Characterizing dw1335-29, a recently discovered dwarf satellite of M83

The number, distribution, and properties of dwarf satellites are crucial probes of the physics of galaxy formation at low masses and the response of satellite galaxies to the tidal and gas dynamical effects of their more massive parent.To make progress, it is necessary to augment and solidify the census of dwarf satellites of galaxies outside the Local Group. M\"uller et al. (2015) presented 16 dwarf galaxy candidates near M83, but lacking reliable distances, it is unclear which candidates are M83 satellites. Using red giant branch stars from the HST/GHOSTS survey in conjunction with ground-based images from VLT/VIMOS, we confirm that one of the candidates, dw1335-29-- with a projected distance of 26 kpc from M83 and a distance modulus of $(m - M)_0 = 28.5^{+0.3}_{-0.1}$ -- is a satellite of M83. We estimate an absolute magnitude $M_V = -10.1 \pm{0.4}$, an ellipticity of $0.40^{+0.14}_{-0.22}$, a half light radius of $656^{+121}_{-170 }$ pc, and [Fe/H] = $-1.3^{+0.3}_{-0.4}$. Owing to dw1335-29's somewhat irregular shape and possible young stars, we classify this galaxy as a dwarf irregular or transition dwarf. This is curious, as with a projected distance of 26 kpc from M83, dw1335-29 is expected to lack recent star formation. Further study of M83's dwarf population will reveal if star formation in its satellites is commonplace (suggesting a lack of a hot gas envelope for M83 that would quench star formation) or rare (suggesting that dw1335-29 has a larger M83-centric distance, and is fortuitously projected to small radii).Comment: 7 pages, 5 figures, accepted for publication in MNRA

Diverse Stellar Haloes in Nearby Milky Way-Mass Disc Galaxies

We have examined the resolved stellar populations at large galactocentric distances along the minor axis (from 10 kpc up to between 40 and 75 kpc), with limited major axis coverage, of six nearby highly-inclined Milky Way-mass disc galaxies using HST data from the GHOSTS survey. We select red giant branch stars to derive stellar halo density profiles. The projected minor axis density profiles can be approximated by power laws with projected slopes of between $-2$ and $-3.7$ and a diversity of stellar halo masses of $1-6\times 10^{9}M_{\odot}$, or $2-14\%$ of the total galaxy stellar masses. The typical intrinsic scatter around a smooth power law fit is $0.05-0.1$ dex owing to substructure. By comparing the minor and major axis profiles, we infer projected axis ratios $c/a$ at $\sim 25$ kpc between $0.4-0.75$. The GHOSTS stellar haloes are diverse, lying between the extremes charted out by the (rather atypical) haloes of the Milky Way and M31. We find a strong correlation between the stellar halo metallicities and the stellar halo masses. We compare our results with cosmological models, finding good agreement between our observations and accretion-only models where the stellar haloes are formed by the disruption of dwarf satellites. In particular, the strong observed correlation between stellar halo metallicity and mass is naturally reproduced. Low-resolution hydrodynamical models have unrealistically high stellar halo masses. Current high-resolution hydrodynamical models appear to predict stellar halo masses somewhat higher than observed but with reasonable metallicities, metallicity gradients and density profiles.Comment: 26 pages, 17 figures. Accepted for publication in MNRA

Manipulation of Virus-Like Particles for the Purpose of Optimizing Immunostimulation

This poster was presented at the Great Plains Honors Conference in South Padre Island, Texas.https://scholarworks.uttyler.edu/student_posters/1015/thumbnail.jp

Engineering Virus Like Particles Towards Directing Immunologic Responses

This poster was presented during the 3rd Annual UT Tyler Faculty Research Poster Showcase.https://scholarworks.uttyler.edu/fac_posters/1008/thumbnail.jp

Constraining the assembly time of the stellar haloes of nearby Milky Way-mass galaxies through AGB populations

The star formation histories (SFHs) of galactic stellar haloes offer crucial insights into the merger history of the galaxy and the effects of those mergers on their hosts. Such measurements have revealed that while the Milky Way's most important merger was 8-10 Gyr ago, M31's largest merger was more recent, within the last few Gyr. Unfortunately, the required halo SFH measurements are extremely observationally expensive outside of the Local Group. Here we use asymptotic giant branch (AGB) stars brighter than the tip of the red giant branch (RGB) to constrain stellar halo SFHs. Both stellar population models and archival datasets show that the AGB/RGB ratio constrains the time before which 90% of the stars formed, $t_{90}$. We find AGB stars in the haloes of three highly-inclined roughly Milky Way-mass galaxies with resolved star measurements from the Hubble Space Telescope; this population is most prominent in the stellar haloes of NGC 253 and NGC 891, suggesting that their stellar haloes contain stars born at relatively late times, with inferred $t_{90}\sim 6\pm1.5$Gyr. This ratio also varies from region to region, tending towards higher values along the major axis and in tidal streams or shells. By combining our measurements with previous constraints, we find a tentative anticorrelation between halo age and stellar halo mass, a trend that exists in models of galaxy formation but has never been elucidated before, i.e, the largest stellar haloes of Milky-Way mass galaxies were assembled more recently.Comment: 20 Pages, 10 Figure

Significant Short-Term Shifts in the Microbiomes of Smokers With Periodontitis After Periodontal Therapy With Amoxicillin & Metronidazole as Revealed by 16S rDNA Amplicon Next Generation Sequencing

The aim of this follow-up study was, to compare the effects of mechanical periodontal therapy with or without adjunctive amoxicillin and metronidazole on the subgingival microbiome of smokers with periodontitis using 16S rDNA amplicon next generation sequencing. Fifty-four periodontitis patients that smoke received either non-surgical periodontal therapy with adjunctive amoxicillin and metronidazole (n = 27) or with placebos (n = 27). Subgingival plaque samples were taken before and two months after therapy. Bacterial genomic DNA was isolated and the V4 hypervariable region of the bacterial 16S rRNA genes was amplified. Up to 96 libraries were normalized and pooled for Illumina MiSeq paired-end sequencing with almost fully overlapping 250 base pairs reads. Exact ribosomal sequence variants (RSVs) were inferred with DADA2. Microbial diversity and changes on the genus and RSV level were analyzed with non-parametric tests and a negative binomial regression model, respectively. Before therapy, the demographic, clinical, and microbial parameters were not significantly different between the placebo and antibiotic groups. Two months after the therapy, clinical parameters improved and there was a significantly increased dissimilarity of microbiomes between the two groups. In the antibiotic group, there was a significant reduction of genera classified as Porphyromonas, Tannerella, and Treponema, and 22 other genera also decreased significantly, while Selenomonas, Capnocytophaga, Actinomycetes, and five other genera significantly increased. In the placebo group, however, there was not a significant decrease in periodontal pathogens after therapy and only five other genera decreased, while Veillonella and nine other genera increased. We conclude that in periodontitis patients who smoke, microbial shifts occurred two months after periodontal therapy with either antibiotics or placebo, but genera including periodontal pathogens decreased significantly only with adjunctive antibiotics

Prospective Genomic Characterization of the German Enterohemorrhagic Escherichia coli O104:H4 Outbreak by Rapid Next Generation Sequencing Technology

An ongoing outbreak of exceptionally virulent Shiga toxin (Stx)-producing Escherichia coli O104:H4 centered in Germany, has caused over 830 cases of hemolytic uremic syndrome (HUS) and 46 deaths since May 2011. Serotype O104:H4, which has not been detected in animals, has rarely been associated with HUS in the past. To prospectively elucidate the unique characteristics of this strain in the early stages of this outbreak, we applied whole genome sequencing on the Life Technologies Ion Torrent PGM™ sequencer and Optical Mapping to characterize one outbreak isolate (LB226692) and a historic O104:H4 HUS isolate from 2001 (01-09591). Reference guided draft assemblies of both strains were completed with the newly introduced PGM™ within 62 hours. The HUS-associated strains both carried genes typically found in two types of pathogenic E. coli, enteroaggregative E. coli (EAEC) and enterohemorrhagic E. coli (EHEC). Phylogenetic analyses of 1,144 core E. coli genes indicate that the HUS-causing O104:H4 strains and the previously published sequence of the EAEC strain 55989 show a close relationship but are only distantly related to common EHEC serotypes. Though closely related, the outbreak strain differs from the 2001 strain in plasmid content and fimbrial genes. We propose a model in which EAEC 55989 and EHEC O104:H4 strains evolved from a common EHEC O104:H4 progenitor, and suggest that by stepwise gain and loss of chromosomal and plasmid-encoded virulence factors, a highly pathogenic hybrid of EAEC and EHEC emerged as the current outbreak clone. In conclusion, rapid next-generation technologies facilitated prospective whole genome characterization in the early stages of an outbreak

The role of methane in future climate strategies: mitigation potentials and climate impacts

This study examines model-specific assumptions and projections of methane (CH4) emissions in deep mitigation scenarios generated by integrated assessment models (IAMs). For this, scenarios of nine models are compared in terms of sectoral and regional CH4 emission reduction strategies, as well as resulting climate impacts. The models’ projected reduction potentials are compared to sector and technology-specific reduction potentials found in literature. Significant cost-effective and non-climate policy related reductions are projected in the reference case (10–36% compared to a “frozen emission factor” scenario in 2100). Still, compared to 2010, CH4 emissions are expected to rise steadily by 9–72% (up to 412 to 654 Mt CH4/year). Ambitious CO2 reduction measures could by themselves lead to a reduction of CH4 emissions due to a reduction of fossil fuels (22–48% compared to the reference case in 2100). However, direct CH4 mitigation is crucial and more effective in bringing down CH4 (50–74% compared to the reference case). Given the limited reduction potential, agriculture CH4 emissions are projected to constitute an increasingly larger share of total anthropogenic CH4 emissions in mitigation scenarios. Enteric fermentation in ruminants is in that respect by far the largest mitigation bottleneck later in the century with a projected 40–78% of total remaining CH4 emissions in 2100 in a strong (2 °C) climate policy case

Boosted decision trees in the era of new physics: a smuon analysis case study

International audienceMachine learning algorithms are growing increasingly popular in particle physics analyses, where they are used for their ability to solve difficult classification and regression problems. While the tools are very powerful, they may often be under- or mis-utilised. In the following, we investigate the use of gradient boosting techniques as applicable to a generic particle physics problem. We use as an example a Beyond the Standard Model smuon collider analysis which applies to both current and future hadron colliders, and we compare our results to a traditional cut-and-count approach. In particular, we interrogate the use of metrics in imbalanced datasets which are characteristic of high energy physics problems, offering an alternative to the widely used area under the curve (auc) metric through a novel use of the F-score metric. We present an in-depth comparison of feature selection and investigation using a principal component analysis, Shapley values, and feature permutation methods in a way which we hope will be widely applicable to future particle physics analyses. Moreover, we show that a machine learning model can extend the 95% confidence level exclusions obtained in a traditional cut-and-count analysis, while potentially bypassing the need for complicated feature selections. Finally, we discuss the possibility of constructing a general machine learning model which is applicable to probe a two-dimensional mass plane