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.     

Residuated bilattices

We introduce a new product bilattice construction that generalizes the well-known one for interlaced bilattices and others that were developed more recently, allowing to obtain a bilattice with two residuated pairs as a certain kind of power of an arbitrary residuated lattice. We prove that the class of bilattices thus obtained is a variety, give a finite axiomatization for it and characterize the congruences of its members in terms of those of their lattice factors. Finally, we show how to employ our product construction to define first-order definable classes of bilattices corresponding to any first-order definable subclass of residuated lattices.     

Spatio-temporal hotspots and application on a disease analysis case via GIS

Hotspot analysis is a spatial analysis that uses cluster techniques for determining areas with elevated concentrations of localized events. We use the consolidated Extended Fuzzy C-Means algorithm to determine the hotspot areas on the map as circles, moreover the advantages of this technique are the linear computational complexity, the robustness to noise and outliers, the automatic determination of the optimal number C of clusters (in the classical FCM algorithm C is chosen a priori). Furthermore it prevents the problem of shifting the clusters with low density area of data points in areas with higher density of such points. We apply this method to study the spatio-temporal variations of the hotspot areas by testing this process on a specific disease problem, precisely we have clusterized 5,000 point-events correspondent to cases of brain cancer detected in the state of New Mexico from 1973 to 1991. We also show that the same results are obtained by using the Extended Gustafson–Kessel algorithm which gives elliptical clusters. We have implemented both algorithms in a Geographic Information System environment. Thus we establish the areas which seem not interested from the incidence of the disease and those areas in which the phenomenon appears to be temporarily attenuated either increased or constant or quite disappeared.     

Robust linear parameter‐varying control of induction motors

The paper deals with the motion control of an induction motor. Because the nonlinear state equations describing the dynamics of such a machine can be embedded into a linear model with the rotor speed ω as a varying parameter, advantage is taken of some recent results on the control of linear parameter‐varying systems, thus ensuring stability independently of how the varying parameter changes in time within a compact set. The adopted control structure consists of a fast inner electric loop that controls the stator currents and an outer mechanical loop that generates the torque acting on the motor shaft. Of crucial importance is the design of the internal model controller for the current loop. In particular, it is proved that an algebraically equivalent electric motor model admits a Lyapunov function that, together with its Lyapunov derivative, is independent of ω and of all motor parameters. This result allows us to find an upper bound on the norm of the Youla–Kucera parameter that ensures robust stability against speed measurement errors. Simulations carried out on a benchmark motor model show that the adopted control strategy performs well. Copyright © 2014 John Wiley & Sons, Ltd.     

Thymosin β4 is differentially expressed in the cerebrospinal fluid of Creutzfeldt‐Jakob disease patients: a MALDI‐TOF MS protein profiling study

MALDI‐TOF protein profiling analysis permits the detection of peptides and small proteins in complex protein mixtures with great accuracy. We applied this analysis to cerebrospinal fluid (CSF) from 15 patients affected by Creutzfeldt‐Jakob disease (CJD). We compared the levels of the normalized ion signals of 11 sporadic and 4 genetic CJD forms with those from ten healthy control subjects and eight non‐CJD relapsing‐remitting multiple sclerosis patients. In so doing, we detected 61 differentially expressed ion signals in CJD samples compared to controls. Among the 61 signals, 3 signals had significantly increased levels with high statistical significance (p <0.0001) and were located at 3238.3 m/z, 4963.7 m/z, and 8565.3 m/z. We characterized the 5.0 and 8.6 kDa proteins as thymosin β4 N‐acetylated and free ubiquitin, respectively, while the 3.2‐kDa peptide remained uncharacterized. Although elevated ubiquitin levels have previously been described in CJD, we have demonstrated for the first time the involvement of thymosin β4 in a neurodegenerative disease. To support the validity of thymosin β4 levels obtained by MALDI‐TOF analysis, an independent enzyme immunoassay analysis was performed. Moreover, a validation cohort consisting of CSF from three CJD patients, five healthy subjects, and six non‐CJD relapsing‐remitting multiple sclerosis patients was analyzed in a similar way, yielding superimposable results. We propose that thymosin β4 is a potential new candidate marker for the ante mortem diagnosis of CJD disease.     

Emerging specializations,competences and firms' proximity in digital industries: The case of London

In order to overcome the limitations of defining industrial specializations in digital industries through SIC codes, this paper suggests measuring the specializations and competences of these industries on the basis of the degree of digital technologies present in the products and services supplied. Metadata from CrunchBase are employed, as proxies of firms' specializations and competences which are defined as the fields of activity in which firms are involved. Applying a network analysis, these specializations and competences are linked to the recognition of emerging digital technologies and the strongest combinations of products and services. We tested the proposed methodology on London, a leading centre for the digital economy.     

Evaluation of log‐of‐power extremum seeking control for wind turbines using large eddy simulations

The extremum seeking control (ESC) algorithm has been proposed to determine operating parameters that maximize power production below rated wind speeds (region II). This is usually done by measuring the turbine's power signal to determine optimal values for parameters of the control law or actuator settings. This paper shows that the standard ESC with power feedback is quite sensitive to variations in mean wind speed, with long convergence time at low wind speeds and aggressive transient response, possibly unstable, at high wind speeds. The paper also evaluates the performance, as measured by the dynamic and steady state response, of the ESC with feedback of the logarithm of the power signal (LP‐ESC). Large eddy simulations (LES) demonstrate that the LP‐ESC, calibrated at a given wind speed, exhibits consistent robust performance at all wind speeds in a typical region II. The LP‐ESC is able to achieve the optimal set‐point within a prescribed settling time, despite variations in the mean wind speed, turbulence, and shear. The LES have been conducted using realistic wind input profiles with shear and turbulence. The ESC and LP‐ESC are implemented in the LES without assuming the availability of analytical gradients.     

Does carbon nanopowder threaten amphibian development?

Lethal and teratogenic potentials of carbon nanoparticles (CNPs) in their amorphous form were investigated by the standardized Frog Embryo Teratogenesis Assay-Xenopus (FETAX), a 96-h in vitro whole-embryo toxicity test based on the amphibian Xenopus laevis. Embryos were acutely exposed to 1, 10, 100 and 500 mg/L CNP suspensions and evaluated for lethality, malformations and growth inhibition. Larvae were processed for histological and ultrastructural analyses to detect the main affected organs, to look for specific lesions at the subcellular level and to image and track CNPs into tissues. Only the highest CNP suspension resulted in being embryolethal for X. laevis larvae, while malformed larva percentages significantly differed from controls starting from 100 mg/L. The stomach and gut were the preferential CNP accumulation sites, on the contrary, the digestive epithelium remained intact. The analyses showed the presence of isolated nanoparticles and/or aggregates in different secondary target organs. CNPs were found in circulating erythrocytes. The research confirms the good tolerance of X. laevis towards pure elemental carbon in its nanoparticulate amorphous form, but highlights the possibility of CNP transfer toward all body areas.     

An exploratory study of the relationship between construction workforce physical strain and task level productivity

The monitoring of construction workforce physical strain can be a valuable management strategy in improving workforce productivity, safety, health, and quality of work. Nevertheless, clear relationships between workforce performance and physical strain have yet to be established. An exploratory investigation of the relationship between task level productivity and physical strain was conducted. Nine participants individually performed a four-hour simulated construction task while a wearable physiological status monitor continuously assessed their physiological condition. Heart rate, relative heart rate, and breathing rate were utilized as predictors of physical strain, and task level–single factor productivity was used as an index of productivity. Numerous regression models were generated using the collected data. This investigation initially unsuccessfully attempted to establish a relationship between physiological condition and productivity at the individual worker level. However, an analysis of the regression models showed that there is a relationship between productivity and either heart rate or relative heart rate at the group level, and that this relationship is parabolic. Breathing rate was proved to not be a significant predictor of productivity. Research results significantly improve understanding of the relationship between work physiology and task productivity. Researchers and practitioners may use the tested monitoring devices, analysis methods, and results to design further applied studies and to improve workforce productivity.