Genotypic and phenotypic diversity among Lactobacillus plantarum and Lactobacillus pentosus isolated from industrial scale cucumber fermentations.

The Lactobacillus plantarum and Lactobacillus pentosus genotypes existing in industrial-scale cucumber fermentations were defined using rep-PCR-(GTG)5. The ability of each genotype to ferment cucumbers under various conditions was evaluated. Rep-PCR-(GTG)5 was the technique capable of illustrating the most intraspecies discrimination compared to the sequencing of housekeeping genes (recA, dnaK, pheS and rpoA), MLST and RAPD with primers LP1, OPL5, M14 and COC. Ten genotypic clusters were defined for the 199 L. pentosus tested and three for the 17 L. plantarum clones. The ability of the 216 clones genotyped and 37 additional cucumber fermentation isolates, of the same species, to rapidly decrease the pH of cucumber juice medium under various combinations of sodium chloride (0 or 6%), initial pH (4.0 or 5.2) and temperatures (15 or 30 °C) was determined using a fractional factorial screening design. A reduced fermentation ability was observed for the L. plantarum strains as compared to L. pentosus, except for clone 3.2.8, which had a ropy phenotype and aligned to genotypic cluster A. L. pentosus strains belonging to three genotypic clusters (B, D and J) were more efficient in cucumber juice fermentation as compared to most L. plantarum strains. This research identified three genetically diverse L. pentosus strains and one L. plantarum as candidates for starter cultures for commercial cucumber fermentations.

Approaching Retinal Ganglion Cell Modeling and FPGA Implementation for Robotics.

Taking inspiration from biology to solve engineering problems using the organizing principles of biological neural computation is the aim of the field of neuromorphic engineering. This field has demonstrated success in sensor based applications (vision and audition) as well as in cognition and actuators. This paper is focused on mimicking the approaching detection functionality of the retina that is computed by one type of Retinal Ganglion Cell (RGC) and its application to robotics. These RGCs transmit action potentials when an expanding object is detected. In this work we compare the software and hardware logic FPGA implementations of this approaching function and the hardware latency when applied to robots, as an attention/reaction mechanism. The visual input for these cells comes from an asynchronous event-driven Dynamic Vision Sensor, which leads to an end-to-end event based processing system. The software model has been developed in Java, and computed with an average processing time per event of 370 ns on a NUC embedded computer. The output firing rate for an approaching object depends on the cell parameters that represent the needed number of input events to reach the firing threshold. For the hardware implementation, on a Spartan 6 FPGA, the processing time is reduced to 160 ns/event with the clock running at 50 MHz. The entropy has been calculated to demonstrate that the system is not totally deterministic in response to approaching objects because of several bioinspired characteristics. It has been measured that a Summit XL mobile robot can react to an approaching object in 90 ms, which can be used as an attentional mechanism. This is faster than similar event-based approaches in robotics and equivalent to human reaction latencies to visual stimulus.

A Binaural Neuromorphic Auditory Sensor for FPGA: A Spike Signal Processing Approach.

This paper presents a new architecture, design flow, and field-programmable gate array (FPGA) implementation analysis of a neuromorphic binaural auditory sensor, designed completely in the spike domain. Unlike digital cochleae that decompose audio signals using classical digital signal processing techniques, the model presented in this paper processes information directly encoded as spikes using pulse frequency modulation and provides a set of frequency-decomposed audio information using an address-event representation interface. In this case, a systematic approach to design led to a generic process for building, tuning, and implementing audio frequency decomposers with different features, facilitating synthesis with custom features. This allows researchers to implement their own parameterized neuromorphic auditory systems in a low-cost FPGA in order to study the audio processing and learning activity that takes place in the brain. In this paper, we present a 64-channel binaural neuromorphic auditory system implemented in a Virtex-5 FPGA using a commercial development board. The system was excited with a diverse set of audio signals in order to analyze its response and characterize its features. The neuromorphic auditory system response times and frequencies are reported. The experimental results of the proposed system implementation with 64-channel stereo are: a frequency range between 9.6 Hz and 14.6 kHz (adjustable), a maximum output event rate of 2.19 Mevents/s, a power consumption of 29.7 mW, the slices requirements of 11141, and a system clock frequency of 27 MHz.

Neuro-inspired spike-based motion: from dynamic vision sensor to robot motor open-loop control through spike-VITE.

In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation.

CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high-speed visual object recognition and tracking.

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies.

[Multifactorial treatment of atherosclerosis]. / Tratamiento multifactorial de la arteriosclerosis.

Cardiovascular diseases, the clinical paradigm of atherosclerosis, are the primary cause of mortality in developed countries. The origin of the atheromatous lesion is multifactorial, as it is the therapeutic approach to its prevention and clinical complications. The initial symptoms of ischemia in a vascular bed are usually evident when the atherosclerotic process is very advanced, being indicative of a diffuse disease and of an elevated future risk for ischemic events in other vascular territories. We perform this review in this clinical scenario, highlighting the preventive and therapeutic aspects of demonstrated clinical efficacy, with no detail of those treatments with benefit insufficiently proven.