Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior.

Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets

Near-Infrared Molecular Hydrogen Emission from the Central Regions of Galaxies: Regulated Physical Conditions in the Interstellar Medium

The central regions of many interacting and early-type spiral galaxies are actively forming stars. This process affects the physical and chemical properties of the local interstellar medium as well as the evolution of the galaxies. We observed near-infrared H2 emission lines: v=1-0 S(1), 3-2 S(3), 1-0 S(0), and 2-1 S(1) from the central ~1 kpc regions of the archetypical starburst galaxies, M82 and NGC 253, and the less dramatic but still vigorously star-forming galaxies, NGC 6946 and IC 342. Like the far-infrared continuum luminosity, the near-infrared H2 emission luminosity can directly trace the amount of star formation activity because the H2 emission lines arise from the interaction between hot and young stars and nearby neutral clouds. The observed H2 line ratios show that both thermal and non-thermal excitation are responsible for the emission lines, but that the great majority of the near-infrared H2 line emission in these galaxies arises from energy states excited by ultraviolet fluorescence. The derived physical conditions, e.g., far-ultraviolet radiation field and gas density, from [C II] and [O I] lines and far-infrared continuum observations when used as inputs to photodissociation models, also explain the luminosity of the observed H2 v=1-0 S(1) line. The ratio of the H2 v=1-0 S(1) line to far-IR continuum luminosity is remarkably constant over a broad range of galaxy luminosities; L_H2/L_FIR = about 10^{-5}, in normal late-type galaxies (including the Galactic center), in nearby starburst galaxies, and in luminous IR galaxies (LIRGs: L_FIR > 10^{11} L_sun). Examining this constant ratio in the context of photodissociation region models, we conclude that it implies that the strength of the incident UV field on typical molecular clouds follows the gas density at the cloud surface.Comment: Accepted for ApJ, 24 pages, 17 figures, for complete PDF file, see http://kao.re.kr/~soojong/mypaper/2004_pak_egh2.pd

The Incidence of Highly-Obscured Star-Forming Regions in SINGS Galaxies

Using the new capabilities of the Spitzer Space Telescope and extensive multiwavelength data from the Spitzer Infrared Nearby Galaxies Survey (SINGS), it is now possible to study the infrared properties of star formation in nearby galaxies down to scales equivalent to large HII regions. We are therefore able to determine what fraction of large, infrared-selected star-forming regions in normal galaxies are highly obscured and address how much of the star formation we miss by relying solely on the optical portion of the spectrum. Employing a new empirical method for deriving attenuations of infrared-selected star-forming regions we investigate the statistics of obscured star formation on 500pc scales in a sample of 38 nearby galaxies. We find that the median attenuation is 1.4 magnitudes in H-alpha and that there is no evidence for a substantial sub-population of uniformly highly-obscured star-forming regions. The regions in the highly-obscured tail of the attenuation distribution (A_H-alpha > 3) make up only ~4% of the sample of nearly 1800 regions, though very embedded infrared sources on the much smaller scales and lower luminosities of compact and ultracompact HII regions are almost certainly present in greater numbers. The highly-obscured cases in our sample are generally the bright, central regions of galaxies with high overall attenuation but are not otherwise remarkable. We also find that a majority of the galaxies show decreasing radial trends in H-alpha attenuation. The small fraction of highly-obscured regions seen in this sample of normal, star-forming galaxies suggests that on 500pc scales the timescale for significant dispersal or break up of nearby, optically-thick dust clouds is short relative to the lifetime of a typical star-forming region.Comment: Accepted for publication in ApJ; emulateapj style, 30 pages, 18 figures (compressed versions), 3 table

Kepler Flares II: The Temporal Morphology of White-Light Flares on GJ 1243

We present the largest sample of flares ever compiled for a single M dwarf, the active M4 star GJ 1243. Over 6100 individual flare events, with energies ranging from $10^{29}$ to $10^{33}$ erg, are found in 11 months of 1-minute cadence data from Kepler. This sample is unique for its completeness and dynamic range. We have developed automated tools for finding flares in short-cadence Kepler light curves, and performed extensive validation and classification of the sample by eye. From this pristine sample of flares we generate a median flare template. This template shows that two exponential cooling phases are present during the white-light flare decay, providing fundamental constraints for models of flare physics. The template is also used as a basis function to decompose complex multi-peaked flares, allowing us to study the energy distribution of these events. Only a small number of flare events are not well fit by our template. We find that complex, multi-peaked flares occur in over 80% of flares with a duration of 50 minutes or greater. The underlying distribution of flare durations for events 10 minutes and longer appears to follow a broken power law. Our results support the idea that sympathetic flaring may be responsible for some complex flare events.Comment: 12 pages, 9 figures, accepted for publication in Ap

V-ATPase-Mediated Granular Acidification Is Regulated by the V-ATPase Accessory Subunit Ac45 in POMC-Producing Cells

The regulation of the V-ATPase, the proton pump mediating intraorganellar acidification, is still elusive. We find that excess of the neuroendocrine V-ATPase accessory subunit Ac45 reduces the intragranular pH and consequently disturbs prohormone convertase activation and prohormone processing. Thus, Ac45 represents the first V-ATPase regulator

Lipoprotein lipase activity is decreased in a large cohort of patients with coronary artery disease and is associated with changes in lipids and lipoproteins

Lipoprotein lipase (LPL) is crucial in the hydrolysis of triglycerides (TG) in TG-rich lipoproteins in the formation of HDL particles. As both these lipoproteins play an important role in the pathogenesis of atherosclerotic vascular disease, we sought to assess the relationship between post-heparin LPL (PH-LPL) activity and lipids and lipoproteins in a large, well-defined cohort of Dutch males with coronary artery disease (CAD). These subjects were drawn from the REGRESS study, totaled 730 in number and were evaluated against 75 healthy, normolipidemic male controls. Fasting mean PH-LPL activity in the CAD subjects was 108 46 mU/ml, compared to 138 44 mU/ml in controls (P < 0.0001). When these patients were divided into activity quartiles, those in the lowest versus the highest quartile had higher levels of TG (P < 0.001), VLDLc and VLDL-TG (P = 0.001). Conversely, levels of TC, LDL, and HDLc were lower in these patients (P = 0.001, P = 0.02, and P = 0.001, respectively). Also, in this cohort PH-LPL relationships with lipids and lipoproteins were not altered by apoE genotypes. The frequency of common mutations in the LPL gene associated with partial LPL deficiency (N291S and D9N carriers) in the lowest quartile for LPL activity was more than double the frequency in the highest quartile (12.0% vs. 5.0%; P = 0.006). By contrast, the frequency of the S447X LPL variant rose from 11.5% in the lowest to 18.3% (P = 0.006) in the highest quartile. This study, in a large cohort of CAD patients, has shown that PH-LPL activity is decreased (22%; P = 0.001) when compared to controls; that the D9N and N291S, and S447X LPL variants are genetic determinants, respectively, in CAD patients of low and high LPL PH-LPL activities; and that PH-LPL activity is strongly associated with changes in lipids and lipoproteins

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

In-vivo generation of bone via endochondral ossification by in-vitro chondrogenic priming of adult human and rat mesenchymal stem cells

Background: Bone grafts are required to repair large bone defects after tumour resection or large tr

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models