dataesr/scientific-paragraphs-categorization huggingface dataset:
The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO 3 ), mixed layer depth (MLD), euphotic layer depth (Z eu ), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean. The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO 3 , MLD, and Z eu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer.
Data and Methods 3. 1. In Situ Net Primary Productivity  (NPP; 1959 -2011)  We built a quality-controlled in situ NPP (mgC m 23 d 21 ) database over five decades  that were sampled mostly in the Chukchi, Beaufort, and Barents Seas as well as the Canadian Archipelago (Figure  1 ). This database consisted of the ARCSS-PP data set  [Matrai et al. , 2013] , available at NOAA/National Oceanographic Data Center (NODC) (http://www. nodc. noaa. gov/cgi-bin/OAS/prd/accession/details/ 63065), and more recent international polar research activities such as CASES (  2004 )  [Brugel et al. , 2009; Tremblay et al. , 2011] ,  ArcticNet (2005 -2008 and 2010 -2011 ) (M. Gosselin, personal communication, 2014) ,  K-PORT (2007 K-PORT ( , 2008 K-PORT ( , and 2010 ) (S. H. Lee, personal communication, 2014) , CFL (2008)  [Mundy et al.
Nitrate (1979 Nitrate ( -2011) )  Vertical profiles (0-100 m) of in situ NO 3 were extracted from the NOAA/NODC ArcNut database  [Codispoti et al. , 2013]  (http://www. nodc. noaa. gov/archive/arc0034/0072133/) as well as additional polar research cruises at the stations where NPP was measured. However, in situ NO 3 profiles were available only at 663 stations (Figure  1 ). Note that, if only combined values of in situ NO 3 and nitrite concentration (hereafter, NO 2 ) were available, those were assumed to be the same as NO 3 because NO 2 in the Arctic Ocean is extremely low  [Codispoti et al. , 2013]. Vertical profiles were sorted by layers  (0-10, 10-20, 20-30, 30-50, and 50-100 m) ; the deepest layer varied depending on station bottom depth, and in most cases (approximately 70%) was measured between 50 and 100 m.
Concentration (1979 Concentration ( -2011) )  Monthly sea ice concentrations (%) at the corresponding location and date of each NPP station were determined at the nearest grid cell (25 km 3 25 km polar stereographic projection), using NOAA/National Snow and Ice Data Center (NSIDC) Climate Data Record of Passive Microwave Sea Ice Concentration, Version 2 (ftp://sidads. colorado. edu/pub/DATASETS/NOAA/G02202_v2/north/monthly/) [Meier et al. , 2013 [Meier et al. , , updated 2015;; Peng et al. , 2013]. These data files include not only monthly sea ice concentration from July 1987 to the present, but also the merged Goddard Space Flight Center NASA Team  [Cavalieri et al. , 1984]  and Bootstrap  [Comiso, 1986]  sea ice concentrations from late 1978 to mid-1987, as an ancillary data set. Sea ice concentration was used to distinguish ice-free (sea ice concentration <10% which is the NSIDC threshold cutoff for sea ice edge detection in  Meier et al.
Mixed Layer Depth (MLD) Because temperature and salinity profile data were often missing or unavailable at the in situ NPP stations (i. e. , separate files archived at different locations), monthly climatological salinity, and temperature from the Polar Science Center Hydrographic Climatology (http://psc. apl. washington. edu/nonwp_projects/PHC/Climatology. html) [Steele et al. , 2001]  were used to estimate MLD. After linearly interpolating the climatological temperature and salinity data in the vertical, the monthly climatological MLD was determined at the nearest grid cell (1 3 1 resolution) for each NPP station by using a threshold criterion of Dr h 5 0. 1 kg m 23   [Peralta-Ferriz and Woodgate, 2015] , where Dr h 5 r h (z) 2 r h (5 m); r h (z) and r h (5 m) are the potential density anomaly (r h 5 q h -1000 kg m 23 , with q h being potential density) at a given depth (z) and 5 m, respectively.
Such a hypothetical extension of sub-Arctic grazing influence to the interior regions of the Arctic Ocean is not currently supported by observations  [Kosobokova and Hopcroft, 2010]. Much is changing with respect to trophic structure in the Arctic Ocean, including grazer community composition and biodiversity  [Bluhm et al. , 2015] , all of which may or may not be simulated by the models, with consequences for estimating iNPP. However, we currently lack a comprehensive review and climatology for in situ micro, meso, and macrozooplankton growth and grazing rates in the Arctic Ocean to broadly assess model relative skill. Our use of an in situ NPP database allowed the assessment of model skill in ice-influenced as well as in icefree waters, overcoming a limitation encountered by previous model intercomparison exercises for the Arctic Ocean [e.
Acknowledgments This project was funded by the  National Aeronautics and Space Agency (NASA) Ocean Biology and Biogeochemistry (OBB) program  ( NNX13AE81G ). We thank the anonymous reviewers for careful reading and constructive comments, and also thank  Charlotte Laufk€  otter for thoughtful suggestions. The project '' Green Mercator '' provided funding to  M. Gehlen  and  C. Perruche  through the national program  CNRS/LEFE/INSU. M. Jin 's contribution was supported by the  NSF Office of Polar Programs  ( PLR-1417925 , and  PLR-1416920 ). A. Samuelsen  acknowledges the projects  FP7 MyOcean2  (project number  283367 ) and  PAVE (Polish-Norwegian Research Program ) and a grant of  CPU  time from the  Norwegian Supercomputing Project  ( NOTUR2 ). J. Tjiputra acknowledges the  Research Council of Norway  funded project  ORGANIC  ( 239965/RU ).
Fragments were amplified by RT-PCR with degenerated primers directed against conserved regions of known hexose transporters  (Roitsch and Tanner, 1994). Sequence analysis of the cloned PCR fragments showed that two different hexose transporters, NtMST2 and NtMST3, were cloned from tobacco anthers. The deduced amino acid sequences of both transporters showed typical monosaccharidetransporter sequence motifs  (Williams et al. , 2000). Nucleotide sequence alignment of NtMST2 and NtMST3 revealed 62. 1% identity between the two sequences, while the cloned region of NtMST2 shows 60. 18% and NtMST3 66% identity with NtMST1, respectively. Comparison of the obtained NtMST2 and NtMST3 sequences to database sequences (NCBI BLAST) identified the highest similarity of NtMST2 to the predicted Nicotiana tomentosiformis sugar transport protein 8-like (accession XM_009622914) and of NtMST3 to the predicted N.
Statistical analysis Statistical analyses were performed based on unpaired Student's t-test on datasets derived from in minimum three independent biological replicates. P values ≤ 0. 05 were considered significant and used as basis for significance groups indicated by individual letters; *, **, and *** indicate significant differences at the 0. 05, 0. 01, and 0. 001 levels of confidence, respectively. Sequence data from this article can be found in the GenBank database accession numbers KT240187 (NtMST2) and KT240188 (NtMST3). This expression is deduced from data obtained with immunocytochemistry and in situ hybridizations. The boxes with arrows correspond to the carbohydrate signal necessary for pollen germination and the square boxes symbolize the carbohydrates uptake to sustain growth of pollen tubes.
ACKNOWLEDGEMENTS We thank  Uwe Kahmann  ( Universität Bielefeld, Germany ) for scanning and transmission electron microscopy, and  Steffen Greiner  and  Thomas Rausch  ( University of Heidelberg, Germany ) for providing plasmid pBK-CMV/P17A. We are grateful to  Edith Stabentheiner  and  Ayesha Farooq  for assistance with in vitro pollen germination. We also thank  G. Stühler ,  P. Schittko ,  E. Herold ,  A. Taffner , and  C. Hampp  for excellent technical assistance and  G. Peissig  and  A. Schmidt  for taking care of the plants. Funding by the  Danish Council for Independent Research ,  Danish Ministry of Higher Education and Science  to  D. K. G. ( Individual Postdoctoral Grant  No. 4093-00255 ) is gratefully acknowledged.
Public Health Action Managing RMP-resistant TB in Zimbabwe 123 reasons why; and, among those who did start treatment, the time taken to initiate MDR-TB treatment. * Error results: probe check control failed and the assay was aborted due to the following: 1) the reaction tube being filled improperly; 2) a reagent probe integrity problem detected; 3) the maximum pressure limits were exceeded; 4) an Xpert module failure. † No results: insufficient data were collected due to 1) power failure; 2) test stopped by the operator. ‡ Invalid results: presence or absence of M. tuberculosis could not be determined due to the following: 1) sample processing control did not meet acceptance criteria; 2) sample was not properly processed; 3) PCR was inhibited. RMP = rifampicin; PCR = polymerase chain reaction.
9 There are a number of challenges with implementation, however. First, national data suggest that the performance of CDST after Xpert detection of RMP resistance is poor, although this has not been formally studied. Second, records indicate that not all patients diagnosed with MDR-TB are initiated on treatment. 2] [13]  More information is needed about what occurs between the diagnosis of MDR-TB and subsequent treatment, and improving this linkage is a key priority of the WHO End TB control strategy. 14 Third, several sites in Zimbabwe have been repeating Xpert assays on the same patient following an initial RMP-resistant Mycobacterium tuberculosis result, which may inadvertently add to the numbers of reported initial losses to follow-up and unnecessarily increase the consumption of expensive Xpert cartridges (approximately US$10 per cartridge).
Data variables, sources of data, data collection tools and data validation Data variables were collected from the Xpert laboratory registers and archive files, the CDST register in the Harare NMRL and the MDR-TB treatment registers. To follow the process of CDST, the names of those found to have M. tuberculosis or RMP resistance detected in the Xpert registers were searched for in the NMRL register in Harare for up to 6 months after the last patient was registered in the Xpert laboratory registers. For data validation, Xpert results in the laboratory registers were cross-checked with data extracted from the Xpert machines, and data in the treatment registers were cross-checked with facility-based patient medical files. Study data were collected into paper-based questionnaires between March and November 2015.
Analysis and statistics Data from the paper-based questionnaires were double entered and analysed using EpiData software (version 3. 1 for entry and version 2. 2. 2. 182 for analysis, Epi-Data Association, Odense, Denmark). Data were analysed by frequencies and percentages, with comparisons between the two provinces and between patient categories assessed using the χ² test, risk ratios and 95% confidence intervals (CI). Levels of significance were set at 5%.
Third, almost half of the patients detected with RMP resistance on Xpert never started MDR-TB treatment, and the reasons were often not documented. Finally, about one third of those who did begin MDR-TB treatment had a treatment delay of 2 weeks from the date of diagnosis. The strengths of this study were that it included all Xpert assays performed in the two provinces during a 12-month period, and included all patients detected with RMP-resistant TB. There was therefore no need for any sampling framework. The study was conducted under routine programmatic conditions, and adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for conducting and reporting on observational studies. 18 Limitations included the retrospective nature of the study using already collected secondary data in registers and archived Xpert files, where some data are missing and some may be inaccurately recorded.
We present measurements of polarization lensing using the 150 GHz maps which include all data taken by the BICEP2 & Keck Array CMB polarization experiments up to and including the 2014 observing season (BK14). Despite their modest angular resolution (∼ 0. 5 • ), the excellent sensitivity (∼ 3µK-arcmin) of these maps makes it possible to directly reconstruct the lensing potential using only information at larger angular scales ( ≤ 700). From the auto-spectrum of the reconstructed potential we measure an amplitude of the spectrum to be A φφ L = 1. 15 ± 0. 36 (Planck ΛCDM prediction corresponds to A φφ L = 1), and reject the no-lensing hypothesis at 5. 8σ, which is the highest significance achieved to date using an EB lensing estimator. Taking the cross-spectrum of the reconstructed potential with the Planck 2015 lensing map yields A φφ L = 1.
2015; Pan & Knox 2015). Lensing can also act as a noise source for primordial B-modes, which peak at degree-scales (e. g. ,  Kesden et al. 2002; Knox & Song 2002). With sufficient sensitivity, a reconstructed lensing potential can be used to predict the degree-scale lensing B-modes, enabling a deeper search for a primordial signal. If the tensorto-scalar ratio r is below 0. 01, such "de-lensing" procedures will become important in the search for inflationary B-modes  (Kesden et al. 2002; Knox & Song 2002; Seljak & Hirata 2004; Smith et al. 2012 ). In the latest BICEP / Keck results we already see a non-negligible lensing contribution at large angular scales ( < 100) (BICEP2 / Keck Array Collaboration VI 2015). Lensing reconstruction from high resolution CMB temperature maps has been performed using data from the Atacama Cosmology Telescope (ACT;  Das et al.
In addition, we cross-correlate our reconstructed lensing map with the published Planck lensing potential  (Planck Collaboration 2015). This cross-spectrum, which is immune to most systematic effects and foregrounds, also detects lensing with high significance. Since the Planck lensing potential is reconstructed primarily using temperature, and that from BICEP / Keck is reconstructed entirely using polarization, the strong correlation of the two maps shows that they are producing a consistent reconstruction of the true lensing potential. The derived lensing amplitudes are consistent with that expected in the ΛCDM cosmology. Taken together, these results imply that the B-mode power in the mul-   2016, hereafter BK-VII). This paper is organized as follows: in Sec. 2 we briefly summarize the data sets that are used in this paper, in Sec.
3 we describe our analysis method for reconstructing the lensing potential from the BICEP / Keck data, in Sec. 4 we give our results including the auto-and cross-spectra of the lensing potential, in Sec. 5 we present consistency and null tests, and in Sec. 6 we conclude. 2. OBSERVED DATA AND SIMULATIONS 2. 1. BICEP2 and Keck Array In this paper we use the BICEP / Keck maps which coadd all data taken up to and including the 2014 observing season-we refer to these as the BK14 maps. These maps were previously described in BK-VI, where they were converted to power spectra, and used to set constraints on the amplitudes of primordial B-modes and foregrounds. In this work we use only the 150 GHz Q/U maps. These have a depth of 3. 0 µK-arcmin over an effective area of ∼ 395 deg 2 , centered on RA 0h, Dec.
LENSING RECONSTRUCTION METHOD It is possible to reconstruct the lensing potential from observed CMB anisotropies because lensing introduces offdiagonal mode-mode covariance within, and between, the T -Eand Bmode sets. An estimator of the lensing potential is then given by a quadratic form in the CMB anisotropies. The power spectrum of the lensing potential C φφ L (lensing potential power spectrum) can be studied by taking the power spectrum of the lensing potential estimator. In this section we describe the method used to reconstruct the lensing potential from the BK14 polarization map, to calculate the lensing potential power spectrum, and to evaluate the amplitudes of the resulting power spectra for the data sets described in Sec. 2.
Input E and B-modes for reconstruction In BICEP / Keck analysis, we use real space matrix operations to process the data into purified Eand B-maps, which are then transformed to multipole space. The sky signal is filtered by the observing strategy, and the analysis process, including the removal of potential systematic errors ("deprojection"). These effects are entirely captured in an observation matrix, R (see  Tolan 2014 and BK-VII). The observed maps, Q obs and U obs , are then given by Q obs U obs = R Q U + Q noise U noise. ( 13 ) Here Q and U are an input signal realization-in this case lensed-ΛCDM+dust-and the second term is a noise realization. The observed map suffers from some mixing of Eand B-modes induced by e. g. the survey boundary and the filtering. To mitigate the mixing between Eand B-modes, the observed Eand B-mode maps are multiplied by purification matrices Π E and Π B respectively to recover pure Eand Bmodes.
Effects of beam systematics Beam shape mismatch of each detector pair leads to a leakage from the bright temperature anisotropies into polarization (e. g. Hu et al. 2003; Miller et al. 2008; Su et al. 2009 ). In our analysis, this leakage is mitigated by deprojecting (or for ellipticity, subtracting) several modes corresponding approximately to the difference of two elliptical Gaussians (see BK-III for details). To assess the level of leakage remaining after deprojection, we use calibration data consisting of high precision, per-detector beam maps described in BK-IV. In special simulations, we explicitly convolve these beam maps onto an input T sky and process the resulting simulated timestream in the normal manner, including deprojection, to produce maps of the "undeprojected residual. " In BK-V, this residual was treated as an upper limit to possible residual systematics.
We leave that to future work. Looking ahead, the reconstructed lensing potential can be used to cross-correlate with other astronomical tracers. However, the reconstruction noise of the BICEP / Keck data will limit its usefulness as a cosmological probe in the era of DES (The Dark Energy Survey Collaboration 2016), DESI (The DESI Collaboration 2013), and LSST (LSST Dark Energy Science Collaboration 2012). As the sensitivity of BICEP / Keck improves, our main objective is to use a well-measured deflection map φ to form a degree-scale B-mode lensing template, which can then be used to improve our final uncertainties on r (i. e. , "delensing"). Multiple studies have shown that high resolution CMB polarization data (e. g. ,  Seljak & Hirata 2004; Smith et al. 2012 ), the CIB  (Simard et al. 2015; Sherwin & Schmittfull 2015) , galaxy clustering  (Namikawa et al.
2016) , or weak lensing  (Sigurdson & Cooray 2005; Marian & Bernstein 2007 ) can all improve measurements of φ. In addition to the lensing potential presented here there already exists in the BICEP / Keck field data from the Planck CIB measurements (Planck Collaboration 2014b,c), as well as high resolution CMB maps from SPTPOL. We are exploring the formation of a lensing template using an optimal combination of these and anticipate using this in our likelihood analysis in the near future. This template will considerably improve as SPT3G  (Benson et al. 2014)   The lensing-induced trispectrum which is relevant to our analysis is given by (see e. g. Lewis & Challinor 2006 ) T 1 2 3 4 ≡ E 1 B 2 E 3 B 4 C δ D 1 + 2+ 3 + 4 [w φ 1 + 2 , 1 w φ 3+ 4 , 3 C φφ | 1 + 2| + w φ 1 + 4 , 1 w φ 2 + 3 , 2 C φφ | 1 + 4 | ] , (A1) where w φ L, is given in Eq.
comes online. The Keck Array project has been made possible through support from the National Science Foundation under Grants ANT-1145172 (Harvard), ANT-1145143 (Minnesota) & ANT-1145248 (Stanford), and from the Keck Foundation (Caltech). The development of antenna-coupled detector technology was supported by the JPL Research and Technology Development Fund and Grants No. 06-ARPA206-0040 and 10-SAT10-0017 from the NASA APRA and SAT programs. The development and testing of focal planes were supported by the Gordon and Betty Moore Foundation at Caltech. Readout electronics were supported by a Canada Foundation for Innovation grant to UBC. The computations in this paper were run on the Odyssey cluster supported by the FAS Science Division Research Computing Group at Harvard University. The analysis effort at Stanford and SLAC is partially supported by the U.
1993; Vitt and Sartorius 1999; Dzialowski and O'Connor 2001; Shine and Kearney 2001; Dzialowski 2005). Alternatively, complex mathematical models predict body temperature from first principles or rely on high-dimensional data sets collected by numerous sensors  (Christian and Weavers 1996; Kearney 2006; Fei et al. 2012a; Barton et al. 2014). We aim to improve the accessibility of predicting body temperature of small reptiles using a simple and cheap method. Our method directly estimates body temperature of small reptiles from temperature data collected by simple temperature sensors (DS18B20 TM , Maxim Integrated Products) and transformed using a single parameter heat transfer equation. By comparison with previously used methods, ours is simple to construct and use. At ca. $US1 per sensor, it is also incredibly cheap compared to cast or printed models  (Watson and Francis 2015)  or other thermal sensors, making it accessible to researchers regardless of funding situation.
US $1 are a cost-effective alternative to copper models for estimating body temperature of field active ectotherms. The most portable method was iButtons, and their independent power and operation means they are suited to any terrain, although they were more labor intensive to deploy, collect, and download than the other methods. Wiring batches of DS18B20 sensors carries an initial time overhead and can make deployment (a 10-m cable) unpleasant in some habitats, although they automatically record to a database, reducing potential human error in labeling and downloading sensors. Our method has some limitations: We used very simple copper models by comparison with some previous studies  (Hertz 1992; Bakken and Angilletta 2014). There is, however, a limited return rate of accuracy in operative temperature estimates for increased precision of copper models: Adding physical structure or color to models can have less influence on predicted temperature than placement  (Shine and Kearney 2001).
Acknowledgments We thank  Jean-Francois Arnoldi  and  Loic Thibaut  for assistance with mathematics and coding,  Ross Alford ,  Matthew Vucko , and Theresa R€ uger for helpful manuscript comments, and the  Lyons family at Wambiana Station  for accommodation, and  Richard Duffy  for everything else. Research was performed under JCU ethics permit number  A1527 , and DERM permit number  A1527. This work is part of the  ANR INDHET   ANR-12-BSV7-0023 ,  LABEX TULIP  ( ANR-10-LABX-41 ).
Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly "under-performing" method (i.