dataesr/scientific-paragraphs-categorization huggingface dataset:
All cases including those where a possible etiology to the SUPC event was found during autopsy or after clinical investigations in survivors were eligible. Inquiries regarding inclusion and exclusion criteria were addressed to Drs. Christian and Anette Poets regarding the two German national inquiries  [3, 12]. In order to illustrate the varying presentations and outcomes of this condition, an examination of three cases of SUPC occurring in presumably healthy newborns at the Karolinska University Hospital-Solna, Stockholm, Sweden, not previously reported, was performed. Characteristics of mothers and infants perinatal, postpartum, events and subsequent investigations, and outcome data at discharge and at 1 year of age follow-up were recorded and analyzed. The study was performed in accordance with European Community guidelines and approved by the regional hospital research ethics committee.
5 years, between January 2010 and July 2012. In this regional cohort study, the SUPC incidence is about half of the recent Spanish report but, nonetheless, nearly tenfold higher than in the UK and German national surveys (Pejovic and Herlenius, unpublished results). SUPC were vigorous stimulation enabled recovery are included in reports by Andres et al. (2 of 6 infants)  [2]  and Grylack et al. (20 infants)   [18] , but these cases are only mentioned in other studies and not included in some calculations of SUPC incidence, i. e. , Poets et al. [12]. Note that only 11 and 8 out of 26 studies provide data for events beyond 24 and 72 h, respectively. The 11 studies that include SUPC events <72 h report 73 cases out of 157 cases that occur between 24 and 72 h PN postnatal, NA not applicable a Inclusion only of the cases not published in Poets et al.
Although deaths in the first week of life are often not included in SIDS datasets, the current and other summaries of SUPC cases and risk factors  [1, 12]  suggest that similar processes to SIDS may account for about half of all sudden unexpected deaths of apparently previously healthy infants in the first week of life  [1]. In ALTE, the role of central respiratory control immaturity is established, and the same risk factors as for SIDS are apparent, at least during the first 24 h of life  [43]. Recommendations that all newborn infants should be placed in supine position within the first few hours after birth are emphasized in the reports of SUPC and in the guidelines to reduce the risk of SIDS  [44]. Recommendations to Promote and Implement to Reduce SUPC Different preventive measures and organizations of care have been strongly recommended by the majority of authors.
Acknowledgments The  Swedish Research Council , the  Stockholm County Council , and  Karolinska Institutet , supported this work as well as a mobility grant from the  "Association de Néonatologie de Port Royal" (Paris, France)  to  Pierre Kuhn. We thank the physicians and midwives that reported these cases. We are indebted to  Drs. Nicolas Pejovic ,  Hugo Lagercrantz ,  Giovanna Marchini ,  Veronica Siljehav ,  Monica Perez , and  Gary Cohen  for valuable discussions, helpful suggestions, and native language assistance.
MATERIALS AND METHODS Human myeloma cell lines. Human myeloma cell lines (HMCLs, N ¼ 40) were obtained as described previously  (Zhang et al, 1994; Rebouissou et al, 1998; Tarte et al, 1999; Gu et al, 2000; Moreaux et al, 2011). Human myeloma cell lines' phenotypic and molecular characteristics have been described previously  (Moreaux et al, 2011). Human myeloma cell lines' microarray data have been deposited in the ArrayExpress public database (accession numbers: E-TABM-937 and E-TABM-1088). Primary MMCs and gene expression profiling. Patients presenting with previously untreated MM (N ¼ 206) or monoclonal gammopathy of undetermined significance (N ¼ 5) at the university hospitals of Heidelberg and Montpellier as well as seven healthy donors have been included in the study with the approval of the ethics committee of Montpellier and Heidelberg after obtaining written informed consent in accordance with the Declaration of Helsinki.
Clinical parameters and treatment regimens of the MM patients included in the Heidelberg-Montpellier cohort were described previously  (Moreaux et al, 2012). Normal BM PCs and myeloma cells were purified as published previously  (Moreaux et al, 2012)  and whole genome gene expression profiling assayed with Affymetrix U133 2. 0 plus microarrays (Affymetrix, Santa Clara, CA, USA) (ArrayExpress accession number E-MTAB-372). Affymetrix data of an independent cohort of 345 MM patients from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA) were also used (Gene Expression Omnibus accession number GSE2658. http://www. ncbi. nlm. nih. gov/geo/). T(4;14) translocation was evaluated using MMSET spike expression  (Kassambara et al, 2012b)  and del17p13 surrogated by TP53 probe set signal  (Xiong et al, 2008)  for UAMS-TT2 patients.
The half inhibitory concentration (IC 50 ) was determined using GraphPad Prism (http://www. graphpad. com/scientificsoftware/prism/). Primary myeloma cells of 13 patients were cultured with or without graded concentrations of TSA and MMC cytotoxicity was evaluated using anti-CD138-PE mAb (Immunotech, Marseille, France) as described  (Mahtouk et al, 2004; Moreaux et al, 2012). Bioinformatics and statistics. Gene expression data were analysed using SAM (Significance Analysis of Microarrays) software  (Cui and Churchill, 2003)  and our bioinformatics platforms (http://rage. montp. inserm. fr/ and http://amazonia. montp. inserm. fr/) (Reme et al, 2008; Tanguy Le Carrour, 2010)  as published  (Moreaux et al, 2012). All computations were performed using R 2. 15. 1 (http://www. r-project. org/) and bioconductor 2.
ACKNOWLEDGEMENTS This work was supported by grants from  ARC  ( SL220110603450 ,  Paris France , to  BK ),  ANR  emergence (ETTMM, to BK) and  FEDER  ( 141 786  and  42667 , to BK and JM) from the  Hopp-Foundation ,  Germany , the  University of Heidelberg, Germany , the  National Centre for Tumour Diseases, Heidelberg, Germany , the  Tumorzentrum Heidelberg/Mannheim, Germany , and the  Deutsche Krebshilfe, Bonn, Germany , the  Deutsche Forschungsgemeinschaft, Bonn, Germany  (to HG). We thank the  Microarray Core Facility of IRB  (http://irb. montp. inserm. fr/en/index. php?page=Plateau&IdEquipe=6) and the cytometry platform of the  Institute of Research in Biotherapy  (http://irb. montp. inserm. fr/en/index. php?page=Plateau&IdEquipe=3, Montpellier Rio Imaging ). This work is published under the standard license to publish agreement.
The ChemCam instrument package on the Mars Science Laboratory rover, Curiosity, is the first planetary science instrument to employ laser-induced breakdown spectroscopy (LIBS) to determine the compositions of geological samples on another planet. Pre-processing of the spectra involves subtracting the ambient light background, removing noise, removing the electron continuum, calibrating for the wavelength, correcting for the variable distance to the target, and applying a wavelength-dependent correction for the instrument response. Further processing of the data uses multivariate and univariate comparisons with a LIBS spectral library developed prior to launch as well as comparisons with several on-board standards post-landing. The level-2 data products include semi-quantitative abundances derived from partial least squares regression.
A LIBS spectral library was developed using 69 rock standards in the form of pressed powder disks, glasses, and ceramics to minimize heterogeneity on the scale of the observation (350-550 μm dia. ). The standards covered typical compositional ranges of igneous materials and also included sulfates, carbonates, and phyllosilicates. The provenance and elemental and mineralogical compositions of these standards are described. Spectral characteristics of this data set are presented, including the size distribution and integrated irradiances of the plasmas, and a proxy for plasma temperature as a function of distance from the instrument. Two laboratory-based clones of ChemCam reside in Los Alamos and Toulouse for the purpose of adding new spectra to the database as the need arises. Sensitivity to differences in wavelength correlation to spectral channels and spectral resolution has been investigated, indicating that spectral registration needs to be within half a pixel and resolution needs to match within 1.
Following this, the data treatment methods are described for quantitative calibration using PLS. Finally, the discussion concludes with a short summary of strategies for analyses on Mars and an initial univariate view of the first data taken on Mars of the onboard calibration targets. An overview of the general multivariate calibration plan is shown in Fig. 1. Standards of known composition were selected during the instrument development time period. LIBS spectra were acquired prior to delivery of the instrument to the rover. These spectra were pre-processed through a number of steps to produce a database from which multivariate training models can be made. Spectra obtained on Mars, indicated in the bottom row, are also pre-processed using exactly the same routines. The team must consider any potential changes to the instrument response on Mars, and must also normalize the spectra to correct for the effects of variable distance.
For this reason, it is important to have the capability to analyze additional standards using laboratory instruments that are sufficiently similar to ChemCam. This is represented in the middle row of Fig. 1. Before accepting standards from laboratory instruments into the ChemCam multivariate training database, a rigorous comparison must be done between spectra taken by ChemCam on standards that were available prior to delivery, with spectra of the same standards taken with laboratory instruments, to ensure that these new spectra match those from ChemCam in terms of resolution, wavelength, spectral range, and plasma temperature (ratios of peak heights for a given composition). The discussion of data processing will follow the different elements in the figure. The accuracy of the model is quantified by performing leave-one-out analyses on the pre-delivery spectral database.
It should be noted that the CMS source clays are mineralogical reference materials and are not accepted geochemical standards; the analyses listed here are published results of representative splits but do not represent precise analysis of the splits used with our LIBS experiments. These data are for comparison only. The SRM kaolinites are accepted geochemical reference materials but are not pure kaolinite; SRM98A in particular is only 40% kaolinite, the rest being mica, quartz, and anatase. SRM97A also contains anatase. 2. 2. 4. 6. Kerogen-rich sediment. Standard SGR1 is an organic-rich material with a kerogen component containing a significant amount of kerogen. Kerogen is X-ray amorphous and was not quantified by XRD. The mineral abundances of quartz, feldspar, dolomite, calcite, and mica reported in Table  4  are corrected from the values determined by XRD assuming ~30% kerogen content.
Data treatment The information of interest in LIBS data is principally contained in the emission lines. Processing of ChemCam data is done in two general steps. The first step, to achieve Level 1 products, is shown in the middle boxes in Fig. 1 , and consists of removing the non-laser-induced background ("Dark Subtract"), de-noising the spectrum, removing the electron continuum, and calibrating for wavelength and instrument response for a sample at a given distance. The result is emission peak spectra in units of photons emitted per pulse in radiance units (photons/pulse/mm 2 /sr/nm). Processing of data to Level 1 is also called pre-processing. Level 2 processing involves using multivariate training models based on spectral libraries to obtain elemental abundances and to classify the sample relative to other spectra.
Non-laser dark subtraction and de-noise A non-laser "dark" spectrum is taken in close temporal proximity to the LIBS spectra. During pre-delivery calibrations a dark spectrum was taken for every two samples analyzed. The dark spectrum typically consists of fifty collects using the same spectrometer settings as the active, but without the laser pulses. With no need to wait for the laser, the dark spectra are taken in rapid succession. The dark subtraction routine finds the non-laser data file nearest in time to the active spectrum of interest and obtains the difference. Prior to subtraction, the routine performs a 'leveling', which uses the means of the 50 non-exposed pixels on either side of the active ones to adjust the non-laser dark spectrum to the appropriate level to subtract from the active spectrum.
In Eq. (  4 ), L and Ω are inversely dependent upon the distance from the ChemCam telescope to the target while A is increasingly dependent on distance. ChemCam level 1 data are provided in two forms: Level 1A data are given in DN, without correction for IR or distance. Level 1B data have the IR applied and are given in photon radiance. Data used for both classification and quantitative elemental compositions, done under Level 2 processing, are mean centered and variance normalized (MCVN) as follows. The mean intensity (m) and standard deviation (s) for each spectral range (UV, VIO, VNIR) are calculated. The MCVN intensity is calculated as: I MCVN ð Þ λ ¼ DN λ -m ð Þ =s ð5Þ such that the average intensity for each spectral range is 0, the minimum value is a negative number, and the standard deviation is 1.
None of these criteria can be checked with the currently available ChemCam data set and it is assumed that the spatially and temporally averaged observations are not in LTE, and as a result, the absolute temperatures may not have physical meaning. However, the relative differences between values obtained at different distances may provide useful information. Prior to the construction of ChemCam, several studies were conducted under Mars-like conditions with equipment anticipated to approximate ChemCam. Plasma temperatures in one such experiment were determined by Sallé et al. [37]  using a gated echelle spectrometer. They found excitation temperatures on six rocks to be between 9000 and 11,000 K using the suite of neutral Fe lines between 350 and 400 nm at a single stand-off distance of 3 m.
Temperatures for three powdered and pressed basalt geochemical reference materials (BEN, BHVO2, and GBW07105) were calculated using the Boltzmann plot method with 29 Ti II lines at 3, 5, and 7 m. Temperatures ranged from 13,900-14,400 K at 3 m, 13,800-14,400 K at 5 m, and 14,400-15,000 K at 7 m (Fig. 6 ); error bars are determined from the standard error of the Boltzmann plot slope. Results from the ChemCam data show no correlation with distance. For each sample, we find that the plasma temperatures at 3, 5, and 7 m are the same to within ~5%. Given the difficulty in calculating the temperature using ChemCam data, within the uncertainty of our measurements we cannot conclude that there is a relationship between plasma temperature and distance. Additional work is needed using a higher resolution gated spectrometer to reduce line overlap before any conclusions should be made.
Coupling efficiency Different materials and different surface textures yield differing fluences of LIBS photons produced by the same laser beam. We refer to this as coupling efficiency. The standards used in this study coupled to the laser to varying degrees, with the strongest coupling materialthe Ti plate used on the rover calibration target assembly for diagnostic purposes-providing about a factor of thirty more photons than the weakest coupling standard. Fig. 7a  shows the relative coupling efficiencies in terms of total emission from peaks, obtained on Level 1 data (after dark subtraction, continuum removal, and instrument response correction) from the cleanroom 3 m data set (Test #6 in Table  1 ). Fig. 7b  compares overall total emission including continuum, taken from the raw data. Titanium was not included in these plots, but was a factor of 3 higher than the strongest coupling geological standard.
A number of other iron-rich standards are nearly as strong in overall emission. However, some of the carbonate standards are still nearly as strong in overall coupling. Another way to consider the coupling efficiency of a sample is to compare the signal strength of an emission line of an element that occurs in every sample observed in this environment: oxygen. There is a triplet that is not resolved by the ChemCam spectrometers at 777 nm. Fig. 7c  shows the radiance in the highest channel for this peak (constitutes 24% of the radiance in the entire peak) for the standards in the cleanroom data set. The aragonite-rich standard (UNSAK) is 25% higher than the next highest standards, a basalt (SR688) and an olivine (DH4912) standard. Note that these differences represent chemical matrix effects, because aragonite does not have a higher oxygen fraction than other carbonates.
LOD 3 gives more rigorous detection limits than LODs 1 and 2, as it includes constraints on the probability of false negatives. This is particularly true for LIBS results in which the uncertainty of the linear regression is relatively high, as is the case in the data examined here. The data set used in this study is Test #1 in Table  1 , with a distance of 3. 0 m. This test gives slightly better results in terms of signal to noise than Test #6 in Table  1  due to the higher laser energy of ~14 mJ resulting from the cooler laser temperature, for which this unit was optimized. By contrast, the laser energy in test series #6 was ~10 mJ. A disadvantage was that Test series #1 involved fewer standards, which may have resulted in decreased regression line quality than an analysis using a larger number of standards.
Feldspathoid minerals are chemically similar to feldspars but have lower SiO 2 contents. Because the PLS model responds to emission lines other than SiO 2 , it overestimates the SiO 2 content of BEN. Granodike is a low-SiO 2 trachybasalt with a Na 2 O content of 4. 51 wt. %. For comparison, the highest Na 2 O content in the cleanroom data set is 5. 91%. The PLS model underestimates the Na 2 O content of Granodike, likely because the sample's spectrum shows relatively weak Na emission compared to the Na 2 O content, as shown in Fig. 11. A similar example, but in the opposite sense, is found in the K 2 O abundance of GUWGNA. In this case the K is significantly overestimated based on anomalously high peak heights for this K abundance. These are examples of chemical matrix effects, where some feature of the sample chemistry apparently prevents the expected Na transition intensity, or boosts the K transition intensity, relative to that expected for the respective abundances.
Steps to improve PLS accuracy There are a number of steps that can be carried out to improve the accuracy of composition estimates derived from the training set obtained in this study. These include culling standards that are far from the compositional range of the Mars samples and from the bulk of the standards. Most of the standards were selected in anticipation of the Mars compositional range, but prior to landing this range was not known. On the other hand, it is relatively easy to determine which standards are outliers relative to the main body of standards. This will be discussed in the next section. Additional gains are certain to be made by increasing the number of standards used in the database. This will involve using instruments designed to mimic the output of ChemCam, as will be discussed later.
The relative accuracy, on the other hand, does show a dip when removing the first few outliers. However, the relative accuracy starts out high in this case because a number of the major elements are near zero wt. % to start with. Overall, this study shows that removing a few outliers significantly helps the accuracy of those spectra which are near the middle of the distribution of spectra in phase space. However, the current training set is not very large, and removing more than a small number of  outliers results in poorer accuracy and less generalizability to unknown samples. 3. 5. 3. 2. Adding new spectra to the database. As indicated above, a key aspect of the ChemCam spectral database is the ability to remove spectra that are not useful in covering the compositional ranges and, more importantly, to add spectra which more closely represent the range of data from Mars.
The original database was built on spectra taken by the flight instrument prior to launch. There are two possibilities for adding new spectra of known compositions after launch and landing. One is to cross-calibrate with the APXS instrument on Mars. This can be done on homogeneous samples for which the difference in sampling size does not matter and for elements for which APXS obtains accurate results, e. g. , for Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Ni, and Zn  [52]. Conversely, APXS cross calibration is not feasible in areas of heterogeneous samples and, more importantly, for elements such as H, Li, Be, B, C, Rb, Sr, Ba that are not generally analyzed by APXS. Although these are not analyzed by PLS, abundances of these elements can be determined by univariate analyses of ChemCam LIBS spectra, when present in sufficient abundances.
Another method of adding spectra to the database is to make LIBS observations on new standards using a surrogate instrument on Earth that matches ChemCam's laser in energy and optical characteristics, and with spectrometers that match the wavelengths and resolutions of ChemCam's. Such instruments were built using flight spare parts and have been installed in laboratories at Los Alamos and Toulouse. It may also be possible to add spectra using slightly less ChemCam-like instruments. To add spectra to the database using any of these instruments one needs to know how accurately each of the instrumental parameters must be matched to ChemCam's. Experience with ChemCam suggests that the spectra are relatively insensitive to the laser energy. We explore the requirements for matching the wavelength and ChemCam's spectral resolution in the next several paragraphs.
To determine the sensitivity of PLS results to the wavelength calibration of the training and test sets, we randomly selected five samples from the cleanroom database to be used as an "unknown" test set. The remaining samples were used to train a PLS model for each of the major elements, with the number of principal components fixed at 15. These models were used to predict the composition of the test set after applying a shift in the wavelength calibration of the test set spectra by up to ± 3 pixels. This shift was applied in increments of 0. 05 pixels up to ± 0. 25 pixels, after which the shift increment was increased to 0. 25 pixels. The calculations were repeated ten times with different sets of five samples held out to serve as the test set. The result of these repeated calculations was a set of ten RMSE curves varying with the magnitude of the wavelength offset for each major element.
We describe the data processing steps to produce Level-1 LIBS data products. The coupling efficiency of potential targets varies by a factor of nearly 30, and the relative coupling efficiencies are described. ChemCam detection limits are investigated, showing, for example, that lithium can be not only detected, but quantified with as little as 25 ppm. The ChemCam LIBS calibration training set was developed and built before the field site (Gale crater on Mars) was selected, so it had to be quite general to start with, but the methods needed to be adaptable to the initial findings. We have shown that such a training set can be built, and that the training set can be later adapted both by culling outliers that turn out to not be needed and, in the near future, by adding to additional standards as initial findings dictate.