Neural Nets and Star/Galaxy Separation in Wide Field Astronomical Images

One of the most relevant problems in the extraction of scientifically useful information from wide field astronomical images (both photographic plates and CCD frames) is the recognition of the objects against a noisy background and their classification in unresolved (star-like) and resolved (galaxies) sources. In this paper we present a neural network based method capable to perform both tasks and discuss in detail the performance of object detection in a representative celestial field. The performance of our method is compared to that of other methodologies often used within the astronomical community.Comment: 6 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199

Some effects of dust on photometry of high-z galaxies: Confounding the effects of evolution

Photometric observations of very distant galaxies--e.g., color vs. z or magnitude vs. z, have been used over the past decade or so in investigations into the evolution of the stellar component. Numerous studies have predicted significant color variations as a result of evolution, in addition to the shifting of different rest wavelengths into the band of observation. Although there is significant scatter, the data can be fit with relatively straightforward, plausible models for galaxian evolution. In very few cases are the effects of dust extinction included in the models. This is due in a large part to the uncertainty about the distribution and optical properties of the grains, and even whether or not they are present in significant numbers in some types of galaxies such as ellipticals. It is likely that the effects of dust on broadband observations are the greatest uncertainty in studies of very distant galaxies. We use a detailed Monte Carlo radiative transfer model within a spherical geometry for different star/dust distributions to examine the effects of dust on the broadband colors of galaxies as a function of redshift. The model fully accounts for absorption and angular redistribution in scattering. In this summary, we consider only the effects on color vs. redshift for three simple geometries each with the same total dust optical depth. Elsewhere at this conference, Capuano, Thronson, & Witt consider other effects of altering the relative dust/star distribution

Star-dust geometries in galaxies: The effect of interstellar matter distributions on optical and infrared properties of late-type galaxies

The presence of substantial amounts of interstellar dust in late-type galaxies affects observable parameters such as the optical surface brightness, the color, and the ratio of far-infrared to optical luminosity of these galaxies. We conducted radiative transfer calculations for late-type galaxy environments to examine two different scenarios: (1) the effects of increasing amounts of dust in two fixed geometries with different star distributions; and (2) the effects of an evolving dust-star geometry in which the total amount of dust is held constant, for three different star distributions. The calculations were done for ten photometric bands, ranging from the far-ultraviolet to the near-infrared (K), and scattered light was included in the galactic surface brightness at each wavelength. The energy absorbed throughout these ten photometric bands was assumed to re-emerge in the far-infrared as thermal dust emission. We also considered the evolutionary contraction of a constant amount of dust relative to pre-existing star distributions

A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

Understanding the role played by genetic variations in diseases, exploring genomic variants, and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, the authors propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis

A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests

Lessons from the Pacific programme to eliminate lymphatic filariasis: a case study of 5 countries

BACKGROUND Lymphatic Filariasis (LF) is an important Neglected Tropical Disease, being a major cause of disability worldwide. The Global Programme to Eliminate Lymphatic Filariasis aims to eliminate LF as a public health problem by the year 2020, primarily through repeated Mass Drug Administration (MDA). The Pacific region programme commenced in 1999. By June 2007, five of the eleven countries classified as endemic had completed five MDA campaigns and post-MDA prevalence surveys to assess their progress. We review available programme data and discuss their implications for other LF elimination programs in developing countries. METHODS Reported MDA coverage and results from initial surveys and post-MDA surveys of LF using the immunochromatographic test (ICT) from these five Pacific Island countries (Tonga, Niue, Vanuatu, Samoa and Cook Islands) were analysed to provide an understanding of their quality and programme progress towards LF elimination. Denominator data reported by each country programme for 2001 was compared to official sources to assess the accuracy of MDA coverage data. RESULTS Initial survey results from these five countries revealed an ICT prevalence of between 2.7 and 8.6 percent in individuals tested prior to commencement of the programme. Country MDA coverage results varied depending on the source of denominator data. Of the five countries in this case study, three countries (Tonga, Niue and Vanuatu) reached the target prevalence of <1% antigenaemia following five rounds of MDA. However, endpoint data could not be reliably compared to baseline data as survey methodology varied. CONCLUSION Accurate and representative baseline and post-campaign prevalence data is crucial for determining program effectiveness and the factors contributing to effectiveness. This is emphasised by the findings of this case study. While three of the five Pacific countries reported achieving the target prevalence of <1% antigenaemia, limitations in the data preclude identification of key determinants of this achievement

Subtle modifications to a thieno[2,3-d]pyrimidine scaffold yield negative allosteric modulators and agonists of the dopamine D2 receptor

We recently described a structurally novel series of negative allosteric modulators (NAMs) of the dopamine D2 receptor (D2R) based on thieno[2,3-d]pyrimidine 1, showing it can be structurally simplified to reveal low molecular weight, fragment-like NAMs that retain robust negative cooperativity, such as 3. Herein, we report the synthesis and functional profiling of analogues of 3, placing specific emphasis on examining secondary and tertiary amino substituents at the 4-position, combined with a range of substituents at the 5/6-positions (e.g. aromatic/aliphatic carbocycles). We identify analogues with diverse pharmacology at the D2R including NAMs (19fc) with sub-?M affinity (9h) and, surprisingly, low efficacy partial agonists (9d and 9i)

A novel synthetic peptide from a tomato defensin exhibits antibacterial activities against Helicobacter pylori

Defensins are a class of cysteine-rich proteins, which exert broad spectrum antimicrobial activity. In this work, we used a bioinformatic approach to identify putative defensins in the tomato genome. Fifteen proteins had a mature peptide that includes the well-conserved tetradisulfide array. We selected a representative member of the tomato defensin family; we chemically synthesized its gamma-motif and tested its antimicrobial activity. Here, we demonstrate that the synthetic peptide exhibits potent antibacterial activity against Gram-positive bacteria, such as Staphylococcus aureus A170, Staphylococcus epidermidis, and Listeria monocytogenes, and Gram-negative bacteria, including Salmonella enterica serovar Paratyphi, Escherichia coli, and Helicobacter pylori. In addition, the synthetic peptide shows minimal (<5%) hemolytic activity and absence of cytotoxic effects against THP-1 cells. Finally, SolyC exerts an anti-inflammatory activity in vitro, as it downregulates the level of the proinflammatory cytokines TNF-alpha and IFN-gamma

Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification

[Abriged] Astronomical Wide Field Imaging performed with new large format CCD detectors poses data reduction problems of unprecedented scale which are difficult to deal with traditional interactive tools. We present here NExt (Neural Extractor): a new Neural Network (NN) based package capable to detect objects and to perform both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first discriminated from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold and then they are classified as stars or as galaxies through diagnostic diagrams having variables choosen accordingly to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of "what an object is" (id est, it keeps all structures composed by more than one pixels) and performs the detection via an unsupervised NN approaching detection as a clustering problem which has been thoroughly studied in the artificial intelligence literature. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features, we use a NN to select the most significant features among the large number of measured ones, and then we use their selected features to perform the classification task. In order to optimise the performances of the system we implemented and tested several different models of NN. The comparison of the NExt performances with those of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at http://www.na.astro.it/~andreon/listapub.htm

Does an antibiotic-loaded hydrogel coating reduce early post-surgical infection after joint arthroplasty?

Background: Infection remains among the main reasons for joint prosthesis failure. Preclinical reports have suggested that antibacterial coatings of implants may prevent bacterial adhesion and biofilm formation. This study presents the results of the first clinical trial on an antibiotic-loaded fast-resorbable hydrogel coating (Defensive Antibacterial Coating, DAC®) in patients undergoing hip or knee prosthesis. Methods: In this multicenter, randomized prospective study, a total of 380 patients, scheduled to undergo primary (n=270) or revision (n=110) total hip (N=298) or knee (N=82) joint replacement with a cementless or a hybrid implant, were randomly assigned, in six European orthopedic centers, to receive an implant either with the antibiotic-loaded DAC coating (treatment group) or without coating (control group). Pre- and postoperative assessment of clinical scores, wound healing, laboratory tests, and x-ray exams were performed at fixed time intervals. Results: Overall, 373 patients were available at a mean follow-up of 14.5 ± 5.5 months (range 6 to 24). On average, wound healing, laboratory and radiographic findings showed no significant difference between the two groups. Eleven early surgical site infections were observed in the control group and only one in the treatment group (6% vs. 0.6%; p=0.003). No local or systemic side effects related to the DAC hydrogel coating were observed, and no detectable interference with implant osteointegration was noted. Conclusions: The use of a fast-resorbable, antibiotic-loaded hydrogel implant coating can reduce the rate of early surgical site infections, without any detectable adverse events or side effects after hip or knee joint replacement with a cementless or hybrid implant