Measuring Young Stars in Space and Time -- II. The Pre-Main-Sequence Stellar Content of N44

The Hubble Space Telescope (HST) survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find $\sim26,700$ candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 18 prominent PMS structures at $1$ $\sigma$ significance above the mean density with sub-clusters persisting up to and beyond $3$ $\sigma$ significance. The most active star-forming center, located at the western edge of N44's bubble, is a subcluster with an effective radius of $\sim 5.6$ pc entailing more than 1,100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H II regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations.Comment: 29 pages, 21 figures, accepted for publication in A

Measuring Young Stars in Space and Time -- I. The Photometric Catalog and Extinction Properties of N44

In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury Program "Measuring Young Stars in Space and Time" (MYSST) targeting the star-forming complex N44 in the Large Magellanic Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and WFC3/UVIS, we built a photometric catalog of 461,684 stars down to $m_\mathrm{F555W} \simeq 29$ mag and $m_\mathrm{F814W} \simeq 28$ mag, corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of $\approx0.09$ $M_\odot$ at the LMC distance. In this first paper we describe the observing strategy of MYSST, the data reduction procedure, and present the photometric catalog. We identify multiple young stellar populations tracing the gaseous rim of N44's super bubble, together with various contaminants belonging to the LMC field population. We also determine the reddening properties from the slope of the elongated red clump feature by applying the machine learning algorithm RANSAC, and we select a set of Upper Main Sequence (UMS) stars as primary probes to build an extinction map, deriving a relatively modest median extinction $A_{\mathrm{F555W}}\simeq0.77$ mag. The same procedure applied to the red clump provides $A_{\mathrm{F555W}}\simeq 0.68$ mag.Comment: 29 pages, 15 figures, accepted for publication in A

Genetic Diversity and Antimicrobial Resistance of Escherichia coli from Human and Animal Sources Uncovers Multiple Resistances from Human Sources

Escherichia coli are widely used as indicators of fecal contamination, and in some cases to identify host sources of fecal contamination in surface water. Prevalence, genetic diversity and antimicrobial susceptibility were determined for 600 generic E. coli isolates obtained from surface water and sediment from creeks and channels along the middle Santa Ana River (MSAR) watershed of southern California, USA, after a 12 month study. Evaluation of E. coli populations along the creeks and channels showed that E. coli were more prevalent in sediment compared to surface water. E. coli populations were not significantly different (P = 0.05) between urban runoff sources and agricultural sources, however, E. coli genotypes determined by pulsed-field gel electrophoresis (PFGE) were less diverse in the agricultural sources than in urban runoff sources. PFGE also showed that E. coli populations in surface water were more diverse than in the sediment, suggesting isolates in sediment may be dominated by clonal populations.Twenty four percent (144 isolates) of the 600 isolates exhibited resistance to more than one antimicrobial agent. Most multiple resistances were associated with inputs from urban runoff and involved the antimicrobials rifampicin, tetracycline, and erythromycin. The occurrence of a greater number of E. coli with multiple antibiotic resistances from urban runoff sources than agricultural sources in this watershed provides useful evidence in planning strategies for water quality management and public health protection

Presence and Growth of Naturalized Escherichia coli in Temperate Soils from Lake Superior Watersheds

The presence of Escherichia coli in water is used as an indicator of fecal contamination, but recent reports indicate that soil populations can also be detected in tropical, subtropical, and some temperate environments. In this study, we report that viable E. coli populations were repeatedly isolated from northern temperate soils in three Lake Superior watersheds from October 2003 to October 2004. Seasonal variation in the population density of soilborne E. coli was observed; the greatest cell densities, up to 3 × 10(3) CFU/g soil, were found in the summer to fall (June to October), and the lowest numbers, ≤1 CFU/g soil, occurred during the winter to spring months (February to May). Horizontal, fluorophore-enhanced repetitive extragenic palindromic PCR (HFERP) DNA fingerprint analyses indicated that identical soilborne E. coli genotypes, those with ≥92% similarity values, overwintered in frozen soil and were present over time. Soilborne E. coli strains had HFERP DNA fingerprints that were unique to specific soils and locations, suggesting that these E. coli strains became naturalized, autochthonous members of the soil microbial community. In laboratory studies, naturalized E. coli strains had the ability to grow and replicate to high cell densities, up to 4.2 × 10(5) CFU/g soil, in nonsterile soils when incubated at 30 or 37°C and survived longer than 1 month when soil temperatures were ≤25°C. To our knowledge, this is the first report of the growth of naturalized E. coli in nonsterile, nonamended soils. The presence of significant populations of naturalized populations of E. coli in temperate soils may confound the use of this bacterium as an indicator of fecal contamination

Presence and Sources of Fecal Coliform Bacteria in Epilithic Periphyton Communities of Lake Superior▿

Epilithic periphyton communities were sampled at three sites on the Minnesota shoreline of Lake Superior from June 2004 to August 2005 to determine if fecal coliforms and Escherichia coli were present throughout the ice-free season. Fecal coliform densities increased up to 4 orders of magnitude in early summer, reached peaks of up to 1.4 × 105 CFU cm−2 by late July, and decreased during autumn. Horizontal, fluorophore-enhanced repetitive-PCR DNA fingerprint analyses indicated that the source for 2% to 44% of the E. coli bacteria isolated from these periphyton communities could be identified when compared with a library of E. coli fingerprints from animal hosts and sewage. Waterfowl were the major source (68 to 99%) of periphyton E. coli strains that could be identified. Several periphyton E. coli isolates were genotypically identical (≥92% similarity), repeatedly isolated over time, and unidentified when compared to the source library, suggesting that these strains were naturalized members of periphyton communities. If the unidentified E. coli strains from periphyton were added to the known source library, then 57% to 81% of E. coli strains from overlying waters could be identified, with waterfowl (15 to 67%), periphyton (6 to 28%), and sewage effluent (8 to 28%) being the major potential sources. Inoculated E. coli rapidly colonized natural periphyton in laboratory microcosms and persisted for several weeks, and some cells were released to the overlying water. Our results indicate that E. coli from periphyton released into waterways confounds the use of this bacterium as a reliable indicator of recent fecal pollution

Hubble Tarantula Treasury Project - VI. Identification of pre-main-sequence stars using machine-learning techniques

The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire starburst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre-main-sequence (PMS) stars of the region, i.e. stars that have not started their lives on the main- sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ machine-learning classification techniques on the HTTP survey of more than 800 000 sources to identify the PMS stellar content of the observed field. Our methodology consists of (1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC 2070, (2) using this sample to train classification algorithms to build a predictive model for PMS stars, and (3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ decision tree, random forest (RF), and support vector machine (SVM) classifiers to categorize the stars as PMS and non-PMS. The RF and SVM provided the most accurate models, predicting about 20 000 sources with a candidateship probability higher than 50 per cent, and almost 10 000 PMS candidates with a probability higher than 95 per cent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex

Measuring Young Stars in Space and Time. I. The Photometric Catalog and Extinction Properties of N44

In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury Program Measuring Young Stars in Space and Time (MYSST) targeting the star-forming complex N44 in the Large Magellanic Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and WFC3/UVIS, we built a photometric catalog of 461,684 stars down to mF555W ≃ 29 mag and mF814W ≃ 28 mag, corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of ≈ 0.09 M⊙ at the LMC distance. In this first paper we describe the observing strategy of MYSST and the data reduction procedure and present the photometric catalog. We identify multiple young stellar populations tracing the gaseous rim of N44's superbubble, together with various contaminants belonging to the LMC field population. We also determine the reddening properties from the slope of the elongated red clump (RC) feature by applying the machine-learning algorithm RANSAC, and we select a set of upper-main-sequence stars as primary probes to build an extinction map, deriving a relatively modest median extinction AF555W ≃ 0.77 mag. The same procedure applied to the RC provides AF555W ≃ 0.68 mag

Measuring Young Stars in Space and Time. II. The Pre-main-sequence Stellar Content of N44

The Hubble Space Telescope survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest-mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine-learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find ∼26,700 candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 16 prominent PMS structures at 1σ significance above the mean density with sub-clusters persisting up to and beyond 3σ significance. The most active star-forming center, located at the western edge of N44's bubble, is a subcluster with an effective radius of ∼5.6 pc entailing more than 1100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H ii regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations. © 2021. The American Astronomical Society. All rights reserved.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Measuring Young Stars in Space and Time. II. The Pre-main-sequence Stellar Content of N44

The Hubble Space Telescope survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest-mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine-learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find ∼26,700 candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 16 prominent PMS structures at 1σ significance above the mean density with sub-clusters persisting up to and beyond 3σ significance. The most active star-forming center, located at the western edge of N44's bubble, is a subcluster with an effective radius of ∼5.6 pc entailing more than 1100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H ii regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations

Measuring Young Stars in Space and Time. I. The Photometric Catalog and Extinction Properties of N44

In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury Program Measuring Young Stars in Space and Time (MYSST) targeting the star-forming complex N44 in the Large Magellanic Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and WFC3/UVIS, we built a photometric catalog of 461,684 stars down to mF555W ≃ 29 mag and mF814W ≃ 28 mag, corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of ≈ 0.09 M⊙ at the LMC distance. In this first paper we describe the observing strategy of MYSST and the data reduction procedure and present the photometric catalog. We identify multiple young stellar populations tracing the gaseous rim of N44's superbubble, together with various contaminants belonging to the LMC field population. We also determine the reddening properties from the slope of the elongated red clump (RC) feature by applying the machine-learning algorithm RANSAC, and we select a set of upper-main-sequence stars as primary probes to build an extinction map, deriving a relatively modest median extinction AF555W ≃ 0.77 mag. The same procedure applied to the RC provides AF555W ≃ 0.68 mag. © 2021. The American Astronomical Society. All rights reserved.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]