In vitro mechanical fatigue behavior of poly-ε-caprolactone macroporous scaffolds for cartilage tissue engineering: Influence of pore filling by a poly(vinyl alcohol) gel

Polymeric scaffolds used in regenerative therapies are implanted in the damaged tissue and subjected to repeated loading cycles. In the case of articular cartilage engineering, an implanted scaffold is typically subjected to long term dynamic compression. The evolution of the mechanical properties of the scaffold during bioresorption has been deeply studied in the past, but the possibility of failure due to mechanical fatigue has not been properly addressed. Nevertheless, the macroporous scaffold is susceptible to failure after repeated loading-unloading cycles. In this work fatigue studies of polycaprolactone scaffolds were carried by subjecting the scaffold to repeated compression cycles in conditions simulating the scaffold implanted in the articular cartilage. The behaviour of the polycaprolactone sponge with the pores filled with a poly(vinyl alcohol) gel simulating the new formed tissue within the pores was compared with that of the material immersed in water. Results were analyzed with Morrow’s criteria for failure and accurate fittings are obtained just up to 200 loading cycles. It is also shown that the presence of poly(vinyl alcohol) increases the elastic modulus of the scaffolds, the effect being more pronounced with increasing the number of freeze/thawing cycles.

Mortars with incorporation of PCM based in different binders: mechanical and thermal behavior

Currently we are witnessing a huge concern of society with the parameters of comfort of the buildings and the energetic consumptions. It is known that there is a huge consumption of non-renewable sources of energy. Thus, it is urgent to develop and explore ways to take advantage of renewable sources of energy by improving the energy efficiency of buildings. The mortars with incorporation of phase change materials (PCM) have the ability to regulate the temperature inside buildings, contributing to the thermal comfort and reduction of the use of heating and cooling equipment, using only the energy supplied by the sun. However, the incorporation of phase change materials in mortars modifies its characteristics. The main purpose of this study was mechanical and thermal characterization of mortars with incorporation of PCM in mortars based in different binders. The binders studied were aerial lime, hydraulic lime, gypsum and cement. For each type of binder a reference composition (0% PCM) and a composition with incorporation of 40% of PCM were developed. It was possible to observe that the incorporation of PCM in mortars caused differences in properties such as workability, compressive strength, flexural strength and adhesion, however leads to an improvement of thermal behavior.

Adult learning and education policies in Germany, Portugal and Sweden: An analysis of national reports to CONFINTEA VI

The chapter presents a theoretical proposal of three analytical models of Adult Learning and Education (ALE) policies. Some analytical categories and the corresponding dimensions are organised according to the ALE rationale which is typical of each social policy model. Historical, cultural and educational features are mentioned in connexion with the different policy models and its interpretative capacity to making sense of policies and practices implemented in Germany, Portugal and Sweden. !e analysis includes the states of the art and the official representations of ALE produced by the respective national authorities through national reports which were presented to CONFINTEA VI (2009).

Enhanced resistive switching and multilevel behavior in bilayered HfAlO/HfAlOx structures for non-volatile memory applications

In this work, hafnium aluminum oxide (HfAlO) thin films were deposited by ion beam sputtering deposition technique on Si substrate. The presence of oxygen vacancies in the HfAlOx layer deposited in oxygen deficient environment is evidenced from the photoluminescence spectra. Furthermore, HfAlO(oxygen rich)/HfAlOx(oxygen poor) bilayer structures exhibit multilevel resistive switching (RS), and the switching ratio becomes more prominent with increasing the HfAlO layer thickness. The bilayer structure with HfAlO/HfAlOx thickness of 30/40 nm displays the enhanced multilevel resistive switching characteristics, where the high resistance state/ intermediate resistance state (IRS) and IRS/low resistance state resistance ratios are 102 and 5 105 , respectively. The switching mechanisms in the bilayer structures were investigated by the temperature dependence of the three resistance states. This study revealed that the multilevel RS is attributed to the coupling of ionic conduction and the metallic conduction, being the first associated to the formation and rupture of conductive filaments related to oxygen vacancies and the second with the formation of a metallic filament. Moreover, the bilayer structures exhibit good endurance and stability in time.

Properties and thermal behavior of natural deep eutectic solvents

Natural deep eutectic solvents (NADES) have shown to be promising sustainable media for a wide range of applications. Nonetheless, very limited data is available on the properties of these solvents. A more comprehensive body of data on NADES is required for a deeper understanding of these solvents at molecular level, which will undoubtedly foster the development of new applications. NADES based on choline chloride, organic acids, amino acids and sugars were prepared, and their density, thermal behavior, conductivity and polarity were assessed, for different NADES compositions. The NADES studied can be stable up to 170 Â°C, depending on their composition. The thermal characterization revealed that all the NADES are glass formers and some, after water removal, exhibit crystallinity. The morphological characterization of the crystallizable materials was performed using polarized optical microscopy which also provided evidence of homogeneity/phase separation. The conductivity of the NADES was also assessed from 0 to 40 Â°C. The more polar, organic acid-based NADES presented the highest conductivities. The conductivity dependence on temperature was well described by the Vogelâ Fulcherâ Tammann equation for some of the NADES studied.

Ranking procedure based on mechanical, durability and thermal behavior of mortars with incorporation of phase change materials

Nowadays, considering the high variety of construction products, adequate material selection, based on their properties and function, becomes increasingly important. In this research, a ranking procedure developed by Czarnecki and Lukowski is applied in mortars with incorporation of phase change materials (PCM). The ranking procedure transforms experimental results of properties into one numerical value. The products can be classified according to their individual properties or even an optimized combination of different properties. The main purpose of this study was the ranking of mortars with incorporation of different contents of PCM based in different binders. Aerial lime, hydraulic lime, gypsum and cement were the binders studied. For each binder, three different mortars were developed. Reference mortars, mortars with incorporation of 40% of PCM and mortars with incorporation of 40% of PCM and 1% of fibers, were tested. Results show that the incorporation of PCM in mortars changes their global performance.

Prenatal dopamine and neonatal behavior and biochemistry

Depressed pregnant women (N=126) were divided into high and low prenatal maternal dopamine (HVA) groups based on a tertile split on their dopamine levels at 20 weeks gestation. The high versus the low dopamine group had lower Center for Epidemiological Studies-Depression Scale (CES-D) scores, higher norepinephrine levels at the 20-week gestational age visit and higher dopamine and serotonin levels at both the 20- and the 32-week gestational age visits. The neonates of the mothers with high versus low prenatal dopamine levels also had higher dopamine and serotonin levels as well as lower cortisol levels. Finally, the neonates in the high dopamine group had better autonomic stability and excitability scores on the Brazelton Neonatal Behavior Assessment Scale. Thus, prenatal maternal dopamine levels appear to be negatively related to prenatal depression scores and positively related to neonatal dopamine and behavioral regulation, although these effects are confounded by elevated serotonin levels.

Using polymers to improve the rutting resistance of asphalt concrete

This study assesses rutting on two types of modified asphalt mixtures containing: (i) amorphous polyolefin polymer and (ii) a particular polymer obtained by combining LDPE (low density polyethylene) and EVA (ethyl-vinyl-acetate). Rutting tests were performed by a wheel tracking device. Stiffness and fatigue tests were carried out to confirm the performance of the asphalt mixtures. The testing showed that polymer modification in this study improved rut resistance without compromising the stiffness and fatigue behavior. The rutting results were fit in the NCHRP 1-37A model and the in situ rutting performance of asphalt mixtures can be predicted.

Mechanical fatigue performance of PCL-chondroprogenitor constructs after cell culture under bioreactor mechanical stimulus

In tissue engineering of cartilage, polymeric scaffolds are implanted in the damaged tissue and subjected to repeated compression loading cycles. The possibility of failure due to mechanical fatigue has not been properly addressed in these scaffolds. Nevertheless, the macroporous scaffold is susceptible to failure after repeated loading-unloading cycles. This is related to inherent discontinuities in the material due to the micropore structure of the macro-pore walls that act as stress concentration points. In this work, chondrogenic precursor cells have been seeded in Poly-ε-caprolactone (PCL) scaffolds with fibrin and some were submitted to free swelling culture and others to cyclic loading in a bioreactor. After cell culture, all the samples were analyzed for fatigue behavior under repeated loading-unloading cycles. Moreover, some components of the extracellular matrix (ECM) were identified. No differences were observed between samples undergoing free swelling or bioreactor loading conditions, neither respect to matrix components nor to mechanical performance to fatigue. The ECM did not achieve the desired preponderance of collagen type II over collagen type I which is considered the main characteristic of hyaline cartilage ECM. However, prediction in PCL with ECM constructs was possible up to 600 cycles, an enhanced performance when compared to previous works. PCL after cell culture presents an improved fatigue resistance, despite the fact that the measured elastic modulus at the first cycle was similar to PCL with poly(vinyl alcohol) samples. This finding suggests that fatigue analysis in tissue engineering constructs can provide additional information missed with traditional mechanical measurements.

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.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.