Probabilistic Cross-Identification of Astronomical Sources

We present a general probabilistic formalism for cross-identifying astronomical point sources in multiple observations. Our Bayesian approach, symmetric in all observations, is the foundation of a unified framework for object matching, where not only spatial information, but physical properties, such as colors, redshift and luminosity, can also be considered in a natural way. We provide a practical recipe to implement an efficient recursive algorithm to evaluate the Bayes factor over a set of catalogs with known circular errors in positions. This new methodology is crucial for studies leveraging the synergy of today's multi-wavelength observations and to enter the time-domain science of the upcoming survey telescopes.Comment: Accepted for publication in the Astrophysical Journal, 8 pages, 1 figure, emulateapj w/ apjfont

Comparative genomics of Cluster O mycobacteriophages

Mycobacteriophages - viruses of mycobacterial hosts - are genetically diverse but morphologically are all classified in the Caudovirales with double-stranded DNA and tails. We describe here a group of five closely related mycobacteriophages - Corndog, Catdawg, Dylan, Firecracker, and YungJamal - designated as Cluster O with long flexible tails but with unusual prolate capsids. Proteomic analysis of phage Corndog particles, Catdawg particles, and Corndog-infected cells confirms expression of half of the predicted gene products and indicates a non-canonical mechanism for translation of the Corndog tape measure protein. Bioinformatic analysis identifies 8-9 strongly predicted SigA promoters and all five Cluster O genomes contain more than 30 copies of a 17 bp repeat sequence with dyad symmetry located throughout the genomes. Comparison of the Cluster O phages provides insights into phage genome evolution including the processes of gene flux by horizontal genetic exchange

Comparative genomics of Cluster O mycobacteriophages

Mycobacteriophages - viruses of mycobacterial hosts - are genetically diverse but morphologically are all classified in the Caudovirales with double-stranded DNA and tails. We describe here a group of five closely related mycobacteriophages - Corndog, Catdawg, Dylan, Firecracker, and YungJamal - designated as Cluster O with long flexible tails but with unusual prolate capsids. Proteomic analysis of phage Corndog particles, Catdawg particles, and Corndog-infected cells confirms expression of half of the predicted gene products and indicates a non-canonical mechanism for translation of the Corndog tape measure protein. Bioinformatic analysis identifies 8-9 strongly predicted SigA promoters and all five Cluster O genomes contain more than 30 copies of a 17 bp repeat sequence with dyad symmetry located throughout the genomes. Comparison of the Cluster O phages provides insights into phage genome evolution including the processes of gene flux by horizontal genetic exchange

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Comparison of classic and novel change point detection methods for time series with changes in variance

Segmentation or change point detection is a very common topic in time series analysis, anomaly detection and pattern recognition. In Breitenberger et al. (2017) the time series generated by sensors with 3D accelerometers were analysed. It was noticed that such series consist of segments of independent and correlated observations. Hence the appropriate methods for change point detection for both data types must be implemented simultaneously. This paper provides an auxiliary comparison analysis which we intend to implement later for the above mentioned acceleration data. The available methods require usually a long execution time, so that it is time-consuming if several methods should be compared. In the framework of the present publication we want to give additional help for detecting a suitable change point detection method and for finding a good parameter setting. Our analysis is performed on simulated time series, that are normally distributed with constant but unknown mean and changes in variance

Comparison of classic and novel change point detection methods for time series with changes in variance

Segmentation or change point detection is a very common topic in time series analysis, anomaly detection and pattern recognition. In Breitenberger et al. (2017) the time series generated by sensors with 3D accelerometers were analysed. It was noticed that such series consist of segments of independent and correlated observations. Hence the appropriate methods for change point detection for both data types must be implemented simultaneously. This paper provides an auxiliary comparison analysis which we intend to implement later for the above mentioned acceleration data. The available methods require usually a long execution time, so that it is time-consuming if several methods should be compared. In the framework of the present publication we want to give additional help for detecting a suitable change point detection method and for finding a good parameter setting. Our analysis is performed on simulated time series, that are normally distributed with constant but unknown mean and changes in variance