885 resultados para Weighted adjacency matrix


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Conventional methods are sometimes insufficient to identify human bacterial pathogens, and alternative techniques, often molecular, are required. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) identified with a valid score 45.9% of 410 clinical isolates from 207 different difficult-to-identify species having required 16S rRNA gene sequencing. MALDI-TOF MS might represent an alternative to 16S rRNA gene sequencing.

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Health assessment and medical surveillance of workers exposed to combustion nanoparticles are challenging. The aim was to evaluate the feasibility of using exhaled breath condensate (EBC) from healthy volunteers for (1) assessing the lung deposited dose of combustion nanoparticles and (2) determining the resulting oxidative stress by measuring hydrogen peroxide (H2O2) and malondialdehyde (MDA). Methods: Fifteen healthy nonsmoker volunteers were exposed to three different levels of sidestream cigarette smoke under controlled conditions. EBC was repeatedly collected before, during, and 1 and 2 hr after exposure. Exposure variables were measured by direct reading instruments and by active sampling. The different EBC samples were analyzed for particle number concentration (light-scattering-based method) and for selected compounds considered oxidative stress markers. Results: Subjects were exposed to an average airborne concentration up to 4.3×10(5) particles/cm(3) (average geometric size ∼60-80 nm). Up to 10×10(8) particles/mL could be measured in the collected EBC with a broad size distribution (50(th) percentile ∼160 nm), but these biological concentrations were not related to the exposure level of cigarette smoke particles. Although H2O2 and MDA concentrations in EBC increased during exposure, only H2O2 showed a transient normalization 1 hr after exposure and increased afterward. In contrast, MDA levels stayed elevated during the 2 hr post exposure. Conclusions: The use of diffusion light scattering for particle counting proved to be sufficiently sensitive to detect objects in EBC, but lacked the specificity for carbonaceous tobacco smoke particles. Our results suggest two phases of oxidation markers in EBC: first, the initial deposition of particles and gases in the lung lining liquid, and later the start of oxidative stress with associated cell membrane damage. Future studies should extend the follow-up time and should remove gases or particles from the air to allow differentiation between the different sources of H2O2 and MDA.

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PURPOSE: At high magnetic field strengths (B0 ≥ 3 T), the shorter radiofrequency wavelength produces an inhomogeneous distribution of the transmit magnetic field. This can lead to variable contrast across the brain which is particularly pronounced in T2 -weighted imaging that requires multiple radiofrequency pulses. To obtain T2 -weighted images with uniform contrast throughout the whole brain at 7 T, short (2-3 ms) 3D tailored radiofrequency pulses (kT -points) were integrated into a 3D variable flip angle turbo spin echo sequence. METHODS: The excitation and refocusing "hard" pulses of a variable flip angle turbo spin echo sequence were replaced with kT -point pulses. Spatially resolved extended phase graph simulations and in vivo acquisitions at 7 T, utilizing both single channel and parallel-transmit systems, were used to test different kT -point configurations. RESULTS: Simulations indicated that an extended optimized k-space trajectory ensured a more homogeneous signal throughout images. In vivo experiments showed that high quality T2 -weighted brain images with uniform signal and contrast were obtained at 7 T by using the proposed methodology. CONCLUSION: This work demonstrates that T2 -weighted images devoid of artifacts resulting from B1 (+) inhomogeneity can be obtained at high field through the optimization of extended kT -point pulses. Magn Reson Med 71:1478-1488, 2014. © 2013 Wiley Periodicals, Inc.

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The SeDeM Diagram Expert System has been used to study excipients, Captopril and designed formulations for their galenic characterization and to ascertain the critical points of the formula affecting product quality to obtain suitable formulations of Captopril Direct Compression SR Matrix Tablets. The application of the Sedem Diagram Expert System enables selecting excipients with in order to optimize the formula in the preformulation and formulation studies. The methodology is based on the implementation of ICH Q8, establishing the design space of the formula with the use of experiment design, using the parameters of the SeDeM Diagram Expert System as system responses.

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This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

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Besides tumor cells, the tumor microenvironment harbors a variety of host-derived cells, such as endothelial cells, fibroblasts, innate and adaptive immune cells. It is a complex and highly dynamic environment, providing very important cues to tumor development and progression. Tumor-associated endothelial cells play a key role in this process. On the one hand, they form tumor-associated (angiogenic) vessels through sprouting from locally preexisting vessels or recruitment of bone marrow-derived endothelial progenitor cells, to provide nutritional support to the growing tumor. On the other hand, they are the interface between circulating blood cells, tumor cells and the extracellular matrix, thereby playing a central role in controlling leukocyte recruitment, tumor cell behavior and metastasis formation. Hypoxia is a critical parameter modulating the tumor microenvironment and endothelial/tumor cell interactions. Under hypoxic stress, tumor cells produce factors that promote tumor angiogenesis, tumor cell motility and metastasis. Among these factors, VEGF, a main angiogenesis modulator, can also play a critical role in the control of immune tolerance. This review discusses some aspects of the role of endothelial cells within tumor microenvironment and emphasizes their interaction with tumor cells, the extracellular matrix and with immune killer cells. We will also address the role played by circulating endothelial progenitor cells and illustrate their features and mechanism of recruitment to the tumor microenvironment and their role in tumor angiogenesis.

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BACKGROUND: The visceral (VAT) and subcutaneous (SCAT) adipose tissues play different roles in physiology and obesity. The molecular mechanisms underlying their expansion in obesity and following body weight reduction are poorly defined. METHODOLOGY: C57Bl/6 mice fed a high fat diet (HFD) for 6 months developed low, medium, or high body weight as compared to normal chow fed mice. Mice from each groups were then treated with the cannabinoid receptor 1 antagonist rimonabant or vehicle for 24 days to normalize their body weight. Transcriptomic data for visceral and subcutaneous adipose tissues from each group of mice were obtained and analyzed to identify: i) genes regulated by HFD irrespective of body weight, ii) genes whose expression correlated with body weight, iii) the biological processes activated in each tissue using gene set enrichment analysis (GSEA), iv) the transcriptional programs affected by rimonabant. PRINCIPAL FINDINGS: In VAT, "metabolic" genes encoding enzymes for lipid and steroid biosynthesis and glucose catabolism were down-regulated irrespective of body weight whereas "structure" genes controlling cell architecture and tissue remodeling had expression levels correlated with body weight. In SCAT, the identified "metabolic" and "structure" genes were mostly different from those identified in VAT and were regulated irrespective of body weight. GSEA indicated active adipogenesis in both tissues but a more prominent involvement of tissue stroma in VAT than in SCAT. Rimonabant treatment normalized most gene expression but further reduced oxidative phosphorylation gene expression in SCAT but not in VAT. CONCLUSION: VAT and SCAT show strikingly different gene expression programs in response to high fat diet and rimonabant treatment. Our results may lead to identification of therapeutic targets acting on specific fat depots to control obesity.

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In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

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Matrix attachment regions (MAR) generally act as epigenetic regulatory sequences that increase gene expression, and they were proposed to partition chromosomes into loop-forming domains. However, their molecular mode of action remains poorly understood. Here, we assessed the possible contribution of the AT-rich core and adjacent transcription factor binding motifs to the transcription augmenting and anti-silencing effects of human MAR 1-68. Either flanking sequences together with the AT-rich core were required to obtain the full MAR effects. Shortened MAR derivatives retaining full MAR activity were constructed from combinations of the AT-rich sequence and multimerized transcription factor binding motifs, implying that both transcription factors and the AT-rich microsatellite sequence are required to mediate the MAR effect. Genomic analysis indicated that MAR AT-rich cores may be depleted of histones and enriched in RNA polymerase II, providing a molecular interpretation of their chromatin domain insulator and transcriptional augmentation activities.

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A particular property of the matched desiredimpulse response receiver is introduced in this paper, namely,the fact that full exploitation of the diversity is obtained withmultiple beamformers when the channel is spatially and timelydispersive. This particularity makes the receiver specially suitablefor mobile and underwater communications. The new structureprovides better performance than conventional and weightedVRAKE receivers, and a diversity gain with no needs of additionalradio frequency equipment. The baseband hardware neededfor this new receiver may be obtained through reconfigurabilityof the RAKE architectures available at the base station. Theproposed receiver is tested through simulations assuming UTRAfrequency-division-duplexing mode.

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Identifiability of the so-called ω-slice algorithm is proven for ARMA linear systems. Although proofs were developed in the past for the simpler cases of MA and AR models, they were not extendible to general exponential linear systems. The results presented in this paper demonstrate a unique feature of the ω-slice method, which is unbiasedness and consistency when order is overdetermined, regardless of the IIR or FIR nature of the underlying system, and numerical robustness.

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The objective of this work was to select the most informative morphoagronomic descriptors for cassava (Manihot esculenta) germplasm and to evaluate the ability of different methods to select the descriptors. Ninety-five accessions were characterized using 51 morphoagronomic descriptors. Data were subjected to a multiple correspondence analysis (MCA), whose information was used in the following four methods of descriptor selection: reverse order of the descriptor for the pth factorial axis of the MCA (Jolliffe); sequential, multiple correspondence analysis (SMCA); mean of the contribution orders of the descriptor in the first three factorial axes (C3PA); and C3PA method weighted by the respective eigenvalues of the full analysis (C3PAWeig). The correlations between the dissimilarity matrix with all descriptors and the most informative descriptors were high and significant (0.75, 0.77, 0.83, and 0.84 for C3PAWeig, C3PA, SMCA, and Jolliffe, respectively). The less informative descriptors were discarded, considering those common among the selection methods and relevant for the breeding interests. Therefore, 32 morphoagronomic descriptors with correlation between the dissimilarity matrices (r=0.81) were selected, due to their high capacity to discriminate cassava germplasm and to their ability to maintain some preliminary agronomic traits, useful for the initial characterization of the germplasm.

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This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos