Constrained semi-analytical models of Galactic outflows

We present semi-analytic models of galactic outflows, constrained by available observations on high redshift star formation and reionization. Galactic outflows are modeled in a manner akin to models of stellar wind blown bubbles. Large scale outflows can generically escape from low mass halos (M<10^9 M_sun) for a wide range of model parameters but not from high mass halos (M> 10^{11} M_sun). The gas phase metallicity of the outflow and within the galaxy are computed. Ionization states of different metal species are calculated and used to examine the detectability of metal lines from the outflows. The global influence of galactic outflows is also investigated. Models with only atomic cooled halos significantly fill the IGM at z~3 with metals (with -2.5>[Z/Z_sun]>-3.7), the actual extent depending on the efficiency of winds, the IMF, the fractional mass that goes through star formation and the reionization history of the universe. In these models, a large fraction of outflows at z~3 are supersonic, hot (T> 10^5 K) and have low density, making metal lines difficult to detect. They may also result in significant perturbations in the IGM gas on scales probed by the Lyman-alpha forest. On the contrary, models including molecular cooled halos with a normal mode of star formation can potentially volume fill the universe at z> 8 without drastic dynamic effects on the IGM, thereby setting up a possible metallicity floor (-4.0<[Z/Z_sun]<-3.6). Interestingly, molecular cooled halos with a ``top-heavy'' mode of star formation are not very successful in establishing the metallicity floor because of the additional radiative feedback, that they induce. (Abridged)Comment: 27 pages, 31 figures, 2 tables, pdflatex. Accepted for publication in MNRA

A search for debris disks in the Herschel-ATLAS

Original article can be found at: http://www.aanda.org/ Copyright The European Southern Observatory (ESO)Aims. We aim to demonstrate that the Herschel-ATLAS (H-ATLAS) is suitable for a blind and unbiased survey for debris disks by identifying candidate debris disks associated with main sequence stars in the initial science demonstration field of the survey. We show that H-ATLAS reveals a population of far-infrared/sub-mm sources that are associated with stars or star-like objects on the SDSS main-sequence locus. We validate our approach by comparing the properties of the most likely candidate disks to those of the known population. Methods. We use a photometric selection technique to identify main sequence stars in the SDSS DR7 catalogue and a Bayesian Likelihood Ratio method to identify H-ATLAS catalogue sources associated with these main sequence stars. Following this photometric selection we apply distance cuts to identify the most likely candidate debris disks and rule out the presence of contaminating galaxies using UKIDSS LAS K-band images. Results. We identify 78 H-ATLAS sources associated with SDSS point sources on the main-sequence locus, of which two are the most likely debris disk candidates: H-ATLAS J090315.8 and H-ATLAS J090240.2. We show that they are plausible candidates by comparing their properties to the known population of debris disks. Our initial results indicate that bright debris disks are rare, with only 2 candidates identified in a search sample of 851 stars. We also show that H-ATLAS can derive useful upper limits for debris disks associated with Hipparcos stars in the field and outline the future prospects for our debris disk search programme.Peer reviewe

Galactic winds driven by cosmic-ray streaming

Galactic winds are observed in many spiral galaxies with sizes from dwarfs up to the Milky Way, and they sometimes carry a mass in excess of that of newly formed stars by up to a factor of ten. Multiple driving processes of such winds have been proposed, including thermal pressure due to supernova-heating, UV radiation pressure on dust grains, or cosmic ray (CR) pressure. We here study wind formation due to CR physics using a numerical model that accounts for CR acceleration by supernovae, CR thermalization, and advective CR transport. In addition, we introduce a novel implementation of CR streaming relative to the rest frame of the gas. We find that CR streaming drives powerful and sustained winds in galaxies with virial masses M_200 < 10^{11} Msun. In dwarf galaxies (M_200 ~ 10^9 Msun) the winds reach a mass loading factor of ~5, expel ~60 per cent of the initial baryonic mass contained inside the halo's virial radius and suppress the star formation rate by a factor of ~5. In dwarfs, the winds are spherically symmetric while in larger galaxies the outflows transition to bi-conical morphologies that are aligned with the disc's angular momentum axis. We show that damping of Alfven waves excited by streaming CRs provides a means of heating the outflows to temperatures that scale with the square of the escape speed. In larger haloes (M_200 > 10^{11} Msun), CR streaming is able to drive fountain flows that excite turbulence. For halo masses M_200 > 10^{10} Msun, we predict an observable level of H-alpha and X-ray emission from the heated halo gas. We conclude that CR-driven winds should be crucial in suppressing and regulating the first epoch of galaxy formation, expelling a large fraction of baryons, and - by extension - aid in shaping the faint end of the galaxy luminosity function. They should then also be responsible for much of the metal enrichment of the intergalactic medium.Comment: 25 pages, 14 figures, accepted by MNRA

Magnetic fields in cosmic particle acceleration sources

We review here some magnetic phenomena in astrophysical particle accelerators associated with collisionless shocks in supernova remnants, radio galaxies and clusters of galaxies. A specific feature is that the accelerated particles can play an important role in magnetic field evolution in the objects. We discuss a number of CR-driven, magnetic field amplification processes that are likely to operate when diffusive shock acceleration (DSA) becomes efficient and nonlinear. The turbulent magnetic fields produced by these processes determine the maximum energies of accelerated particles and result in specific features in the observed photon radiation of the sources. Equally important, magnetic field amplification by the CR currents and pressure anisotropies may affect the shocked gas temperatures and compression, both in the shock precursor and in the downstream flow, if the shock is an efficient CR accelerator. Strong fluctuations of the magnetic field on scales above the radiation formation length in the shock vicinity result in intermittent structures observable in synchrotron emission images. Resonant and non-resonant CR streaming instabilities in the shock precursor can generate mesoscale magnetic fields with scale-sizes comparable to supernova remnants and even superbubbles. This opens the possibility that magnetic fields in the earliest galaxies were produced by the first generation Population III supernova remnants and by clustered supernovae in star forming regions.Comment: 30 pages, Space Science Review

Modelling high redshift Lyman-alpha Emitters

We present a new model for high redshift Lyman-Alpha Emitters (LAEs) in the cosmological context which takes into account the resonant scattering of Ly-a photons through expanding gas. The GALICS semi-analytic model provides us with the physical properties of a large sample of high redshift galaxies. We implement a gas outflow model for each galaxy based on simple scaling arguments. The coupling with a library of numerical experiments of Ly-a transfer through expanding or static dusty shells of gas allows us to derive the Ly-a escape fractions and profiles. The predicted distribution of Ly-a photons escape fraction shows that galaxies with a low star formation rate have a f_esc of the order of unity, suggesting that, for those objects, Ly-a may be used to trace the star formation rate assuming a given conversion law. In galaxies forming stars intensely, the escape fraction spans the whole range from 0 to 1. The model is able to get a good match to the UV and Ly-a luminosity function (LF) data at 3 < z < 5. We find that we are in good agreement with both the bright Ly-a data and the faint population observed by Rauch et al. (2008) at z=3. Most of the Ly-a profiles of our LAEs are redshifted by the diffusion in the outflow which suppresses IGM absorption. The bulk of the observed Ly-a equivalent width (EW) distribution is recovered by our model, but we fail to obtain the very large values sometimes detected. Predictions for stellar masses and UV LFs of LAEs show a satisfactory agreement with observational estimates. The UV-brightest galaxies are found to show only low Ly-a EWs in our model, as it is reported by many observations of high redshift LAEs. We interpret this effect as the joint consequence of old stellar populations hosted by UV-bright galaxies, and high HI column densities that we predict for these objects, which quench preferentially resonant Ly-a photons via dust extinction.Comment: 17 pages, 12 figures, 3 tables, accepted for publication in MNRA

Characterization of shape and dimensional accuracy of incrementally formed titanium sheet parts with intermediate curvatures between two feature types

Single point incremental forming (SPIF) is a relatively new manufacturing process that has been recently used to form medical grade titanium sheets for implant devices. However, one limitation of the SPIF process may be characterized by dimensional inaccuracies of the final part as compared with the original designed part model. Elimination of these inaccuracies is critical to forming medical implants to meet required tolerances. Prior work on accuracy characterization has shown that feature behavior is important in predicting accuracy. In this study, a set of basic geometric shapes consisting of ruled and freeform features were formed using SPIF to characterize the dimensional inaccuracies of grade 1 titanium sheet parts. Response surface functions using multivariate adaptive regression splines (MARS) are then generated to model the deviations at individual vertices of the STL model of the part as a function of geometric shape parameters such as curvature, depth, distance to feature borders, wall angle, etc. The generated response functions are further used to predict dimensional deviations in a specific clinical implant case where the curvatures in the part lie between that of ruled features and freeform features. It is shown that a mixed-MARS response surface model using a weighted average of the ruled and freeform surface models can be used for such a case to improve the mean prediction accuracy within ±0.5 mm. The predicted deviations show a reasonable match with the actual formed shape for the implant case and are used to generate optimized tool paths for minimized shape and dimensional inaccuracy. Further, an implant part is then made using the accuracy characterization functions for improved accuracy. The results show an improvement in shape and dimensional accuracy of incrementally formed titanium medical implants

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype

Evidence of Yersinia pestis DNA from fleas in an endemic plague area of Zambia

BACKGROUND: Yersinia pestis is a bacterium that causes plague which infects a variety of mammals throughout the world. The disease is usually transmitted among wild rodents through a flea vector. The sources and routes of transmission of plague are poorly researched in Africa, yet remains a concern in several sub-Saharan countries. In Zambia, the disease has been reported on annual basis with up to 20 cases per year, without investigating animal reservoirs or vectors that may be responsible in the maintenance and propagation of the bacterium. In this study, we undertook plague surveillance by using PCR amplification of the plasminogen activator gene in fleas. FINDINGS: Xenopsylla species of fleas were collected from 83 rodents trapped in a plague endemic area of Zambia. Of these rodents 5 had fleas positive (6.02%) for Y. pestis plasminogen activator gene. All the Y. pestis positive rodents were gerbils. CONCLUSIONS: We conclude that fleas may be responsible in the transmission of Y. pestis and that PCR may provide means of plague surveillance in the endemic areas of Zambia

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined