DNA Methylation Signatures of Bone Metabolism in Osteoporosis and Osteoarthritis Aging-Related Diseases: An Updated Review

DNA methylation is one of the most studied epigenetic mechanisms that play a pivotal role in regulating gene expression. The epigenetic component is strongly involved in aging-bone diseases, such as osteoporosis and osteoarthritis. Both are complex multi-factorial late-onset disorders that represent a globally widespread health problem, highlighting a crucial point of investigations in many scientific studies. In recent years, new findings on the role of DNA methylation in the pathogenesis of aging-bone diseases have emerged. The aim of this systematic review is to update knowledge in the field of DNA methylation associated with osteoporosis and osteoarthritis, focusing on the specific tissues involved in both pathological conditions.     

Building kernels from binary strings for image matching.

In the statistical learning framework, the use of appropriate kernels may be the key for substantial improvement in solving a given problem. In essence, a kernel is a similarity measure between input points satisfying some mathematical requirements and possibly capturing the domain knowledge. In this paper, we focus on kernels for images: we represent the image information content with binary strings and discuss various bitwise manipulations obtained using logical operators and convolution with nonbinary stencils. In the theoretical contribution of our work, we show that histogram intersection is a Mercer's kernel and we determine the modifications under which a similarity measure based on the notion of Hausdorff distance is also a Mercer's kernel. In both cases, we determine explicitly the mapping from input to feature space. The presented experimental results support the relevance of our analysis for developing effective trainable systems.     

Promoting the integration of technology in teaching: An analysis of the factors that increase the intention to use technologies among Italian teachers

Background: The rapid spread of COVID-19 forced many countries to adopt severe containment measures, transferring all didactic activities into virtual environments. However, the integration of technology in teaching may present difficulties, especially in some countries, such as Italy. Objectives: The present study analyzed how the two main factors of the Technology Acceptance Model (TAM), namely perceived usefulness and perceived ease of use, combined with online teaching self-efficacy, were associated with the intention to use technology to teach. We posited a moderated moderation model in which perceived usefulness represented the main predictor, with self-efficacy and perceived ease of use as moderators of intention to use technology to teach. Method: The model was tested through multiple regressions, using the PROCESS macro on SPSS 26 with a sample of 178 upper secondary school teachers in Italy. Results and conclusions: Regressions showed that each variable significantly predicted the intention to use technology. In addition, a moderation effect of self-efficacy on the perceived usefulness of using technology was found for medium and high-level of perceived ease of use of technology. Implications: The present study provides targeted implications for distance education policy and practice to promote its adoption (or the blended modality) in Italian upper schools.     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

Synthesis and Evaluation of Voltage-Gated Sodium Channel Blocking Pyrroline Derivatives Endowed with Both Antiarrhythmic and Antioxidant Activities

Under the hypothesis that cardioprotective agents might benefit from synergism between antiarrhythmic activity and antioxidant properties, a small series of mexiletine analogues were coupled with the 2,2,5,5-tetramethylpyrroline moiety, known for its antioxidant effect, in order to obtain dual-acting drugs potentially useful in the protection of the heart against post-ischemic reperfusion injury. The pyrroline derivatives reported herein were found to be more potent as antiarrhythmic agents than mexiletine and displayed antioxidant activity. The most interesting tetramethylpyrroline congener, a tert-butyl-substituted analogue, was at least 100 times more active as an antiarrhythmic than mexiletine.     

Electrochemical characterization of 8-hydroxyquinoline-5-sulphonate/aluminium(III) aqueous solutions

8-Hydroxyquinoline-5-sulphonate/Al(III) aqueous solutions were studied both by potentiometric titrations and voltammetric measurements, in order to obtain the number, the stoichiometry and the stability constants of the complexes formed at equilibrium, and to evaluate the redox and (electro)kinetic properties of the free ligand and of the metal/ligand complexes. The complexes formed in 0.2 m (Na)Cl aqueous solution (stability log beta values ± standard deviation) are AlL⁺ (8.95 ± 0.05), AlL₂⁻ (17.43 ± 0.03) and AlL₃³⁻ (24.58 ± 0.05), where “L” denotes the free ligand in the completely deprotonated form (L²⁻, pK_a1 = 3.910 ± 0.008, pK_a2 = 8.319 ± 0.004). AlL₃³⁻ is the predominant Al(III) species in a very wide range of pH, metal and ligand concentrations and metal-to-ligand ratios. The free ligand shows an oxidation wave at 0.62 V versus SCE. The proposed oxidation mechanism includes a first reversible one-electron oxidation of the ligand, followed by a coupling reaction and by a second reversible one-electron oxidation, and finally by a decomposition reaction. The addition of Al(III) lowers the intensity of the oxidation wave due to the formation of the redox-inactive complex AlL₃³⁻. A residual low signal was attributed to the free ligand produced by the complex dissociation, AlL₃³⁻ = AlL₂⁻ + L²⁻. All the kinetic parameters involved in the ligand oxidation and in the complex disruption were calculated on the basis of the agreement between experimental and simulated linear sweep and cyclic voltammetries. Correctness of the mechanisms proposed was further confirmed “a posteriori” by the agreement between potentiometric and linear sweep voltammetric results. The low residual signal observed in the presence of fully formed complex was attributed to the free ligand produced by the complex dissociation, having a kinetic constant estimated 0.2 s⁻¹.     

Conformational transitions in poly(butyl methacrylate)

Summary Temperature dependence of viscosity data was investigated on poly(butyl methacrylate) fractions in dilute solution. A discontinuity in intrinsic viscosity was observed in the 25°C–35°C temperature range. Sharp changes of unperturbed dimensions were visualized from this behaviour. The phenomenon can be accounted for by assuming conformational changes of the chain in different solvents.     

Valorization of Al slag in the production of green ceramic tiles: Effect of experimental conditions on microstructure and crystalline phase composition

This work reports the results of an investigation aimed at the development of sintered glass-ceramic tiles by the sinter-crystallization of mixtures composed of aluminum slag and reclaimed packaging glass. The thermal behaviors of mixtures incorporating 50 and 60 wt% Al slag were established by differential thermal analysis (DTA). Green compacts were sintered in a temperature range of 800°C-1050°C and then soaked for 10-60 minutes. The mineralogical characterizations of the sintered materials were obtained by X-ray diffraction (XRD). The achieved results indicated that the sintering of aluminum slag and packaging glass reclaimed led to a glass-ceramic material composed mainly of needle-like crystals of wollastonite (CaSiO₃). The bloating of samples during firing was evaluated according to a Cougny predictive diagram. After initial observations and according to bending strength characterizations, sintered tiles prepared from aluminum slag and glassy sand are appropriate for floor pavement and wall covering.     

Immersive and desktop virtual reality in virtual fashion stores: a comparison between shopping experiences

Virtual Reality - With the high growth and prosperity of e-commerce, the retail industry needs to explore new technologies that improve digital shopping experiences. In the current technological...