Convolutional neural networks for transient candidate vetting in large-scale surveys

Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional pre-processing steps – eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3 per cent of all ‘real’ and 99.7 per cent of all ‘bogus’ instances on a test set containing 1942 ‘bogus’ and 227 ‘real’ instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all.FG and VARMR acknowledge financial support from the Radboud Excellence Initiative. VARMR further acknowledges financial support from Fundac¸ao para a Ci ˜ encia e a Technologia (FCT) ˆ in the form of an exploratory project of reference IF/00498/2015, from Center for Research & Development in Mathematics and Applications (CIDMA) strategic project UID/MAT/04106/2013 and from Enabling Green E-science for the Square Kilometer Array Research Infrastructure (ENGAGE SKA), POCI-01-0145- FEDER-022217, funded by Programa Operacional Competitividade e Internacionalizac¸eo (COMPETE 2020) and FCT, Portugal

Convolutional neural networks for transient candidate vetting in large-scale surveys

Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional pre-processing steps – eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3 per cent of all ‘real’ and 99.7 per cent of all ‘bogus’ instances on a test set containing 1942 ‘bogus’ and 227 ‘real’ instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all

Phylogenetic and functional marker genes to study ammonia-oxidizing microorganisms (AOM) in the environment

The oxidation of ammonia plays a significant role in the transformation of fixed nitrogen in the global nitrogen cycle. Autotrophic ammonia oxidation is known in three groups of microorganisms. Aerobic ammonia-oxidizing bacteria and archaea convert ammonia into nitrite during nitrification. Anaerobic ammonia-oxidizing bacteria (anammox) oxidize ammonia using nitrite as electron acceptor and producing atmospheric dinitrogen. The isolation and cultivation of all three groups in the laboratory are quite problematic due to their slow growth rates, poor growth yields, unpredictable lag phases, and sensitivity to certain organic compounds. Culture-independent approaches have contributed importantly to our understanding of the diversity and distribution of these microorganisms in the environment. In this review, we present an overview of approaches that have been used for the molecular study of ammonia oxidizers and discuss their application in different environments

Biofilm plaque and hydrodynamic effects on mass transfer, fluoride delivery and caries.

Dental plaque is a dynamic community of microor-ganisms, developing continually and reshaping the microenvironment in which they live.1,2 Bacteria and other organisms in the plaque take nutrients from our saliva and the food we eat to proliferate. Immediately after tooth cleaning, bacteria left on the tooth surface and those attaching to the tooth surface from other parts of the oral cavity such as the tongue, gingivae and cheek mucosa begin to regrow. As the biofilm grows, it forms an irregular heterogeneous structure containing clusters of cells surrounded by channels through which liquid, such as saliva, can flow.3,4 Aerobic organisms on the periphery of the cell clusters remove dissolved oxygen (DO) rapidly, creating favorable microniches for pathogenic anaerobic bacteria to thrive. Thus, as the biofilm develops, it may be thought of as an ecosystem, containing many habitats and organisms. Bacteria modify the local environment through the production of acid from the fermentation of sucrose and other fermentable sugars in the diet, which then may increase demineralization of the enamel surface, leading to, or accelerating, the development of caries.5 The literature contains many excellent reviews regarding the microbial ecology and management of dental plaque biofilms.1,2,6 However, it is the goal of this review to concentrate on the effect that the interactions between biofilm and hydrodynamics have on the delivery of fluoride ion (F–) to the tooth surface, and the effect that F– might have on biofilm physiology and, consequently, the cariogenic process

Fluorescence “in situ” hybridization for the detection of biofilm in the middle ear and upper respiratory tract mucosa

Most chronic bacterial infections are associated with biofilm formation wherein the bacteria attach to mucosal surfaces, wound tissue, or medical device surfaces in the human body via the formation of an extracellular matrix. Biofilms assume complex three-dimensional structures dependent on the species, the strain, and the prevailing environmental conditions and are composed of both the bacteria and the extracellular slime-like matrices, which surround the bacteria. Bacteria deep in the biofilm live under anaerobic conditions and must use alternatives to O(2) as a terminal electron acceptor. Thus, the metabolic rates of these deep bacteria are greatly reduced, which renders them extremely resistant to antibiotic treatment, and for reasons not clearly understood, it is often very difficult to culture biofilm bacteria using traditional microbiologic techniques. To directly identify and visualize biofilm bacteria in a species-specific manner, we developed a confocal laser scanning microscopy (CLSM)-based 16S rRNA fluorescence in situ hybridization (FISH) protocol, to find biofilm bacteria in middle ear and upper respiratory tract mucosa, which preserves the three-dimensional structure of the biofilm and avoids the use of traditional culture techniques

Impact of nitrate on bacterial structure and function in injection-water biofilms.

We studied the impact of NO3– on the bacterial community composition, diversity, and function in in situ industrial, anaerobic biofilms by combining microsensor profiling, 15N and 35S labeling, and 16S rRNA gene-based fingerprinting. Biofilms were grown on carbon steel coupons within a system designed to treat seawater for injection into an oil field for pressurized oil recovery. NO3– was added to the seawater in an attempt to prevent bacterial H2S generation and microbially influenced corrosion in the field. Microprofiling of nitrogen compounds and redox potential inside the biofilms showed that the zone of highest metabolic activity was located close to the metal surface, correlating with a high bacterial abundance in this zone. Upon addition, NO3– was mainly reduced to NO2–. In biofilms grown in the absence of NO3–, redox potentials of <–450 mV at the metal surface suggested the release of Fe2+. NO3– addition to previously untreated biofilms induced a decline (65%) in bacterial species richness, with Methylophaga- and Colwellia-related sequences having the highest number of obtained clones in the clone library. In contrast, no changes in community composition and potential NO3– reduction occurred upon subsequent withdrawal of NO3–. Active sulfate reduction was below detection levels in all biofilms, but S isotope fractionation analysis of sulfide deposits suggested that it must have occurred either at low rates or episodically. Scanning electron microscopy revealed that pitting corrosion occurred on all coupons, independent of the treatment. However, uniform corrosion was clearly mitigated by NO3– addition