TargetFish industry forum on DNA vaccination: Where do we stand and what's next?

Maybe most characteristic of the TargetFish1 project, which kicked off some five years ago with 30 partners from 10 EU member states, two associated countries (Norway, Israel) and one international cooperation partner country (Chile), has been the close cooperation between research groups and enterprises; more or less equally represented in this large consortium. In this respect, TargetFish has been revolutionary validating by this close cooperation fundamental knowledge for the development of next generation vaccines and different routes of vaccine administration. TargetFish had the ambition to demonstrate market applicability of improved vaccines or new prototype vaccines that would come forward from the project. Via frequent joint meetings of its partners, be it research group or enterprise, TargetFish aimed to drive vaccine development in an industrial applicable way. This could facilitate adoption of new intellectual property and stimulate the presentation of new fish vaccines on the market. The industry forum has been a platform for a continuing validation of the applied potential of the research outcomes. Workshops were organised at the different EAFP meetings to communicate the validation process to those not directly involved with the project but interested in the fish vaccine market. After a kick-off meeting during the EAFP in Tampere, Finland fours years ago and a second meeting at the EAFP in Las Palmas, Spain, two years ago, at the present EAFP in Belfast, Northern Ireland a final meeting was organised. This report is a summary of the 'Industrial Forum workshop' held at the EAFP in Belfast 2017 and provides a short overview of the highlights presented to, and discussed with, those present and interested in DNA vaccine development, policies and laws, production and delivery routes

Markovian regularization of latent-variable-models mixture for New multi-component image reduction/segmentation scheme

This paper proposes a new framework for multi-component images segmentation which plays an increasing role in many imagery applications like astronomy, medicine, remote sensing, chemistry, biology etc. In fact, inference on such images is a very difficult task when the number of components increases due to the well-known Hughes phenomenon. A common solution is to reduce dimensionality, keeping only relevant information before segmentation. Linear models usually fail with complex data structure, and mixture of linear models, each of which modeling a local cluster of the data, is more suitable. Moreover, a probabilistic formulation based on linear latent variable models allows efficient solution using a maximum-likelihood-based decision to recover the clusters. However, for multi-component image classification, this is not enough because it completely neglects the spatial positions of the multi-dimensional pixels on the lattice. Therefore, we propose to consider the neighborhood by introducing a Markovian a priori to efficiently regularize pixel classification. As a consequence, segmentation and reduction are performed simultaneously in an efficient and robust way. In this paper, we focus on the Probabilistic Principal Component Analysis (PPCA) as a latent variable model, and the Hidden Markov quad-Tree (HMT) as an a priori for regularization. The method performs well both on synthetic and real remote sensing and Stokes-Mueller images. © 2007 Springer-Verlag London Limited

Driving Distraction Analysis by ECG Signals An Entropy Analysis

This paper presents a novel method in driving distraction analysis: entropy analysis of ECG signals. ECG signals were recorded continuously while 15 drivers were driving with a simulator. Mental computation task was employed as driving distraction. Sample entropy and power spectrum entropy of drivers. ECG signals while they were driving with and without distraction were derived. The result indicated that entropy of drivers ECG signals was sensitive to driving distraction and were potential significant metrics in driving distraction measurement.</p

Efeito de substratos porosos no enraizamento in vitro do porta-enxerto de macieira M-9 (Malus pumilla) Effect of porous substrates in vitro rooting of M-9 apple rootstock (Malus pumilla)

O presente trabalho foi conduzido com o objetivo de avaliar os efeitos de substratos no enraizamento in vitro do porta-enxerto de macieira M-9. Foram testados três substratos: ágar, vermiculita (nº 2, granulometria média) e cinza vegetal, como suporte físico no enraizamento das miniestacas. Para os tratamento com vermiculita e cinza vegetal, meio nutritivo MS, reduzido à metade da concentração, foi adicionado em frascos de vidro de 250 mL contendo 15 g dos respectivos substratos. Brotações de 2,5 a 3,0 cm de comprimento, com dois pares de folhas, foram transferidas para os frascos, os quais foram mantidos durante 35 dias em sala de crescimento com temperatura de 25 ±1,5ºC, fotoperíodo de 16 horas e intensidade luminosa de 75 µmol.m-2.s-1. As maiores percentagens de enraizamento (88,4 e 87,9%) foram observadas nos tratamentos com vermiculita e cinza vegetal, respectivamente. Após a avaliação do enraizamento, as plantas foram transferidas para bandejas de isopor alveoladas com 128 células e mantidas por 40 dias em casa de vegetação. A maior taxa de sobrevivência de plantas aclimatizadas (93,5%) foi obtida com as miniestacas produzidas em meio contendo vermiculita.<br>the present work was carried out with the objective to evaluate the effect of substrates in the rooting in vitro of the M-9 apple rootstock. Three substrates were tested: ágar, vermiculite (number 2, medium granulometry) and vegetal ash as physical support in the rooting of the shoots. For the treatment with vermiculite and vegetal ash, MS medium nutritive, with half of the concentration, was added in glass bottles of 250 mL with 15g of respective substrates. The shoots of 2,5 to 3,0 cm of length and two pair of leaves had been transferred to the bottles. After the inoculation the bottles were kept during 35 days in a growing chamber, at 25 ±1ºC, for a 16 hour photoperiod with light intensity of 75 µmol.m-2.s-1. The higher percentages of rooting (88,4 and 87,9%) were observed for the treatments with vermiculite and vegetal ash, respectively. The number of roots per shoot was not affected by the applied treatments. After the evaluation of the rooting, the plants were transferred to a greenhouse for 40 days, in isopor alveolated containers with 128 cells. For the survival of the acclimatized plants the best treatment was vermiculite (93,5%)

Cooperative coevolution for agrifood process modeling

On the contrary to classical schemes of evolutionary optimisations algorithms, single population Cooperative Co-evolution techniques (CCEAs, also called "Parisian" approaches) make it possible to represent the evolved solution as an aggregation of several individuals (or even as a whole population). In other words, each individual represents only a part of the solution. This scheme allows simulating the principles of Darwinian evolution in a more economic way, which results in gain in robustness and efficiency. The counterpart however is a more complex design phase. In this chapter, we detail the design of efficient CCEAs schemes on two applications related to the modeling of an industrial agri-food process. The experiments correspond to complex optimisations encountered in the modeling of a Camembert-cheese ripening process. Two problems are considered: A deterministic modeling problem, phase prediction, for which a search for a closed form tree expression is performed using genetic programming (GP). A Bayesian network structure estimation problem. The novelty of the proposed approach is based on the use of a two step process based on an intermediate representation called independence model. The search for an independence model is formulated as a complex optimisation problem, for which the CCEA scheme is particularly well suited. A Bayesian network is finally deduced using a deterministic algorithm, as a representative of the equivalence class figured by the independence model