Fluorescence-based approaches towards the assessment of structural and functional alterations in lysosomes and vacuoles induced by lactoferrin and acetic acid

Dissertação de mestrado em Biofísica e Bionanossistemas

Establishing guidelines for obtaining optimal cell sources to use in tendon tissue engineering strategies

Cell-based approaches in tissue engineering (TE) have been barely explored for the treatment of tendon and ligament (T/L) tissues, requiring the establishment of a widely available cell source with tenogenic potential. As T/L cells are scarce, stem cells may provide a good alternative. Understanding how resident cells behave in vitro, might be useful for recapitulating the tenogenic potential of stem cells for tendon TE applications. Therefore, we propose to isolate and characterize human T/L-derived cells (hTDCs and hLDCs) and compare their regenerative potential with stem cells from adipose tissue (hASCs) and amniotic fluid (hAFSCs)(1). T/L cells were isolated using different procedures and stem cells isolated as described elsewhere(1). Moreover, T/L cells were stimu- lated into the three mesenchymal lineages, using standard differentia- tion media. Cells were characterized for the typical stem cell markers as well as T/L related markers, namely tenascin-C, collagen I and III, decorin and scleraxis, using different complementary techniques such as real time RT-PCR, immunocytochemistry and flow cytometry. No differences were observed between T/L in gene expression and protein deposition. T/L cells were mostly positive for stem ness markers (CD73/CD90/CD105), and have the potential to differentiate towards osteogenesis, chondrogenesis and adipogenesis, demonstrated by the positive staining for AlizarinRed, SafraninO, ToluidineBlue and OilRed. hASCs and hAFSCs exhibit positive expression of all tenogenic mark- ers, although at lower levels than hTDCs and hLDCs. Nevertheless, stem cells availability is key factor in TE strategies, despite that it’s still required optimization to direct their tenogenic phenotype.

Tunnel engineering – influence of the type and the quantity of measurements in the back analysis of geomechanical parameters

The monitoring data collected during tunnel excavation can be used in inverse analysis procedures in order to identify more realistic geomechanical parameters that can increase the knowledge about the interested formations. These more realistic parameters can be used in real time to adapt the project to the real structure in situ behaviour. However, monitoring plans are normally designed for safety assessment and not especially for the purpose of inverse analysis. In fact, there is a lack of knowledge about what types and quantity of measurements are needed to succeed in identifying the parameters of interest. Also, the optimisation algorithm chosen for the identification procedure may be important for this matter. In this work, this problem is addressed using a theoretical case with which a thorough parametric study was carried out using two optimisation algorithms based on different calculation paradigms, namely a conventional gradient-based algorithm and an evolution strategy algorithm. Calculations were carried for different sets of parameters to identify several combinations of types and amount of monitoring data. The results clearly show the high importance of the available monitoring data and the chosen algorithm for the success rate of the inverse analysis process.

Flow updating: fault-tolerant aggregation for dynamic networks

Documento submetido para revisão pelos pares. A publicar em Journal of Parallel and Distributed Computing. ISSN 0743-7315

Comparative genomics reveals adaptations of a halotolerant thaumarchaeon in the interfaces of brine pools in the Red Sea

The bottom of the Red Sea harbors over 25 deep hypersaline anoxic basins that are geochemically distinct and characterized by vertical gradients of extreme physicochemical conditions. Because of strong changes in density, particulate and microbial debris get entrapped in the brine-seawater interface (BSI), resulting in increased dissolved organic carbon, reduced dissolved oxygen toward the brines and enhanced microbial activities in the BSI. These features coupled with the deep-sea prevalence of ammonia-oxidizing archaea (AOA) in the global ocean make the BSI a suitable environment for studying the osmotic adaptations and ecology of these important players in the marine nitrogen cycle. Using phylogenomic-based approaches, we show that the local archaeal community of five different BSI habitats (with up to 18.2% salinity) is composed mostly of a single, highly abundant Nitrosopumilus-like phylotype that is phylogenetically distinct from the bathypelagic thaumarchaea; ammonia-oxidizing bacteria were absent. The composite genome of this novel Nitrosopumilus-like subpopulation (RSA3) co-assembled from multiple single-cell amplified genomes (SAGs) from one such BSI habitat further revealed that it shares [sim]54% of its predicted genomic inventory with sequenced Nitrosopumilus species. RSA3 also carries several, albeit variable gene sets that further illuminate the phylogenetic diversity and metabolic plasticity of this genus. Specifically, it encodes for a putative proline-glutamate 'switch' with a potential role in osmotolerance and indirect impact on carbon and energy flows. Metagenomic fragment recruitment analyses against the composite RSA3 genome, Nitrosopumilus maritimus, and SAGs of mesopelagic thaumarchaea also reiterate the divergence of the BSI genotypes from other AOA.

Pre-clinical trials for Machado-Joseph Disease: Hypothesis-based and hypothesis-free therapeutic approaches

Tese de Doutoramento em Ciências da Saúde

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

Acellular Gellan-gum based bilayered structures for the regeneration of osteochondral defects: a preclinical study

 In orthopaedics, the management and treatment of osteochondral (OC) defects remains an ongoing clinical challenge. Autologous osteochondral mosaicplasty has been used as a valid option for OC treatments although donor site morbidity remains a source of concern [1]. Engineering a whole structure capable of mimicking different tissues (cartilage and subchondral bone) in an integrated manner could be a possible approach to regenerate OC defects. In our group we have been proposing the use of bilayered structures to regenerate osteochondral defects [2,3]. The present study aims to investigate the pre-clinical performance of bilayered hydrogels and spongy-like hydrogels in in vivo  models (mice and rabbit, respectively), in both subcutaneous and orthotopic models. The bilayered structures were produced from Low Acyl Gellan Gum (LAGG) from Sigma-Aldrich, USA. Cartilage-like layers were obtained from a 2wt% LAGG solution. The bone-like layers were made of 2wt% LAGG with incorporation of hydroxyapatite at 20% and 30% (w/v). Hydrogels and spongy-like were subcutaneouly implanted in mice to evaluate the inflammatory response. Then, OC defects were induced in rabbit knee to create a critical size defect (4 mm diameter and 5 mm depth), and then hydrogels and sponges implanted. Both structures followed different processing methods. The hydrogels were injected allowing in situ  crosslinking. Unlike, the spongy-like were pre-formed by freeze-drying. The studies concerning subcutaneous implantation and critical size OC defect were performed for 2 and 4 weeks time, respectively. Cellular behavior and inflammatory responses were assessed by means of histology staining and biochemical function and matrix deposition by immunohistochemistry. Additionally, both OC structures stability and new cartilage and bone formation were evaluated by using vivo- computed tomography (Scanco 80). The results showed no acute inflammatory response for both approaches. New tissue formation and integration in the adjacent tissues were also observed, which present different characteristic behaviors when comparing hydrogels and sponges response. As future insights, a novel strategy for regeneration of OC defects can be designed encompassing both, hydrogels and spongy-like structures and cellular approaches. References: 1. Espregueira-Mendes J. et al. Osteochondral transplantation using autografts from the upper tibio-fibular joint for the treatment of knee cartilage lesions. Knee Surgery, Sports Traumatology, Arthroscopy 20,1136, 2012. 2. Oliveira JM. et al, Novel hydroxyapatite/chitosan bilayered scaffold for osteochondral tissue-engineering applications: Scaffold design and its performance when seeded with goat bone marrow stromal cells. Biomaterials 27, 6123, 2006. 3. Pereira D R. et al. Gellan Gum-Based Hydrogel Bilayered Scaffolds for Osteochondral Tissue Engineering. Key Engineering Materials 587, 255, 2013.

Fucoidan-based particles for diabetes mellitus treatment

Marine organisms are rich in a variety of materials with potential use in Tissue Engineering and Regenerative Medicine. One important example is fucoidan, a sulfated polysaccharide extracted from the cell wall of brown seaweeds.  Fucoidan is composed by L-fucose, sulfate groups and glucuronic acid. It has important bioactive properties such as anti-oxidative, anticoagulant, anticancer and reducing the blood glucose (1). In this work, the biomedical potential of fucoidan-based materials as drug delivery system was assessed by processing modified fucoidan (MFu) into particles by photocrosslinking using superamphiphobic surfaces and visible light. Fucoidan was modified by methacrylation reaction using different concentrations of methacrylate anhydride, namely 8% v/v (MFu1) and 12% v/v (MFu2). Further, MFu particles with and without insulin (5% w/v) were produced by pipetting a solution of 5% MFu with triethanolamine and eosin-y onto a superamphiphobic surface and then photocrosslinking using visible light (2). The developed particles were characterized to assess their chemistry, morphology, swelling behavior, drug release, insulin content and encapsulation efficiency. Moreover, the viability assays of fibroblast L929 cells in contact with MFu particles showed good adhesion and proliferation up to 14 days. Furthermore, the therapeutic potential of these particles using human beta cells is currently under investigation. Results obtained so far suggest that modified fucoidan particles could be a good candidate for diabetes mellitus therapeutic approaches.  

Experimental and numerical approaches for structural assessment in new footbridge designs (SFRSCC–GFPR hybrid structure)

Within the civil engineering field, the use of the Finite Element Method has acquired a significant importance, since numerical simulations have been employed in a broad field, which encloses the design, analysis and prediction of the structural behaviour of constructions and infrastructures. Nevertheless, these mathematical simulations can only be useful if all the mechanical properties of the materials, boundary conditions and damages are properly modelled. Therefore, it is required not only experimental data (static and/or dynamic tests) to provide references parameters, but also robust calibration methods able to model damage or other special structural conditions. The present paper addresses the model calibration of a footbridge bridge tested with static loads and ambient vibrations. Damage assessment was also carried out based on a hybrid numerical procedure, which combines discrete damage functions with sets of piecewise linear damage functions. Results from the model calibration shows that the model reproduces with good accuracy the experimental behaviour of the bridge.

An agent-based model to assess the attractiveness of industrial estates

This article describes the main approaches adopted in a study focused on planning industrial estates on a sub-regional scale. The study was supported by an agent-based model, using firms as agents to assess the attractiveness of industrial estates. The simulation was made by the NetLogo toolkit and the environment represents a geographical space. Three scenarios and four hypotheses were used in the simulation to test the impact of different policies on the attractiveness of industrial estates. Policies were distinguished by the level of municipal coordination at which they were implemented and by the type of intervention. In the model, the attractiveness of industrial estates was based on the level of facilities, amenities, accessibility and on the price of land in each industrial estate. Firms are able to move and relocate whenever they find an attractive estate. The relocating firms were selected by their size, location and distance to an industrial estate. Results show that a coordinated policy among municipalities is the most efficient policy to promote advanced-qualified estates. In these scenarios, it was observed that more industrial estates became attractive, more firms were relocated and more vacant lots were occupied. Furthermore, the results also indicate that the promotion of widespread industrial estates with poor-quality infrastructures and amenities is an inefficient policy to attract firms.

An immunity based configuration for multilayer single featured biometric authentication algorithms

Immune systems have been used in the last years to inspire approaches for several computational problems. This paper focus on behavioural biometric authentication algorithms’ accuracy enhancement by using them more than once and with different thresholds in order to first simulate the protection provided by the skin and then look for known outside entities, like lymphocytes do. The paper describes the principles that support the application of this approach to Keystroke Dynamics, an authentication biometric technology that decides on the legitimacy of a user based on his typing pattern captured on he enters the username and/or the password and, as a proof of concept, the accuracy levels of one keystroke dynamics algorithm when applied to five legitimate users of a system both in the traditional and in the immune inspired approaches are calculated and the obtained results are compared.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

MOEA/VAN: Multiobjective Evolutionary Algorithm based on Vector Angle Neighborhood

Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.