dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter eliminates an artificial CA trend of about 4 % over the observation period of 2004 to 2016: The magnitude of cloud amount and effective low-level cloud amount deseasonalized variations lies within 1 % and that of effective highlevel cloud amount lies within 0. 5 % over this period. Ancillary data from the meteorological reanalyses ERA-Interim have been interpolated to the observation times of AIRS and IASI. Additional ancillary data, established from NASA AIRS retrievals, permitted us to iteratively make adjustments to both sets of ancillary data for optimal results in cloud properties and to estimate uncertainties in cloud amounts. Since the cloud detection depends on the coherence of spectral cloud emissivity, the surface temperature influences only slightly the cloud amount (in particular the one of low-level clouds).
AIRS total cloud amount is 70 % (67 %), high-level cloud amount is 27 % (27 %) and low-level cloud amount is 29 % (27 %), using ERA-Interim (AIRS-NASA) ancillary data. This corresponds to uncertainty estimates of 5 % and 10 % on global averages of CA and CAL, respectively. Uncertainties are larger over land and ice or snow than over ocean, in particular because T surf of ERA-Interim is underestimated in the afternoon and T surf of AIRS-NASA is underestimated during night due to cloud contamination. In the future, the CIRS cloud retrieval might use ancillary data from the new ECMWF meteorological reanalysis ERA5, with a better temporal and spatial resolution. The 5 % annual mean excess in UT cloud amount in the NH compared to the SH has a pronounced seasonal cycle with a maximum of 25 % in boreal summer have been related to the characteristics of the ITCZ.
The annual mean ITCZ peak latitude lies about 5 • N with a maximum of 10 • N in boreal summer. At that time the ITCZ width is also narrower and the peak slightly larger. This suggests that the NH-SH excess in CAH is mostly determined by the position of the ITCZ. To illustrate the added value of the CIRS cloud data for climate studies, we have finally presented geographical patterns in changes of amount of high opaque, cirrus and thin cirrus with respect to global mean T surf changes. These are in agreement with earlier studies, while an examination of changes in tropical high cirrus and thin cirrus amounts relative to total cloud amount revealed that these are more frequent among all clouds when T surf gets warmer. Even though the change in mean T surf is mostly linked to ENSO variability over the studied period and it is still uncertain how to relate these to long-term patterns due to anthropogenic climate warming, the large difference in geographical patterns in changes of amounts of high opaque clouds and thin cirrus, relative to total cloud amount, indicates that their response to climate change may be different.
This might then have consequences on the atmospheric circulation. To get a better understanding on the underlying feedback processes, one has to consider the heating rates of these UT cloud systems and link them to the dynamics. Therefore the AIRS-CIRS and IASI-CIRS cloud data have been further used to build UT cloud systems (based on p cld ) and then to distinguish convective cores, cirrus anvil and thin cirrus according to ε cld  (Protopapadaki et al. , 2017). These data are being further exploited, together with other data and modelling on different scales, within the framework of the GEWEX Process Evaluation Study on Upper Tropospheric Clouds and Convection (UTCC PROES;  Stubenrauch and Stephens, 2017)  to advance our understanding on UT cloud feedbacks. The AIRS-CIRS and IASI-CIRS cloud climatologies will be made available at the French data centre AERIS, which also will continue their production.
Acknowledgements. This work has been financially supported by  CNRS , by the  ESA   Cloud_cci  project (contract no. 4000109870/13/I-NB ) and by  CNES. The authors thank the members of the  IASI, AIRS, CALIPSO and CloudSat science teams  for their efforts and cooperation in providing the data as well as the engineers and space agencies who control the data quality. We thank the  Aeris data infrastructure  for providing access to the data used in this study and for continuing the data production. We also thank  Filipe Aires  for providing the surface emissivity climatology built from IASI. In addition, we thank two anonymous referees for their thoughtful comments, which improved the quality of the manuscript. Edited by:  Stefan Buehler Reviewed  by: two anonymous referees.
Data availability. AIRS L1 data are available at https://mirador. gsfc. nasa. gov/ (AIRS Science Team/Chahine, 2007). The NASA Science Team L2 standard products (version 6; AIRS Science Team/Texeira, 2013) are available at https://mirador. gsfc. nasa. gov/. IASI L1 data are available at the French Data Centre AERIS. The ARSA database can be obtained at http://ara. abct. lmd. polytechnique. fr/index. php?page=arsa. The operational version of the 4A radiative transfer model  (Scott and Chédin, 1981 ) is available at http://4aop. noveltis. com/. The cloud climatologies of the GEWEX Cloud Assessment database are available at http://climserv. ipsl. polytechnique. fr/gewexca/. The AIRS-CIRS and IASI-CIRS cloud climatologies will be made available by the French Data Centre AERIS. The Supplement related to this article is available online at https://doi.
Background. Antibiotics are life-saving drugs but severely affect the gut microbiome with short term consequences including diarrhoea, Clostridium difficile infections and selection of antibiotic-resistant bacteria. Long-term links to allergy and obesity are also suggested. We devised a product, DAV132, and previously showed its ability to deliver a powerful adsorbent, activated charcoal, in the late ileum of human volunteers. Methods. We performed a randomized controlled trial (ClinicalTrials. gov NCT02176005) in 28 human volunteers treated with a 5-day clinical regimen of the fluoroquinolone antibiotic moxifloxacin in two parallel groups, with or without DAV132 co-administration. Two control goups of 8 volunteers each receiving DAV132 alone, or a non-active substitute, were added. Results.
Volunteers were randomised (Supplementary Material) to receive either MXF alone (n=14), MXF+DAV132 (n=14), DAV132 alone (n=8), or negative control (CTL) (n=8) (Figure  1 ). The study was carried out after authorisations from French Health Authorities and the Independent Ethics Committee ("Comité de Protection des Personnes Ile-de-France IV", Paris, France) had been obtained (January and February 2014, respectively). It was declared in ClinicalTrials. gov (identifier NCT02176005) and the French (ID-RCB number 2013-A01504-41) registers of clinical trials. A study-specific Scientific Committee (A. A. , V. A. , A. Duc. , A. Duf. , X. D. , C. F. , J. G. , F. M. , and M. V. ) was set up to ensure scientific integrity.
Statistical methods The primary objective was to evaluate the influence of DAV132 on free fecal MXF concentrations between D1 and D16 by comparing individuals in the MXF and MXF+DAV132 treated groups. The primary endpoint was the AUCD1-D16 of free fecal MXF concentrations. The study sample size was calculated at the time of study design. Assuming an AUCD1-D16 variability similar to that of the AUCD1-D14 previously measured from individual data  [23] , a sample size of 11 subjects in each MXF treated group (MXF and MXF+DAV132) would allow to detect a 2-fold change between these groups (90% power, two-sided test, type I error 0. 05). For security, we included 14 subjects in each of these groups. Additionally, we randomized two groups of 8 volunteers without MXF, but with DAV132 or CTL to study secondary objectives (DAV132 safety and intestinal microbiota composition).
As preplanned for the primary objective, comparison of log(AUCD1-D16) of free MXF fecal concentrations, in groups treated with MXF+DAV132 and MXF alone, was performed using a general linear model. AUCD1-D16 were calculated by the trapezoidal method using the actual time of stool emission and the results were expressed as geometric means of AUCD1-D16 and coefficient of variation. For MXF plasma concentrations, comparisons of log(AUC0-24h) and log(Cmax), in groups treated with MXF+DAV132 and MXF alone, were perfomed using a general linear model. AUC0-24h was calculated by the trapezoidal method. Statistical analysis of clinical and pharmacokinetic data were performed using the SAS 9. 4 software (SAS Institute, Cary, NC, USA).
Metagenomic methods and analysis Analysis of metagenomic data was exploratory and not prespecified. Essentially, total fecal DNA was extracted as described  [24, 25]  and sequenced using SOLiD 5500 Wildfire (Life Technologies) resulting in 67. 2±19. 8M (mean ±SD) sequences of 35-base-long single-end reads. High-quality reads were generated with quality score cut-off >20. Reads with a positive match with human, plant, cow or SOLiD adapter sequences were removed. Filtered highquality reads were mapped to the MetaHIT 3. 9M gene catalog  [26]  using METEOR software  [27]. The read alignments were performed in colorspace with Bowtie software (version 1. 1. 0) [28]  with options: -v 3 (maximum number of mismatch) and -k 10000 (maximum number of alignment per reads). The raw SOLiD read data were deposited in the EBI European Nucleotide Archive under accession number PRJEB12391.
Acknowledgements. We thank  M. Ghidi  and  M. N. Bouverne  ( Da Volterra ), as well as  C. Féger  ( Emibiothech ) for coordination and management of the clinical study,  E. Arcaraz ,  E. Desmartin ,  A. Toutin ,  T. Mezzasalma  and  C. Toutin  ( Amatsi Group ) for the development and validation of bioanalytical methods, and for performing the fecal and plasma pharmacokinetic analyses,  I. Wieder  and  C. Bourseau  for performing the phenotypic microbiological analyses,  Pr A. Dufour  for biopharmacological analysis,  P. Clerson  ( Orgamétrie ) for statistical analysis of the clinical data,  N. Galleron  and  B. Quinquis  ( Metagenopolis ) for generating sequencing data and  P. Leonard  ( Metagenopolis ) for informatics support.
The randomised clinical trial was sponsored by  Da Volterra (Paris)  and funded in part by the  European Union Seventh Framework Programme  ( FP7-HEALTH-2011single-stage ) under grant agreement n°  282004 , EvoTAR. Additional funding was from the  Metagenopolis  grant  ANR-11-DPBS-0001 , and from  BPIFrance  under the NOSOBIO collaborative programme. ID week, October 2015,.
Funding: The randomised clinical trial was sponsored by Da Volterra (Paris) and funded in part by the European Union Seventh Framework Programme (FP7-HEALTH-2011-singlestage) under grant agreement n° 282004, EvoTAR. Additional funding was from the Metagenopolis grant ANR-11-DPBS-0001, and from BPIFrance under the NOSOBIO collaborative programme.
e. , host satellite cells) were 42 ± 3 and 38 ± 11 in control and obestatin-treated muscles, respectively. To further determine whether donor-derived Pax7 + cells were in the satellite cell position, we performed triple labeling of human Lamin A/C, Pax7, and laminin. Donor-derived Pax7 + cells were located both in the satellite cell position (underneath the basal lamina of myofibers; Figure  5 ) and outside the basal lamina. There were 1. 0 ± 0. 3 in the control group and 14. 8 ± 1. 2 in the obestatin group human Pax7 + /Lamin A/C + cells per 100 fibers located inside the basal lamina. These data indicate that 21. 4% ± 1. 7% of Pax7 + donor cells are in the position of satellite cells, the remainder being undifferentiated interstitial myoblasts located outside the basal lamina. We further quantified the number of human cells that had not been incorporated into the muscle fibers but remained located in an interstitial position outside the basal lamina of the fibers.
ACKNOWLEDGMENTS This work was supported by grants from the  Instituto de Salud Carlos III , the  European Regional Development Fund (ISCIII and Fondos FEDER; MINECO, Spain ; grant  PI15/01537 ), and the  Association Française contre les Myopathies (AFM-Téléthon ). The work of J. P. C. and Y. P. is funded by the  SERGAS  through a research staff stabilization contract. An  EMBO  Fellowship supported the work of I. S. -Z. Marta Picado Barreiro  from  IDIS (Santiago de Compostela, Spain)  is greatly acknowledged for assistance with microscope experiments, as well as the platform for immortalization of human cells from the  Myology Institute (Paris, France ).
Data originating from the deep-sea observatories MoMAR, now EMSO-Azores, on the slow-spreading Mid-Atlantic Ridge (MAR) and NEPTUNE, now called Ocean Networks Canada (ONC), on the faster-spreading Juan de Fuca Ridge (Northeast Pacific, NEP), featuring the same time span and resolution, have been analysed. The two oceans are characterised by different vent fauna, with a visual predominance of Bathymodiolus mussels in the shallower (< 2300 m) Atlantic and Ridgeia tubeworms in the NEP, but they do share higher taxonomic groups. Following key questions are put forward: (i) Are there rhythms discernible in both hydrothermal settings? (ii) Is there a lag/time difference in community dynamics and environmental variables observed between the two oceans? (iii) Which environmental variables influence community dynamics? And, finally, (iv) do the shared taxa occupy similar microhabitats and possible niches in each ocean? Answering these questions will provide new insights in understanding local vent community dynamics and will enlighten us on similarities and differences between oceanic ridges and oceans.
Data collection and recordings Data collected consisted of video imagery recordings, temperature measurements, iron and oxygen concentrations, and turbidity measurements (the latter for MAR only) (Table  1 ;  Cannat et al. , 2015  for MAR), which were recorded jointly for the period 7-30 October 2011. Differences in recording resolutions were mainly due to different observatory set-ups and more particularly due to the cabled or wireless network characteristics and their inherent energy limitations (continuous power vs. battery dependence). Lights were powered on with the same frequency as the imagery recording (every 6 h) at MAR, contrastingly to NEP where lights were on continuously during the period analysed (23 days). At NEP, TEMPO-mini was equipped with a thermistor array in which two probes (T602 and T603) were deployed on an assemblage most similar to the one filmed (see  Cuvelier et al.
, 2014). Therefore, only those two probes were used in the comparison to the MAR temperature data, which were recorded directly on the filmed assemblage. Iron (from here on referred to as Fe) concentrations were measured on top of the assemblage and within the field of view (FOV) at MAR  (Laës-Huon et al. , 2015; Sarradin et al. , 2015)  and below the FOV at NEP. An in situ calibration was performed at NEP, analysing two Fe standards a day of 20 and 60 µmol L -1 ; no such calibration took place at MAR. At NEP, sampling frequency was changed from twice (30 September-18 October 2011) to once a day (19 October 2011-31 January 2012) due to rapidly decreasing reagents. Fe concentrations were analysed over a longer term and used to explore the differences between the observatory settings. Closer examination of data recorded by the optode revealed some inconsistencies between the measured temperature and the O 2 concentrations.
Imagery analysis The variations occurring in the faunal assemblages in the two hydrothermal vent settings were analysed for 23 days. For this period, a screen still was taken every 6 h at 00:00, 06:00, 12:00 and 18:00 UTC. For each site, these screen stills were used as a template in Photoshop© to map and count faunal abundances. Faunal densities were quantified at a 6 h frequency, while the microbial coverage was assessed every 12 h. To pursue the latter, the microbial cover was marked in white and the rest of the image rendered in black. Using the "magic wand tool" of the ImageJ image analysis software  (Rasband, 2012) , the surface covered by microorganisms was quantified and converted to percentages. Due to gaps in the data recordings different numbers of images were analysed for MAR and NEP (Table  2 ).
The MRT partition was then subjected to a search for indicator taxa (IndVal analysis, Dufrêne and Legendre, 1997; multipatt function in R package Indicspecies, De Caceres and  Legendre, 2009). The IndVal index combined a measure of taxon specificity with a measure of fidelity to a group and thus revealed which taxon was significantly more or less abundant in the group before than after the split. Its significance was assessed a posteriori through a permutation test  (Borcard et al. , 2011). The observed temporally consistent groups were delineated by colour codes within a redundancy analysis (RDA) ordination plot; RDAs were carried out on the Hellinger-transformed faunal densities and environmental variables to visualise the possible influence of the environmental constraints on the temporal groups found in the faunal density matrices.
Long-term temporal analyses For the time period 29 September 2011 to 19 June 2012, environmental data spanning 9 months of temperature and Fe were available for compared analyses, while turbidity was only available for the MAR. The oxygen time series revealed the issues explained previously (see Sect. 2. 2) and were not subject to temporal analyses but the differences in concentrations measured between the two observatory locations were addressed. Faunal densities could not be assessed on the longer term due to the lack of regular imagery recordings for MAR and NEP as well as changes in zoom and subsequently image quality for the NEP. Long-term time series analyses in the form of WR periodograms were carried out on the hourly data for temperature and turbidity and daily/12 h (NEP/MAR respectively) frequency for Fe to allow comparison between MAR and NEP.
Their distribution was rather heterogeneous but mostly associated with the mussel beds and shrimp presence. A Cataetyx laticeps fish was observed five times within the analysed time seriesmostly in the background and not interacting actively with the other organisms. Its presence was only discernible based on the video footage (and not on the screen stills). The small patch of anemones observed below the probe featured 33 individuals. No changes were documented over time for this taxon. For NEP, 88 images were analysed from the TEMPOmini module; there were five gaps in the imagery dataset (Table  2 ). Ridgeia piscesae tubeworms were the most abundant taxon assessed on imagery, adding up to several hundred visible (outside their tubes) individuals and with their tubes providing a secondary surface for the other organisms to occupy.
For Fe, a negative, almost significant trend (p > 0. 05) was observed for 6 months of data (30 September 2011-29 March 2012) from the MAR featuring two Fe measurements a day (at 00:00 and 12:00 UTC) (Fig. 9 ). Minimum and maximum concentrations were 0. 25 and 2. 61 µmol L -1 , respectively, with a mean of 0. 98 ± 0. 43 µmol L -1 , which was lower than the averaged concentrations of the other deployment years (with 2. 12 ± 2. 66 µmol L -1 averaged over  2006, 2010-2011, 2012-2013 and 2013-2014). Periodogram analyses revealed a peak at 108 h (4. 5 days) and a more pronounced one at 180 h (7. 5 days), but neither of these were significant. For the NEP, a time series of one Fe measurement a day (at 06:00 UTC), consisting out of four sampling cycles spanning > 4 months, was analysed (20 October 2011-26 March 2012).
Acknowledgements. We thank the captains and crews of the R/V Pourquoi pas? and the  R/V Thomas G. Thompson  for their steadfast collaboration in the success of the MoMARSAT and  NEPTUNE (Ocean Networks Canada ) cruises. We are grateful to the  Victor6000  and ROPOS ROV pilots for their patience and constant support. The authors would like to thank the LEP technical team for its valuable help both at sea and in the lab, the TEMPO and TEMPO-mini engineers and technicians who developed and maintained the modules and the  Ocean Networks Canada  scientists and engineers, whose engagement and professionalism made this study possible. Daphne Cuvelier  is supported by a post-doctoral scholarship ( SFRH/BPD/110278/2015 ) from  FCT  and this study had also the support of  Fundação para a Ciência e Tecnologia (FCT) , through the strategic project  UID/MAR/04292/2013  granted to MARE.
25, 26 As alternative financial sources for research on neglected diseases are limited and highly divided among individual researchers, open science and data sharing have received a growing interest as means of leveraging and combining the available resources to accelerate drug discovery efforts. 27 This community-based concept for a new drug discovery model led to the London Declaration on Neglected Tropical Diseases in 2012 for the control, elimination, or eradication of neglected tropical diseases (http://unitingtocombatntds. org). Many proteins are potential targets for drug interventions that control human diseases. The most recent number of drug targets was estimated to be in the hundreds, based on an analysis made before 2007. 28 However, the number of druggable proteins is substantially greater according to the DrugBank database Web site (http://www.
drugbank. ca/). The current version of this database (5. 0) contains 8206 drug entries that are linked to 4333 nonredundant (nr) protein sequences (ie, drug target/enzyme/transporter/carrier). The discovery of drugs based on in silico docking of the inhibitors in models of the 3-dimensional (3D) structures of the protein targets has proven to be of great value in the processes of rational drug design as well as drug screening and has effectively contributed to conserving resources in the area of drug discovery. The use of this information may allow a substantial savings in the cost and time involved in the process of drug release. 29 nfortunately, all druggable protein targets are not necessarily suitable for a therapeutic design because of several constraints, including noxious side effects to patients.
fiocruz. br), uses the protein sequences stored in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www. genome. jp/kegg/). AnEnPi implements algorithms that can perform the following tasks: (1) the clustering of protein sequences by homology using Basic Local Alignment Search Tool (BLAST); (2) the classification of sequences in different homologous clusters as analogous enzymes when they have the same enzymatic function, ie, Enzyme Commission (EC) number 33 ; (3) the detection of specific enzymes 34 ; (4) the annotation of protein functions using BLAST or HMMER; and (5) the generation of metabolic maps using the tools provided by KEGG (http://www. genome. jp/kegg/tool/map_pathway1. html). ] [37]  Any error associated with a classification process can be qualified as follows: (1) false positives, which occur when a Leishmania major enzyme is classified as specific or analogous while at least 1 gene for a homologous enzyme exists in the Homo sapiens genome; (2) false negatives, which occur when the classification of a homologous pair is erroneous, and it should be classified as a specific or an analogous enzyme because of an incorrect annotation; (3) true positives, which occur when a pair of specific or analogous genes is correctly classified; and (4) true negatives, which occur when a pair of homologous genes indeed does not refer to specific or analogous genes.