Use of waste materials for Lactococcus lactis development

BACKGROUND: Lactococcus lactis is an interesting microorganism with several industrial applications, particularly in the food industry. As well as being a probiotic species, L. lactis produces several metabolites with interesting properties, such as lactic acid (LA) and biosurfactants. Nevertheless, L. lactis is an especially demanding species since it has strong nutritional requirements, implying the use of complex and expensive culture media. RESULTS: The results showed the potential of L. lactis CECT‐4434 as a LA and biosurfactant producer. The economical cost of L. lactis cultures can be reduced by replacing the MRS medium by the use of two waste materials: trimming vine shoots as C source, and 20 g L^?1 distilled wine lees (vinasses) as N, P and micronutrient sources. From the hemicellulosic fraction, 14.3 g L^?1 LA and 1.7 mg L^?1 surfactin equivalent were achieved after 74 h (surface tension reduction of 14.4 mN m^?1); meanwhile, a simultaneous saccharification and fermentation process allowed the generation of 10.8 g L^?1 LA and 1.5 mg L^?1 surfactin equivalent after 72 h, reducing the surface tension by 12.1 units at the end of fermentation. CONCLUSIONS: Trimming vine shoots and vinasses can be used as alternative economical media for LA and cell‐bound biosurfactant production. Copyright © 2010 Society of Chemical Industry     

Architectural and Basic Circuit Considerations for a Flexible 128 × 128 Mixed-Signal SIMD Vision Chip

From a system level perspective, this paper presents a 128 × 128 flexible and reconfigurable Focal-Plane Analog Programmable Array Processor, which has been designed as a single chip in a 0.35 m standard digital 1P-5M CMOS technology. The core processing array has been designed to achieve high-speed of operation and large-enough accuracy (7 bits) with low power consumption. The chip includes on-chip program memory to allow for the execution of complex, sequential and/or bifurcation flow image processing algorithms. It also includes the structures and circuits needed to guarantee its embedding into conventional digital hosting systems: external data interchange and control are completely digital. The chip contains close to four million transistors, 90% of them working in analog mode. The chip features up to 330 GOPs (Giga Operations per second), and uses the power supply (180 GOP/Joule) and the silicon area (3.8 GOPS/mm²) efficiently, and is able to maintain VGA processing throughputs of 100 Frames/s with about 10–20 basic image processing tasks on each frame.     

New learning rules for the ASSOM network

The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonens method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one.     

Vegetation responses to precipitation and temperature: a spatiotemporal analysis of ecoregions in the Colorado River Basin

Predicting vegetation response to precipitation and temperature anomalies, particularly during droughts, is of great importance in semi-arid regions, because ecosystem and hydrologic processes depend on vegetation conditions. This article studies vegetation responses to precipitation and temperature in 10 ecological regions within the semi-arid Colorado River Basin (CRB). The Normalized Difference Vegetation Index (NDVI) from Global Inventory Modeling and Mapping Studies (GIMMS) database and the Standardized Precipitation Index (SPI) and temperature series from Parameter-Elevation Regressions on Independent Slope Models (PRISM) database were jointly evaluated for the period 1986–2006, using Multichannel Singular Spectrum Analysis (MSSA) to determine common oscillations and significant lags in vegetation response to seasonal and annual precipitation and temperature. Results show high correlations between lagged SPI series and standardized NDVI: from 1-month lag in the warm deserts (Sonora, Chihuahua and Mojave) to two months in the Temperate Sierras and Semi-Arid Highlands and three months in the Colorado and Arizona/New Mexico Plateaus and the Western Cordillera. Temperature anomalies are negatively correlated to NDVI in the lower CRB and positively correlated in the upper CRB. Notably, we see a basin-wide response to SPI anomalies, and consequently, the identified latitudinal and altitudinal lags between SPI and NDVI will allow an early, basin-wide assessment of lagged vegetation responses to precipitation along the CRB ecoregions.     

Reconciling continuum and non-continuum data with industrial application

In order to perform data reconciliation, it is important that noises in the data have well-defined distributions. The motivation behind this study was to enable the comparison between a discrete and continuous data set so that means can be compared for gross error over the short term; this required that local variables exhibit similar distributions.A case study was done on a system where non-continuum loads from a dump truck were to be reconciled with two downstream continuum weightometers. An algorithm was developed using the binomial distribution and time delay in order to simulate the effect of the dump pocket.Regression analysis based on principal components was used to evaluate the performance of the smoothing algorithm and to determine the most likely maximum hopper capacity that locates between the two weightometers.     

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.     

On the microstructure of single wall carbon nanotubes reinforced ceramic matrix composites

A microstructural modelling of the microstructure in single wall carbon nanotubes reinforced alumina ceramics has been developed. The model accounts for the main microstructural features, being quite useful to describe the carbon nanotube distribution along the ceramic matrix. The microstructural analysis derived from this model is found to give a deeper insight into the high-temperature creep of these composites.