Biosynthesis, detection and toxicology of ochratoxins - optimisation of production

Ochratoxin A (OTA) is a very well known mycotoxin found in several food commodities for which maximum limits are being discussed in EC in other to produce appropriate regulations. OTA is one of several ochratoxins produced by Aspergillus and Penicillium species. All the compounds in this group have a molecular structure very similar to OTA and some were already isolated from natural substrates. Several of these compounds such as ochratoxin , methyl and ethyl ester of ochratoxin A, 4-R and S-hydroxyochratoxin A, 10-hydroxyochratoxin A and ochratoxin A open lactone are commercially unavailable. However, they can be easily synthesized through OTA modification. With the main objective of its application on further research works, OTA production, isolation and purification has been optimised from an A. alliaceus strain grown on wheat medium. Synthesis and purification of some OTA derivatives has been achieved and an HPLC method for their detection was optimised. Data about their production by several species of Aspergillus will be presented. The toxicological properties of ochratoxins are still not very clear and a future EC safety limit for OTA will depend on e.g., a better clarification of its carcinogenity. Could OTA derivatives play a role here?

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Assistive locomotion strategies for active lower limb devices

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Optimization of peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) for the detection of bacteria: the effect of pH, dextran sulfate and probe concentration

Fluorescence in situ hybridization (FISH) is a molecular technique widely used for the detection and characterization of microbial populations. FISH is affected by a wide variety of abiotic and biotic variables and the way they interact with each other. This is translated into a wide variability of FISH procedures found in the literature. The aim of this work is to systematically study the effects of pH, dextran sulfate and probe concentration in the FISH protocol, using a general peptide nucleic acid (PNA) probe for the Eubacteria domain. For this, response surface methodology was used to optimize these 3 PNA-FISH parameters for Gram-negative (Escherichia coli and Pseudomonas fluorescens) and Gram-positive species (Listeria innocua, Staphylococcus epidermidis and Bacillus cereus). The obtained results show that a probe concentration higher than 300 nM is favorable for both groups. Interestingly, a clear distinction between the two groups regarding the optimal pH and dextran sulfate concentration was found: a high pH (approx. 10), combined with lower dextran sulfate concentration (approx. 2% [w/v]) for Gram-negative species and near-neutral pH (approx. 8), together with higher dextran sulfate concentrations (approx. 10% [w/v]) for Gram-positive species. This behavior seems to result from an interplay between pH and dextran sulfate and their ability to influence probe concentration and diffusion towards the rRNA target. This study shows that, for an optimum hybridization protocol, dextran sulfate and pH should be adjusted according to the target bacteria.

A spectrum-driven damage Identification technique : application and validation through the numerical simulation of the Z24 Bridge

The present paper focuses on a damage identification method based on the use of the second order spectral properties of the nodal response processes. The explicit dependence on the frequency content of the outputs power spectral densities makes them suitable for damage detection and localization. The well-known case study of the Z24 Bridge in Switzerland is chosen to apply and further investigate this technique with the aim of validating its reliability. Numerical simulations of the dynamic response of the structure subjected to different types of excitation are carried out to assess the variability of the spectrum-driven method with respect to both type and position of the excitation sources. The simulated data obtained from random vibrations, impulse, ramp and shaking forces, allowed to build the power spectrum matrix from which the main eigenparameters of reference and damage scenarios are extracted. Afterwards, complex eigenvectors and real eigenvalues are properly weighed and combined and a damage index based on the difference between spectral modes is computed to pinpoint the damage. Finally, a group of vibration-based damage identification methods are selected from the literature to compare the results obtained and to evaluate the performance of the spectral index.

Semipermeable capsules wrapping a multifunctional and self-regulated co-culture microenvironment for osteogenic differentiation

A new concept of semipermeable reservoirs containing co-cultures of cells and supporting microparticles is presented, inspired by the multi-phenotypic cellular environment of bone. Based on the deconstruction of the â stem cell nicheâ , the developed capsules are designed to drive a self-regulated osteogenesis. PLLA microparticles functionalized with collagen I, and a co-culture of adipose stem (ASCs) and endothelial (ECs) cells are immobilized in spherical liquified capsules. The capsules are coated with multilayers of poly(L-lysine), alginate, and chitosan nano-assembled through layer-by-layer. Capsules encapsulating ASCs alone or in a co-culture with ECs are cultured in endothelial medium with or without osteogenic differentiation factors. Results show that osteogenesis is enhanced by the co-encapsulation, which occurs even in the absence of differentiation factors. These findings are supported by an increased ALP activity and matrix mineralization, osteopontin detection, and the up regulation of BMP-2, RUNX2 and BSP. The liquified co-capsules also act as a VEGF and BMP-2 cytokines release system. The proposed liquified capsules might be a valuable injectable self-regulated system for bone regeneration employing highly translational cell sources.