Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean

The distribution of any non-conservative variable in the deep open ocean results from the circulation and mixing of water masses (WMs) of contrasting origin and from the initial preformed composition, modified during ongoing simultaneous biological and/or geochemical processes. Estimating the contribution of the WMs composing a sample is useful to trace the distribution of each water mass and to quantitatively separate the physical (mixing) and biogeochemical components of the variability of any, non- conservative variable (e.g., dissolved organic carbon, prokaryote biomass) in the ocean. Other than potential temperature and salinity, additional semi-conservative and non-conservative variables have been used to solve the mixing of more than three water masses using Optimum Multi-Parameter (OMP) approaches. Successful application of an OMP analysis requires knowledge of the characteristics of the water masses in their source regions as well as their circulation and mixing patterns. Here, we propose the application of multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). Our model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Therefore, basic hydrographic data collected during typical research cruises or autonomous systems can be used as input variables and provide results in real time. The model can be fed with new solutions from compatible OMP analyses as well as with new water masses not previously considered in it. Our tool will provide knowledge on water mass composition and distribution to a broader community of marine scientists not specialized in OMP analysis and/or in the oceanography of the studied area. This will allow a quantitative analysis of the effect of water mass mixing on the variables or processes under study

SIZE-REACTIVITY OF DISSOLVED ORGANIC MATTER IN THE CAPE VERDE FRONTAL ZONE

Oral communicationDissolved organic matter (DOM) plays a major role in the recycling, export and sequestration of biogenic organic carbon, being a key component of ocean biogeochemical cycles and of the biological and microbial carbon pumps. Microbial degradation of DOM not only produces CO 2 but also generates dissolved molecules of decreasing bioavailability that can accumulate in the oceans for hundreds to thousands of years. The size-reactivity continuum (SRC) model is the conceptual framework to explain the DOM reactivity on a size basis, although field tests are still scarce and some of the pieces of this puzzle remain unclear. Taking advantage of the FLUXES-I cruise in the Cape Verde Frontal Zone (CVFZ), we have studied the size fractionated reactivity of the high (HMW; >1 KDa) and low (LMW; <1 KDa) molecular weight fractions of the DOM from surface down to 4000 m, using a high-efficiency and low-concentration-factor ultrafiltration cell. The wide ageing range covered by the water masses of the CVFZ makes it an excellent site to test the SRC model. Regarding the bulk C and N pools, the water masses with higher oxygen utilization were more depleted in HMW molecules, with a significant preference for the degradation of large N-containing compounds. Accordingly, preferential degradation of HMW fluorescent protein-like compounds was observed. In parallel, fluorescent humic-like compounds of both HMW and LMW were generated as by-product of the degradation of HMW organic compounds, and the remineralization of the DOM increases the aromaticy of both fractions, but especially the LMW one.ASL

Prokaryotic capability to use organic substrates across the global tropical and subtropical ocean

Prokaryotes play a fundamental role in decomposing organic matter in the ocean, but little is known about how microbial metabolic capabilities vary at the global ocean scale and what are the drivers causing this variation. We aimed at obtaining the first global exploration of the functional capabilities of prokaryotes in the ocean, with emphasis on the under-sampled meso- and bathypelagic layers. We explored the potential utilization of 95 carbon sources with Biolog GN2 plates® in 441 prokaryotic communities sampled from surface to bathypelagic waters (down to 4,000 m) at 111 stations distributed across the tropical and subtropical Atlantic, Indian, and Pacific oceans. The resulting metabolic profiles were compared with biological and physico-chemical properties such as fluorescent dissolved organic matter (DOM) or temperature. The relative use of the individual substrates was remarkably consistent across oceanic regions and layers, and only the Equatorial Pacific Ocean showed a different metabolic structure. When grouping substrates by categories, we observed some vertical variations, such as an increased relative utilization of polymers in bathypelagic layers or a higher relative use of P-compounds or amino acids in the surface ocean. The increased relative use of polymers with depth, together with the increases in humic DOM, suggest that deep ocean communities have the capability to process complex DOM. Overall, the main identified driver of the metabolic structure of ocean prokaryotic communities was temperature. Our results represent the first global depiction of the potential use of a variety of carbon sources by prokaryotic communities across the tropical and the subtropical ocean and show that acetic acid clearly emerges as one of the most widely potentially used carbon sources in the ocean

Global diversity and biogeography of deep-sea pelagic prokaryotes

The deep-sea is the largest biome of the biosphere, and contains more than half of the whole ocean/'s microbes. Uncovering their general patterns of diversity and community structure at a global scale remains a great challenge, as only fragmentary information of deep-sea microbial diversity exists based on regional-scale studies. Here we report the first globally comprehensive survey of the prokaryotic communities inhabiting the bathypelagic ocean using high-throughput sequencing of the 16S rRNA gene. This work identifies the dominant prokaryotes in the pelagic deep ocean and reveals that 50{\%} of the operational taxonomic units (OTUs) belong to previously unknown prokaryotic taxa, most of which are rare and appear in just a few samples. We show that whereas the local richness of communities is comparable to that observed in previous regional studies, the global pool of prokaryotic taxa detected is modest (\~{}3600 OTUs), as a high proportion of OTUs are shared among samples. The water masses appear to act as clear drivers of the geographical distribution of both particle-attached and free-living prokaryotes. In addition, we show that the deep-oceanic basins in which the bathypelagic realm is divided contain different particle-attached (but not free-living) microbial communities. The combination of the aging of the water masses and a lack of complete dispersal are identified as the main drivers for this biogeographical pattern. All together, we identify the potential of the deep ocean as a reservoir of still unknown biological diversity with a higher degree of spatial complexity than hitherto considered.En prensa8,951

<i>Gaia</i> Data Release 1. Summary of the astrometric, photometric, and survey properties

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the HIPPARCOS and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ∼3000 Cepheid and RR-Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr−1 for the proper motions. A systematic component of ∼0.3 mas should be added to the parallax uncertainties. For the subset of ∼94 000 HIPPARCOS stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr−1. For the secondary astrometric data set, the typical uncertainty of the positions is ∼10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ∼0.03 mag over the magnitude range 5 to 20.7. Conclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data

Gaia Data Release 1: Testing parallaxes with local Cepheids and RR Lyrae stars

Context. Parallaxes for 331 classical Cepheids, 31 Type II Cepheids, and 364 RR Lyrae stars in common between Gaia and the Hipparcos and Tycho-2 catalogues are published in Gaia Data Release 1 (DR1) as part of the Tycho-Gaia Astrometric Solution (TGAS). Aims. In order to test these first parallax measurements of the primary standard candles of the cosmological distance ladder, which involve astrometry collected by Gaia during the initial 14 months of science operation, we compared them with literature estimates and derived new period-luminosity (PL), period-Wesenheit (PW) relations for classical and Type II Cepheids and infrared PL, PL-metallicity (PLZ), and optical luminosity-metallicity (M V -[Fe/H]) relations for the RR Lyrae stars, with zero points based on TGAS. Methods. Classical Cepheids were carefully selected in order to discard known or suspected binary systems. The final sample comprises 102 fundamental mode pulsators with periods ranging from 1.68 to 51.66 days (of which 33 with σ Ω /Ω < 0.5). The Type II Cepheids include a total of 26 W Virginis and BL Herculis stars spanning the period range from 1.16 to 30.00 days (of which only 7 with σ Ω /Ω < 0.5). The RR Lyrae stars include 200 sources with pulsation period ranging from 0.27 to 0.80 days (of which 112 with σ Ω /Ω < 0.5). The new relations were computed using multi-band (V,I,J,K s ) photometry and spectroscopic metal abundances available in the literature, and by applying three alternative approaches: (i) linear least-squares fitting of the absolute magnitudes inferred from direct transformation of the TGAS parallaxes; (ii) adopting astrometry-based luminosities; and (iii) using a Bayesian fitting approach. The last two methods work in parallax space where parallaxes are used directly, thus maintaining symmetrical errors and allowing negative parallaxes to be used. The TGAS-based PL,PW,PLZ, and M V - [Fe/H] relations are discussed by comparing the distance to the Large Magellanic Cloud provided by different types of pulsating stars and alternative fitting methods. Results. Good agreement is found from direct comparison of the parallaxes of RR Lyrae stars for which both TGAS and HST measurements are available. Similarly, very good agreement is found between the TGAS values and the parallaxes inferred from the absolute magnitudes of Cepheids and RR Lyrae stars analysed with the Baade-Wesselink method. TGAS values also compare favourably with the parallaxes inferred by theoretical model fitting of the multi-band light curves for two of the three classical Cepheids and one RR Lyrae star, which were analysed with this technique in our samples. The K-band PL relations show the significant improvement of the TGAS parallaxes for Cepheids and RR Lyrae stars with respect to the Hipparcos measurements. This is particularly true for the RR Lyrae stars for which improvement in quality and statistics is impressive. Conclusions. TGAS parallaxes bring a significant added value to the previous Hipparcos estimates. The relations presented in this paper represent the first Gaia-calibrated relations and form a work-in-progress milestone report in the wait for Gaia-only parallaxes of which a first solution will become available with Gaia Data Release 2 (DR2) in 2018. © ESO, 2017

Gaia Data Release 1: Open cluster astrometry: performance, limitations, and future prospects

Context. The first Gaia Data Release contains the Tycho-Gaia Astrometric Solution (TGAS). This is a subset of about 2 million stars for which, besides the position and photometry, the proper motion and parallax are calculated using Hipparcos and Tycho-2 positions in 1991.25 as prior information.Aims. We investigate the scientific potential and limitations of the TGAS component by means of the astrometric data for open clusters.Methods. Mean cluster parallax and proper motion values are derived taking into account the error correlations within the astrometric solutions for individual stars, an estimate of the internal velocity dispersion in the cluster, and, where relevant, the effects of the depth of the cluster along the line of sight. Internal consistency of the TGAS data is assessed.Results. Values given for standard uncertainties are still inaccurate and may lead to unrealistic unit-weight standard deviations of least squares solutions for cluster parameters. Reconstructed mean cluster parallax and proper motion values are generally in very good agreement with earlier HIPPARCOS-based determination, although the Gaia mean parallax for the Pleiades is a significant exception. We have no current explanation for that discrepancy. Most clusters are observed to extend to nearly 15 pc from the cluster centre, and it will be up to future Gaia releases to establish whether those potential cluster-member stars are still dynamically bound to the clusters.Conclusions. The Gaia DR1 provides the means to examine open clusters far beyond their more easily visible cores, and can provide membership assessments based on proper motions and parallaxes. A combined HR diagram shows the same features as observed before using the HIPPARCOS data, with clearly increased luminosities for older A and F dwarfs

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

The Gaia mission

Gaia is a cornerstone mission in the science programme of the EuropeanSpace Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page. http://www.cosmos.esa.int/gai

DataSheet_1_Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean.zip

The distribution of any non-conservative variable in the deep open ocean results from the circulation and mixing of water masses (WMs) of contrasting origin and from the initial preformed composition, modified during ongoing simultaneous biological and/or geochemical processes. Estimating the contribution of the WMs composing a sample is useful to trace the distribution of each water mass and to quantitatively separate the physical (mixing) and biogeochemical components of the variability of any, non- conservative variable (e.g., dissolved organic carbon, prokaryote biomass) in the ocean. Other than potential temperature and salinity, additional semi-conservative and non-conservative variables have been used to solve the mixing of more than three water masses using Optimum Multi-Parameter (OMP) approaches. Successful application of an OMP analysis requires knowledge of the characteristics of the water masses in their source regions as well as their circulation and mixing patterns. Here, we propose the application of multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). Our model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Therefore, basic hydrographic data collected during typical research cruises or autonomous systems can be used as input variables and provide results in real time. The model can be fed with new solutions from compatible OMP analyses as well as with new water masses not previously considered in it. Our tool will provide knowledge on water mass composition and distribution to a broader community of marine scientists not specialized in OMP analysis and/or in the oceanography of the studied area. This will allow a quantitative analysis of the effect of water mass mixing on the variables or processes under study.</p