dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, as already pointed out by  Wanninkhof (1992) , the use of monthly averaged values of U 10 instead of the 6 hour U 2 10 has a much bigger effect on F CO 2 , with an underestimation reaching over 20 %. It is also interesting to compare our reported F CO 2 uncertainties to those introduced by the choice of a given pCO 2 product. The application of distinct interpolation techniques in recent years has led to the publication of several global pCO 2 products that are largely based on the same observational dataset (i. e. , SOCAT;  Bakker et al. , 2016). To quantify the uncertainty introduced by the choice of the pCO 2 field,  Rödenbeck et al. (2015)  applied an identical parameterization of the CO 2 exchange at the air-water interface to 14 pCO 2 data products. The global F CO 2 ranged from -1.
0. CO;2) and the NCEP/DOE AMIP-II Reanalysis (NCEP2;  Kanamitsu et al. , 2002 , https://doi. org/10. 1175 /BAMS-83-11-1631) provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www. esrl. noaa. gov/psd/data/gridded/. The NOAA High Resolution SST data are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www. esrl. noaa. gov/psd/. Appendix A Table  A1. 21 year mean U 10 ( U 10 , m s -1 ) and F CO 2 (F CO 2 , Tg C yr -1 ) in the 11 oceanic RECCAP regions calculated using the different global k relationships combined with four different wind products. C * refers to F CO 2 calculated with a quadratic k relationship where c is recalibrated for each wind product to fit a global average k value of 16 cm h -1 for the period of the study.
Interim datasets (ERA; Dee et al. , 2011; https://doi. org/10. 1002/qj. 828) are accessible on the European Centre for Medium-Range Weather Forecasts (ECMWF) web site. The Cross-Calibrated Multi-Platform Ocean Wind Vector 3. 0 datasets (CCMP; Atlas et al. , 2011, https://doi. org/10. 1175/2010BAMS2946. 1) are provided by the NASA/GSFC/NOAA (2009). The NCEP/NCAR reanalysis 1 (NCEP1;.
Acknowledgements. Goulven G. Laruelle  was funded by the  F. R. S. -FNRS  as a postdoctoral researcher when this study was carried out. Peter Landschützer  is supported by the  Max Planck Society for the Advancement of Science. The research leading to these results has received funding from the  European Union 's  Horizon 2020 research and innovation programs  under the  Marie Sklodowska-Curie  grant agreement no. 643052  ( C-CASCADES  project) and under a grant agreement no. 776810  ( VERIFY  project). Edited by: Alexey V. Eliseev Reviewed by: two anonymous referees.
Interim datasets (ERA; Dee et al. , 2011; https://doi. org/10. 1002/qj. 828) are accessible on the European Centre for Medium-Range Weather Forecasts (ECMWF) web site. The Cross-Calibrated Multi-Platform Ocean Wind Vector 3. 0 datasets (CCMP; Atlas et al. , 2011, https://doi. org/10. 1175/2010BAMS2946. 1) are provided by the NASA/GSFC/NOAA (2009). The NCEP/NCAR reanalysis 1 (NCEP1; Data availability. The observation-based global monthly gridded sea surface pCO 2 product is provided by Land- schützer et al. (2015b, https://www. nodc. noaa. gov/ocads/oceans/ SPCO2_1982_2011_ETH_SOM_FFN. html). The global at- mospheric reanalysis ERA-.
I. INTRODUCTION Cosmology is steadily maturing from a phase driven by high precision to a high-accuracy science. While statistical estimators and observational strategies have been designed to minimise random errors, the focus is now shifting towards systematic uncertainties and their impact on the total error budget. Various strategies have been adopted to tackle this problem. The most effective one is to estimate the same quantity using different techniques applied to independent datasets. Considering the linear growth rate of matter density fluctuations, f , as an example, redshift-space distortions (RSDs) have been recognised as the most promising method to estimate this important quantity [e. g. ,  1, 2]  and are now adopted as its standard probe in next-generation galaxy redshift surveys [e.
g. ,  3, 4]. Peculiar velocities measured from distance indicators have traditionally represented an alternative probe which has provided a precious, though very local, robustness test for analyses based on RSDs. More recently, yet another technique has been proposed to infer the growth rate from a photometric dataset complemented with spectroscopic redshift information  [5, 6]. Thanks to this luminosity method, we now have independent and consistent f -estimates out to redshifts z ∼ 0. 1. The luminosity method can also be used to assess the coherence of the peculiar velocity field, most notably the * Electronic address: feix@iap. fr † Electronic address: branchin@fis. uniroma3. it ‡ Electronic address: adi@physics. technion. ac. il bulk flow, i. e. the volume average of the peculiar velocity field.
Here φ(M ) denotes the galaxy luminosity function (LF) which is determined from the very same dataset, and M ± are the limiting magnitudes which depend on the survey's flux cuts and individual radial velocities field through the cosmological redshift z c specified in Eq. (1). The rationale of this approach is to find the set of model parameters which yield the minimal spread in the observed magnitudes. The luminosity method may be used to constrain the peculiar velocity field in different ways. In this study, we focus on two types of measurements. To estimate cosmic bulk flows, we simply set the peculiar velocity model to V (r, z) = r • v B , where the components of the bulk flow vector v B are the free model parameters determined by the likelihood procedure  [7, 8]. Since the peculiar velocity field is spatially coherent on large-scales, another possibility is to independently predict it from the observed galaxy distribution in redshift space  [18] [19] [20].
06 < z < 0. 12 and selecting only galaxies within -33 • < η < 36 • and -48 • < λ < 51. 5 • , where η and λ are the SDSS survey latitude and survey longitude, respectively. This leads to a spatially connected sample volume at z = 0. 1 for which reliable reconstructions of the peculiar velocity field can be obtained. The sample contains a total of roughly 1. 7 × 10 5 galaxies which is around 15% less compared to the size of the data sample used in  [6]. Because we are dealing with galaxy samples limited to z ≈ 0. 2, we follow the lines of  [9]  and assume that the luminosity evolution depends linearly on redshift, i. e. Q(z) = Q 0 × (z -z 0 ), (4) where the pivotal redshift is set to z 0 = 0. 1. Results from applying the luminosity method to these datasets are robust with respect to different models for luminosity evolution as well as K-corrections of individual galaxies  [9].
B. Mock galaxy catalogues To aid the interpretation of bulk flow measurements, we consider a suite of 269 mock galaxy catalogues which were modelled after the SDSS DR7 and allow for an assessment of effects due to known systematics, incompleteness, and cosmic variance. A detailed description of how these mocks were constructed can be found in  [9]. Since the number of galaxies in the DR7 and DR13 samples is comparable and the photometric recalibration of r-band magnitudes in DR13 introduced only slight changes at the level of a few mmag, the mock catalogues remain a suitable choice for the present study. Crossmatching DR13 and DR7 galaxies within one arcsec, for example, we find that more than 95% of all galaxies in luminosityA are included in the corresponding DR7 sample of  [9]. As we show in section IV A, a further indication is provided by a very similar LF estimate between the two datasets.
To maximise P tot , we adopt the spline-based LF which is fixed to its estimate for zero galaxy velocities. Our analysis also accounts for a slight statistical bias which emerges from the partial sky coverage of the considered SDSS datasets. All details regarding the calculation are summarised in  [9]. Presented are results based on galaxies from SDSS DR13 (filled circles) and DR7 (open circles). The estimates assume the power spectrum amplitude of L ⋆ -galaxies given by  [32]. Vertical lines indicate the result inferred from Planck data (TT+lowP+lensing)  [34]  and its 95% confidence limits. Carrying out the above steps, we find β = 0. 54 ± 0. 18 for the DR13 galaxy sample. This new estimate is around 30% larger than our previous result (β = 0. 42 ± 0. 14 for DR7), but consistent within the quoted uncertainties which have been obtained from approximating the loglikelihood near its maximum to quadratic order.
For velocity models which exclude more of the low multipoles (e. g. , models with l > 5), we observe a similar trend. Assuming the power spectrum amplitude of L ⋆ -galaxies inferred in  [32] , we may rewrite the result in terms of f σ 8 , which allows for a comparison to measurements based on different datasets  [33]  and where σ 8 is the amplitude of matter fluctuations in spheres of 8h -1 Mpc radius. This yields a value of f σ 8 = 0. 48 ± 0. 16 (and f σ 8 = 0. 37 ± 0. 13 for DR7; see Figure  3 ) close to the ΛCDM estimate at z = 0. 1 based on the Planck data [f σ 8 ≈ 0. 45; shown as the solid vertical line in Figure  3 ; 34]. Our result is independent of the Hubble constant and quite insensitive to the precise choice of the cosmological parameters, the removal of high-l modes in the velocity models  [6] , or the used LF model.
ACKNOWLEDGMENTS This research was supported by the  I-CORE Program of the Planning and Budgeting Committee ,  THE IS-RAEL SCIENCE FOUNDATION  (grants No. 1829/12  and No. 203/09 ), the  German-Israeli Foundation for Research and Development , the  Asher Space Research Institute , and in part by the  Lady Davis Foundation. M. F. acknowledges support by the grant  ANR-13-BS05-0005  of the  French National Research Agency. E. B. is supported by  INFN -PD51 INDARK, MIUR PRIN 2011  " The dark Universe and the cosmic evolution of baryons: from current surveys to Euclid", and the  Agenzia Spaziale Italiana  from the agreement  ASI/INAF/I/023/12/0. Funding for the SDSS and SDSS-II has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , the  U.
1 f σ 8 18 FIG. 3. Estimated ∆χ 2 (and its quadratic approximation) as a function of f σ8 for linear velocity models with l > 1. Presented are results based on galaxies from SDSS DR13 (filled circles) and DR7 (open circles). The estimates assume the power spectrum amplitude of L ⋆ -galaxies given by [32]. Vertical lines indicate the result inferred from Planck data (TT+lowP+lensing) [34]  and its 95% confidence limits.
However, we expect that (i) the number of studies using RNA-seq will increase dramatically and (ii) these methodologies will help to bring the knowledge of the BBB to a new level. We are currently developing "BBBHub"-a web based data hub platform (i) to collect existing and new data (ii) to integrate and organize the omics-data (mainly RNA-seq) (iii) to implement data-mining tools and (iv) to provide the data to both researchers and general public. For the implementation of the BBBHub, we will use the frame work "open-BIS", an open source platform for managing scientific information, as its backbone. Furthermore, we are developing an easy-to-use frontend system to allow all researchers to use it independently of their computational expertise. In the context of the BBBHub, we are setting up standardized data analysis workflows to allow for a more efficient comparison between datasets and are currently working on quality control determination features.
The frequency, duration, and intensity of each running session were monitored for each mouse via a direct data link to a computer and the running data are analyzed by Clocklab The microvascular endothelial cells in the brain, connected by tight junctions, are surrounded by pericytes, astrocytes, microglia and neurons. Together they act as a functional unit, named the neurovascular unit (NVU). Proper functioning of the NVU in general is critical for normal brain functions and homeostasis. Accumulated recent data from human and animal studies reveal that increased permeability of the blood-brain barrier (BBB) is a major determinant in common neurological disorders. We have previously shown that long-lasting dysfunction of the BBB leads to astrocytic transformation and epileptogenesis  [1, 2, 3, 4].
The mouse line enables us to specifically explore effects of pericyte-mediated HIF-1 signaling on stroke outcome and BBB modulation after ischemic insult. We subjected pericyte HIF-1 KO and WT mice to transient middle cerebral artery occlusion (tMCAo) for 45 mins using intraluminal filament method followed by reperfusion up to 14 days. Ischemic damage, BBB permeability, neurological scores were assessed. Intriguingly, our data suggested stabilization of HIF-1 in pericytes differentially modulates barrier integrity and stroke outcome during stroke progression. Firstly, at 72h post-tMCAo HIF-1 deletion reduced IgG extravasation and Evans blue leakage. Although overall infarct volume was unaffected, reduced neuronal degeneration, less cell apoptosis and more astrocyte activation were observed in HIF-1 KO peri-infarct cortex by immunostaining.
LPS treatment decreased expression of Cldn2 in SD and LD. Leptin did not affect Aqp1 and Cldn2. In case of VEGF/VEGFR system, the impact of LPS and leptin was photoperiodically differentiated. LPS did not affect Vegf120 and Vegf164, however increased (p<0. 05) VegfR1 and VegfR2 but only in SD. In SD leptin increased (p<0. 05) Vegf164 and VegfR1 and increasing tendency was observed for Vegf120 (p= 0. 06). During LD VegfRs increased only in leptin/LPS group. This results, confirmed hypothesis that leptin modulates CP response to immune stress in photoperiodically differentiated way. Funded by the National Science Centre of Poland, grant SONATA no 2013/11/D/NZ9/02536. Participation of AS, MK and JS was supported by statutory research founds of Ministry of Higher Education. A55 Pericyte HIF- 1 , Agata Krawczynska 2 , Andrzej P.
The Global Action Plan on antimicrobial resistance calls for the use of antimicrobial medicines in human and animal health to be optimized, in tandem with a strengthening of the knowledge and evidence base through surveillance and research. However, there is a paucity of consumption data for African countries such as South Africa. Determining antimicrobial consumption data in low-resource settings remains a challenge. This article describes alternative mechanisms of assessing antimicrobial consumption data, such as the use of Intercontinental Marketing Services (IMS) data and contract data arising from tenders (an open Request for Proposal, RFP), as opposed to the international norms of daily defined doses per 100 patient-days or per 1000 population. Despite their limitations, these serve as indicators of antimicrobial exposure at the population level and represent an alternative method for ascertaining antimicrobial consumption in human health.
It identified several constraints limiting the implementation of good infection control practices and antimicrobial stewardship programmes in humans and animals  (Global Antibiotic Resistance Partnership, 2011). One such constraint was the availability of procurement data from Intercontinental Marketing Services (IMS), currently IMS Health, which was only forthcoming from the private sector and focused exclusively on antibiotics. Infection represents the major burden of disease in South Africa, largely HIV and tuberculosis (TB), as well as high rates of community-and hospital-acquired infections  (South African Medical Research Council, 2016). As of 2016, an estimated 7. 03 million people were living with HIV in South Africa, giving an overall estimated HIV prevalence rate of 12. 7%  (Statistics South Africa, 2016).
Hence, the data from IMS in the 2011 analysis fell considerably short of the real picture of antimicrobial consumption in South Africa. The collection of antimicrobial consumption data in the public sector was in its infancy, requiring manual computation from aggregate data methods to elucidate consumption in healthcare facilities. The acquisition of accurate consumption data from communities was precluded by fluid catchment populations. A lack of reliable data hampers antimicrobial stewardship efforts and the evaluation of stewardship interventions  (Global Antibiotic Resistance Partnership, 2011; South African Antibiotic Stewardship Programme, 2017; National Department of Health, 2015a; National Department of Health, 2015b; Essack et al. , 2011). The last 6 years has seen major shifts in policy towards combating AMR in South Africa.
Quantifying population-level antimicrobial data in humans The data sources presented for antimicrobial procurement differ between the public and private health sectors. Variations in presentation of antimicrobial data are largely based on the differences in patient management systemsmostly manual/ paper-based in the public sector, compared to electronic recordkeeping for the private sector. Private sector data were obtained from IMS Health. Although IMS Health does not report in standard units of consumption, i. e. DDDs, their units do help to show trends over time. IMS Health collects data from a variety of sources of healthcare information, including sales, de-identified prescription data, medical claims, electronic medical records, and social media. Conversely, public sector data were obtained from contract data arising from tenders from wholesalers (an open Request for Proposal, RFP) where the NDOH solicits bids from suppliers and publishes this on the NDOH website (National Department of Health, 2017).
e. beds, admissions, and discharges. This type of surveillance data for antimicrobial usage is not routinely available in South Africa, although increasingly implemented as the AMR strategic framework is progressively rolled out. Antimicrobial data include three major categories of antimicrobials as per the World Health Organization (WHO) ATC classification system (World Health Organization, 2015c): Antibacterials for systemic use (ATC group J01) Antiretrovirals for systemic use (ATC group J05) Anti-tuberculosis agents for systemic use (ATC group J01GB and J04) The selection of antibiotics and antiretrovirals for this analysis was classified based on the WHO classification system and was cross-referenced to the NDOH tender data. The selection of anti-TB medicines was based on the South African treatment guidelines for TB (National Department of Health, 2014; National Department of Health, 2015c), which include those recommended for multidrugresistant (MDR) and extensively drug-resistant (XDR) TB (National Department of Health, 2015d).
, 2014). The use of high-dose isoniazid to overcome resistance caused by mutations in the inhA promoter region may also have played a minor role in the increasing consumption. In the public sector, the order quantities of ethionamide (J04AD), a drug commonly used for MDR-TB, increased by more than 30%. The IMS data reflect all clinical indications for a particular medicine and it was therefore not possible to determine which products were used specifically for TB treatment and which were used for other clinical indications. For example, linezolid, levofloxacin, and moxifloxacin are used in the treatment of drug-resistant TB and for other bacterial infections. However, the a Total QTY units: total number of antimicrobial units. The number of units (tablets, vials, capsules, etc. ) was determined by taking into account the pack size (units per pack) and the quantity of packs awarded in the contract to arrive at quantity per unit.
Limitations The analysis presented in this article is subject to limitations, one of the most important being the absence of true patient-level consumption data, measured using the DDD (introduced by the WHO). The DDD is generally preferred over standard units because it allows for a comparison between different antimicrobials and across different healthcare environments. However, antimicrobial data collected for a developing country such as South Africa, using IMS Health and tender data, could potentially be regarded as a good source of information when surveillance networks are either missing or weak. It might also be relevant to other low and middleincome countries that cannot at this stage provide DDD consumption data.
To make comparisons between geographical areas possible, the number of DDDs per 1000 inhabitants per day (DID) may be calculated. If this is not attainable in all hospitals across the country, procurement data as used in this article are a useful alternative to describe trends in antimicrobial use (not consumption). However, set standards for measurement should be used, e. g. CAGR, MAT units, and total number of antimicrobial units (QTY units), obtained from either IMS Health (where possible) or tender data. The collection and quantification of data on antibiotic consumption in animals and use in the environment. Two quality indicators for future implementation include tailoring empirical antibiotic therapy according to national antibiotic guidelines and assessing antibiotics prescribed against recommended national antibiotic guidelines.
Cochrane Database of Systematic Reviews Delivery arrangements include changes in who receives care and when, who provides care, the working conditions of those who provide care, coordination of care amongst di erent health care providers, where care is provided, the use of information and communication technology to deliver care, and quality and safety systems. How services are delivered can have impacts on the e ectiveness, e iciency and equity of health systems. This overview can help policymakers and other stakeholders to identify evidence-informed strategies to improve the delivery of services.
Search methods for identification of reviews We searched Health Systems Evidence in November 2010 using the following filters. • Health system topics = delivery arrangements. • Type of synthesis = systematic review or Cochrane Review. • Type of question = e ectiveness. • Publication date range = 2000 to 2010. We conducted subsequent searches using PDQ ('pretty darn quick')-Evidence, which was launched in 2012. We searched PDQ up to 17 December 2016, using the filter 'Systematic reviews' with no other restrictions. We updated that search, excluding records that were entered into PDQ-Evidence prior to the date of the last search. PDQ-Evidence is a database of evidence for decisions about health systems, which is derived from the Epistomonikos database of systematic reviews  (Rada 2013). It includes systematic reviews, overviews of reviews (including evidence-based policy briefs) and studies included in systematic reviews.
The following databases are included in Epistomonikos and PDQ-Evidence searches, with no language or publication status restrictions. We describe the detailed search strategies for Pubmed, Embase, LILACS, CINAHL and PsycINFO in Appendix 2. We screened all records in the other databases. PDQ sta and volunteers update these searches weekly for PubMed and monthly for the other databases, screening records continually and adding new reviews to the database daily. In addition, we screened all of the Cochrane E ective Practice and Organisation of Care (EPOC) Group systematic reviews in Archie (i. e. the Cochrane Collaboration's central server for managing documents) and the reference lists of relevant policy briefs and overviews of reviews.
Data synthesis We describe the methods used to prepare a SUPPORT Summary of each review in detail on the SUPPORT Summaries website. Briefly, for each included systematic review we prepared a table summarising what the review authors searched for and what they found, we prepared 'Summary of findings' tables for each main comparison, and we assessed the relevance of the findings for lowincome countries. The SUPPORT Summaries include key messages, important background information, a summary of the findings of the review, and structured assessments of the relevance of the review for low-income countries. The SUPPORT Summaries were reviewed by the lead author of each review, at least one content area expert, people with practical experience in lowincome settings, and a Cochrane EPOC Group editor (AO or SL).