Segmentation and intensity estimation for microarray images with saturated pixels

<p>Abstract</p> <p>Background</p> <p>Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶- 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.</p> <p>Results</p> <p>We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.</p> <p>Conclusions</p> <p>The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.</p

Quinine doped hybrid sol-gel coatings for wave guiding and optical applications

Pure and quinine doped silica coatings have been prepared over sodalime glasses. The coatings were consolidated at low temperature (range 60-180 A degrees C) preserving optical activity of quinine molecule. We designed a device to test the guiding properties of the coatings. We confirmed with this device that light injected in pure silica coatings is guided over distances of meters while quinine presence induces isotropic photoluminescence. With the combined use of both type of coatings, it is possible to design light guiding devices and illuminate regions in glass elements without electronic circuits

VAMOS: a Pathfinder for the HAWC Gamma-Ray Observatory

VAMOS was a prototype detector built in 2011 at an altitude of 4100m a.s.l. in the state of Puebla, Mexico. The aim of VAMOS was to finalize the design, construction techniques and data acquisition system of the HAWC observatory. HAWC is an air-shower array currently under construction at the same site of VAMOS with the purpose to study the TeV sky. The VAMOS setup included six water Cherenkov detectors and two different data acquisition systems. It was in operation between October 2011 and May 2012 with an average live time of 30%. Besides the scientific verification purposes, the eight months of data were used to obtain the results presented in this paper: the detector response to the Forbush decrease of March 2012, and the analysis of possible emission, at energies above 30 GeV, for long gamma-ray bursts GRB111016B and GRB120328B.Comment: Accepted for pubblication in Astroparticle Physics Journal (20 pages, 10 figures). Corresponding authors: A.Marinelli and D.Zaboro

bioNMF: a versatile tool for non-negative matrix factorization in biology

BACKGROUND: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment. RESULTS: In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process. CONCLUSION: bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at

Atypical pathogens in hospitalized patients with community-acquired pneumonia: A worldwide perspective

Background: Empirical antibiotic coverage for atypical pathogens in community-acquired pneumonia (CAP) has long been debated, mainly because of a lack of epidemiological data. We aimed to assess both testing for atypical pathogens and their prevalence in hospitalized patients with CAP worldwide, especially in relation with disease severity. Methods: A secondary analysis of the GLIMP database, an international, multicentre, point-prevalence study of adult patients admitted for CAP in 222 hospitals across 6 continents in 2015, was performed. The study evaluated frequency of testing for atypical pathogens, including L. pneumophila, M. pneumoniae, C. pneumoniae, and their prevalence. Risk factors for testing and prevalence for atypical pathogens were assessed through univariate analysis. Results: Among 3702 CAP patients 1250 (33.8%) underwent at least one test for atypical pathogens. Testing varies greatly among countries and its frequency was higher in Europe than elsewhere (46.0% vs. 12.7%, respectively, p &lt; 0.0001). Detection of L. pneumophila urinary antigen was the most common test performed worldwide (32.0%). Patients with severe CAP were less likely to be tested for both atypical pathogens considered together (30.5% vs. 35.0%, p = 0.009) and specifically for legionellosis (28.3% vs. 33.5%, p = 0.003) than the rest of the population. Similarly, L. pneumophila testing was lower in ICU patients. At least one atypical pathogen was isolated in 62 patients (4.7%), including M. pneumoniae (26/251 patients, 10.3%), L. pneumophila (30/1186 patients, 2.5%), and C. pneumoniae (8/228 patients, 3.5%). Patients with CAP due to atypical pathogens were significantly younger, showed less cardiovascular, renal, and metabolic comorbidities in comparison to adult patients hospitalized due to non-atypical pathogen CAP. Conclusions: Testing for atypical pathogens in patients admitted for CAP in poorly standardized in real life and does not mirror atypical prevalence in different settings. Further evidence on the impact of atypical pathogens, expecially in the low-income countries, is needed to guidelines implementation

Prevalence and etiology of community-acquired pneumonia in immunocompromised patients

Background. The correct management of immunocompromised patients with pneumonia is debated. We evaluated the prevalence, risk factors, and characteristics of immunocompromised patients coming from the community with pneumonia. Methods. We conducted a secondary analysis of an international, multicenter study enrolling adult patients coming from the community with pneumonia and hospitalized in 222 hospitals in 54 countries worldwide. Risk factors for immunocompromise included AIDS, aplastic anemia, asplenia, hematological cancer, chemotherapy, neutropenia, biological drug use, lung transplantation, chronic steroid use, and solid tumor. Results. At least 1 risk factor for immunocompromise was recorded in 18% of the 3702 patients enrolled. The prevalences of risk factors significantly differed across continents and countries, with chronic steroid use (45%), hematological cancer (25%), and chemotherapy (22%) the most common. Among immunocompromised patients, community-acquired pneumonia (CAP) pathogens were the most frequently identified, and prevalences did not differ from those in immunocompetent patients. Risk factors for immunocompromise were independently associated with neither Pseudomonas aeruginosa nor non\u2013community-acquired bacteria. Specific risk factors were independently associated with fungal infections (odds ratio for AIDS and hematological cancer, 15.10 and 4.65, respectively; both P = .001), mycobacterial infections (AIDS; P = .006), and viral infections other than influenza (hematological cancer, 5.49; P < .001). Conclusions. Our findings could be considered by clinicians in prescribing empiric antibiotic therapy for CAP in immunocompromised patients. Patients with AIDS and hematological cancer admitted with CAP may have higher prevalences of fungi, mycobacteria, and noninfluenza viruses