dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation results from a comparison between Measurement Of Pollution In The Troposphere (MOPITT) V5 Near InfraRed (NIR) carbon monoxide (CO) total column measurements and Measurement of Ozone and Water Vapour on Airbus in-service Aircraft (MOZAIC)/In-Service Aircraft for a Global Observing System (IAGOS) aircraft measurements are presented. A good agreement is found between MOPITT and MOZAIC/IAGOS measurements, consistent with results from earlier studies using different validation data and despite large variability in MOPITT CO total columns along the spatial footprint of the MOZAIC/IAGOS measurements. Validation results improve when taking the large spatial footprint of the MOZAIC/IAGOS data into account. No statistically significant drift was detected in the validation results over the period 2002-2010 at global, continental and local (airport) scales.
The MOPITT instrument has been providing the vertical distribution of CO from measuring thermal In-fraRed (IR) radiation emitted by Earth and its atmosphere. MOPITT data have been widely used in many applications (e. g. Worden et al. , 2013, and  references therein; see also http://www2. acd. ucar. edu/mopitt/refereed-publications). Apart from measuring IR radiation around 4. 7 µm, the MOPITT instrument also measures in the 2. 3 µm wavelength range (also known as Near InfraRed or NIR). It was already known -based on measurements by the SCIAMACHY instrument -that useful information on CO could be derived from NIR wavelengths (e. g. Buchwitz et al. , 2004 Buchwitz et al. , , 2006 Buchwitz et al. , , 2007;; Dils et al. , 2006; Sussmann and Buchwitz, 2005; Warneke et al. , 2005; Gloudemans et al.
, 2006). However, due to instrumental issues affecting the data, a useful MO-PITT NIR CO product had been still lacking for some time. Only in 2009 was the NIR CO data product delivered by the MOPITT team  (Deeter et al. , 2009). In recent years, MOPITT CO measurements have also been incorporated into data assimilation systems (El  Amraoui et al. , 2010; Elguindi et al. , 2010; Huijnen et al. , 2012; Inness et al. , 2013; Stein et al. , 2014). The assimilation of CO assists in improving long-range transport of air pollution, for example from biomass burning, for improving understanding of stratosphere-troposphere exchange and its representations in numerical weather forecast models as well as for providing the best analysis of the 3-D global distribution of trace gas concentrations and assessing the quality of historical emission databases.
Since the release of MOPITT NIR CO, a first validation study using NOAA (National Oceanic and Atmospheric Administration) aircraft data has shown that the NIR measurements are in good agreement with observations  (Deeter et al. , 2013) , with an average bias of approximately 5-10 % and a 10-year drift of approximately 0. 15 % year -1 , both thus being insignificant. However, the NOAA measurements have a limited spatial coverage, only providing data over North America, and more validation of MOPITT NIR would be beneficial (opposite to the MOPITT TIR data, which has been much more extensively validated;  Deeter et al. , 2013, and references therein). An alternative validation data set is available via regular In-Service Aircraft for a Global Ob-serving System (IAGOS) measurements on board of several commercial aircraft.
IAGOS is a continuation of the Measurement of Ozone and Water Vapour on Airbus in-service Aircraft (MOZAIC) project that has been providing measurements made on board commercial aircraft since the late 1990s. This data set has already been used for the validation of SCIAMACHY CO measurements  (de Laat et al. , 2012). Results of validation of MOPITT NIR measurements are presented in this paper. Important aspects of the validation are (1) how our results compare with the validation results of  Deeter et al. (2013) , (2) can we find indications of spatial biases and (  3 ) is there any long-term drift in the MOPITT measurements, which is important for long-term monitoring of CO. Furthermore, although used for validation of SCIA-MACHY CO, there still is an important open question with regard to the use of MOZAIC/IAGOS data for satellite validation: how representative are the vertically integrated MOZAIC/IAGOS CO profile measurements of a true vertical CO column? Because the MOZAIC/IAGOS profile measurements used for validation of CO profiles and total columns are obtained during take-off and landing of aircraft, these profiles cover a certain horizontal distance (typically 200-400 km, the so-called "profile flight path").
Because of the lower data quality of SCIAMACHY CO, considerable spatio-temporal averaging of SCIAMACHY was required to reduce SCIAMACHY CO measurement errors to acceptable levels in the study by  de Laat et al. (2012). Hence, the question related to the representativity of the MOZAIC/IAGOS CO profiles did not have to be addressed in that study. However, given that MOPITT NIR measurements are of higher quality than those of SCIAMACHYthus not requiring extensive spatio-temporal averaging -and that the size of the MOPITT pixel is considerably smaller (approximately 25 km) than the typical profile flight path distance, the question should be asked whether -and how -a single MOPITT measurement should be compared with the MOZAIC/IAGOS profiles. In  Deeter et al. (2013)  it is assumed that the in situ profile measurements used for validation are representative for a certain horizontal distance, which is not further discussed.
MOZAIC/IAGOS MOZAIC was initiated in 1993 by European scientists, aircraft manufacturers and airline companies to better understand the natural variability of the chemical composition of the atmosphere and how it is changing under the influence of human activity, with a particular focus on the effects of aircraft. IAGOS is both a continuation and an extension of the MOZAIC research infrastructure. MOZAIC/IAGOS consists of automatic and regular measurements of reactive gases by several long-distance passenger airliners. A large database of measurements (about 30 000 flights since 1994) allows studies of chemical and physical processes in the atmosphere and also validation of global chemistry transport models and satellite retrievals. MOZAIC/IAGOS data provide detailed climatologies of trace gases at 9-12 km and also provide frequent vertical profiles close to a large number of airports.
Carbon monoxide is measured using a Gas Filter Correlation principle, in which infrared absorption of the 4. 67 µm fundamental vibration-rotation band of CO is used. Different filters are used to modify the infrared radiation between an infrared source and a detector. By comparing the measurements for different filters information about CO is obtained -see  Nédélec et al. (2003)  for a detailed explanation of the measurement principle and instrument. The vertical profile measurements of CO will be used to calculate CO total columns. Evaluation of MOZAIC/IAGOS CO measurements indicates a precision of ±5 %, which is sufficiently accurate for validation purposes  (Nédélec et al. , 2003). The vertical resolution of raw measurements is 150 m (≈ 2 % of the pressure altitude). The MOZAIC/IAGOS database provides automatically selected profiles, based on the distance to the starting point, the pressure difference between the bottom and top of the profile and the fraction of measurements for which there is a consistent change in pressure.
35 NA oil immersion objective lens. DAPI, Alexa Fluor 488, and Alexa Fluor 568 fluorescence was sequentially excited using lasers with wavelengths of 405 for DAPI, 488 (Leica) or 473 (Olympus) for Alexa Fluor 488, and 543 nm for Alexa Fluor 568. Z-series from the top to the bottom of fibers were sequentially collected for each channel with a step of 0. 9-1 µm between each frame. Imaging was performed at room temperature using Type F immersion oil (Leica). Images (1024 × 1024 pixels) were saved as TIFF files in Olympus FV-1000 software and Leica confocal software, and input levels were adjusted in Adobe Photoshop. Image quantification was performed using ImageJ (National Institutes of Health).
Fig. S4  compares the effect of siRNA-mediated depletion of CHC, AP1, AP2, Hip1R, and DNM2 on actin distribution, and characterizes localization of DNM2 in control or CHC-and AP2-depleted myotubes. Fig. S5  shows that in vivo CHC depletion induces a strong inflammatory response but has no effect of -dystroglycan and dystrophin distribution. Online supplemental material is available at http://www. jcb. org/cgi/ content/full/jcb. 201309096/DC1. Additional data are available in the JCB DataViewer at http://dx. doi. org/10. 1083/jcb. 201309096. dv.
Histomorphological and ultrastructural analyses Samples were frozen in liquid nitrogen-cooled isopentane. Transverse sections of TA muscle (8-µm thick) were stained with H&E by standard methods. Light microscopy was performed using an upright microscope (DMR; Leica) and a 40×/NA 0. 85 HCX Plan Apo objective (Leica). Images were captured using a monochrome camera (DS-Ri1; Nikon) and NIS-Elements BR software (Nikon). For all imaging, exposure settings were identical between compared samples and viewed at room temperature. For morphological electron microscopy, muscles were fixed by intraaortic perfusion with 2% paraformaldehyde and 2% glutaraldehyde in 0. 1 M phosphate buffer (pH 7. 4). TA samples were post-fixed with 2% OsO 4 , in 0. 1 M phosphate buffer (pH 7. 4) for 1 h, then dehydrated in a graded series of acetone including a 1% uranyl acetate staining step in 70% acetone and finally embedded in epon resin.
Similarly, the GEBA project will aim to fully cover a defined portion of the extant diversity by targeting the approximately 11,000 type strains that represent the complete current list of Bacteria and Archaea with validly published names. Given that the richest metadata is associated with the type strains, a focused, in-depth survey such as this will offer significant benefits by providing genomic data to complement the wealth of information already acquired for these organisms. The metadata, such as the physiology of the organism, will reciprocate by validating the genome-based metabolic reconstructions. To keep track of the constantly increasing number of type strains for Bacteria and Archaea and the list of those that have a genome sequencing project, the Microbial Earth Project (MEP) was recently launched.
MEP (http://www. microbialearth. org/) is a public resource providing frequently updated information on the status of sequencing coverage of the type strains. The resource, maintained at the DOE Joint Genome Institute, provides data based on the type-strain information available from N4L (http://namesforlife. com/) and genome projects available from GenomesonLine Database (http://www. genomesonline. org/) [13]. MEP displays the list of type strains with and without genome sequencing projects as a list or as an interactive map (Figure  2 ). novelty does in fact exist in nature. Gaining a deeper understanding of that genetic novelty demands the systematic genomic characterization of ultimately all bacterial and archaeal species across the ToL. Toward that end, the CyanoGEBA project took a phylum-level approach to sequence 54 phylogenetically and phenotypically diverse strains of cyanobacteria  [23].
More recently, the aptly named GEBA-Microbial Dark Matter (GEBA-MDM) (http://genome. jgi. doe. gov/MDM/ MDM. home. html) explored the diversity of the vast universe of uncultured microbes by using high-throughput single-cell sequencing to generate a reference dataset of 201 single-cell genomes from candidate phyla  [24]. At the same time, these  initiatives have also stimulated the quest for novel organisms in these previously uncultivated groups, further increasing the number of strains available for study. The National Institutes of Health (NIH)-funded HMP project broke new ground in microbial genomics by virtue of the unprecedented volume of sequence data generated by sequencing approximately 1,000 microbial genomes  [25]. Of even greater consequence is the distribution of the work across several large-scale sequencing facilities (i.
These serve as international standards in the way organisms are characterized at this level. The requirement that type strains be deposited in two collections in two different countries also ensures long-term availability of this biological reference material, as well as introducing a verification step during the process of accession. The synergy of these three elements will provide an unprecedented set of standards that will serve to significantly improve the quality of the data obtained. Such transformation of the existing research infrastructure into a globally distributed and digitally integrated network for microbial research, including computational science and automated knowledge discovery, would require overcoming obsolete and science-hostile database protection laws as well as highly restrictive licensing practices of biological materials  [37].
The design over-sampled younger birth cohorts. The response rate was 50. 1%. Weights adjust for the sampling design and the national distribution of age, gender, federal state, and size of the community. In the United States, the survey used a stratified multistage procedure that oversamples youth and young adults. The response rate was 74. 6%, and weights account for selection probability and nonresponse. Due to confidentiality concerns, the public use NSDUH data for the USA reports age in the following categories rather than the exact age:  18-20, 21-25, 26-34, 35-44, 45-49, and 50-64  years. These categories correspond to three broad age groups of 18-34, 35-49, and 50-64 years used in the analysis, but were recoded within the age groups to the midpoints of  19, 23, 30, 40, 47, and 57.
Limitations Limitations of the findings relate to the cross-sectional data. First, the self-reported retrospective measures of age of experimentation may be biased by recall error. Although retrospective measures are generally found to be reliable for tobacco  (Kenkel, Lillard, & Mathios, 2003)  and cannabis  (Johnson & Mott, 2001; Labouvie, Bates, & Pandina, 1997) , differences in recall by age could affect the cohort comparisons and more specifically the least educated groups and ethnic minorities  (Johnson & Mott, 2001). Second, the cross-sectional nature of the data makes it hard to disentangle the causal relationships between education and experimentation. Many youths experiment with cannabis before completing their education, requiring longitudinal data to draw causal conclusions about the relationship.
Single genome amplification of P. vivax sequences To examine the evolutionary relationships of ape and human parasites, we amplified the complete P. vivax mitochondrial genome in three partially overlapping fragments (Supplementary Fig. 1a ). This was done using single genome amplification (SGA) followed by direct amplicon sequencing, which eliminates Taq polymerase-induced recombination and nucleotide substitution errors, and provides a proportional representation of the parasite sequences that are present in vivo  10, 20. Alignment of these sequences revealed two single nucleotide variants (SNVs) that distinguished all ape from all human parasites (Supplementary Fig. 1b ; a third previously proposed SNV  13  was polymorphic among the ape samples in our dataset). We thus designed PCR primers to amplify a fragment (fragment D) that included both SNVs on the same SGA amplicon (Supplementary Fig.
1 ). Although only a subset of P. vivax positive faecal samples yielded this larger mtDNA fragment, we were able to generate fragment D sequences from 22 chimpanzees and 9 gorillas, 17 of which were sampled in the wild (Supplementary Table  3 ). Since most database sequences are derived by conventional PCR approaches, we also used SGA to amplify fragment D sequences from the blood of P. vivax infected humans, to produce Taq polymerase error free sequences  20. These samples included 94 international travellers, who had acquired P. vivax while visiting malaria endemic areas, as well as 25 P. vivax infected individuals from China, Thailand, Myanmar and India who sought treatment for clinical malaria (Supplementary Table  5 ), and thus provide a globally representative sample of human P. vivax infections.
Inclusion of previously published non-SGA sequences confirmed this topology, although many of the database sequences exhibited long branches suggestive of PCR errors (Supplementary Fig. 4a ). Interestingly, the one P. vivax sequence recently identified in a European traveller who became infected after working in a central African forest  13  did not fall within the human P. vivax lineage, but clustered with parasites obtained from wild-living chimpanzees and gorillas (Supplementary Fig. 4a ). This confirms the suspicion that this traveller acquired his infection by cross-species transmission from a wild ape. To examine the robustness of these phylogenetic relationships, we selected additional genomic regions that had previously been used for evolutionary studies of P. vivax  7,26. These included portions of the apicoplast caseinolytic protease C (clpC) gene as well as the nuclear genes encoding lactate dehydrogenase (ldh), adenylosuccinate lyase (asl), cell division cycle 2-related kinase (crk2) and β-tubulin (β-tub).
Ape P. vivax infection rates For sites where the number of sampled chimpanzees was known (Supplementary Table  1 ), P. vivax infection rates were estimated based on the proportion of positive individuals (but correcting for test sensitivity; see below), with 95% confidence limits determined assuming binomial sampling. For field sites where the number of sampled individuals was not known, infection rates were estimated based on the number of faecal samples, but correcting for specimen degradation, oversampling and the sensitivity of the diagnostic PCR test. The latter was estimated by determining the proportion of PCR positive specimens from P. vivax-infected apes that were sampled more than once on the same day, as previously described  10. Including data from 14 such apes (6 central chimpanzees, 5 eastern chimpanzees and 3 western gorillas), we estimated the sensitivity to be 32% (17 of 53 samples were positive; confidence limits were determined assuming binomial sampling).
Human sequences are depicted by haplotype (rectangles) and labeled according to their geographic origin in Oceania (light grey), Africa (white), South and Central America (black), South and South East Asia (striped) and the Middle East (dark grey). Haplotypes that include more than one sequence are indicated, with the numbers of sequences listed to the right. A second lineage of related parasite sequences from chimpanzee samples DGptt540 and BQptt392 likely represents a new Plasmodium species. The tree was inferred using maximum likelihood methods  56. Numbers above and below nodes indicate bootstrap values (≥ 70%) and Bayesian posterior probabilities (≥ 0. 95), respectively (the scale bar represents 5 nucleotide substitutions). 6 7 8 for GenBank accession numbers). Newly derived ape P. vivax sequences are labeled and color-coded as in Fig.
# All human parasite sequences were derived using SGA methods from a global sampling of P. vivax strains (Supplementary Table  S4 ), except for five completely sequenced reference genomes from the database (Salvador I, Brazil I, Mauritania I, North Korea, India VII). Ape P. vivax sequences were also derived by SGA from faecal and blood samples of wild-living and sanctuary apes (Supplementary Table  S3 ). † Nucleotide diversity (π). Human and ape sequences were compared over the same length of sequence. ‡ Diversity (or distance) value expressed relative to that for mtDNA.
Acknowledgments We thank the staff of project  PRESICA , the  World Wildlife Fund for Nature (WWF/DRC) , the  Institut National de Recherches Biomédicales (INRB, Kinshasa, DRC) ,  Global Viral Cameroon , the  Bonobo Conservation Initiative  and  Vie Sauvage , as well as  Didier Mazongo ,  Octavie Lunguya ,  Muriel Aloni  and  Valentin Mbenz  for field work in Cameroon and the  DRC ; field assistants in  Gombe National Park and Ugalla  for sample collection in Tanzania, the staff of the  Sanaga Yong, Limbe and Ape Action Africa/Mfou National Park Wildlife Rescue Centres  for collecting blood samples from captive apes; the  Cameroonian Ministries of Health, Forestry and Wildlife, and Scientific Research and Innovation for permission  to collect samples in Cameroon; the Water and  Forest Ministry  for permission to collect samples in the Central African Republic; the  Ministry of Forest Economy  and  Sustainable Development  for permission to collect samples in the  Republic of Congo ; the  Ministry of Scientific Research and Technology , the  Department of Ecology and Management of Plant and Animal Resources of the University of Kisangani , the  Ministries of Health and Environment  and the  National Ethics Committee  for permission to collect samples in the DRC; the  Tanzania Commission for Science and Technology  and the  Tanzania Wildlife Research Institute  for permission to conduct research in  Gombe National Park and Ugalla.
Photometry The CoRoT data were reduced using a standard procedure  (Samadi et al. 2007; Auvergne et al. 2009 ) and delivered in the form of a light curve. The light curve was processed using a sigma-clipping filter that removed outliers (mostly related to South Atlantic Anomaly crossings) and hot pixels, and corrected for the effects associated with entrance into and exit from Earth eclipses. The light curve was also rebinned to 512 s and corresponds to the integrated flux in the CoRoT mask. The OPD photometric observations were carried out on five nights between March 19 and 27, 2008. The 60 cm telescope was equipped with the CCD camera #106 (SITe SI003AB) of 1024×1024 pixels and Johnson/Cousins BV(RI) c filters. Integration times ranged from 200 s to 400 s, depending on the filter. We processed raw images with the usual techniques within the IRAF environment, which included bias subtraction and flat-field calibration.
Spectroscopy The spectroscopic observations were collected on eight nights between March 12 and 28, 2008. We obtained ten highresolution spectra at the 1. 93 m OHP telescope with the SOPHIE dual fiber échelle spectrograph  (Perruchot et al. 2008) , which yields 41 orders covering the 3872 Å to 6943 Å domain at a mean spectral resolution of λ/∆λ ≃ 75 000 (high-resolution mode), and simultaneously records the object and the neighboring sky. All spectra have the same exposure time of 3600 s. The data were automatically reduced via a standard procedure, adapted from the software used with the HARPS spectrograph designed at the Geneva Observatory. The reduction procedure includes bias subtraction, optimum extraction of the orders, removal of cosmic rays, flat-fielding achieved through a tungsten lamp exposure, wavelength calibration with a thorium lamp exposure, cross-correlation with an appropriate numerical mask, and reconnection of spectral orders with barycentric correction.
However, the period of 3 days has to be confirmed with a A&A proofs: manuscript no. v354mon_fin richer dataset because we have only a few points to investigate additional periods. The Hα profiles are shown as a function of rotational phase with photometric period (Fig. 6 ) to identify similarities and differences between the spectra that are close in phase position. The top plot helps to locate the spectroscopic observations in the light curve and to identify a correlation between them. The profile of March 28 resembles the profile of March 18, with a slight difference in the redshifted absorption component. They are close to each other in phase, 0. 11 and 0. 20, and at similar points of the light-curve profile, in which there is a flux decrease before a small local peak. Similarly, the spectra of days 12, 28, 23, and 18 are similar, with an asymmetric profile and a more intense redshifted side.