dataesr/scientific-paragraphs-categorization huggingface dataset:
The in situ pCO 2 data are well distributed over the 12 months (Fig. 2 ). January is the month for which the number of data is lower (i. e. below 80), but the other months have 110-155 data points each. In our case, each in situ data point is characterized by SST, Chl a, CDOM, NPP, and MLD as well as information on the date the measurements were made. This temporal information was normalized by sine and cosine, as follows: time(cosine) = cos Days • 2π 365 , (1) time (sine) = sin Days • 2π 365 , (2) where "Days" represents the Julian day. This definition of time is used to render the values continuous over the course of the year, sidestepping the artificial numerical transition from the last day of one year to the first day of the next, to be able to situate the process in relation to its seasonality.
Although time itself is not affecting the pCO 2 , the inclusion of time (time(cosine) and time(sine)) as a parameter is important since, in the database, some situations have similar values for SST, Chl a, CDOM, NPP, and MLD but A principal component analysis (PCA) was conducted to highlight the importance of the parameters in the pCO 2 variability (Fig. 3 ). PCA is a method of analysis which involves finding the linear combination of a set of variables that has maximum variance and removing its effect, repeating this successively  (Jolliffe, 2002). The percent of variance explained by each axis of the PCA is shown in Table  1. The results of this PCA indicate that the percentages of variance explained by all axes beyond the first do not differ greatly, with the notable exception of the last two axes, indicating that most parameters can be discriminant in the definition of the SOM states.
Methodology The relationship between pCO 2 and the environmental parameters is highly non-linear: a slight variation in some of the environmental parameters could correspond to significant variations in pCO 2. We chose to use the SOMLO methodology, which combines two statistical approaches: SOMs (Kohonen, 1990) and linear regression. SOMs are a subfamily of neural network algorithms used to perform multidimensional classification. A defining characteristic of SOMs is that their classes can represent a Gaussian distribution centred around the typical profile of environmental parameters, if there is high discretization of the training data set  (Dreyfus, 2005). We use this hypothesis to classify the environmental parameter data set and then estimate the parameters of a linear regression for each class.
Self-organizing maps Self-organizing topological maps comprise a clustering method based on neural networks. They cluster a learning data set into a reduced number of subsets, called classes, with common statistical characteristics. Generating a SOM requires the creation of a training database that contains homogenous vectors. After a training phase, we obtain a SOM. The term "map" corresponds to a 2-D matrix that stores, for each class, its referent vector, r i , which approximates the mean value of the elements belonging to it, and its index, which positions it in the map matrix used to situate it in relation to the other classes (Fig. 4 ). SOMs are also called self-organizing topological maps, "topological" indicating that the SOM training algorithm forces a topological ordering of the classes in the matrix, meaning that any two neighbouring classes C i and C j on the map matrix have referent vectors r i and r j that are close in the Euclidean sense in the data space.
Multiple linear regression MLR is a modelling method that expresses the value of one response variable, V (in our study pCO 2 ), as a linear function of other explicative variables, i. e. X = X 1 , X 2 ,. , X i (in our study SST, Chl a, CDOM, NPP, MLD, time sin , and time cos ). An MLR is generally performed either to interpret the relationship between the variable y and each of the other predictive variables X i or else to predict, from a data set of vectors containing the values of X, the corresponding value of y. In this paper, we used both aspects of MLR. However, to perform MLRs in the present case, we had to take into account their limitations and the nature of the problem. Specifically, to perform an MLR we are obliged to assume that the relationship between the predictor variables and the response variable is linear.
Statistical imputation As described in Sect. 2. 4, both the satellite and measured data available for the application present missing values. To complete these data sets, we chose to use imputation methods similar to those described by  Schafer and Graham (2002)  and  Malek et al. (2008). The main idea of these methods is to use the classifying abilities of the SOMs to regroup the data in typical situations and replace the missing explicative data values with the corresponding values of the referent vector of the class to which it belongs. We first selected the database containing SST, Chl a, CDOM, NPP, MLD, time sine , and time cosine. The vectors were sorted according to the number of values missing from each vector and noting the locations of these missing values. We chose all complete data vectors and the first 5 % of the sorted vectors containing missing data, and we trained a SOM.
Most of the data range between 180 µatm (value observed in summer) and 550 µatm (observed in winter). The SST (Fig. 8b ) is very representative of the variability in the Baltic Sea, with a maximum occurring between July and September in all basins around 18 • C  (Siegel and Gerth, 2012). The NPP variability is fairly homogenous, except for the peak at 10 mg C m -2 d -1. This peak occurs because the first model providing us the NPP values has a set maximum of NPP at 10 mg C m -2 d -1 ; therefore, the correction of the NPP satellite data takes this maximum into account. The variability of chlorophyll results in one subset of the data with a low value and another subset with a value higher than 6 mg m -3 , which can be explained by the fact that the Baltic Sea is a narrow sea, with important coastal regions, and that two blooms take place, in spring and in summer.
Topological map We classified the explicative variables (i. e. SST, Chl a, CDOM, NPP, MLD, time sin , and time cos ) into classes that share similar characteristics. In order to optimize the SOM map size for the method and to calculate the method's performances, we randomly sampled 90 % of our completed data set (1300 vectors) to be used for the training phase, keeping 10 % (144 vectors) to be used for computing the performances of the method. We iterated this process, selecting many different random samplings for each map size, and selected the map size with the best average reconstruction when applying the SOMLO methodology. At the end of our optimization, we selected a SOM consisting of 77 classes. The number of observations captured by each class ranges from 0 to 38 (Fig. 9 ). The order of magnitude of the number of observations is constant throughout the SOM, and we can regard the classes as having spread in multidimensional space in order to accurately represent the data space of the explanatory parameters.
12 ), the root mean square (rms) being 36. 7 µatm: 12 % of the validation data have a value higher than 20 µatm, and 45 % have a value between 20 and 30 µatm (Fig. 13 ). The characteristics of time, SST, MLD, CDOM, Chl a, and NPP do not explain the difference observed in the reconstruction. A reconstruction has been done using the satellite data from 1998 to 2011. The seasonal cycle of pCO 2 is well reproduced and in agreement with the results of other studies. The maximum is observed in winter, with a pCO 2 of 437 µatm on average, while the level is 274 µatm in summer. These values are comparable to the averages estimated in the central Baltic Sea of 500 µatm in summer and 150 µatm in winter  (Wesslander, 2011). The pCO 2 decreases in April due to the biological activity and increases slowly in September (Fig.
Improved satellite data availability could therefore also improve the performance of our reconstruction. This study could be further developed so as to reconstruct the spatial fields of pCO 2. Specifically, one could imagine a Bayesian approach that would select which class to use for the MLR by also taking into account the potential classes attributed to the neighbouring grid points of a geographic study area. This, however, remains dependent on the acquisition of additional in situ measurements to allow for the robust estimation of such Bayesian probabilities. Many programs exist for the acquisition of new data. Data from the Östergarnsholm site are still being acquired; 2012 did not yield much data from this site, and the data from 2013 and 2014 still need to be validated. In time, the SMHI station could also supply additional data.
e. based on the lower spatial resolution CDOM data set. We obtain values for five parameters from various sources: SST: several data sets are used for SST, and we combine two types of data sets for 2007 and 2011. For 2005-2011, we use data from the Federal Maritime and Hydrographic Agency (BSH), which processed data from the NOAA Advanced Very High Resolution Radiometer (AVHRR), and data from the Group for High-Resolution Sea Surface Temperature (GRHSST) data set for the Baltic Sea, 2007-2011. The spatial resolution is 0. 03 • at a daily temporal resolution (http://podaac. jpl. nasa. gov/dataset/ DMI-L4UHfnd-NSEABALTIC-DMI_OI). For 1998-2004, the data come from a reanalysis of the NOAA/NASA AVHRR data stream conducted by the University of Miami's Rosenstiel School of Marine and Atmospheric Science (RSMAS) and the NOAA National Oceanographic Data Center (NODC).
Acknowledgements. We wish to thank the two reviewers for their useful comments. The authors would like to thank  Tiit Kutser  and  Melissa Chierici  for their valuable help with data collection. We would like to thank  Sylvie Thiria  for her advice on the SOM classifications and  Marion Leduc-Leballeur  for her help with the method of filling the gaps in satellite data. We wish to thank  Maria Norman  for providing us with the model output. We would also like to thank  Bernd Schneider  and the  BONUS/Baltic-C  programs for cargo ship observations. We would like to thank the  Swedish National Space Board  (number  120/11:3 ) for financing the project. Edited by:  C. Garbe.
Data collection Quantitative data and setting information. Data were collected between April 2011 and December 2012 by quarter and recorded in an electronic laboratory register developed for routine Xpert data collection. At each site, the period of use was divided into two phases: the "implementation phase", covering the first four months of Xpert use and the "routine activity phase" covering the subsequent months of routine testing that were covered by the monitoring period. Projects also provided information on the month in which the use of new G4 cartridge version began. G4 was considered introduced from the middle of the month onwards. Additionally, each project reported any module replacement throughout the monitoring period. All projects provided information about the facility level of the laboratory where Xpert was implemented, including a description of infrastructure, environmental conditions, workload in the laboratory and sample collection strategy.
The research leading to these results has received funding from the European Community's Seventh Framework Programme  (FP7/2007 (FP7/ -2013) )  under grant agreement number RG226604 (OPTICON). This work makes use of observations from the LCOGT network. Appendices are available in electronic form at http://www. aanda. org CoRoT-28b is a hot Jupiter orbiting an evolved star. We investigated the stellar and planetary properties, in particular the rotation and metallicity of the star, and found a lithium abundance higher than expected for its evolutionary state. CoRoT-29b is a bit of a riddle. The transit discovered by CoRoT is significantly asymmetric, a signature that the host star could have a non-spherical shape. The asymmetry measured by CoRoT has been confirmed by ground-based observations at several observatories.
CoRoT observations and data reduction Notes. ( a) Provided by ExoDat  (Deleuil et al. 2009 ); (b) from 2MASS catalogue; (c) WISE photometry from  Wright et al. (2010). and 30 September. The CoRoT data for the run LRc08 has been public since 7 June 2013 and can be obtained through the IAS CoRoT Archive 1. The coordinates, identification labels, and magnitudes of the stars CoRoT-28 and CoRoT-29 are given in Table  1. CoRoT-28 is a relatively bright target and it was assigned an observing cadence rate of 32 s from the start of the observations, collecting 225 087 measurements with a chromatic mask. CoRoT-29 is a fainter target and was assigned the cadence rate of 512 s. When the first transits were discovered by the Alarm Mode, 1 http://idoc-corot. ias. u-psud. fr/ the target was assigned a sampling rate of 32 s, collecting in total 121 288 measurements with a monochromatic mask.
CoRoT-29b In the case of CoRoT-29, the standard reduction procedures described in Sect. 2. 2 revealed a feature with the shape of non-zero slope in the bottom of the transit shape, which is otherwise expected to be reasonably flat. The slope of the bottom part of the transit is non-zero with 95% confidence level in the 32 s sampled data by CoRoT, and only non-zero with 1σ significance in the 512 s sampled data set. But at the same time, CoRoT-29 is a fainter star than CoRoT-28 and its light curve has more noise at the timescales comparable with the transit length. We therefore decided to use a more robust filtering for this light curve. We followed the procedure described in  Alapini & Aigrain (2009) , which iteratively fits the planetary parameters, subtracts the planetary model from the data, creates a model of the stellar variability, then subtracts the stellar variability model from the original data, and then fits the planetary parameters again until the method converges to a final set of planetary parameters and stellar activity pattern.
This effect can be mitigated by modifying the corresponding background correction for each star as a function of its position on the CCD, a feature that will be introduced in future releases of CoRoT data. The CoRoT pipeline is currently implementing an improved background correction to mitigate this effect. We used the data of this improved pipeline (version 3. 5 3  ). However, the final planetary parameters determined when using the previous (version 3. 0) or the new data (version 3. 5) are not significantly different. Our modelling fits the background contamination as a free parameter, and can deal with the residuals left by the background correction from the pipeline. The incomplete background correction pipeline dilutes the transit signal as if there was an additional contribution of light from a background star, a well-known effect that can be corrected with standard procedures.
CoRoT-28b Photometric follow-up of CoRoT-28b was performed with the IAC80 (Tenerife) and the Euler (La Silla) telescopes. Photometric data were acquired at the 80 cm telescope of the IAC on Tenerife (IAC80). Measurements during the transit (on) obtained on 9 October 2011 and out-of-transit (off) data  taken on 25 October 2011 showed an on-off brightness variation of 0. 3%, but at a low level of confidence. Each observing run lasted 33 minutes. Conversely, relevant brightness variations could be excluded with high confidence on the neighboring stars (stars 2, 3, 6, 13, and 16 in Fig. 1 ). A star some 17 SSW of the target, on which photometry was performed, was found to be 5. 8 mag fainter than the target and hence too faint to cause a false alarm; the recognizable stars that were closest to the target were even fainter (about 4 N and 5 S; no photometry could be performed on them).
CoRoT-29b Photometric follow-up observations of CoRoT-29b were performed on several occasions. A full transit was obtained on 28 May 2012 with the 2m Faulkes Telescope North (FTN). The analysis of this data showed a clear transit on CoRoT-29 itself. There are several nearby stars which fall within the same point spread function (PSF) and aperture in the CoRoT data, contaminating flux of the mask of CoRoT-29. Most of this contamination is due to a similarly bright star at a distance of 10 NW which can be well separated in the ground based images. Therefore the real depth of the transit, as it is observed from ground is 1. 5% (measured from FTN observations) instead of the 0. 6% in the CoRoT light curve, because the contaminating star accounts for about 50% of the flux in the CoRoT mask (see Table  6 ).
The FTN observations show also the non-flat feature at the bottom of the transit observed by CoRoT (see Fig. 5 ). Given the on-target detection of the transit, contamination from eclipsing binaries at distances larger than 2 could be excluded as a source of false alarm. Further ON-OFF photometry undertaken by the 3. 5 m CFHT on 10 May 2013 also showed an on-target transit with a depth of 1. 4 ± 0. 3%. A full transit of CoRoT-29b was observed with IAC80 in the night of 14 July 2014. The transit centre time, at BJD 2 456 853. 435 ± 0. 005, was used to refine the ephemeris Fig. 6. Superposition of the transits of CoRoT-29b observed by CoRoT (small gray points) in 2011 and the transit observed by IAC80 in 2014 (large blue points). Same conditions as in Fig. 5. in Table  6. It also shows some degree of asymmetry, though it is difficult to assess it with the same confidence level as for FTN data (see Fig.
We reduced the data using a customized IDL software suite, which includes bias subtraction, flat fielding, order tracing and extraction, and wavelength calibration. We obtained radial velocity measurements performing a multi-order cross-correlation with the RV standard star HD 182572, observed with the same instrument set-up as the target object, and for which we adopted an heliocentric radial velocity of -100. 350 km s -1 , as measured by  Udry et al. (1999). The radial velocity measurements with the best orbital solution are shown in Fig. 7 , assuming the period and transit epoch determined by the CoRoT light curve. Finally, the crosscorrelation function bisector span shows no correlation with the radial velocities (see Fig. 8 ), reinforcing the conclusion that the radial velocity variations are not due to a blending effect.
Spectroscopic characterization To derive the stellar parameters from the spectroscopic data, we used the Spectroscopy Made Easy (SME) package (version 5, February 2014). The development and structure and We subtracted the systemic velocities in the x axis. The uncertainties in the bisector span is assumed to be twice the uncertainties in the radial velocities. The bisector span shows no correlation with the radial velocities. performance of SME is described in  Valenti & Piskunov (1996)  and in e. g. Valenti & Fischer (2005). Briefly, this code uses a grid of stellar models to which is fitted the observed spectrum. This is done by calculating a synthetized spectrum and minimizing the discrepancies through a non-linear, least-squares algorithm. The analysis is based on the latest generation of MARCS model atmospheres  (Gustafsson et al.
We would like to thank the workshops and the night assistants at the observatory in Tautenburg, Germany. We are thankful to A. Almazan for the observations taken for this paper. This research has made use of the ExoDat database, operated at LAM-OAMP, Marseille, France, on behalf of the CoRoT/Exoplanet program. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. Funding for the Sloan Digital Sky Survey (SDSS) has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Aeronautics and Space Administration, the National Science Foundation, the US Department of Energy, the Japanese Monbukagakusho, and the Max Planck Society.
Community under the FP7 through Starting Grant agreement number 239953. is supported by the European Union under a Marie Curie Intra-European Fellowship for Career Development with reference FP7-PEOPLE-2013-IEF, number 627202. S. C. C. B. thanks CNES for the grant 98761. M. A. was supported by DLR (Deutsches Zentrum für Luft-und Raumfahrt) under the project 50 OW 0204. H. D. and P. K. acknowledge support by grant AYA2012-39346-C02-02 of the Spanish Secretary of State for R&D&i (MICINN). R. A. acknowledges the Spanish Ministry of Economy and Competitiveness (MINECO) for the financial support under the Ramón y Cajal program RYC-2010-06519, and by grant ESP2013-48391-C4-2-R.
Acknowledgements. We would like to thank the anonymous referee for comments, which led to substantial improvements of this paper. We are thankful for fruitful discussions to  J. -P. Zahn  ( LUTh, Observatoire de Paris ) and  T. Borkovits  ( Baja Astronomical Observatory, Hungary ). The team at  LAM  acknowledges support by  CNES  grants  98761  (SCCB), 251091 (JMA), and 426808 (CD). R. F. D. was supported by  CNES  via  its postdoctoral fellowship program. A. S. acknowledges support from the  European Research Council /European.
, 2011; Bhatt et al. , 2007; Sacco et al. , 2007). Pharmaceutically relevant inhibitors of M. tuberculosis fatty-acid synthesis are included in the scheme: pyrazinamide (PZA;  Sayahi et al. , 2011), isoxyl (ISO; Gannoun-Zaki et al. , 2013) , thiolactomycin (TLM;  Kremer et al. , 2000) , isoniazid (INH), ethionamide (ETH) and triclosan (TRC)  (Sullivan et al. , 2006; Banerjee et al. , 1994). (b) Phylogenetic analysis of selected bacterial and fungal FAS I. For the calculation of the phylogenetic tree, dual-chain fungal FAS I were submitted as /-fused pseudo-single chains. For the analysis, FAS I with the following GenBank accession Nos. were used: Candida albicans (X74952. 1, L29063. 1), Saccharomyces cerevisiae (CAA82025. 1, CAA97948. 1), Cryptococcus neoformans (AAW43793. 1, AAW43793. 1), Ustilago maydis (XP_759118.
Macromolecule production The cloning and purification of M. tuberculosis FAS I (UniProt accession code P95029) and C. ammoniagenes FAS I (UniProt accession code Q04846) have been described in detail elsewhere  (Kikuchi et al. , 1992) ; i. e. cloning with an infusion cloning reaction (Takara Bio, Japan) and purification with engineered N-terminal Strep-II Tags  (Schmidt & Skerra, 2007). The coding sequence for C. efficiens FAS I (UniProt accession code Q8FMV7) was amplified by PCR from C. efficiens genomic DNA (DSM 44549). The gene was inserted into an SalI/XhoI-digested pME164 plasmid with an in-fusion cloning reaction (Takara Bio, Japan) to yield pME300 [pME164 is a pET-22b(+) (Merck, Germany) derivative with a N-terminal fused Strep-II Tag-encoding sequence]. Plasmid pME300 was modified using site-directed mutagenesis (forward primer GCG GCG CGC CGC AAC CAG CTG C; reverse primer GGT TGC GGC GCG CCG CGA CAC CCT CCA CGA GTG TC) to generate plasmid pME301 with S1778A and S1779A mutated ACP.
After 16 h, C 17 -CoA (Sigma-Aldrich, USA) was added as an internal standard and the reactions were stopped by adding precooled (À20 C) acetone. These solutions were mixed for 20 s and stored for 60 min at À20 C. After centrifugation for 5 min at 20 000g, the supernatants were evaporated in a SpeedVac at 4 C. The CoA esters were dissolved in 60 ml Milli-Q water and, after ultrasonication, were analysed by HPLC-UV-MS [Ultimate 3000 RSLC (Thermo Fisher Scientific, USA) coupled to a micrOTOF-Q II system (Bruker Daltonics, Germany)]. CoA esters were separated on an RP-18 column (100 Â 2. 1 mm, particle size 1. 7 mm; Waters, USA) in gradients of solvent A (water, 10 mM triethylamine/acetic acid buffer pH 9. 0) and B (acetonitrile). Data were analyzed using the DataAnalysis 4. 0 software package (Bruker Daltonics, Germany).