenomena linked to the use of an elasto-plastic adhesive. In addition, a parameter based on a microscopic measurement has been defined which allows us to describe the onset of plasticity and predict maximum capacity.