Macrophage inhibitory cytokine-1 (MIC-1/GDF15) slows cancer development but increases metastases in TRAMP prostate cancer prone mice

Macrophage inhibitory cytokine-1 (MIC-1/GDF15), a divergent member of the TGF-β superfamily, is over-expressed by many common cancers including those of the prostate (PCa) and its expression is linked to cancer outcome. We have evaluated the effect of MIC-1/GDF15 overexpression on PCa development and spread in the TRAMP transgenic model of spontaneous prostate cancer. TRAMP mice were crossed with MIC-1/GDF15 overexpressing mice (MIC-1fms) to produce syngeneic TRAMPfmsmic-1 mice. Survival rate, prostate tumor size, histopathological grades and extent of distant organ metastases were compared. Metastasis of TC1-T5, an androgen independent TRAMP cell line that lacks MIC-1/GDF15 expression, was compared by injecting intravenously into MIC-1fms and syngeneic C57BL/6 mice. Whilst TRAMPfmsmic-1 survived on average 7.4 weeks longer, had significantly smaller genitourinary (GU) tumors and lower PCa histopathological grades than TRAMP mice, more of these mice developed distant organ metastases. Additionally, a higher number of TC1-T5 lung tumor colonies were observed in MIC-1fms mice than syngeneic WT C57BL/6 mice. Our studies strongly suggest that MIC-1/GDF15 has complex actions on tumor behavior: it limits local tumor growth but may with advancing disease, promote metastases. As MIC-1/GDF15 is induced by all cancer treatments and metastasis is the major cause of cancer treatment failure and cancer deaths, these results, if applicable to humans, may have a direct impact on patient care.

Ingenuity pathways analysis of urine metabonomics phenotypes toxicity of gentamicin in multiple organs

We introduce the use of Ingenuity Pathway Analysis to analyzing global metabonomics in order to characterize phenotypically biochemical perturbations and the potential mechanisms of the gentamicin-induced toxicity in multiple organs. A single dose of gentamicin was administered to Sprague Dawley rats (200 mg/kg, n = 6) and urine samples were collected at -24-0 h pre-dosage, 0-24, 24-48, 48-72 and 72-96 h post-dosage of gentamicin. The urine metabonomics analysis was performed by UPLC/MS, and the mass spectra signals of the detected metabolites were systematically deconvoluted and analyzed by pattern recognition analyses (Heatmap, PCA and PLS-DA), revealing a time-dependency of the biochemical perturbations induced by gentamicin toxicity. As result, the holistic metabolome change induced by gentamicin toxicity in the animal's organisms was characterized. Several metabolites involved in amino acid metabolism were identified in urine, and it was confirmed that gentamicin biochemical perturbations can be foreseen from these biomarkers. Notoriously, it was found that gentamicin induced toxicity in multiple organs system in the laboratory rats. The proof-of-knowledge based Ingenuity Pathway Analysis revealed gentamicin induced liver and heart toxicity, along with the previously known toxicity in kidney. The metabolites creatine, nicotinic acid, prostaglandin E2, and cholic acid were identified and validated as phenotypic biomarkers of gentamicin induced toxicity. Altogether, the significance of the use of metabonomics analyses in the assessment of drug toxicity is highlighted once more; furthermore, this work demonstrated the powerful predictive potential of the Ingenuity Pathway Analysis to study of drug toxicity and its valuable complementation for metabonomics based assessment of the drug toxicity.

Neural activity associated with semantic versus phonological anomia treatments in aphasia

Naming impairments in aphasia are typically targeted using semantic and/or phonologically based tasks. However, it is not known whether these treatments have different neural mechanisms. Eight participants with aphasia received twelve treatment sessions using an alternating treatment design, with fMRI scans pre- and post-treatment. Half the sessions employed Phonological Components Analysis (PCA), and half the sessions employed Semantic Feature Analysis (SFA). Pre-treatment activity in the left caudate correlated with greater immediate treatment success for items treated with SFA, whereas recruitment of the left supramarginal gyrus and right precuneus post-treatment correlated with greater immediate treatment success for items treated with PCA. The results support previous studies that have found greater treatment outcome to be associated with activity in predominantly left hemisphere regions, and suggest that different mechanisms may be engaged dependent on the type of treatment employed.

Fuzzy waste load allocation model: a multiobjective approach

Fuzzy Waste Load Allocation Model (FWLAM), developed in an earlier study, derives the optimal fractional levels, for the base flow conditions, considering the goals of the Pollution Control Agency (PCA) and dischargers. The Modified Fuzzy Waste Load Allocation Model (MFWLAM) developed subsequently is a stochastic model and considers the moments (mean, variance and skewness) of water quality indicators, incorporating uncertainty due to randomness of input variables along with uncertainty due to imprecision. The risk of low water quality is reduced significantly by using this modified model, but inclusion of new constraints leads to a low value of acceptability level, A, interpreted as the maximized minimum satisfaction in the system. To improve this value, a new model, which is a combination Of FWLAM and MFWLAM, is presented, allowing for some violations in the constraints of MFWLAM. This combined model is a multiobjective optimization model having the objectives, maximization of acceptability level and minimization of violation of constraints. Fuzzy multiobjective programming, goal programming and fuzzy goal programming are used to find the solutions. For the optimization model, Probabilistic Global Search Lausanne (PGSL) is used as a nonlinear optimization tool. The methodology is applied to a case study of the Tunga-Bhadra river system in south India. The model results in a compromised solution of a higher value of acceptability level as compared to MFWLAM, with a satisfactory value of risk. Thus the goal of risk minimization is achieved with a comparatively better value of acceptability level.

Development of Novel Assays for Measuring Different Molecular Forms of Prostate Specific Antigen

Prostate cancer (PCa) is the most commonly diagnosed non-skin cancer and second leading cause of cancer-related death of men in developed countries. Measurement of prostate specific antigen (PSA) is a very sensitive method for diagnosing and monitoring of prostate cancer (PCa), but the specificity needs improvement. Measurements of different molecular forms of PSA have been shown to improve differentiation between PCa and benign prostatic diseases. However, accurate measurement of some isoforms has not been achieved in previous assays. The aim of the present study was to develop new assays that reliably measure enzymatically active PSA, PSA-α1-chymotryposin (PSA-ACT) and PSA-α1-protease inhibitor (PSA-API), and to evaluate their diagnostic value. Double-label immunofluorometric assays using a novel monoclonal antibody (MAb) and another antibody to either free PSA (fPSA) or total PSA (tPSA) were developed and used to measure PSA-ACT and fPSA or tPSA at the same time. These assays provide enough sensitivity for measurement of PSA-ACT in sera with low PSA levels. The results obtained confirmed that proportion of PSA-ACT to tPSA (%PSA-ACT) was as useful as proportion of fPSA to tPSA (%fPSA) for discrimination between PCa and benign prostatic hyperplasia (BPH). We developed an immunoassay for detection of PSA-API based on proximity ligation, which improved assay sensitivity 10-fold compared with conventional assays. Our results confirmed previous findings that the PSA-API level is somewhat lower in men with than without PCa, and the combination of %fPSA and proportion of PSA-API to tPSA (%PSA-API) provides diagnostic improvement compared with either method alone. Assays based on this principle should be applicable to other immunoassays in which the nonspecific background is a problem. An immunopeptidometric sandwich assay (IPMA) was developed to measure the enzymatically active PSA. This assay showed high specificity, but sensitivity was not good enough for measurement of PSA concentrations in the gray zone, 2-10 µg/L, in which tPSA does not efficiently differentiate between PCa and BPH. We further developed a solid-phase proximity ligation immunoassay, which provided a 10-fold improvement in sensitivity. This proof of concept study shows that peptides reacting with proteins are potentially useful for sensitive and specific measurement of protein variants for which specific MAbs cannot be obtained.

River water quality response under hypothetical climate change scenarios in Tunga-Bhadra river, India

Analysis of climate change impacts on streamflow by perturbing the climate inputs has been a concern for many authors in the past few years, but there are few analyses for the impacts on water quality. To examine the impact of change in climate variables on the water quality parameters, the water quality input variables have to be perturbed. The primary input variables that can be considered for such an analysis are streamflow and water temperature, which are affected by changes in precipitation and air temperature, respectively. Using hypothetical scenarios to represent both greenhouse warming and streamflow changes, the sensitivity of the water quality parameters has been evaluated under conditions of altered river flow and river temperature in this article. Historical data analysis of hydroclimatic variables is carried out, which includes flow duration exceedance percentage (e.g. Q90), single low- flow indices (e.g. 7Q10, 30Q10) and relationships between climatic variables and surface variables. For the study region of Tunga-Bhadra river in India, low flows are found to be decreasing and water temperatures are found to be increasing. As a result, there is a reduction in dissolved oxygen (DO) levels found in recent years. Water quality responses of six hypothetical climate change scenarios were simulated by the water quality model, QUAL2K. A simple linear regression relation between air and water temperature is used to generate the scenarios for river water temperature. The results suggest that all the hypothetical climate change scenarios would cause impairment in water quality. It was found that there is a significant decrease in DO levels due to the impact of climate change on temperature and flows, even when the discharges were at safe permissible levels set by pollution control agencies (PCAs). The necessity to improve the standards of PCA and develop adaptation policies for the dischargers to account for climate change is examined through a fuzzy waste load allocation model developed earlier. Copyright (C) 2011 John Wiley & Sons, Ltd.

As práticas de saúde do enfermeiro e do agente comunitário de saúde na visita domiciliar da Estratégia Saúde da Família

Esta pesquisa teve como objetivo conhecer os sentidos atribuídos pelos enfermeiros e agentes comunitários de saúde da Estratégia Saúde da Família do município do Rio de Janeiro/RJ acerca das práticas de saúde desenvolvidas na visita domiciliar. É um estudo descritivo, de natureza qualitativa e teve como abordagem metodológica a hermenêutica-dialética. O cenário foi a cidade do Rio de Janeiro/RJ, em duas Unidades Básicas de Saúde da Família (UBSF) da Área Programática 3.1. Os sujeitos foram 08 enfermeiros e 07 agentes comunitários de saúde (ACSs) atuantes nas UBSF selecionadas. A coleta de dados foi realizada entre janeiro e março de 2010, por meio de entrevistas semi-estruturadas e para a avaliação dos resultados utilizou-se a técnica de análise de conteúdo proposta por Bardin. A partir dos resultados alcançados foi possível elaborar três categorias de estudo: a primeira trata das práticas de saúde do enfermeiro e do ACS na Estratégia Saúde da Família (ESF); a segunda aborda a visita domiciliar do enfermeiro e do ACS, a qual inclui subcategorias sobre o trabalho em equipe na visita domiciliar, as dificuldades na realização da visita domiciliar, o planejamento da visita domiciliar, o vínculo entre enfermeiro, ACS e família na visita domiciliar e a interação profissional do enfermeiro e do ACS na visita domiciliar; a última categoria trata dos sentidos atribuídos pelos enfermeiros e ACSs acerca das práticas de saúde desenvolvidas na visita domiciliar, as quais incluem subcategorias sobre as práticas de saúde do enfermeiro e do ACS na visita domiciliar e as opiniões sobre a visita domiciliar. Com a análise dos dados constatou-se que os enfermeiros e os ACS's desenvolvem diversas práticas de saúde na ESF, com destaque para as práticas de cuidado. As práticas de cuidado do enfermeiro na visita domiciliar estão voltadas para a investigação das necessidades de saúde e realização das atividades assistenciais. Já as do ACS estão voltadas para a identificação de demandas. A escuta ativa, a observação da estrutura física, da alimentação e das relações familiares e a educação em saúde são as principais práticas de cuidado realizadas em conjunto por estes profissionais na visita domiciliar. O percentual de visitas domiciliares semanais do enfermeiro está abaixo do esperado, sendo que a principal justificativa para este baixo índice é a sobrecarga de trabalho na UBSF. Ficou evidente que a interação profissional entre enfermeiro e ACS na visita domiciliar é pequena, pois diversas vezes, o ACS está presente na visita domiciliar do enfermeiro apenas como acompanhante. Por fim, pode-se constatar que o cuidado desenvolvido por enfermeiros e por ACSs é distinto. A prática de cuidado que o enfermeiro desenvolve na visita domiciliar é específica, destinada às famílias com prioridades de saúde e a que o ACS desenvolve é mais ampla, voltada para todas as famílias da microárea. Estas conclusões demonstram a necessidade de estimular enfermeiros e ACSs a (re)pensarem as práticas de saúde desenvolvidas na visita domiciliar, bem como a compreenderem e discutirem seus papéis e a interação nesta atividade.

LC-MS based methods for phytosterols determination in oenological matrices. Targeted metabolomic study of Rioja grape varieties

221 p.+ anexos

Problemas inversos em processos difusivos com retenção

Um Estudo para a solução numérica do modelo de difusão com retenção, proposta por Bevilacqua et al. (2011), é apresentado, bem como uma formulação implícita para o problema inverso para a estimativa dos parâmetros envolvidos na formulação matemática do modelo. Através de um estudo minucioso da análise de sensibilidade e do cálculo do coeficiente de correlação de Pearson, são identificadas as chances de se obter sucesso na solução do problema inverso através do método determinístico de Levenberg-Marquardt e dos métodos estocásticos Algoritmo de Colisão de Partículas (Particle Collision Algorithm - PCA) e Evolução Diferencial (Differential Evolution - DE). São apresentados os resultados obtidos através destes três métodos de otimização para três casos de conjunto de parâmetros. Foi observada uma forte correlação entre dois destes três parâmetros, o que dificultou a estimativa simultânea dos mesmos. Porém, foi obtido sucesso nas estimativas individuais de cada parâmetro. Foram obtidos bons resultados para os fatores que multiplicam os termos diferenciais da equação que modela o fenômeno de difusão com retenção.

Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.

黄土丘陵沟壑区纸坊沟小流域农业生态安全态势变化的驱动力分析

以黄土丘陵沟壑区纸坊沟小流域为研究对象,利用1985—2006年调查和监测资料,综合现有研究成果,将主成分分析和通径分析方法相结合,分析可能影响农业生态安全态势变化的各因子,探讨农业生态安全态势变化的驱动力。结果表明:自然条件是影响黄土丘陵沟壑区纸坊沟小流域农业生态安全态势变化的基础,人文社会经济条件是其变化的主要驱动力,具体可归纳为经济发展、人口压力、流域产业结构调整和农业科技进步4类因子;此外,国家和区域的相关政策、农业产业链与资源量相关度对农业生态安全也有一定影响。

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78-0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1-6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI. © Springer-Verlag London Limited 2008.

Chemometric techniques in multivariate statistical modelling of process plant

The techniques of principal component analysis (PCA) and partial least squares (PLS) are introduced from the point of view of providing a multivariate statistical method for modelling process plants. The advantages and limitations of PCA and PLS are discussed from the perspective of the type of data and problems that might be encountered in this application area. These concepts are exemplified by two case studies dealing first with data from a continuous stirred tank reactor (CSTR) simulation and second a literature source describing a low-density polyethylene (LDPE) reactor simulation.

Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches

Systematic principal component analysis (PCA) methods are presented in this paper for reliable islanding detection for power systems with significant penetration of distributed generations (DGs), where synchrophasors recorded by Phasor Measurement Units (PMUs) are used for system monitoring. Existing islanding detection methods such as Rate-of-change-of frequency (ROCOF) and Vector Shift are fast for processing local information, however with the growth in installed capacity of DGs, they suffer from several drawbacks. Incumbent genset islanding detection cannot distinguish a system wide disturbance from an islanding event, leading to mal-operation. The problem is even more significant when the grid does not have sufficient inertia to limit frequency divergences in the system fault/stress due to the high penetration of DGs. To tackle such problems, this paper introduces PCA methods for islanding detection. Simple control chart is established for intuitive visualization of the transients. A Recursive PCA (RPCA) scheme is proposed as a reliable extension of the PCA method to reduce the false alarms for time-varying process. To further reduce the computational burden, the approximate linear dependence condition (ALDC) errors are calculated to update the associated PCA model. The proposed PCA and RPCA methods are verified by detecting abnormal transients occurring in the UK utility network.