Fat Distribution and Lipid Profile of Young Adults with Congenital Adrenal Hyperplasia Due to 21-Hydroxylase Enzyme Deficiency

We aimed to compare detailed fat distribution and lipid profile between young adults with congenital adrenal hyperplasia due to 21-hydroxylase enzyme deficiency and a control group. We also verified independent associations of treatment duration and daily hydrocortisone dose equivalent (HDE) with lipid profile within patients. This case–control study included 23 patients (7 male and 16 female) matched by an age range of young adults (18–31 years) with 20 control subjects (8 male and 12 female). Dual energy X-ray absorptiometry was used to measure the fat distribution. Male patients demonstrated elevated indices of fat mass for total (7.7 ± 2.1 vs. 4.5 ± 1.3 kg/m², p = 0.003), trunk (4.0 ± 1.2 vs. 2.2 ± 0.8 kg/m², p = 0.005), android (0.63 ± 0.24 vs. 0.32 ± 0.15 kg/m², p = 0.008), gynoid (1.34 ± 0.43 vs. 0.74 ± 0.24 kg/m², p = 0.005), arm (0.65 ± 0.16 vs. 0.39 ± 0.10 kg/m², p = 0.009), and leg regions (2.7 ± 0.8 vs. 1.6 ± 0.4 kg/m², p = 0.005) than the control group, but not in females. However, female patients demonstrated elevated ratio of low-density lipoprotein cholesterol to high-density lipoprotein cholesterol (1.90 ± 0.46 vs. 1.39 ± 0.47, p = 0.009) than the control group, but not in males. Total fat mass was inversely correlated with total testosterone (r = −0.64, p = 0.014) and positively correlated with leptin in males (r = 0.75, p = 0.002). An elevated daily HDE (β = 0.43, p = 0.038 and β = 0.47, p = 0.033) and trunk to total fat mass ratio (β = 0.46, p = 0.025, and β = 0.45, p = 0.037) were independently correlated with impaired lipid profile markers. Although there is no altered lipid profile, male patients demonstrated an increased fat distribution. However, female patients presented with an impaired lipid profile marker but demonstrated close values of normal fat distribution. Interestingly, the dose of glucocorticoid therapy can have some role in the lipid mechanisms.     

A rubber-modified thermoplastic where the morphology produced by phase-separation induced by polymerization disappears at high conversions

An unexpected experimental finding is reported where the primary morphology developed during polymerization-induced phase separation in a rubber-modified thermoplastic practically disappears at high conversions. This process was evidenced by light scattering (LS) and scanning electron microscopy (SEM) for a particular composition of solutions of polyisobutylene oligomers (PIB) in isobornylmethacrylate (IBoMA), during the free-radical polymerization of the monomer. The primary phase separation produced a dispersion of domains rich in PIB containing significant amounts of the monomer (IBoMA). Polymerization of the monomer in these domains occurred at high overall conversions producing the filling of dispersed domains with a PIBoMA-PIB blend that could not be distinguished from the matrix. Under these conditions the final material had the appearance of a homogeneous blend.     

Effect of different surface treatments on polypropylene composites reinforced with yerba mate fibers: Physical,mechanical, chemical,and morphological properties

This study presents the preparation of post-consumer polypropylene (r-PP) composites filled with 30 wt% yerba mate (YM) stick particles. To improve the fiber–matrix adhesion, three surface treatments were performed: alkaline treatment with sodium hydroxide (NaOH) and use of 3-aminopropyltriethoxysilane (APTS) and maleic anhydride graft polypropylene copolymer (PP-g-MA) as coupling agents. Mechanical properties including tensile, flexural, and impact resistance were determined, and chemical (Fourier transform infrared spectroscopy [FTIR]), physical (water absorption), and morphological analyses were performed. The main findings show that the treatments were efficient in improving the mechanical properties of the composites, with emphasis on the r-PP/YM30/APTS and r-PP/YM30/PP-g-MA composites, which proved to be superior in tensile, flexion and impact strength and absorption of water compared to the untreated composite. The morphological analysis showed a better interaction between the fiber and the polymeric matrix for the composites with YM/APTS and YM/PP-g-MA, which corroborates the results of tensile and flexural strength, as well as with the spectra of FTIR in which the chemical modification of the fibers is observed. However, the results show that these treatments are promising in obtaining composites with recycled matrix with better properties.     

Stochastic approximation learning for mixtures of multivariate elliptical distributions

Most of the current approaches to mixture modeling consider mixture components from a few families of probability distributions, in particular from the Gaussian family. The reasons of these preferences can be traced to their training algorithms, typically versions of the Expectation-Maximization (EM) method. The re-estimation equations needed by this method become very complex as the mixture components depart from the simplest cases. Here we propose to use a stochastic approximation method for probabilistic mixture learning. Under this method it is straightforward to train mixtures composed by a wide range of mixture components from different families. Hence, it is a flexible alternative for mixture learning. Experimental results are presented to show the probability density and missing value estimation capabilities of our proposal.     

New learning rules for the ASSOM network

The ASSOM is a self-organising neural network with the capability of adapting to linear subspaces. Here we propose two new methods to train the ASSOM network. A nonlinear system of equations is derived for network training. This system can be solved by a gradient-based approach or by the Levenberg–Marquardt method. Each of these two approaches gives a different learning rule. A comparison is carried out among the original Kohonens method and the proposed learning rules. Experimental results are reported, including a convergence speed experiment and a speech processing application, which show that the new learning rules have better performance than the original one.     

Internal structure analysis of Polypropylene/quartz composites related to their toughness

The mechanical performance of composite materials has been related to a wide range of factors. The complexity of analysis lays on the fact that any single variation normally affects many characteristics or properties of the composite material. For this reason, different theoretical and experimental characterizations should be considered. In this work the internal structure, interactions and fracture surfaces of PP‐based composites reinforced with quartz particles are investigated. Particle size distributions, rheological measurements and multifractal spectra suggest favorable filler dispersion into the PP matrix. A yield strength model and rheological data evidence low internal interactions. In addition, these composites characteristics could promote the effective activation of energy consumption mechanisms improving the material toughness. The fracture surfaces analysis allows correlating fractography, multifractal spectra and material toughness. However, the experimental procedure of multifractal theory should be improved to define the most sensitive parameter for fractographic studies. POLYM. COMPOS., 37:1488–1496, 2016. © 2014 Society of Plastics Engineers     

An 8-Bit Digitally Controlled Programmable Phase Shifter Circuit for Sinusoidal Signals with 252∘ Phase Control Range

In this paper, the synthesis, design, and implementation of a programmable phase shifter circuit for sinusoidal signals is presented. The proposed circuit, built-up herein with operational amplifiers (OPAMPs), high precision resistors and low voltage switches, consists of a digitally controlled amplitude attenuator in combination with a single-tone orthogonalizer. Experimental results agree with theoretical background: the attained phase range was 252^° in 256 steps with a median step of 0.9^°. The inaccuracy of the circuit was determined to be of 0.03 %. Contrary to other OPAMP approaches for sinusoidal signals reported in the literature and based on a first-order all-pass filter structure, the approximation suggested in this work is based on a different concept. The achieved results demonstrate the functionality of the system for the case of a sinusoidal signal with frequency of 1 kHz. Notwithstanding, the proposed architecture can be extended to operate at higher frequencies by using different building blocks with larger bandwidth. Furthermore, it can be extended as well to work out with other periodic input waveforms, like triangular shapes or square waves, with the use of an appropriate orthogonalizer.     

Self-Organizing Dynamic Graphs

We propose a new self-organizing neural model that considers a dynamic topology among neurons. This leads to greater plasticity with respect to the self-organizing neural network (SOFM). Theorems are presented and proved that ensure the stability of the network and its ability to represent the input distribution. Finally, simulation results are shown to demonstrate the performance of the model, with an application to colour image compression.     

Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation

In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech recognition system.