The Caterpillar Project: A Large Suite of Milky Way Sized Halos

We present the largest number of Milky Way sized dark matter halos simulated at very high mass ($\sim$$10^4$ M$_\odot$/particle) and temporal resolution ($\sim$5 Myrs/snapshot) done to date, quadrupling what is currently available in the literature. This initial suite consists of the first 24 halos of the $Caterpillar$ $Project$ (www.caterpillarproject.org) whose project goal of 60 - 70 halos will be made public when complete. We resolve $\sim$20,000 gravitationally bound subhalos within the virial radius of each host halo. Over the ranges set by our spatial resolution our convergence is excellent and improvements were made upon current state-of-the-art halo finders to better identify substructure at such high resolutions (e.g., on average we recover $\sim$4 subhalos in each host halo above 10$^8$ M$_\odot$ which would have otherwise not been found using conventional methods). For our relaxed halos, the inner profiles are reasonably fit by Einasto profiles ($\alpha$ = 0.169 $\pm$ 0.023) though this depends on the relaxed nature and assembly history of a given halo. Averaging over all halos, the substructure mass fraction is $f_{m,subs} = 0.121 \pm 0.041$, and mass function slope is d$N$/d$M\propto M^{-1.88 \pm 0.10}$ though we find scatter in the normalizations for fixed halo mass due to more concentrated hosts having less subhalos at fixed subhalo mass. There are no biases stemming from Lagrangian volume selection as all Lagrangian volume types are included in our sample. Our detailed contamination study of 264 low resolution halos has resulted in obtaining very large and unprecedented, high-resolution regions around our host halos for our target resolution (sphere of radius $\sim$$1.4 \pm 0.4$ Mpc) allowing for accurate studies of low mass dwarf galaxies at large galactocentric radii and the very first stellar systems at high redshift ($z \geq$ 10).Comment: 19 pages; 14 figures; 6 tables; Received September 3, 2015; Accepted November 15, 2015; Published February 2, 201

The Effects of Varying Cosmological Parameters on Halo Substructure

We investigate how different cosmological parameters, such as those delivered by the WMAP and Planck missions, affect the nature and evolution of dark matter halo substructure. We use a series of flat $\Lambda$ cold dark matter ($\Lambda$CDM) cosmological $N$-body simulations of structure formation, each with a different power spectrum but the same initial white noise field. Our fiducial simulation is based on parameters from the WMAP 7th year cosmology. We then systematically vary the spectral index, $n_s$, matter density, $\Omega_M$, and normalization of the power spectrum, $\sigma_8$, for 7 unique simulations. Across these, we study variations in the subhalo mass function, mass fraction, maximum circular velocity function, spatial distribution, concentration, formation times, accretion times, and peak mass. We eliminate dependence of subhalo properties on host halo mass and average over many hosts to reduce variance. While the "same" subhalos from identical initial overdensity peaks in higher $\sigma_8, n_s$, and $\Omega_m$ simulations accrete earlier and end up less massive and closer to the halo center at $z=0$, the process of continuous subhalo accretion and destruction leads to a steady state distribution of these properties across all subhalos in a given host. This steady state mechanism eliminates cosmological dependence on all properties listed above except subhalo concentration and $V_{max}$, which remain greater for higher $\sigma_8, n_s$ and $\Omega_m$ simulations, and subhalo formation time, which remains earlier. We also find that the numerical technique for computing scale radius and the halo finder used can significantly affect the concentration-mass relationship computed for a simulation.Comment: 15 pages, 15 figures, Accepted to ApJ on March 15, 201

Descendants of the first stars: the distinct chemical signature of second generation stars

Extremely metal-poor (EMP) stars in the Milky Way (MW) allow us to infer the properties of their progenitors by comparing their chemical composition to the metal yields of the first supernovae. This method is most powerful when applied to mono-enriched stars, i.e. stars that formed from gas that was enriched by only one previous supernova. We present a novel diagnostic to identify this subclass of EMP stars. We model the first generations of star formation semi-analytically, based on dark matter halo merger trees that yield MW-like halos at the present day. Radiative and chemical feedback are included self-consistently and we trace all elements up to zinc. Mono-enriched stars account for only $\sim 1\%$ of second generation stars in our fiducial model and we provide an analytical formula for this probability. We also present a novel analytical diagnostic to identify mono-enriched stars, based on the metal yields of the first supernovae. This new diagnostic allows us to derive our main results independently from the specific assumptions made regarding Pop III star formation, and we apply it to a set of observed EMP stars to demonstrate its strengths and limitations. Our results may provide selection criteria for current and future surveys and therefore contribute to a deeper understanding of EMP stars and their progenitors.Comment: 18 pages, 20 figures, published in MNRA

AAOmega spectroscopy of 29 351 stars in fields centered on ten Galactic globular clusters

Galactic globular clusters have been pivotal in our understanding of many astrophysical phenomena. Here we publish the extracted stellar parameters from a recent large spectroscopic survey of ten globular clusters. A brief review of the project is also presented. Stellar parameters have been extracted from individual stellar spectra using both a modified version of the Radial Velocity Experiment (RAVE) pipeline and a pipeline based on the parameter estimation method of RAVE. We publish here all parameters extracted from both pipelines. We calibrate the metallicity and convert this to [Fe/H] for each star and, furthermore, we compare the velocities and velocity dispersions of the Galactic stars in each field to the Besan\c{c}on Galaxy model. We find that the model does not correspond well with the data, indicating that the model is probably of little use for comparisons with pencil beam survey data such as this.Comment: 6 pages, 5 figures, 4 tables. Accepted for publication in A&A. Data described in tables will be available on CDS (at http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/530/A31) once publishe

Globular cluster formation within the Aquarius simulation

The Aquarius project is a very high-resolution simulation capable of resolving the full mass range of potential globular cluster (GC) formation sites. With a particle mass mp= 1.4 × 104 M¿, Aquarius yields more than 100 million particles within the virial radius of the central halo which has a mass of 1.8 × 1012 M¿, similar to that of the Milky Way. With this particle mass, dark matter concentrations (haloes) that give rise to GCs via our formation criteria contain a minimum of ~2000 particles. Here, we use this simulation to test a model of metal-poor GC formation based on collapse physics. In our model, GCs form when the virial temperatures of haloes first exceed 104 K as this is when electronic transitions allow the gas to cool efficiently. We calculate the ionizing flux from the stars in these first clusters and stop the formation of new clusters when all the baryonic gas of the Galaxy is ionized. This is achieved by adopting reasonable values for the star formation efficiencies and escape fraction of ionizing photons which result in similar numbers and masses of clusters to those found in the Milky Way. The model is successful in that it predicts ages (peak age ~13.3 Gyr) and a spatial distribution of metal-poor GCs which are consistent with the observed populations in the Milky Way. The model also predicts that less than 5 per cent of GCs within a radius of 100 kpc have a surviving dark matter halo, but the more distant clusters are all found in dark matter concentrations. We then test a scenario of metal-rich cluster formation by examining mergers that trigger star formation within central gas discs. This results in younger (~7¿13.3 Gyr), more centrally located clusters (40 metal-rich GCs within 18 kpc from the centre of the host) which are consistent with the Galactic metal-rich population. We test an alternate model in which metal-rich GCs form in dwarf galaxies that become stripped as they merge with the main halo. This process is inconsistent with observed metal-rich globulars in the Milky Way because it predicts spatial distributions that are far too extended

The illustris simulation: Public data release

We present the full public release of all data from the Illustris simulation project. Illustris is a suite of large volume, cosmological hydrodynamical simulations run with the moving-mesh code Arepo and including a comprehensive set of physical models critical for following the formation and evolution of galaxies across cosmic time. Each simulates a volume of (106.5 Mpc)3and self-consistently evolves five different types of resolution elements from a starting redshift of z=127 to the present day, z=0. These components are: dark matter particles, gas cells, passive gas tracers, stars and stellar wind particles, and supermassive black holes. This data release includes the snapshots at all 136 available redshifts, halo and subhalo catalogs at each snapshot, and two distinct merger trees. Six primary realizations of the Illustris volume are released, including the flagship Illustris-1 run. These include three resolution levels with the fiducial "full" baryonic physics model, and a dark matter only analog for each. In addition, we provide four distinct, high time resolution, smaller volume "subboxes". The total data volume is ~265 TB, including ~800 full volume snapshots and ~30,000 subbox snapshots. We describe the released data products as well as tools we have developed for their analysis. All data may be directly downloaded in its native HDF5 format. Additionally, we release a comprehensive, web-based API which allows programmatic access to search and data processing tasks. In both cases we provide example scripts and a getting-started guide in several languages: currently, IDL, Python, and Matlab. This paper addresses scientific issues relevant for the interpretation of the simulations, serves as a pointer to published and on-line documentation of the project, describes planned future additional data releases, and discusses technical aspects of the release

RPPA-based proteomics recognizes distinct epigenetic signatures in chronic lymphocytic leukemia with clinical consequences

The chronic lymphocytic leukemia (CLL) armamentarium has evolved significantly, with novel therapies that inhibit Bruton Tyrosine Kinase, PI3K delta and/or the BCL2 protein improving outcomes. Still, the clinical course of CLL patients is highly variable and most previously recognized prognostic features lack the capacity to predict response to modern treatments indicating the need for new prognostic markers. In this study, we identified four epigenetically distinct proteomic signatures of a large cohort of CLL and related diseases derived samples (n = 871) using reverse phase protein array technology. These signatures are associated with clinical features including age, cytogenetic abnormalities [trisomy 12, del(13q) and del(17p)], immunoglobulin heavy-chain locus (IGHV) mutational load, ZAP-70 status, Binet and Rai staging as well as with the outcome measures of time to treatment and overall survival. Protein signature membership was identified as predictive marker for overall survival regardless of other clinical features. Among the analyzed epigenetic proteins, EZH2, HDAC6, and loss of H3K27me3 levels were the most independently associated with poor survival. These findings demonstrate that proteomic based epigenetic biomarkers can be used to better classify CLL patients and provide therapeutic guidance

Design and Synthesis of High Affinity Inhibitors of Plasmodium falciparum and Plasmodium vivax N-Myristoyltransferases Directed by Ligand Efficiency Dependent Lipophilicity (LELP)

N-Myristoyltransferase (NMT) is an essential eukaryotic enzyme and an attractive drug target in parasitic infections such as malaria. We have previously reported that 2-(3-(piperidin-4-yloxy)benzo[b]thiophen-2-yl)-5-((1,3,5-trimethyl-1H-pyrazol-4-yl)methyl)-1,3,4-oxadiazole (34c) is a high affinity inhibitor of both Plasmodium falciparum and P. vivax NMT and displays activity in vivo against a rodent malaria model. Here we describe the discovery of 34c through optimization of a previously described series. Development, guided by targeting a ligand efficiency dependent lipophilicity (LELP) score of less than 10, yielded a 100-fold increase in enzyme affinity and a 100-fold drop in lipophilicity with the addition of only two heavy atoms. 34c was found to be equipotent on chloroquine-sensitive and -resistant cell lines and on both blood and liver stage forms of the parasite. These data further validate NMT as an exciting drug target in malaria and support 34c as an attractive tool for further optimization

A randomised clinical study to determine the effect of a toothpaste containing enzymes and proteins on plaque oral microbiome ecology

The numerous species that make up the oral microbiome are now understood to play a key role in establishment and maintenance of oral health. The ability to taxonomically identify community members at the species level is important to elucidating its diversity and association to health and disease. We report the overall ecological effects of using a toothpaste containing enzymes and proteins compared to a control toothpaste on the plaque microbiome. The results reported here demonstrate that a toothpaste containing enzymes and proteins can augment natural salivary defences to promote an overall community shift resulting in an increase in bacteria associated with gum health and a concomitant decrease in those associated with periodontal disease. Statistical analysis shows significant increases in 12 taxa associated with gum health including Neisseria spp. and a significant decrease in 10 taxa associated with periodontal disease including Treponema spp. The results demonstrate that a toothpaste containing enzymes and proteins can significantly shift the ecology of the oral microbiome (at species level) resulting in a community with a stronger association to health