Exploring the emission intensities of ICP OES aided by chemometrics in the geographical discrimination of mineral waters

A rapid method for classification of mineral waters is proposed. The discrimination power was evaluated by a novel combination of chemometric data analysis and qualitative multi-elemental fingerprints of mineral water samples acquired from different regions of the Brazilian territory. The classification of mineral waters was assessed using only the wavelength emission intensities obtained by inductively coupled plasma optical emission spectrometry (ICP OES), monitoring different lines of Al, B, Ba, Ca, Cl, Cu, Co, Cr, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Sb, Si, Sr, Ti, V, and Zn, and Be, Dy, Gd, In, La, Sc and Y as internal standards. Data acquisition was done under robust (RC) and non-robust (NRC) conditions. Also, the combination of signal intensities of two or more emission lines for each element were evaluated instead of the individual lines. The performance of two classification-k-nearest neighbor (kNN) and soft independent modeling of class analogy (SIMCA)-and preprocessing algorithms, autoscaling and Pareto scaling, were evaluated for the ability to differentiate between the various samples in each approach tested (combination of robust or non-robust conditions with use of individual lines or sum of the intensities of emission lines). It was shown that qualitative ICP OES fingerprinting in combination with multivariate analysis is a promising analytical tool that has potential to become a recognized procedure for rapid authenticity and adulteration testing of mineral water samples or other material whose physicochemical properties (or origin) are directly related to mineral content.

Estimation of hexenuronic acids and kappa number in kraft pulps of Eucalyptus globulus by Fourier transform near infrared spectroscopy and multivariate analysis

Fourier transform near infrared (FT-NIR) spectroscopy was evaluated as an analytical too[ for monitoring residual Lignin, kappa number and hexenuronic acids (HexA) content in kraft pulps of Eucalyptus globulus. Sets of pulp samples were prepared under different cooking conditions to obtain a wide range of compound concentrations that were characterised by conventional wet chemistry analytical methods. The sample group was also analysed using FT-NIR spectroscopy in order to establish prediction models for the pulp characteristics. Several models were applied to correlate chemical composition in samples with the NIR spectral data by means of PCR or PLS algorithms. Calibration curves were built by using all the spectral data or selected regions. Best calibration models for the quantification of lignin, kappa and HexA were proposed presenting R-2 values of 0.99. Calibration models were used to predict pulp titers of 20 external samples in a validation set. The lignin concentration and kappa number in the range of 1.4-18% and 8-62, respectively, were predicted fairly accurately (standard error of prediction, SEP 1.1% for lignin and 2.9 for kappa). The HexA concentration (range of 5-71 mmol kg(-1) pulp) was more difficult to predict and the SEP was 7.0 mmol kg(-1) pulp in a model of HexA quantified by an ultraviolet (UV) technique and 6.1 mmol kg(-1) pulp in a model of HexA quantified by anion-exchange chromatography (AEC). Even in wet chemical procedures used for HexA determination, there is no good agreement between methods as demonstrated by the UV and AEC methods described in the present work. NIR spectroscopy did provide a rapid estimate of HexA content in kraft pulps prepared in routine cooking experiments.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Recognition of noisy images by PLL networks

We use networks composed of three phase-locked loops (PLLs), where one of them is the master, for recognizing noisy images. The values of the coupling weights among the PLLs control the noise level which does not affect the successful identification of the input image. Analytical results and numerical tests are presented concerning the scheme performance. (c) 2008 Elsevier B.V. All rights reserved.

Identification of protein coding regions using the modified Gabor-wavelet transform

An important topic in genomic sequence analysis is the identification of protein coding regions. In this context, several coding DNA model-independent methods based on the occurrence of specific patterns of nucleotides at coding regions have been proposed. Nonetheless, these methods have not been completely suitable due to their dependence on an empirically predefined window length required for a local analysis of a DNA region. We introduce a method based on a modified Gabor-wavelet transform (MGWT) for the identification of protein coding regions. This novel transform is tuned to analyze periodic signal components and presents the advantage of being independent of the window length. We compared the performance of the MGWT with other methods by using eukaryote data sets. The results show that MGWT outperforms all assessed model-independent methods with respect to identification accuracy. These results indicate that the source of at least part of the identification errors produced by the previous methods is the fixed working scale. The new method not only avoids this source of errors but also makes a tool available for detailed exploration of the nucleotide occurrence.

Development and validation of sensitive methods for determination of sibutramine hydrochloride monohydrate and direct enantiomeric separation on a protein-based chiral stationary phase

Sibutramine hydrochloride monohydrate, chemically 1-(4-chlorophenyl)-N,N-dimethyl-alpha-(2-methylpropyl) hydrochloride monohydrate (SB center dot HCl center dot H2O), was approved by the U.S. Food and Drug Administration for the treatment of obesity. The objective of this study was to develop, validate, and compare methods using UV-derivative spectrophotometry (UVDS) and reversed-phase high-performance liquid chromatography (HPLC) for the determination of SB center dot HCl center dot H2O in pharmaceutical drug products. The UVDS and HPLC methods were found to be rapid, precise, and accurate. Statistically, there was no significant difference between the proposed UVDS and HPLC methods. The enantiomeric separation of SB was obtained on an alpha-1 acid glycoprotein column. The R- and S-sibutramine were eluted in < 5 min with baseline separation of the chromatographic peaks (alpha = 1.9 and resolution = 1.9).

Development and validation of high performance liquid chromatographic and UV-derivative spectrophotometric methods for the determination of sotalol hydrochloride in tablets

High performance liquid chromatographic (HPLC) and UV derivative spectrophotometric (UVDS) methods were developed and validated for the quantitative determination of sotalol hydrochloride in tablets. The HPLC method was performed on a C18 column with fluorescence detection. The excitation and emission wavelengths were 235 and 310nm, respectively. The mobile phase was composed of acetonitrile-water containing 0.1% trietylamine (7:93v/v) and pH adjusted to 4.6 with formic acid. The UVDS method was performed taking a signal at 239.1nm in the first derivative. The correlation coefficients (r) obtained were 0.9998 and 0.9997 for HPLC and UVDS methods, respectively. The proposed methods are simple and adaptable to routine analysis.

Kinetic analysis of vascular marker distribution in perfused rat livers after regeneration following partial hepatectomy

Background/Aims: Liver clearance models are based on information (or assumptions) on solute distribution kinetics within the microvasculatory system, The aim was to study albumin distribution kinetics in regenerated livers and in livers of normal adult rats, Methods: A novel mathematical model was used to evaluate the distribution space and the transit time dispersion of albumin in livers following regeneration after a two-thirds hepatectomy compared to livers of normal adult rats. Outflow curves of albumin measured after bolus injection in single-pass perfused rat livers were analyzed by correcting for the influence of catheters and fitting a long-tailed function to the data. Results: The curves were well described by the proposed model. The distribution volume and the transit time dispersion of albumin observed in the partial hepatectomy group were not significantly different from livers of normal adult rats. Conclusions: These findings suggest that the distribution space and the transit time dispersion of albumin (CV2) is relatively constant irrespective of the presence of rapid and extensive repair. This invariance of CV2 implies, as a first approximation, a similar degree of intrasinusoidal mixing, The finding that a sum of two (instead of one) inverse Gaussian densities is an appropriate empirical function to describe the outflow curve of vascular indicators has consequences for an improved prediction of hepatic solute extraction.

Testing hypotheses about the relationship between cannabis use and psychosis

Aim: To model the impact of rising rates of cannabis use on the incidence and prevalence of psychosis under four hypotheses about the relationship between cannabis use and psychosis. Methods: The study modelled the effects on the prevalence of schizophrenia over the lifespan of cannabis in eight birth cohorts: 1940-1944, 1945-1949, 1950-1954, 1955-1959, 1960-1964, 1965-1969, 1970-1974, 1975-1979. It derived predictions as to the number of cases of schizophrenia that would be observed in these birth cohorts, given the following four hypotheses: (1) that there is a causal relationship between cannabis use and schizophrenia; (2) that cannabis use precipitates schizophrenia in vulnerable persons; (3) that cannabis use exacerbates schizophrenia; and (4) that persons with schizophrenia are more liable to become regular cannabis users. Results: There was a steep rise in the prevalence of cannabis use in Australia over the past 30 years and a corresponding decrease in the age of initiation of cannabis use. There was no evidence of a significant increase in the incidence of schizophrenia over the past 30 years. Data on trends the age of onset of schizophrenia did not show a clear pattern. Cannabis use among persons with schizophrenia has consistently been found to be more common than in the general population. Conclusions: Cannabis use does not appear to be causally related to the incidence of schizophrenia, but its use may precipitate disorders in persons who are vulnerable to developing psychosis and worsen the course of the disorder among those who have already developed it. (C) 2003 Elsevier Science Ireland Ltd. All rights reserved.

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood and adult-onset schizophrenia, bipolar disorder, attention-deficit/ hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages. (C) 2004 Published by Elsevier Inc.

HPLC-FLD methods to quantify chloroaluminum phthalocyanine in nanoparticles, plasma and tissue: application in pharmacokinetic and biodistribution studies

Analytical and bioanalytical methods of high-performance liquid chromatography with fluorescence detection (HPLC-FLD) were developed and validated for the determination of chloroaluminum phthalocyanine in different formulations of polymeric nanocapsules, plasma and livers of mice. Plasma and homogenized liver samples were extracted with ethyl acetate, and zinc phthalocyanine was used as internal standard. The results indicated that the methods were linear and selective for all matrices studied. Analysis of accuracy and precision showed adequate values, with variations lower than 10% in biological samples and lower than 2% in analytical samples. The recoveries were as high as 96% and 99% in the plasma and livers, respectively. The quantification limit of the analytical method was 1.12 ng/ml, and the limits of quantification of the bioanalytical method were 15 ng/ml and 75 ng/g for plasma and liver samples, respectively. The bioanalytical method developed was sensitive in the ranges of 15-100 ng/ml in plasma and 75-500 ng/g in liver samples and was applied to studies of biodistribution and pharmacokinetics of AlClPc. (C) 2011 Elsevier B.V. All rights reserved.

Gingival capillary changes and oral motor weakness in juvenile dermatomyositis

Objective. We assessed the orofacial involvement in JDM, and evaluated the possible association of gingival and mandibular mobility alterations with demographic data, periodontal indices, clinical features, muscle enzyme levels, JDM scores and treatment. Methods. Twenty-six JDM patients were studied and compared with 22 healthy controls. Orofacial evaluation included clinical features, dental and periodontal assessment, mandibular function and salivary flow. Results. The mean current age was similar in patients with JDM and controls (P > 0.05). A unique gingival alteration characterized by erythema, capillary dilation and bush-loop formation was observed only in JDM patients (61 vs 0%, P = 0.0001). The frequencies of altered mandibular mobility and reduced mouth opening were significantly higher in patients with JDM vs controls (50 vs 14%, P = 0.013; 31 vs 0%, P = 0.005). Comparison of the patients with and without gingival alteration showed that the former had lower values of median of cementoenamel junction (-0.26 vs -0.06 mm, P = 0.013) and higher gingival bleeding index (27.7 vs 14%, P = 0.046). This pattern of gingival alteration was not associated with periodontal disease [plaque index (P = 0.332) and dental attachment loss (P = 0.482)]. The medians for skin DAS and current dose of MTX were higher in JDM with gingival alteration (2.5 vs 0.5, P = 0.029; 28.7 vs 15, P = 0.012). A significant association of lower median manual muscle testing with a reduced ability to open the mouth was observed in patients with JDM than those without this alteration (79 vs 80, P = 0.002). Conclusions. The unique gingival pattern associated with cutaneous disease activity, distinct from periodontal disease, suggests that gingiva is a possible target tissue for JDM. In addition, muscle weakness may be a relevant factor for mandibular mobility.

Distribution of HDL-cholesterol and non-HDL-cholesterol in Brazilian children and adolescents - The Floripa study

Background and aims: HDL-cholesterol (HDL-C) and non-HDL-cholesterol (nHDL-C) are involved in atherosclerosis. The aim of this study was to determine the distribution of HDL-C and nHDL-C and its association with cardiovascular and socio-cultural variables in a pediatric Brazilian sample. Methods and results: Children and adolescents from Florianopolis were randomly selected and a structured questionnaire was administered, a physical examination was performed and a blood sample was collected. Enzymatic and Direct methods in vitro were used to determine the total cholesterol and HDL-cholesterol levels. The associations among HDL-C and nHDL-C and the described variables were tested by odds ratio and logistic regression. A total of 1009 individuals were examined. Based on the Brazilian criteria, 23% were classified with low levels of HDL-C and 25% with high levels of non-HDL-C. After multivariate analysis there were significant associations among low HDL-C and high C-reactive protein (OR, 3.3; 95% CI, 2.1-5.2), paternal tobacco use (OR, 1.5; 95% CI, 1.1-2.1), and high triceps-to-subscapular index (OR, 1.5; 95% CI, 1.1-2.2). There were also significant associations among high nHDL-C and high waist circumference (OR, 1.95; 95% CI, 1.16-3.29), black skin color (OR, 1.78; 95% CI, 1.06-3.06), and high income (OR, 1.48; 95% CI, 1.09-2.02). Conclusions: In this sample, low levels of HDL-C were associated with other clinical variables such as a centripetal fat pattern and C-reactive protein, and n-HDL-C was associated with abdominal obesity, skin color and economic class. (C) 2009 Elsevier B. V. All rights reserved.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Microangiopathy of the inner ear, deafness, and cochlear implantation in a patient with Susac syndrome

Conclusion: The cochlear implant was beneficial as an attempt to restore hearing and improve communication abilities in this patient with profound sensorineural hearing loss secondary to Susac syndrome. Objective: To report the audiological outcomes of cochlear implantation (CI) in a young woman with Susac syndrome after a 6-month follow-up period. Susac syndrome is a rare disorder. It is clinically characterized by a typical triad of sensorineural deafness, encephalopathy, and visual defect, due to microangiopathy involving the brain, inner ear, and retina. Methods: This was a retrospective review of a case at a tertiary referral center. After diagnosis, the patient was evaluated by a multidisciplinary team and received a cochlear implant in her right ear. Results: The patient achieved 100% open-set sentence recognition in noise conditions and 92% monosyllable and 68% medial consonant recognition in quiet conditions after 6 months of implant use. She reported the use of the telephone 3 months after activation.