895 resultados para Culture-dependent analysis
Resumo:
Background: Chrysotile is considered less harmful to human health than other types of asbestos fibers. Its clearance from the lung is faster and, in comparison to amphibole forms of asbestos, chrysotile asbestos fail to accumulate in the lung tissue due to a mechanism involving fibers fragmentation in short pieces. Short exposure to chrysotile has not been associated with any histopathological alteration of lung tissue. Methods: The present work focuses on the association of small chrysotile fibers with interphasic and mitotic human lung cancer cells in culture, using for analyses confocal laser scanning microscopy and 3D reconstructions. The main goal was to perform the analysis of abnormalities in mitosis of fibers-containing cells as well as to quantify nuclear DNA content of treated cells during their recovery in fiber-free culture medium. Results: HK2 cells treated with chrysotile for 48 h and recovered in additional periods of 24, 48 and 72 h in normal medium showed increased frequency of multinucleated and apoptotic cells. DNA ploidy of the cells submitted to the same chrysotile treatment schedules showed enhanced aneuploidy values. The results were consistent with the high frequency of multipolar spindles observed and with the presence of fibers in the intercellular bridge during cytokinesis. Conclusion: The present data show that 48 h chrysotile exposure can cause centrosome amplification, apoptosis and aneuploid cell formation even when long periods of recovery were provided. Internalized fibers seem to interact with the chromatin during mitosis, and they could also interfere in cytokinesis, leading to cytokinesis failure which forms aneuploid or multinucleated cells with centrosome amplification.
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Background and Objective: Impaired cell metabolism and increased cell death in fibroblast cells are physiological features of chronic tendinopathy. Although several studies have shown that low-level laser therapy (LLLT) at certain parameters has a biostimulatory effect on fibroblast cells, it remains uncertain if LLLT effects depend on the physiological state. Study Design/Material and Methods: High-metabolic immortal cell culture and primary human keloid fibroblast cell culture were used in this study. Trypan blue exclusion and the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test were used to determine cell viability and proliferation. Propidium iodide stain was used for cell-cycle analysis by flow cytometry. Laser irradiation was performed daily on three consecutive days with a GaAlAs 660-nm laser (mean output: 50 mW, spot size 2 mm(2), power density = 2.5 W/cm(2)) and a typical LLLT dose and a high LLLT dose (irradiation times: 60 or 420 s; fluences: 150 or 1050 J/cm(2); energy delivered: 3 or 21 J). Results: Primary fibroblast cell culture from human keloids irradiated with 3 J showed significant proliferation by the trypan blue exclusion test (p < 0.05), whereas the 3T3 cell culture showed no difference using this method. Propidium iodide staining flow cytometry data showed a significant decrease in the percentage of cells being in proliferative phases of the cell cycle (S/g(2)/M) when irradiated with 21 J in both cell types (hypodiploid cells increased). Conclusions: Our data support the hypothesis that the physiological state of the cells affects the LLLT results, and that high-metabolic rate and short-cell-cycle 3T3 cells are not responsive to LLLT. In conclusion, LLLT with a dose of 3 J reduced cell death significantly, but did not stimulate cell cycle. A LLLT dose of 21 J had negative effects on the cells, as it increased cell death and inhibited cell proliferation.
Resumo:
P>The aim of the present work was to evaluate the use of the kefir grains as a starter culture for tradicional milk kefir beverage and for cheese whey-based beverages production. Fermentation was performed by inoculating kefir grains in milk (ML), cheese whey (CW) and deproteinised cheese whey (DCW). Erlenmeyers containing kefir grains and different substrates were statically incubated for 72 h at 25 degrees C. Lactose, ethanol, lactic acid, acetic acid, acetaldehyde, ethyl acetate, isoamyl alcohol, isobutanol, 1-propanol, isopentyl alcohol and 1-hexanol were identified and quantified by high-performance liquid chromatography and GC-FID. The results showed that kefir grains were able to utilise lactose in 60 h from ML and 72 h from CW and DCW and produce similar amounts of ethanol (similar to 12 g L-1), lactic acid (similar to 6 g L-1) and acetic acid (similar to 1.5 g L-1) to those obtained during milk fermentation. Based on the chemical characteristics and acceptance in the sensory analysis, the kefir grains showed potential to be used for developing cheese whey-based beverages.
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A liquid chromatography method is described for the analysis of fluoxetine and norfluoxetine enantiomers in fungi cultures. The analytes were separated simultaneously by LC employing a serial system. The resolution was performed using a mobile phase of ethanol: 15 mM ammonium acetate buffer solution, pH 5.9: acetonitrile (77.5:17.5:5, v/v/v). UV detection was at 227 nm. Hexane: isoamyl alcohol (98:2, v/v) was used as extractor solvent. The calibration curves were linear over the concentration range of 12.5-3,750 ng mL(-1) (r a parts per thousand yen 0.996). The values for intra- and inter-day precision and accuracy were a parts per thousand currency sign10% for all analytes. The validated method was used to evaluate fluoxetine biotransformation to its mammalian metabolite, norfluoxetine, by selected endophytic fungi. Although the desired biotransformation was not observed in the conditions used here, the method could be used to evaluate the biotransformation of fluoxetine by other fungi or to be extended to other matrices with adequate procedures for sample preparation.
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Calcineurin plays an important role in the control of cell morphology and virulence in fungi. Calcineurin is a serine/threonine-specific protein phosphatase heterodimer consisting of a catalytic subunit A and a regulatory subunit B. A mutant of Aspergillus fumigatus lacking the calcineurin A (calA) catalytic subunit exhibited defective hyphal morphology related to apical extension and branching growth, which resulted in drastically decreased filamentation. Here, we investigated which pathways are influenced by A. fumigatus calcineurin during proliferation by comparatively determining the transcriptional profile of A. fumigatus wild type and Delta calA mutant strains. Our results showed that the mitochondrial copy number is reduced in the Delta calA mutant strain, and the mutant has increased alternative oxidase (aoxA) mRNA accumulation and activity. Furthermore, we identified four genes that encode transcription factors that have increased mRNA expression in the Delta calA mutant. Deletion mutants for these transcription factors had reduced susceptibility to itraconazole, caspofungin, and sodium dodecyl sulfate (SDS). (C) 2009 Elsevier Inc. All rights reserved.
Resumo:
Arylamine N-acetyltransferase-1 (NAT1) is a polymorphically expressed enzyme that is widely distributed throughout the body. In the present study, we provide evidence for substrate-dependent regulation of this enzyme. Human peripheral blood mononuclear cells cultured in medium supplemented with p-aminobenzoic acid (PABA; 6 mu M) for 24 h showed a significant decrease (50-80%) in NAT1 activity. The loss of activity was concentration-dependent (EC50 similar to 2 mu M) and selective because PABA had no effect on the activity of constitutively expressed lactate dehydrogenase or aspartate aminotransferase. PABA also induced down-regulation of NAT1 activity in several human cell lines grown at confluence. Substrate-dependent downregulation was not restricted to PABA. Addition of other NAT1 substrates, such as p-aminosalicylic acid, ethyl-p-aminobenzoate, or p-aminophenol to peripheral blood mononuclear cells in culture also resulted in significant (P < .05) decreases in NAT1 activity. However, addition of the NAT2-selective substrates sulfamethazine, dapsone, or procainamide did not alter NAT1 activity. Western blot analysis using a NAT1-specific antibody showed that the loss of NAT1 activity was associated with a parallel reduction in the amount of NAT1 protein (r(2) = 0.95). Arylamines that did not decrease NAT1 activity did not alter NAT1 protein levels. Semiquantitative reverse transcriptase polymerase chain reaction of mRNA isolated from treated and untreated cells revealed no effect of PABA on NAT1 mRNA levels. We conclude that NAT1 can be down-regulated by arylamines that are themselves NAT1 substrates. Because NAT1 is involved in the detoxification/activation of various drugs and carcinogens, substrate-dependent regulation may have important consequences with regard to drug toxicity and cancer risk.
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We present the first comprehensive study, to our knowledge, on genomic chromosomal analysis in syndromic craniosynostosis. In total, 45 patients with craniosynostotic disorders were screened with a variety of methods including conventional karyotype, microsatellite segregation analysis, subtelomeric multiplex ligation-dependent probe amplification) and whole-genome array-based comparative genome hybridisation. Causative abnormalities were present in 42.2% (19/45) of the samples, and 27.8% (10/36) of the patients with normal conventional karyotype carried submicroscopic imbalances. Our results include a wide variety of imbalances and point to novel chromosomal regions associated with craniosynostosis. The high incidence of pure duplications or trisomies suggests that these are important mechanisms in craniosynostosis, particularly in cases involving the metopic suture.
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Concurrent deletion at 1p/19q is a common signature of oligodendrogliomas, and it may, be identified in low-grade tumours (grade II) suggesting it represents an early event in the development of these brain neoplasms. Additional non-random changes primarily involve CDKN2A, PTEN and EGFR. Identification of all of these genetic changes has become an additional parameter in the evaluation of the clinical patients` prognosis, including good response to conventional chemotherapy. Multiple ligation-dependent probe amplification (MLPA) analysis is a new methodology that allows an easy identification of the oligodendrogliomas` abnormalities in a single step. No need of the respective constitutional DNA from each patient is another advantage of this method. We used MLPA kits P088 and P105 to determine the molecular characteristics of a series of 40 oligodendrogliomas. Deletions at I p and 19q were identified in 45% and 65% of cases, respectively. Alterations of EGFR, CDKN2A, ERBB2, PTEN and TP53 were also identified in variable frequencies among 7% to 35% of tumours. These findings demonstrate that MLPA is a reliable technique to the detection of molecular genetic changes in oligodendrogliomas.
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Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.
Resumo:
Rapid accumulation of few polyhedra (FP) mutants was detected during serial passaging of Helicoverpa armigera nucleopolyhedrovirus (HaSNPV) in cell culture. 100% FP infected cells were observed by passage 6. The specific yield decreased from 178 polyhedra per cell at passage 2 to two polyhedra per cell at passage 6. The polyhedra at passage 6 were not biologically active, with a 28-fold reduction in potency compared to passage 3. Electron microscopy studies revealed that very few polyhedra were produced in an FP infected cell (< 10 polyhedra per section) and in most cases these polyhedra contained no virions. A specific failure in the intranuclear nucleocapsid envelopment process in the FP infected cells, leading to the accumulation of naked nucleocapsids, was observed. Genomic restriction endonuclease digestion profiles of budded virus DNA from all passages did not indicate any large DNA insertions or deletions that are often associated with such FP phenotypes for the extensively studied Autographa californica nucleopolyhedrovirus and Gaileria mellonella nucleopolyhedrovirus. Within an HaSNPV 25K FP gene homologue, a single base-pair insertion (an adenine residue) within a region of repetitive sequences (seven adenine residues) was identified in one plaque-purified HaSNPV FP mutant. Furthermore, the sequences obtained from individual clones of the 25KFP gene PCR products of a late passage revealed point mutations or single base-pair insertions occurring throughout the gene. The mechanism of FP mutation in HaSNPV is likely similar to that seen for Lymantria dispar nucleopolyhedrovirus, involving point mutations or small insertions/deletions of the 25K FP gene.
Resumo:
An increasing number of studies shows that the glycogen-accumulating organisms (GAOs) can survive and may indeed proliferate under the alternating anaerobic/aerobic conditions found in EBPR systems, thus forming a strong competitor of the polyphosphate-accumulating organisms (PAOs). Understanding their behaviors in a mixed PAO and GAO culture under various operational conditions is essential for developing operating strategies that disadvantage the growth of this group of unwanted organisms. A model-based data analysis method is developed in this paper for the study of the anaerobic PAO and GAO activities in a mixed PAO and GAO culture. The method primarily makes use of the hydrogen ion production rate and the carbon dioxide transfer rate resulting from the acetate uptake processes by PAOs and GAOs, measured with a recently developed titration and off-gas analysis (TOGA) sensor. The method is demonstrated using the data from a laboratory-scale sequencing batch reactor (SBR) operated under alternating anaerobic and aerobic conditions. The data analysis using the proposed method strongly indicates a coexistence of PAOs and GAOs in the system, which was independently confirmed by fluorescent in situ hybridization (FISH) measurement. The model-based analysis also allowed the identification of the respective acetate uptake rates by PAOs and GAOs, along with a number of kinetic and stoichiometric parameters involved in the PAO and GAO models. The excellent fit between the model predictions and the experimental data not involved in parameter identification shows that the parameter values found are reliable and accurate. It also demonstrates that the current anaerobic PAO and GAO models are able to accurately characterize the PAO/GAO mixed culture obtained in this study. This is of major importance as no pure culture of either PAOs or GAOs has been reported to date, and hence the current PAO and GAO models were developed for the interpretation of experimental results of mixed cultures. The proposed method is readily applicable for detailed investigations of the competition between PAOs and GAOs in enriched cultures. However, the fermentation of organic substrates carried out by ordinary heterotrophs needs to be accounted for when the method is applied to the study of PAO and GAO competition in full-scale sludges. (C) 2003 Wiley Periodicals, Inc.
Resumo:
Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. The performance of the method is illustrated using simulated and real data.
Resumo:
The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.
Resumo:
This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.