Surface modification of bovine bone ash prepared by milling and acid washing process

Bovine bone ash is the main raw material for fabrication of bone china, a special kind of porcelain that has visual and mechanical advantages when compared to usual porcelains. The properties of bone china are highly dependent on the characteristics of the bone ash. However, despite a relatively common product, the science behind formulations and accepted fabrication procedures for bone china is not completely understood and deserves attention for future processing optimizations. In this paper, the influence of the preparation steps (firing, milling, and washing of the bones) on the physicochemical properties of bone ash particles was investigated. Bone powders heat-treated at temperatures varying from 700 to 1000 degrees C were washed and milled. The obtained materials were analyzed in terms of particle size distribution, chemical composition, density, specific surface area, FTIR spectroscopy, dynamic electrophoretic mobility, crystalline phases and scanning electron microscopy. The results indicated that bone ash does not significantly change in terms of chemistry and physical features at calcination temperatures above 700 degrees C. After washing in special conditions, one could only observe hydroxyapatite in the diffraction pattern. By FTIR it was observed that carbonate seems to be mainly concentrated on the surface of the powders. Since this compound can influence in the dispersion stability, and consequently in the quality of the final bone china product, and considering optimal washing parameters based on the dynamic electrophoretic mobility results, we describe a procedure for surface cleaning. (c) 2009 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Anodic porous alumina structural characteristics study based on SEM image processing and analysis

The present work reports the porous alumina structures fabrication and their quantitative structural characteristics study based on mathematical morphology analysis by using the SEM images. The algorithm used in this work was implemented in 6.2 MATLAB software. Using the algorithm it was possible to obtain the distribution of maximum, minimum and average radius of the pores in porous alumina structures. Additionally, with the calculus of the area occupied by the pores, it was possible to obtain the porosity of the structures. The quantitative results could be obtained and related to the process fabrication characteristics, showing to be reliable and promising to be used to control the pores formation process. Then, this technique could provide a more accurate determination of pore sizes and pores distribution. (C) 2008 Elsevier Ltd. All rights reserved.

An integrated molecular map of yellow passion fruit based on simultaneous maximum-likelihood estimation of linkage and linkage phases

The development of genetic maps for auto-incompatible species, such as the yellow passion fruit (Passiflora edulis Sims f.flavicarpa Deg.) is restricted due to the unfeasibility of obtaining traditional mapping populations based on inbred lines. For this reason, yellow passion fruit linkage maps were generally constructed using a strategy known as two-way pseudo-testeross, based on monoparental dominant markers segregating in a 1:1 fashion. Due to the lack of information from these markers in one of the parents, two individual (parental) maps were obtained. However, integration of these maps is essential, and biparental markers can be used for such an operation. The objective of our study was to construct an integrated molecular map for a full-sib population of yellow passion fruit combining different loci configuration generated from amplified fragment length polymorphisms (AFLPs) and microsatellite markers and using a novel approach based on simultaneous maximum-likelihood estimation of linkage and linkage phases, specially designed for outcrossing species. Of the total number of loci, approximate to 76%, 21%, 0.7%, and 2.3% did segregate in 1:1, 3:1, 1:2:1, and 1:1:1:1 ratios, respectively. Ten linkage groups (LGs) were established with a logarithm of the odds (LOD) score >= 5.0 assuming a recombination fraction : <= 0.35. On average, 24 markers were assigned per LG, representing a total map length of 1687 cM, with a marker density of 6.9 cM. No markers were placed as accessories on the map as was done with previously constructed individual maps.

Characterization of Brazilian lager and brown ale beers based on color, phenolic compounds, and antioxidant activity using chemometrics

BACKGROUND: Epidemiological studies have shown that beer has positive effects on inhibiting atherosclerosis, decreasing the content of serum low-density lipoprotein cholesterol and triglycerides, by acting as in vivo free radical scavenger. In this research, the antioxidant activity of commercial Brazilian beers (n = 29) was determined by the oxygen radical absorbance capacity (ORAC) and 1,1 -diphenyl-2-picrylhydrazyl (DPPH(center dot)) assays and results were analyzed by chemometrics. RESULTS: The brown ale samples (n = 11) presented higher (P < 0.05) flavonoids (124.01 mg L(-1)), total phenolics (362.22 mg L(-1)), non-flavonoid phenolics (238.21 mg L(-1)), lightness (69.48), redness (35.75), yellowness (55.71), color intensity (66.86), hue angle (59.14), color saturation (0.9620), DPPH(center dot) values (30.96% inhibition), and ORAC values (3,659.36 mu mol Trolox equivalents L(-1)), compared to lager samples (n = 18). Brown ale beers presented higher antioxidant properties (P < 0.05) measured by ORAC (1.93 times higher) and DPPH (1.65 times higher) compared to lager beer. ORAC values correlated well with the content of flavonoids (r = 0.47; P = 0.01), total phenolic compounds (r = 0.44; P < 0.01) and DPPH (r = 0.67; P < 0.01). DPPH values also correlated well to the content of flavonoids (r = 0.69; P < 0.01), total phenolic compounds (r = 0.60; P < 0.01), and non-flavonoid compounds (r = 0.46; P = 0.01). CONCLUSION: The results suggest that brown ale beers, and less significantly lager beers, could be sources of bioactive compounds with suitable free radical scavenging properties. (C) 2010 Society of Chemical Industry

Molecular dynamics, density functional, ADMET predictions, virtual screening, and molecular interaction field studies for identification and evaluation of novel potential CDK2 inhibitors in cancer therapy

In this work, we have used molecular dynamics, density functional theory, virtual screening, ADMET predictions, and molecular interaction field studies to design and propose eight novel potential inhibitors of CDK2. The eight molecules proposed showed interesting structural characteristics that are required for inhibiting the CDK2 activity and show potential as drug candidates for the treatment of cancer. The parameters related to the Rule of Five were calculated, and only one of the molecules violated more than one parameter. One of the proposals and one of the drug-like compounds selected by virtual screening indicated to be promising candidates for CDK2-based cancer therapy.

Synthesis and Characterization of Semi-Interpenetrating Networks Based on Poly(dimethylsiloxane) and Poly(vinyl alcohol)

Semi-interpenetrating networks (Semi-IPNs) with different compositions were prepared from poly(dimethylsiloxane) (PDMS), tetraethylorthosilicate (TEOS), and poly (vinyl alcohol) (PVA) by the sol-gel process in this study. The characterization of the PDMS/PVA semi-IPN was carried out using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and swelling measurements. The presence of PVA domains dispersed in the PDMS network disrupted the network and allowed PDMS to crystallize, as observed by the crystallization and melting peaks in the DSC analyses. Because of the presence of hydrophilic (-OH) and hydrophobic (Si-(CH(3))(2)) domains, there was an appropriate hydrophylic/hydrophobic balance in the semi-IPNs prepared, which led to a maximum equilibrium water content of similar to 14 wt % without a loss in the ability to swell less polar solvents. (C) 2009 Wiley Periodicals, Inc. J Appl Polym Sci 115: 158-166, 2010

Extrapolation from ten sections can make CT-based quantification of lung aeration more practicable

Clinical applications of quantitative computed tomography (qCT) in patients with pulmonary opacifications are hindered by the radiation exposure and by the arduous manual image processing. We hypothesized that extrapolation from only ten thoracic CT sections will provide reliable information on the aeration of the entire lung. CTs of 72 patients with normal and 85 patients with opacified lungs were studied retrospectively. Volumes and masses of the lung and its differently aerated compartments were obtained from all CT sections. Then only the most cranial and caudal sections and a further eight evenly spaced sections between them were selected. The results from these ten sections were extrapolated to the entire lung. The agreement between both methods was assessed with Bland-Altman plots. Median (range) total lung volume and mass were 3,738 (1,311-6,768) ml and 957 (545-3,019) g, the corresponding bias (limits of agreement) were 26 (-42 to 95) ml and 8 (-21 to 38) g, respectively. The median volumes (range) of differently aerated compartments (percentage of total lung volume) were 1 (0-54)% for the nonaerated, 5 (1-44)% for the poorly aerated, 85 (28-98)% for the normally aerated, and 4 (0-48)% for the hyperaerated subvolume. The agreement between the extrapolated results and those from all CT sections was excellent. All bias values were below 1% of the total lung volume or mass, the limits of agreement never exceeded +/- 2%. The extrapolation method can reduce radiation exposure and shorten the time required for qCT analysis of lung aeration.

Effect of the bilateral hip bone density measurement on clinical practice in elderly subjects

Objectives: The aim of this study was to determine whether the addition of the measurement of bilateral hip bone mineral density (BMD) has an impact on indications for osteoporosis (OP) treatment in community-dwelling elderly individuals, based on criteria from the National Osteoporosis Foundation (NOF). Methods: In total, 605 consecutive community-dwelling elderly individuals who were 65 years and older were evaluated. Dual energy X-ray absorptiometry was used to determine the lowest T-score in the lumbar spine + unilateral hip, the bilateral hips, and the lumbar spine + bilateral hips. Risk factors associated with the lowest T-score in these three conditions were applied to indicate treatment in accordance with NOF criteria. McNemar`s test was used to assess the difference of adding bilateral hip BMD measurements. Results: There was a significant difference in the frequency of pharmacological indication using NOF criteria together with the lowest T-score for the three tests (72.8% for lumbar spine + bilateral hips and 71.2% for lumbar spine + unilateral hip; p=0.002). A higher frequency of treatment indication was also observed for lumbar spine + unilateral hip (71.2%) compared to bilateral hips (61.1%) (p<0.001). The discrepancies in treatment appeared to be more evident in women when analyzed by gender distribution. Conclusion: Our finding supports the theory that evaluation of the bilateral hips with the lumbar spine seems to be more sensitive measure for identifying patients with an osteoporosis treatment indication. Furthermore, despite the well-known artifact in the lumbar spine, this site should not be excluded when determining the indication for OP treatment in elderly people. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer`s Disease

Here, we examine morphological changes in cortical thickness of patients with Alzheimer`s disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification of AD patients using cortical and volumetric data. Cortical thickness of AD patients (n = 14) was measured using MRI cortical surface-based analysis and compared with healthy subjects (n = 20). Data was analyzed using an automated algorithm for tissue segmentation and classification. A Support Vector Machine (SVM) was applied over the volumetric measurements of subcortical and cortical structures to separate AD patients from controls. The group analysis showed cortical thickness reduction in the superior temporal lobe, parahippocampal gyrus, and enthorhinal cortex in both hemispheres. We also found cortical thinning in the isthmus of cingulate gyrus and middle temporal gyrus at the right hemisphere, as well as a reduction of the cortical mantle in areas previously shown to be associated with AD. We also confirmed that automatic classification algorithms (SVM) could be helpful to distinguish AD patients from healthy controls. Moreover, the same areas implicated in the pathogenesis of AD were the main parameters driving the classification algorithm. While the patient sample used in this study was relatively small, we expect that using a database of regional volumes derived from MRI scans of a large number of subjects will increase the SVM power of AD patient identification.

Assessment of immediate conservative breast surgery reconstruction: A classification system of defects revisited and an algorithm for selecting the appropriate technique

Background: Although various techniques have been used for breast conservation surgery reconstruction, there are few studies describing a logical approach to reconstruction of these defects. The objectives of this study were to establish a classification system for partial breast defects and to develop a reconstructive algorithm. Methods: The authors reviewed a 7-year experience with 209 immediate breast conservation surgery reconstructions. Mean follow-up was 31 months. Type I defects include tissue resection in smaller breasts (bra size A/B), including type IA, which involves minimal defects that do not cause distortion; type III, which involves moderate defects that cause moderate distortion; and type IC, which involves large defects that cause significant deformities. Type II includes tissue resection in medium-sized breasts with or without ptosis (bra size C), and type III includes tissue resection in large breasts with ptosis (bra size D). Results: Eighteen percent of patients presented type I, where a lateral thoracodorsal flap and a latissimus dorsi flap were performed in 68 percent. Forty-five percent presented type II defects, where bilateral mastopexy was performed in 52 percent. Thirty-seven percent of patients presented type III distortion, where bilateral reduction mammaplasty was performed in 67 percent. Thirty-five percent of patients presented complications, and most were minor. Conclusions: An algorithm based on breast size in relation to tumor location and extension of resection can be followed to determine the best approach to reconstruction. The authors` results have demonstrated that the complications were similar to those in other clinical series. Success depends on patient selection, coordinated planning with the oncologic surgeon, and careful intraoperative management.

Regional gray matter abnormalities in panic disorder: A voxel-based morphometry study

Although abnonnalities in brain structures involved in the neurobiology of fear and anxiety have been implicated in the pathophysiology of panic disorder (PD), relatively few studies have made use of voxel-based morphometry (VBM) magnetic resonance imaging (MRI) to determine structural brain abnormalities in PD. We have assessed gray matter volume in 19 PD patients and 20 healthy volunteers using VBM. Images were acquired using a 1.5 T MRI scanner, and were spatially normalized and segmented using optimized VBM. Statistical comparisons were performed using the general linear model. A relative increase in gay matter volume was found in the left insula of PD patients compared with controls. Additional structures showing differential increases were the left superior temporal gyrus, the midbrain, and the pons. A relative gray matter deficit was found in the right anterior cingulate cortex. The insula and anterior cingulate abnormalities may be relevant to the pathophysiology of PD, since these structures participate in the evaluation process that ascribes negative emotional meaning to potentially distressing cognitive and interoceptive sensory information. The abnormal brain stem structures may be involved in the generation of panic attacks. (C) 2007 Elsevier Ireland Ltd. All rights reserved.

An MRI-based approach for the measurement of the dorsolateral prefrontal cortex in humans

The dorsolateral prefrontal cortex (DLPFC) has been implicated in the pathophysiology of mental disorders. Previous region-of-interest MRI studies that attempted to delineate this region adopted various landmarks and measurement techniques, with inconsistent results. We developed a new region-of-interest measurement method to obtain morphometric data of this region from structural MRI scans, taking into account knowledge from cytoarchitectonic postmortem studies and the large inter-individual variability of this region. MRI scans of 10 subjects were obtained, and DLPFC tracing was performed in the coronal plane by two independent raters using the semi-automated software Brains2. The intra-class correlation coefficients between two independent raters were 0.94 for the left DLPFC and 0.93 for the right DLPFC. The mean +/- S.D. DLPFC volumes were 9.23 +/- 2.35 ml for the left hemisphere and 8.20 +/- 2.08 ml for the right hemisphere. Our proposed method has high inter-rater reliability and is easy to implement, permitting the standardized measurement of this region for clinical research applications. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Right adolescent idiopathic thoracic curve (Lenke 1 A and B): does cost of instrumentation and implant density improve radiographic and cosmetic parameters?

In adolescent idiopathic scoliosis (AIS) there has been a shift towards increasing the number of implants and pedicle screws, which has not been proven to improve cosmetic correction. To evaluate if increasing cost of instrumentation correlates with cosmetic correction using clinical photographs. 58 Lenke 1A and B cases from a multicenter AIS database with at least 3 months follow-up of clinical photographs were used for analysis. Cosmetic parameters on PA and forward bending photographs included angular measurements of trunk shift, shoulder balance, rib hump, and ratio measurements of waist line asymmetry. Pre-op and follow-up X-rays were measured for coronal and sagittal deformity parameters. Cost density was calculated by dividing the total cost of instrumentation by the number of vertebrae being fused. Linear regression and spearman`s correlation were used to correlate cost density to X-ray and photo outcomes. Three independent observers verified radiographic and cosmetic parameters for inter/interobserver variability analysis. Average pre-op Cobb angle and instrumented correction were 54A degrees (SD 12.5) and 59% (SD 25) respectively. The average number of vertebrae fused was 10 (SD 1.9). The total cost of spinal instrumentation ranged from $6,769 to $21,274 (Mean $12,662, SD $3,858). There was a weak positive and statistically significant correlation between Cobb angle correction and cost density (r = 0.33, p = 0.01), and no correlation between Cobb angle correction of the uninstrumented lumbar spine and cost density (r = 0.15, p = 0.26). There was no significant correlation between all sagittal X-ray measurements or any of the photo parameters and cost density. There was good to excellent inter/intraobserver variability of all photographic parameters based on the intraclass correlation coefficient (ICC 0.74-0.98). Our method used to measure cosmesis had good to excellent inter/intraobserver variability, and may be an effective tool to objectively assess cosmesis from photographs. Since increasing cost density only improves mildly the Cobb angle correction of the main thoracic curve and not the correction of the uninstrumented spine or any of the cosmetic parameters, one should consider the cost of increasing implant density in Lenke 1A and B curves. In the area of rationalization of health care expenses, this study demonstrates that increasing the number of implants does not improve any relevant cosmetic or radiographic outcomes.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.