Tendon regeneration through a scaffold-free approach: development of tenogenic magnetic hASCs sheets

Tendon's regeneration is limited, demanding for cell-based strategies to fully restore their functionality upon injury. The concept of magnetic force-based TE(1), generally using magnetic nanoparticles may enable, for example, stem cell stimulation and/or remote control over TE constructs. Thus, we originally propose the development of magnetic cell sheets (magCSs) with tenogenic capability, aimed at promoting tendon's regeneration. A Tenomodulin (TNMD+) subpopulation was sorted from human adipose stem cells (hASCs), using TNMD-coated immunomagnetic beads(2) and used as cell source for the development of magCSs. Briefly, cells were labeled with iron oxide composite particles (Micromod) and cultured for 7 days in α-MEM medium with or without magnetic stimulation provided by a magnetic device (nanoTherics). CSs were retrieved from the plates using magnet attraction as contiguous sheets of cells within its own deposited ECM.

Spatio-temporal modelling of environmental data

Programa Doutoral em Matemática e Aplicações.

A high order model for piezoelectric rods: an asymptotic approach

Preprint submitted to International Journal of Solids and Structures. ISSN 0020-7683

A continuous solvent- and oil-free method to prepare injectable PEGylated fibrinogen cell-laden microparticles

Injectable biomaterials with in situ cross-linking reactions have been suggested to minimize the invasiveness associated with most implantation procedures. However, problems related with the rapid liquid-to-gel transition reaction can arise because it is difficult to predict the reliability of the reaction and its end products, as well as to mitigate cytotoxicity to the surrounding tissues. An alternative minimally invasive approach to deliver solid implants in vivo is based on injectable microparticles, which can be processed in vitro with high fidelity and reliability, while showing low cytotoxicity. Their delivery to the defect can be performed by injection through a small diameter syringe needle. We present a new methodology for the continuous, solvent- and oil-free production of photopolymerizable microparticles containing encapsulated human dermal fibroblasts. A precursor solution of cells in photo-reactive PEG-fibrinogen (PF) polymer was transported through a transparent injector exposed to light-irradiation before being atomized in a jet-in-air nozzle. Shear rheometry data provided the cross-linking kinetics of each PF/cell solution, which was then used to determine the amount of irradiation required to partially polymerize the mixture prior to atomization. The partially polymerized drops fell into a gelation bath for further polymerization. The system was capable of producing cell-laden microparticles with high cellular viability, with an average diameter of between 88.1 µm to 347.1 µm and a dispersity of between 1.1 and 2.4, depending on the parameters chosen.

Fatores de sucesso e dificuldades das spin-offs empresariais: o caso das agências de mediação de seguros

Dissertação de mestrado em Marketing e Estratégia

Analytical approach for the flexural analysis of RC beams strengthened with prestressed CFRP

The objective of this paper is to propose a simplified analytical approach to predict the flexural behavior of simply supported reinforced-concrete (RC) beams flexurally strengthened with prestressed carbon fiber reinforced polymer (CFRP) reinforcements using either externally bonded reinforcing (EBR) or near surface mounted (NSM) techniques. This design methodology also considers the ultimate flexural capacity of NSM CFRP strengthened beams when concrete cover delamination is the governing failure mode. A moment–curvature (M–χ) relationship formed by three linear branches corresponding to the precracking, postcracking, and postyielding stages is established by considering the four critical M–χ points that characterize the flexural behavior of CFRP strengthened beams. Two additional M–χ points, namely, concrete decompression and steel decompression, are also defined to assess the initial effects of the prestress force applied by the FRP reinforcement. The mid-span deflection of the beams is predicted based on the curvature approach, assuming a linear curvature variation between the critical points along the beam length. The good predictive performance of the analytical model is appraised by simulating the force–deflection response registered in experimental programs composed of RC beams strengthened with prestressed NSM CFRP reinforcements.

Assessment of the effectiveness of steel fibre reinforcement for the punching resistance of flat slabs by experimental research and design approach

The present paper deals with the experimental assessment of the effectiveness of steel fibre reinforcement in terms of punching resistance of centrically loaded flat slabs, and to the development of an analytical model capable of predicting the punching behaviour of this type of structures. For this purpose, eight slabs of 2550 x 2550 x 150 mm3 dimensions were tested up to failure, by investigating the influence of the content of steel fibres (0, 60, 75 and 90 kg/m3) and concrete strength class (50 and 70 MPa). Two reference slabs without fibre reinforcement, one for each concrete strength class, and one slab for each fibre content and each strength class compose the experimental program. All slabs were flexurally reinforced with a grid of ribbed steel bars in a percentage to assure punching failure mode for the reference slabs. Hooked ends steel fibres provided the unique shear reinforcement. The results have revealed that steel fibres are very effective in converting brittle punching failure into ductile flexural failure, by increasing both the ultimate load and deflection, as long as adequate fibre reinforcement is assured. An analytical model was developed based on the most recent concepts proposed by the fib Mode Code 2010 for predicting the punching resistance of flat slabs and for the characterization of the behaviour of fibre reinforced concrete. The most refined version of this model was capable of predicting the punching resistance of the tested slabs with excellent accuracy and coefficient of variation of about 5%.

A model to simulate the moment-rotation and crack width of FRC members reinforced with longitudinal bars

The present work describes a model for the determination of the moment–rotation relationship of a cross section of fiber reinforced concrete (FRC) elements that also include longitudinal bars for the flexural reinforcement (R/FRC). Since a stress–crack width relationship (σ–w)(σ–w) is used to model the post-cracking behavior of a FRC, the σ–w directly obtained from tensile tests, or derived from inverse analysis applied to the results obtained in three-point notched beam bending tests, can be adopted in this approach. For a more realistic assessment of the crack opening, a bond stress versus slip relationship is assumed to simulate the bond between longitudinal bars and surrounding FRC. To simulate the compression behavior of the FRC, a shear friction model is adopted based on the physical interpretation of the post-peak compression softening behavior registered in experimental tests. By allowing the formation of a compressive FRC wedge delimited by shear band zones, the concept of concrete crushing failure mode in beams failing in bending is reinterpreted. By using the moment–rotation relationship, an algorithm was developed to determine the force–deflection response of statically determinate R/FRC elements. The model is described in detail and its good predictive performance is demonstrated by using available experimental data. Parametric studies were executed to evidence the influence of relevant parameters of the model on the serviceability and ultimate design conditions of R/FRC elements failing in bending.

Plastic-damage smeared crack model to simulate the behaviour of structures made by cement based materials

This work proposes a constitutive model to simulate nonlinear behaviour of cement based materials subjected to different loading paths. The model incorporates a multidirectional fixed smeared crack approach to simulate crack initiation and propagation, whereas the inelastic behaviour of material between cracks is treated by a numerical strategy that combines plasticity and damage theories. For capturing more realistically the shear stress transfer between the crack surfaces, a softening diagram is assumed for modelling the crack shear stress versus crack shear strain. The plastic damage model is based on the yield function, flow rule and evolution law for hardening variable, and includes an explicit isotropic damage law to simulate the stiffness degradation and the softening behaviour of cement based materials in compression. This model was implemented into the FEMIX computer program, and experimental tests at material scale were simulated to appraise the predictive performance of this constitutive model. The applicability of the model for simulating the behaviour of reinforced concrete shear wall panels submitted to biaxial loading conditions, and RC beams failing in shear is investigated.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Child abuse monitor system model: a health care critical knowledge monitor system

The Childhood protection is a subject with high value for the society, but, the Child Abuse cases are difficult to identify. The process from suspicious to accusation is very difficult to achieve. It must configure very strong evidences. Typically, Health Care services deal with these cases from the beginning where there are evidences based on the diagnosis, but they aren’t enough to promote the accusation. Besides that, this subject it’s highly sensitive because there are legal aspects to deal with such as: the patient privacy, paternity issues, medical confidentiality, among others. We propose a Child Abuses critical knowledge monitor system model that addresses this problem. This decision support system is implemented with a multiple scientific domains: to capture of tokens from clinical documents from multiple sources; a topic model approach to identify the topics of the documents; knowledge management through the use of ontologies to support the critical knowledge sensibility concepts and relations such as: symptoms, behaviors, among other evidences in order to match with the topics inferred from the clinical documents and then alert and log when clinical evidences are present. Based on these alerts clinical personnel could analyze the situation and take the appropriate procedures.

Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables

Lecture Notes in Computer Science, 9273

Connecting free volume with shape memory properties in noncytotoxic gamma-irradiated polycyclooctene

The free volume holes of a shape memory polymer have been analysed considering that the empty space between molecules is necessary for the molecular motion, and the shape memory response is based on polymer segments acting as molecular switches through variable flexibility with temperature or other stimuli. Therefore, thermomechanical analysis (TMA) and positron annihilation lifetime spectroscopy (PALS) have been applied to analyse shape recovery and free volume hole sizes in gamma irradiated polycyclooctene (PCO) samples, as a non-cytotoxic alternative to more conventional PCO crosslinked via peroxide for future applications in medicine. Thus, a first approach relating structure, free volume holes and shape memory properties in gamma irradiated PCO is presented. The results suggest that free volume holes caused by gamma irradiation in PCO samples facilitate the recovery process by improving movement of polymer chains and open t possibilities for the design and control of the macroscopic response.

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.