Advances and Perspectives in Dental Pulp Stem Cell Based Neuroregeneration Therapies

Human dental pulp stem cells (hDPSCs) are some of the most promising stem cell types for regenerative therapies given their ability to grow in the absence of serum and their realistic possibility to be used in autologous grafts. In this review, we describe the particular advantages of hDPSCs for neuroregenerative cell therapies. We thoroughly discuss the knowledge about their embryonic origin and characteristics of their postnatal niche, as well as the current status of cell culture protocols to maximize their multilineage differentiation potential, highlighting some common issues when assessing neuronal differentiation fates of hDPSCs. We also review the recent progress on neuroprotective and immunomodulatory capacity of hDPSCs and their secreted extracellular vesicles, as well as their combination with scaffold materials to improve their functional integration on the injured central nervous system (CNS) and peripheral nervous system (PNS). Finally, we offer some perspectives on the current and possible future applications of hDPSCs in neuroregenerative cell therapies.     

Plant oil bioconversion into increase biological activity through lipases derived from wastes

The aim of this study was to show that abundant and inexpensive plant oils can be biotransformed to increase biological activity (antioxidant, antimicrobial and cytotoxicity) through hydrolysis reaction catalysed by lipases. We tested homemade and commercial lipases through the biotransformation of nine different plant oils in forty different combinations. First, the chemical composition of the samples was investigated. Thereafter, biological tests were conducted to assess the antioxidant and antimicrobial activity of the sampled biotransformation products, as well as analyzed their influence on the viability of healthy and cancer cells. Summarising, sunflower, corn and olive oils modified by orange waste-obtained lipases presented the most promising results, reaching up to 90% of antioxidant activity increase and significant growth inhibition of bacteria colonies belonging to genera Escherichia, Listeria, Staphylococcus, Pseudomonas and Salmonella. In addition, those compounds affected human oral squamous carcinoma cells. The bioconversion of plant oils through lipases improves their biological properties and might be an option for biotechnological application.     

Modification of coconut fibers with polyaniline for manufacture of pressure‐sensitive devices

This article studies the modification of coconut fibers with polyaniline (PAni) with help of statistical experimental design techniques. The main factors studied here were the techniques used for particle dispersion (sonication vs. magnetic stirring) and the type of initiator (ammonium persulfate, APS, vs. ammonium cerium sulfate dihydrate, Ce(IV)). The obtained materials were characterized through low field nuclear magnetic resonance, small‐and wide‐angle X‐ray scattering, scanning electron microscopy, fourier transform infrared spectroscopy, and ultraviolet–visible spectroscopy. Additionally, the electrical resistivities and respective sensitivities to variations of the applied pressure were evaluated for all obtained samples, with help of standard volume resistivity measurements and electro‐mechanical tests. The materials prepared through magnetic stirring with APS presented the best electrical and electromechanical properties, although materials prepared through sonication with Ce(IV) also presented good electrical and electromechanical properties and could be prepared much faster. As a consequence, modification of coconut fibers with PAni, using sonication as the particle dispersion technique and Ce(IV) as initiator, constitutes a very promising procedure for manufacture of pressure‐sensitive devices. POLYM. ENG. SCI., 54:2887–2895, 2014. © 2014 Society of Plastics Engineers     

Molds and Resists Studies for Nanoimprint Lithography of Electrodes in Low-Voltage Polymer Thin-Film Transistors

A low-cost patterning of electrodes was investigated looking forward to replacing conventional photolithography for the processing of low-operating voltage polymeric thin-film transistors. Hard silicon, etched by sulfur hexafluoride and oxygen gas mixture, and flexible polydimethylsiloxane imprinting molds were studied through atomic force microscopy (AFM) and field emission gun scanning electron microscopy. The higher the concentration of oxygen in reactive ion etching, the lower the etch rate, sidewall angle, and surface roughness. A concentration around 30 % at 100 mTorr, 65 W and 70 sccm was demonstrated as adequate for submicrometric channels, presenting a reduced etch rate of 176 nm/min. Imprinting with positive photoresist AZ1518 was compared to negative SU-8 2002 by optical microscopy and AFM. Conformal results were obtained only with the last resist by hot embossing at 120 °C and 1 kgf/cm² for 2 min, followed by a 10 min post-baking at 100 °C. The patterning procedure was applied to define gold source and drain electrodes on oxide-covered substrates to produce bottom-gate bottom-contact transistors. Poly(3-hexylthiophene) (P3HT) devices were processed on high-κ titanium oxynitride (TiO _x N _y ) deposited by radiofrequency magnetron sputtering over indium tin oxide-covered glass to achieve low-voltage operation. Hole mobility on micrometric imprinted channels may approach amorphous silicon (～0.01 cm²/V s) and, since these devices operated at less than 5 V, they are not only suitable for electronic applications but also as sensors in aqueous media.     

Generalized Hermitian Radial Basis Functions Implicits from polygonal mesh constraints

In this work we investigate a generalized interpolation approach using radial basis functions to reconstruct implicit surfaces from polygonal meshes. With this method, the user can define with great flexibility three sets of constraint interpolants: points, normals, and tangents; allowing to balance computational complexity, precision, and feature modeling. Furthermore, this flexibility makes possible to avoid untrustworthy information, such as normals estimated on triangles with bad aspect ratio. We present results of the method for applications related to the problem of modeling 2D curves from polygons and 3D surfaces from polygonal meshes. We also apply the method to problems involving subdivision surfaces and front-tracking of moving boundaries. Finally, as our technique generalizes the recently proposed HRBF Implicits technique, comparisons with this approach are also conducted.     

Parallelization of the FMM on distributed-memory GPGPU systems for acoustic-scattering prediction

In this work, we carry out the parallelization of the single level Fast Multipole Method (FMM) for solving acoustic-scattering problems (using the Helmholtz equation) on distributed-memory GPGPU systems. With the aim of enlarging the scope of feasible simulations, the presented solution combines the techniques developed for our distributed-memory CPU solver with our shared-memory GPGPU solver. The performance of the developed solution is proved using two different GPGPU clusters: the first one consists of two workstations with NVIDIA GTX 480 GPUs linked by a Gigabit Ethernet network, and the second one comprises four nodes with NVIDIA Tesla M2090 GPUs linked by an Infiniband network.     

Dynamic Epistemic Logic for Implicit and Explicit Beliefs

Epistemic logic with its possible worlds semantic model is a powerful framework that allows us to represent an agent’s information not only about propositional facts, but also about her own information. Nevertheless, agents represented in this framework are logically omniscient: their information is closed under logical consequence. This property, useful in some applications, is an unrealistic idealisation in some others. Many proposals to solve this problem focus on weakening the properties of the agent’s information, but some authors have argued that solutions of this kind are not completely adequate because they do not look at the heart of the matter: the actions that allow the agent to reach such omniscient state. Recent works have explored how acts of observation, inference, consideration and forgetting affect an agent’s implicit and explicit knowledge; the present work focuses on acts that affect an agent’s implicit and explicit beliefs. It starts by proposing a framework in which these two notions can be represented, and then it looks into their dynamics, first by reviewing the existing notion of belief revision, and then by introducing a rich framework for representing diverse forms of inference that involve both knowledge and beliefs.     

Forecasting Financial Failure of Firms via Genetic Algorithms

Given a wide amount of possible ratios available for constructing a LOGIT model for forecasting bankruptcy, this paper provides a computational search methodology, only guided by data, for selecting the financial ratios employed in the model. This procedure is based on genetic algorithms which are used to explore the universe of models made available by all possible existing financial ratios (with very redundant information). This search process of the correct model is guided by the Schwarz information criterion. As an empirical illustration, the methodology is applied to forecasting the failure of firms in the Spanish building industry using annual public accounting information.