Oceanic emissions of dimethyl sulfide and methanethiol and their contribution to sulfur dioxide production in the marine atmosphere

Oceanic emissions of dimethyl sulfide (CH3SCH3, DMS) have long been recognized to impact aerosol particle composition and size, the concentration of cloud condensation nuclei (CCN), and Earth's radiation balance. The impact of oceanic emissions of methanethiol (CH3SH, MeSH), which is produced by the same oceanic precursor as DMS, on the volatile sulfur budget of the marine atmosphere is largely unconstrained. Here we present direct flux measurements of MeSH oceanic emissions using the eddy covariance (EC) method with a high-resolution proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToFMS) detector and compare them to simultaneous flux measurements of DMS emissions from a coastal ocean site. Campaign mean mixing ratios of DMS and MeSH were 72&thinsp;ppt (28&ndash;90&thinsp;ppt interquartile range) and 19.1&thinsp;ppt (7.6&ndash;24.5&thinsp;ppt interquartile range), respectively. Campaign mean emission fluxes of DMS (FDMS) and MeSH (FMeSH) were 1.13&thinsp;ppt&thinsp;m&thinsp;s&minus;1 (0.53&ndash;1.61&thinsp;ppt&thinsp;m&thinsp;s&minus;1 interquartile range) and 0.21&thinsp;ppt&thinsp;m&thinsp;s&minus;1 (0.10&ndash;0.31&thinsp;ppt&thinsp;m&thinsp;s&minus;1 interquartile range), respectively. Linear least squares regression of observed MeSH and DMS flux indicates the emissions are highly correlated with each other (R2=0.65) over the course of the campaign, consistent with a shared oceanic source. The campaign mean DMS to MeSH flux ratio (FDMS:FMeSH) was 5.5&thinsp;&plusmn;&thinsp;3.0, calculated from the ratio of&nbsp;304 individual coincident measurements of FDMS and FMeSH. Measured FDMS:FMeSH was weakly correlated (R2=0.15) with ocean chlorophyll concentrations, with FDMS:FMeSH reaching a maximum of 10.8&thinsp;&plusmn;&thinsp;4.4 during a phytoplankton bloom period. No other volatile sulfur compounds were observed by PTR-ToFMS to have a resolvable emission flux above their flux limit of detection or to have a gas-phase mixing ratio consistently above their limit of detection during the study period, suggesting DMS and MeSH are the dominant volatile organic sulfur compounds emitted from the ocean at this site. The impact of this MeSH emission source on atmospheric budgets of sulfur dioxide (SO2) was evaluated by implementing observed emissions in a coupled ocean&ndash;atmosphere chemical box model using a newly compiled MeSH oxidation mechanism. Model results suggest that MeSH emissions lead to afternoon instantaneous SO2 production of 2.5&thinsp;ppt&thinsp;h&minus;1, which results in a 43&thinsp;% increase in total SO2 production compared to a case where only DMS emissions are considered and accounts for 30% of the instantaneous SO2 production in the marine boundary layer at the mean measured FDMS and FMeSH. This contribution of MeSH to SO2 production is driven by a higher effective yield of SO2 from MeSH oxidation and the shorter oxidation lifetime of MeSH compared to DMS. This large additional source of marine SO2 has not been previously considered in global models of marine sulfur cycling. The field measurements and modeling results presented here demonstrate that MeSH is an important contributor to volatile sulfur budgets in the marine atmosphere and must be measured along with DMS in order to constrain marine sulfur budgets. This large additional source of marine&ndash;reduced sulfur from MeSH will contribute to particle formation and growth and CCN abundance in the marine atmosphere, with subsequent impacts on climate. &nbsp;</p

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

Bacillus anthracis Peptidoglycan Stimulates an Inflammatory Response in Monocytes through the p38 Mitogen-Activated Protein Kinase Pathway

We hypothesized that the peptidoglycan component of B. anthracis may play a critical role in morbidity and mortality associated with inhalation anthrax. To explore this issue, we purified the peptidoglycan component of the bacterial cell wall and studied the response of human peripheral blood cells. The purified B. anthracis peptidoglycan was free of non-covalently bound protein but contained a complex set of amino acids probably arising from the stem peptide. The peptidoglycan contained a polysaccharide that was removed by mild acid treatment, and the biological activity remained with the peptidoglycan and not the polysaccharide. The biological activity of the peptidoglycan was sensitive to lysozyme but not other hydrolytic enzymes, showing that the activity resides in the peptidoglycan component and not bacterial DNA, RNA or protein. B. anthracis peptidoglycan stimulated monocytes to produce primarily TNFα; neutrophils and lymphocytes did not respond. Peptidoglycan stimulated monocyte p38 mitogen-activated protein kinase and p38 activity was required for TNFα production by the cells. We conclude that peptidoglycan in B. anthracis is biologically active, that it stimulates a proinflammatory response in monocytes, and uses the p38 kinase signal transduction pathway to do so. Given the high bacterial burden in pulmonary anthrax, these findings suggest that the inflammatory events associated with peptidoglycan may play an important role in anthrax pathogenesis

Brain-age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.</p

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Brain‐age prediction: systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics

Brain‐age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.<br/

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to examine age‐related trajectories inferred from cross‐sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter‐individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age‐related morphometric patterns