The LOFAR Transients Pipeline

Current and future astronomical survey facilities provide a remarkably rich opportunity for transient astronomy, combining unprecedented fields of view with high sensitivity and the ability to access previously unexplored wavelength regimes. This is particularly true of LOFAR, a recently-commissioned, low-frequency radio interferometer, based in the Netherlands and with stations across Europe. The identification of and response to transients is one of LOFAR's key science goals. However, the large data volumes which LOFAR produces, combined with the scientific requirement for rapid response, make automation essential. To support this, we have developed the LOFAR Transients Pipeline, or TraP. The TraP ingests multi-frequency image data from LOFAR or other instruments and searches it for transients and variables, providing automatic alerts of significant detections and populating a lightcurve database for further analysis by astronomers. Here, we discuss the scientific goals of the TraP and how it has been designed to meet them. We describe its implementation, including both the algorithms adopted to maximize performance as well as the development methodology used to ensure it is robust and reliable, particularly in the presence of artefacts typical of radio astronomy imaging. Finally, we report on a series of tests of the pipeline carried out using simulated LOFAR observations with a known population of transients.Comment: 30 pages, 11 figures; Accepted for publication in Astronomy & Computing; Code at https://github.com/transientskp/tk

Impact of the SPOP Mutant Subtype on the Interpretation of Clinical Parameters in Prostate Cancer.

Purpose: Molecular characterization of prostate cancer, including The Cancer Genome Atlas, has revealed distinct subtypes with underlying genomic alterations. One of these core subtypes, SPOP (speckle-type POZ protein) mutant prostate cancer, has previously only been identifiable via DNA sequencing, which has made the impact on prognosis and routinely used risk stratification parameters unclear. Methods: We have developed a novel gene expression signature, classifier (Subclass Predictor Based on Transcriptional Data), and decision tree to predict the SPOP mutant subclass from RNA gene expression data and classify common prostate cancer molecular subtypes. We then validated and further interrogated the association of prostate cancer molecular subtypes with pathologic and clinical outcomes in retrospective and prospective cohorts of 8,158 patients. Results: The subclass predictor based on transcriptional data model showed high sensitivity and specificity in multiple cohorts across both RNA sequencing and microarray gene expression platforms. We predicted approximately 8% to 9% of cases to be SPOP mutant from both retrospective and prospective cohorts. We found that the SPOP mutant subclass was associated with lower frequency of positive margins, extraprostatic extension, and seminal vesicle invasion at prostatectomy; however, SPOP mutant cancers were associated with higher pretreatment serum prostate-specific antigen (PSA). The association between SPOP mutant status and higher PSA level was validated in three independent cohorts. Despite high pretreatment PSA, the SPOP mutant subtype was associated with a favorable prognosis with improved metastasis-free survival, particularly in patients with high-risk preoperative PSA levels. Conclusion: Using a novel gene expression model and a decision tree algorithm to define prostate cancer molecular subclasses, we found that the SPOP mutant subclass is associated with higher preoperative PSA, less adverse pathologic features, and favorable prognosis. These findings suggest a paradigm in which the interpretation of common risk stratification parameters, particularly PSA, may be influenced by the underlying molecular subtype of prostate cancer

An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: a case study of nitrogen dioxide concentrations in Scotland

The long-term health effects of air pollution can be estimated using a spatio-temporal ecological study, where the disease data are counts of hospital admissions from populations in small areal units at yearly intervals. Spatially representative pollution concentrations for each areal unit are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over grid level concentrations from an atmospheric dispersion model. We propose a novel fusion model for estimating spatially aggregated pollution concentrations using both the modelled and monitored data, and relate these concentrations to respiratory disease in a new study in Scotland between 2007 and 2011

Evolutionary associations between sand seatrout (Cynoscion arenarius) and silver seatrout (C. nothus) inferred from morphological characters, mitochondrial DNA, and microsatellite markers

The evolutionary associations between closely related fish species, both contemporary and historical, are frequently assessed by using molecular markers, such as microsatellites. Here, the presence and variability of microsatellite loci in two closely related species of marine fishes, sand seatrout (Cynoscion arenarius) and silver seatrout (C. nothus), are explored by using heterologous primers from red drum (Sciaenops ocellatus). Data from these loci are used in conjunction with morphological characters and mitochondrial DNA haplotypes to explore the extent of genetic exchange between species offshore of Galveston Bay, TX. Despite seasonal overlap in distribution, low genetic divergence at microsatellite loci, and similar life history parameters of C. arenarius and C. nothus, all three data sets indicated that hybridization between these species does not occur or occurs only rarely and that historical admixture in Galveston Bay after divergence between these species was unlikely. These results shed light upon the evolutionary history of these fishes and highlight the genetic properties of each species that are influenced by their life history and ecology

Earthquake Arrival Association with Backprojection and Graph Theory

The association of seismic wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become more sensitive, and particularly when earthquake rates are high. For instance, seismic waves arriving across a monitoring network from several sources may overlap in time, false arrivals may be detected, and some arrivals may be of unknown phase (e.g., P- or S-waves). We propose an automated method to associate arrivals with earthquake sources and obtain source locations applicable to such situations. To do so we use a pattern detection metric based on the principle of backprojection to reveal candidate sources, followed by graph-theory-based clustering and an integer linear optimization routine to associate arrivals with the minimum number of sources necessary to explain the data. This method solves for all sources and phase assignments simultaneously, rather than in a sequential greedy procedure as is common in other association routines. We demonstrate our method on both synthetic and real data from the Integrated Plate Boundary Observatory Chile (IPOC) seismic network of northern Chile. For the synthetic tests we report results for cases with varying complexity, including rates of 500 earthquakes/day and 500 false arrivals/station/day, for which we measure true positive detection accuracy of > 95%. For the real data we develop a new catalog between January 1, 2010 - December 31, 2017 containing 817,548 earthquakes, with detection rates on average 279 earthquakes/day, and a magnitude-of-completion of ~M1.8. A subset of detections are identified as sources related to quarry and industrial site activity, and we also detect thousands of foreshocks and aftershocks of the April 1, 2014 Mw 8.2 Iquique earthquake. During the highest rates of aftershock activity, > 600 earthquakes/day are detected in the vicinity of the Iquique earthquake rupture zone

HOME RANGE AND MICROHABITAT ASSOCIATIONS OF THE SOUTHERN RED-BACKED VOLE (MYODES GAPPERI) IN NEW HAMPSHIRE FORESTS

Resources, such as food and shelter, are unevenly distributed across the landscape at both macro and micro scales. Home range is one measure of space use that reflects an individual’s resource requirements (e.g., microhabitat characteristics) and competition for those resources (e.g., density dependence). This study focuses on the home range of the southern red-backed vole (Myodes gapperi), comparing field methods for estimating home range and modeling the microhabitat characteristics that define the core area of the home range. Southern red-backed voles (Myodes gapperi) are common to boreal forests, most often found in coniferous or mixed deciduous stands, and in the northeast, have an affinity for eastern hemlock (Tsuga canadensis). With eastern hemlock populations in decline due to the invasive eastern hemlock woolly adelgid (Adelges tsugae), it is unknown how M. gapperi space use will be affected. From 2014-2017, southern red-backed voles were censused across 12 (~1 ha) grids using mark-recapture methods and for a subset of individuals radiotelemetry. Individual home range size, core area size, and core area overlap were calculated for adults using kernel density estimators from both mark recapture live trapping and radiotelemetry data. At each capture point, forest structure, ground cover, and geographic features were measured to assess influence of microhabitat on home range and core area. Density was calculated on each grid for each year of the study using the POPAN parameterization of the Jolly-Seber model. In this thesis, Chapter One presents the effects of M. gapperi density on individual home range and core area. Differences in size and overlap are examined within and between sexes, and estimates compared between the two field techniques, mark-recapture and radiotelemetry, often used to delineate home range and core area. Density did not affect space use and female voles shared area more often with males than other females. The home range size of males was larger than that of females, however, core area was consistently about 30% of total home range. Area estimates generated under mark-recapture and radiotelemetry were similar for females, but differed for males with larger home ranges calculated using radiotelemetry. Mark-recapture methods may have underestimated male home range as a consequence of the trapping grid being smaller than male home range. Chapter Two identifies habitat characteristics at the macro and micro scale that influence M. gapperi space use. Macrohabitat differences were evaluated between trap stations that were visited and were not visited by M. gapperi and microhabitat characteristics were modeling within female M. gapperi core areas. Myodes gapperi are found in areas with higher eastern hemlock basal area and more coarse woody debris. Within these stands, female M. gapperi select for core areas closer to water, with greater red maple basal area, deeper leaf litter, and a greater density of hemlock stems