The Conditional Colour-Magnitude Distribution: I. A Comprehensive Model of the Colour-Magnitude-Halo Mass Distribution of Present-Day Galaxies

We formulate a model of the conditional colour-magnitude distribution (CCMD) to describe the distribution of galaxy luminosity and colour as a function of halo mass. It consists of two populations of different colour distributions, dubbed pseudo-blue and pseudo-red, respectively, with each further separated into central and satellite galaxies. We define a global parameterization of these four colour-magnitude distributions and their dependence on halo mass, and we infer parameter values by simultaneously fitting the space densities and auto-correlation functions of 79 galaxy samples from the Sloan Digital Sky Survey defined by fine bins in the colour-magnitude diagram (CMD). The model deprojects the overall galaxy CMD, revealing its tomograph along the halo mass direction. The bimodality of the colour distribution is driven by central galaxies at most luminosities, though at low luminosities it is driven by the difference between blue centrals and red satellites. For central galaxies, the two pseudo-colour components are distinct and orthogonal to each other in the CCMD: at fixed halo mass, pseudo-blue galaxies have a narrow luminosity range and broad colour range, while pseudo-red galaxies have a narrow colour range and broad luminosity range. For pseudo-blue centrals, luminosity correlates tightly with halo mass, while for pseudo-red galaxies colour correlates more tightly (redder galaxies in more massive haloes). The satellite fraction is higher for redder and for fainter galaxies, with colour a stronger indicator than luminosity. We discuss the implications of the results and further applications of the CCMD model.Comment: 32 pages, 26 figures, accepted for publication in MNRA

Constraining the HI-Halo Mass Relation From Galaxy Clustering

We study the dependence of galaxy clustering on atomic gas mass using a sample of $\sim$16,000 galaxies with redshift in the range of $0.0025<z<0.05$ and HI mass of $M_{\rm HI}>10^8M_{\odot}$, drawn from the 70% complete sample of the Arecibo Legacy Fast ALFA survey. We construct subsamples of galaxies with $M_{\rm HI}$ above different thresholds, and make volume-limited clustering measurements in terms of three statistics: the projected two-point correlation function, the projected cross-correlation function with respect to a reference sample selected from the Sloan Digital Sky Survey, and the redshift-space monopole moment. In contrast to previous studies, which found no/weak HI-mass dependence, we find both the clustering amplitude on scales above a few Mpc and the bias factors to increase significantly with increasing HI mass for subsamples with HI mass thresholds above $10^9M_{\odot}$. For HI mass thresholds below $10^9M_{\odot}$, while the measurements have large uncertainties caused by the limited survey volume and sample size, the inferred galaxy bias factors are systematically lower than the minimum halo bias factor from mass-selected halo samples. The simple halo model, in which galaxy content is only determined by halo mass, has difficulties in interpreting the clustering measurements of the HI-selected samples. We extend the simple model by including the halo formation time as an additional parameter. A model that puts HI-rich galaxies into halos that formed late can reproduce the clustering measurements reasonably well. We present the implications of our best-fitting model on the correlation of HI mass with halo mass and formation time, as well as the halo occupation distributions and HI mass functions for central and satellite galaxies. These results are compared with the predictions from semi-analytic galaxy formation models and hydrodynamic galaxy formation simulations.Comment: Accepted for publication in ApJ. The 2PCF measurements are available at http://sdss4.shao.ac.cn/guoh

Why Is CA3 More Vulnerable Than CA1 in Experimental Models of Controlled Cortical Impact-Induced Brain Injury?

One interesting finding of controlled cortical impact (CCI) experiments is that the CA3 region of the hippocampus, which is positioned further from the impact than the CA1 region, is reported as being more injured. The current literature has suggested a positive correlation between brain tissue stretch and neuronal cell loss. However, it is counterintuitive to assume that CA3 is stretched more during CCI injury. Recent mechanical studies of the brain have reported on a level of spatial heterogeneity not previously appreciated—the finding that CA1 was significantly stiffer than all other regions tested and that CA3 was one of the most compliant. We hypothesized that mechanical heterogeneity of anatomical structures could underlie the proposed heterogeneous mechanical response and hence the pattern of cell death. As such, we developed a three-dimensional finite element (FE) rat brain model representing detailed hippocampal structures and simulated various CCI experiments. Four groups of material properties based on recent experiments were tested. In group 1, hyperelastic material properties were assigned to various hippocampal structures, with CA3 more compliant than CA1. In group 2, linear viscoelastic material properties were assigned to hippocampal structures, with CA3 more com- pliant than CA1. In group 3, the hippocampus was represented by homogenous linear viscoelastic material properties. In group 4, a homogeneous nonlinear hippocampus was adopted. Simulation results demonstrated that for CCI with a 5-mm diameter, flat shape impactor, CA3 experienced increased tensile strains over a larger area and to a greater magnitude than did CA1 for group 1, which best explained why CA3 is more sensitive to CCI injury. However, for groups 2-4, the total volume with high strain (\u3e 30%) in CA3 was smaller than that in CA1. The FE rat brain model, with detailed hippocampal structures presented here, will help to engineer desired experimental neurotrauma models by virtually characterizing brain biomechanics before testing

Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes

On-line optimization of glutamate production based on balanced metabolic control by RQ

In glutamate fermentations by Corynebacterium glutamicum, higher glutamate concentration could be achieved by constantly controlling dissolved oxygen concentration (DO) at a lower level; however, by-product lactate also severely accumulated. The results of analyzing activities changes of the two key enzymes, glutamate and lactate dehydrogenases involved with the fermentation, and the entire metabolic network flux analysis showed that the lactate overproduction was because the metabolic flux in TCA cycle was too low to balance the glucose glycolysis rate. As a result, the respiratory quotient (RQ) adaptive control based “balanced metabolic control” (BMC) strategy was proposed and used to regulate the TCA metabolic flux rate at an appropriate level to achieve the metabolic balance among glycolysis, glutamate synthesis, and TCA metabolic flux. Compared with the best results of various DO constant controls, the BMC strategy increased the maximal glutamate concentration by about 15% and almost completely repressed the lactate accumulation with competitively high glutamate productivity

Groups and protocluster candidates in the CLAUDS and HSC-SSP joint deep surveys

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMUsing the extended halo-based group finder developed by Yang et al., which is able to deal with galaxies via spectroscopic and photometric redshifts simultaneously, we construct galaxy group and candidate protocluster catalogs in a wide redshift range (0 2.0. By checking the galaxy number distributions within a 5-7 h -1Mpc projected separation and a redshift difference Δz ≤ 0.1 around those richest groups at redshift z > 2, we identify lists of 761, 343, and 43 protocluster candidates in the redshift bins 2 ≤ z < 3, 3 ≤ z < 4, and z ≥ 4, respectively. In general, these catalogs of galaxy groups and protocluster candidates will provide useful environmental information in probing galaxy evolution along cosmic tim

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: modelling of the luminosity and colour dependence in the Data Release 10

We investigate the luminosity and colour dependence of clustering of CMASS galaxies in the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey Data Release 10, focusing on projected correlation functions of well-defined samples extracted from the full catalogue of ∼540 000 galaxies at z ∼ 0.5 covering about 6500 deg2. The halo occupation distribution framework is adopted to model the measurements on small and intermediate scales (from 0.02 to 60 h-1 Mpc), infer the connection of galaxies to dark matter haloes and interpret the observed trends. We find that luminous red galaxies in CMASS reside in massive haloes of mass M ∼ 1013–1014 h-1 M⊙ and more luminous galaxies are more clustered and hosted by more massive haloes. The strong small-scale clustering requires a fraction of these galaxies to be satellites in massive haloes, with the fraction at the level of 5–8 per cent and decreasing with luminosity. The characteristic mass of a halo hosting on average one satellite galaxy above a luminosity threshold is about a factor of 8.7 larger than that of a halo hosting a central galaxy above the same threshold. At a fixed luminosity, progressively redder galaxies are more strongly clustered on small scales, which can be explained by having a larger fraction of these galaxies in the form of satellites in massive haloes. Our clustering measurements on scales below 0.4 h-1 Mpc allow us to study the small-scale spatial distribution of satellites inside haloes. While the clustering of luminosity-threshold samples can be well described by a Navarro–Frenk–White profile, that of the reddest galaxies prefers a steeper or more concentrated profile. Finally, we also use galaxy samples of constant number density at different redshifts to study the evolution of luminous red galaxies, and find the clustering to be consistent with passive evolution in the redshift range of 0.5 ≲ z ≲ 0.6