dataesr/scientific-paragraphs-categorization huggingface dataset:
3 , 3 , HYY, YYH, RWR, RW (1)2 , IR (2), RWN, FC IV Omega-secalins***** 18), EL (1)  (2), EYY (2) , RYY, YYI (1)2 , YYL (2), YYS, SYY (1) 2, LWG, PHQ, LK (3), KP (1) 2 , LW, WG (1) 2 , IY, YYQ, PHY, PWS, RHV, LQPGQGQQ (2), LQPGQGQQG (2), PW, HL (1) (2) 2 , AY (5) (6) (8), AH (1) 3 , LHA (1) 2 , PHS (1) 2 , TY (1) 2 , LHT, LLR, LLPF (2), LPM, IRn T -the number of total proteins including all protein fragments available from the BIOPEP-UWM database (access on August/September 2019 and from the 1st to the 10th December 2019. The data were updated as of 10 December 2019), n-the number of analysed proteins including selected fragments, c, f -protein sequence status ( c -complete, f -a protein fragment), x-a mean value, *NAAR-the number of amino acid residues found in the analysed protein or protein fragment, **NAFS-the number of potential sequence fragments with an antioxidant activity, ***A-the A parameter (the frequency of bioactive fragment occurrence in a protein sequence) (defined in the "Materials and Methods" section of the study), ****AAS-amino acid sequence presented in the form of a single-letter amino acid code: A-Alanine, R-Arginine, N-Asparagine, D-Aspartic acid, C-Cysteine, Q-Glutamine, E-Glutamic acid, G-Glycine, H-Histidine, I-Isoleucine, L-Leucine, K-Lysine, M-Methionine, F-Phenylalanine, P-Proline, S-Serine, T-Threonine, W-Tryptophan, Y-Tyrosine, V-Valine.
Ordinary chondrites (OCs) are classified into three groups, according to their oxidation state, which increases from the H to L to LL groups. This is demonstrated by the decrease in metal content (H=~8 vol%, L=~4 vol%, and LL=~2 vol%), and by a positive correlation between Δ 17 O and %Fa through the OC sequence. Compared to other chondrites, OCs exhibit the largest variation in oxidation state but there is an ongoing debate on the processes that control this variation. To constrain the causes of the variations in the oxidation state with respect to the associated nebular versus parent bodies processes, we investigated the elemental and isotopic isotopic metal-silicate datasets, such as the magmatic iron meteorites, the Earth silicate reservoirs. We propose that the Δ 74/70 Ge metal-silicate and the negative δ 74/70 Ge values of OCs are inherited from metal-silicate melting and partial exchange before planetesimal accretion in a light isotope-enriched gas.
Acknowledgments The authors thank  Damien Cividini  for his valuable assistance on the NeptunePlus MC-ICP-MS and his advice on germanium chemistry, and  Delphine Yeghicheyan  ( SARM "Service d'Analyses des Roches et des Minéraux", CRPG-CNRS, Nancy ) for contribution to elemental analyses. The authors also acknowledge  Andreas Pack  for oxygen isotopic measurements. We thank  M. Gounelle  and the " Comité de gestion" at the  " Muséum National d'Histoire Naturelle" (MNHN) of Paris ,  T. McCoy  and  J. Hoskin  from the  Smithsonian Institute , and  A. Nieto Codina  from the  "Museo Nacional de Ciencias Naturales" (MNCN)  of  Madrid  for providing the meteorite samples. Jesse Davenport is thanked for his kind and careful correction of the manuscript. Finally, we thank  Fang Huang  for editorial handling, and three anonymous reviewers for their thorough and constructive reviews that helped to improve the manuscript.
Introduction The Soil Moisture and Ocean Salinity (SMOS) mission is the second Earth Explorer mission of the European Space Agency (ESA). The satellite was launched in November 2009 and has been continuously operating ever since, with an excellent health status. Data acquisition is in the order of 99. 88%, and processing performance is above 99%. As such, the ESA has continuously provided nominal and near-real-time data for the past 10 years since the end of the commissioning phase. The improvements in the new L1OP were applied to the calibration process and the image reconstruction processing. The improvements for these two categories are presented below in separate sub-sections.
Later, during SMOS's first mission reprocessing, an antenna thermal model was introduced to correct for variations observed during NIR external calibration manoeuvres. However, the antenna thermal model was quickly abandoned, as the instrument became more stable following the initial months in orbit. For the second mission reprocessing, the team derived a method to calibrate the antenna losses in orbit  [8]. Antenna losses were measured every 15 days, and, since the values were stable, the average value was used for the entire reprocessing. This correction was key to improve the stability of the data in the second mission reprocessing. The calibration procedure could only measure the antenna losses for whole of the antenna patch and the inner part of the antenna (L1 and L2 losses respectively).
For the second mission reprocessing, the team derived a method to calibrate the antenna losses in orbit  [8]. Antenna losses were measured every 15 days, and, since the values were stable, the average value was used for the entire reprocessing. This correction was key to improve the stability of the data in the second mission reprocessing. The calibration procedure could only measure the antenna losses for whole of the antenna patch and the inner part of the antenna (L 1 and L 2 losses respectively). But the antenna patch and the innermost part of the antenna in SMOS suffer different temperature excursions. Introducing the correct split in the total antenna loss between L 1 and L 2 is key for obtaining good instrument stability. This split was obtained by assessing the brightness temperature variations over the ocean against an ocean forward model for Stokes-1 measurements and applying the same antenna loss value at the H and V polarisations.
Sea-Ice Mask The calculation of the artificial scene used in the Gibbs-2 algorithm described above is based on the use of a fixed global land-ocean mask. In fact, we identify the land and ocean pixels within the field of view and assign a constant value over land and the Fresnel forward model over ocean. To improve the accuracy of the artificial scene in seasonal sea-ice growth, we have developed an operational strategy to measure the sea-ice extension from the actual SMOS measurements and apply this extension to the artificial scene. Measurements are collected for 10 days, then a mask of the percentage of sea ice over the ocean is derived and this mask is used in the Gibbs-2 algorithm with a constant value of 250 K (same as for land pixels), as can be seen in Figure  11. In the third mission reprocessing data, the mask computation will be aligned with the data that is applied.
Measurements are collected for 10 days, then a mask of the percentage of sea ice over the ocean is derived and this mask is used in the Gibbs-2 algorithm with a constant value of 250 K (same as for land pixels), as can be seen in Figure  11. In the third mission reprocessing data, the mask computation will be aligned with the data that is applied. However, in nominal operations, the mask will be applied, typically with a 12-day delay from the moment it first started estimating the extension. Errors derived from this 12-day delay were analysed and resulted to be much lower than those present when not applying the correction at all. Figure  11  shows that the sea ice detected by SMOS BT measurements goes beyond the continental surfaces.
Land-Sea Contamination Another critical aspect of the SMOS radiometric measurements is the land-sea contamination. This refers to the increase in the bias that occurs in the ocean measurements near land masses and vice versa. The adjustments in calibration and the new Gibbs-2 image reconstruction technique have achieved a significant improvement in land-sea contamination, even though this is still substantially present in the third mission reprocessing. Figure  1  shows the biases, over ocean, in the global maps for one particular month of data (June 2016) for the four polarisations for the second  [21]  and third mission reprocessing. The improvements are most noticeable in Y polarisation and in the fourth.
Land-Sea Contamination Another critical aspect of the SMOS radiometric measurements is the land-sea contamination. This refers to the increase in the bias that occurs in the ocean measurements near land masses and vice versa. The adjustments in calibration and the new Gibbs-2 image reconstruction technique have achieved a significant improvement in land-sea contamination, even though this is still substantially present in the third mission reprocessing. Figure  17  shows the biases, over ocean, in the global maps for one particular month of data (June 2016) for the four polarisations for the second  [21]  and third mission reprocessing. The improvements are most noticeable in Y polarisation and in the fourth Stokes parameter. On the other hand, the contamination in Tx has changed but remains at similar levels, and similarly for the third Stokes parameter.
χ 2 Test χ 2 is an important metric to assess the quality of the soil moisture retrievals and is used widely in many retrieval processes. It provides a measure of the agreement (best fit) between the geophysical modelling that resulted in the retrieved parameters and the L 1 data that were used accounting for the expected noise on the observed data. In this study, we considered rather the reduced χ 2 r form, which is χ 2 divided by the number of degrees of freedom. Using χ 2 r introduces a normalisation, which is preferable as the Level 2 processor includes L 1 data filtering that may result in slightly different numbers of degrees of freedom between the two L 1 datasets. Figure  20  shows the changes of the χ 2 r between v620 and v724, computed as the ratio χ 2 r v724 divided by χ 2 r v620 for June 2014 for ascending and descending orbits.
It provides a measure of the agreement (best fit) between the geophysical modelling that resulted in the retrieved parameters and the L1 data that were used accounting for the expected noise on the observed data. In this study, we considered rather the reduced   2 form, which is χ 2 divided by the number of degrees of freedom. Using   2 introduces a normalisation, which is preferable as the Level 2 processor includes L1 data filtering that may result in slightly different numbers of degrees of freedom between the two L1 datasets. Figure  20  shows the changes of the   2 between v620 and v724, computed as the ratio   2 v724 divided by   2 v620 for June 2014 for ascending and descending orbits. Very similar maps are observed at other months of the year. Ratios >> 1 (toward red colours) indicate degraded (increased) v724   2 with respect to v620 and ratios << 1 (toward blue colours) indicate improved (decreased) v724   2 with respect to v620.
Below dense forest v724, soil moisture and opacity tend to decrease, with patterns changing in position between ascending and descending orbits, e. g. , the North American east coast, Amazonian forest, and African Congo forest. This is probably a signature of the Gibbs-2 correction, as the contrast of land/sea masses is not similar within the SMOS field of view for these locations depending on the orbit pass. The L1C v724 data generate significant changes compared to the L1C v720, and the question whether those changes are in the right direction is addressed by the two following sections.
χ Test is an important metric to assess the quality of the soil moisture retrievals and is used widely in many retrieval processes. It provides a measure of the agreement (best fit) between the geophysical modelling that resulted in the retrieved parameters and the L1 data that were used accounting for the expected noise on the observed data. In this study, we considered rather the reduced form, which is χ 2 divided by the number of degrees of freedom. Using introduces a normalisation, which is preferable as the Level 2 processor includes L1 data filtering that may result in slightly different numbers of degrees of freedom between the two L1 datasets. Figure  20  shows the changes of the between v620 and v724, computed as the ratio v724 divided by v620 for June 2014 for ascending and descending orbits.
In-Situ Soil Moisture Several networks of in-situ soil moisture measurements stations (ISMN) can be used to assess the quality of SMOS-retrieved soil moisture. SMOS coarse resolution observations and ultra-local in-situ measurement are not necessarily fully comparable, but become useful when assessing relative differences between two versions of processing of the same satellite data. SMOS soil moisture retrievals obtained from L1OP v620 data and from L1OP v724 data are compared against in-situ soil moisture time series of 250 validation sites taken from 11 in-situ soil moisture networks (Figure  21 ). SMOS retrieval data and in-situ data are first co-located in space and time by taking the SMOS grid-points closest to the stations and by pairing in time SMOS and in-situ data of less than 7.
In-Situ Soil Moisture Several networks of in-situ soil moisture measurements stations (ISMN) can be used to assess the quality of SMOS-retrieved soil moisture. SMOS coarse resolution observations and ultra-local insitu measurement are not necessarily fully comparable, but become useful when assessing relative differences between two versions of processing of the same satellite data. SMOS soil moisture retrievals obtained from L1OP v620 data and from L1OP v724 data are compared against in-situ soil moisture time series of 250 validation sites taken from 11 in-situ soil moisture networks (Figure  21 ).
χ 2 Test  2 is an important metric to assess the quality of the soil moisture retrievals and is used widely in many retrieval processes. It provides a measure of the agreement (best fit) between the geophysical modelling that resulted in the retrieved parameters and the L1 data that were used accounting for the expected noise on the observed data. In this study, we considered rather the reduced   2 form, which is χ 2 divided by the number of degrees of freedom. Using   2 introduces a normalisation, which is preferable as the Level 2 processor includes L1 data filtering that may result in slightly different numbers of degrees of freedom between the two L1 datasets. Figure  20  shows the changes of the   2 between v620 and v724, computed as the ratio  strong negative asymmetry. Many continental areas show a deep blue colour, which indicates very significant improvements that also appear to be very stable in time for different seasons and different years.
In-Situ Soil Moisture Several networks of in-situ soil moisture measurements stations (ISMN) can be used to assess the quality of SMOS-retrieved soil moisture. SMOS coarse resolution observations and ultra-local insitu measurement are not necessarily fully comparable, but become useful when assessing relative differences between two versions of processing of the same satellite data. SMOS soil moisture retrievals obtained from L1OP v620 data and from L1OP v724 data are compared against in-situ soil moisture time series of 250 validation sites taken from 11 in-situ soil moisture networks (Figure  21 ). SMOS retrieval data and in-situ data are first co-located in space and time by taking the SMOS grid-points closest to the stations and by pairing in time SMOS and in-situ data of less than 7. 5 markers, respectively.
Nephelometric measurements and data analysis Sterile 96 UV-star, 48 and 24 cell culture Greiner microplates were filled with 300 µL, 1 mL and 2 mL samples, respectively. Nephelometric measurements were recorded with a NEPHELOstar Omega (BMG Labtech, Offenburg) reader equipped with a 635 nm laser. Each well was measured for 0. 1 s with a laser beam focus of 2. 5 mm and 80% laser intensity. Filaments and clumps of E. siliculosus, M. pyrifera, H. pluvialis, P. sedebokerense, P. littoralis data were measured using a well-scan protocol and a 5x5 pixel grid covering 5 mm in diameter (48-well microplates) or a 7x7 pixel grid covering 10 mm diameter (24-well microplates). Spores of S. latissima and other microalgae were measured using an endpoint protocol. The time-course of S. latissima spore release was recorded using a time-course of endpoint (plate mode) protocol, during which the 24-well plates were subjected to shaking at 175 rpm for 5 min every 10 min.
To check for the possible presence and subsequently control for any artefact caused by a meniscus in the nephelometric well scans, the data analysis was conducted on the average measurement recorded in a centred disc of 3. 5 mm diameter using the corresponding function in the Mars software (BMG Labtech, Offenburg). CSV files were exported and further processed in Microsoft Excel and R  [23]. Regression curves were compared using the ANCOVA test implemented in Graphpad Prism 7. At all times, the background signal was measured in duplicate using appropriate mock samples, and the corresponding mean values were subtracted from all measurements. For time courses, an initial relative nephelometric unit (RNU) value was calculated as the mean of the initial three measurements and then subtracted from each curve value as described by Joubert et al.
Spectrophotometry and Fluorometry For comparative purposes, absorbance and fluorescence data were recorded on the samples used in nephelometry using a SPECTROstar Omega (BMG Labtech, Offenburg). The recording wavelength (450 nm or rarely, 250 nm) was selected to give the most sensitive measurements, depending on the species used. Chlorophyll fluorescence was recorded using a 12 nm band pass excitation filter, centred on 485 nm, and a 50 nm band pass emission filter, centred on 655 nm. Unless specified otherwise, all experiments were performed on at least three (and up to six) biological replicates, each composed of 3 technical replicates.
Microscopy Algal material was observed directly in the multiwell plates with an AxioObserver (Zeiss) microscope, using differential interference contrast and phase contrast. For epifluorescence, culture aliquots were mounted on microscope slides. Photographs were taken using an Axiocam HRc (Zeiss) and processed with Axiovision software (Zeiss, version 4. 7 or 4. 8).
As different type of possible application, we show that combining image analysis with nephelometry provides a rapid and simple estimation of the reproductive effort of kelp sporophytes, with minimal damage to the plants. Evaluating of the fitness in kelps species is a main challenge because fitness is related to the growth and the fertility of both the sporophytes and gametophytes. While morphological traits are easily measured on macroscopic sporophytes, our growth data E. siliculosus open interesting perspectives to monitor the growth of gametophytes and young sporophytes. Second, our real-time quantification of spore release shows that nephelometry is applicable to characterize the reproductive traits of propagules (i. e. spores or gametes), release, opening the prospect of exploring both sporogenesis and gametogenesis.
Small portions of the probe molecule, Py (C5H5N, Acros Organics, 99. 5%, dried over 3A molecular sieve), were admitted into the cell, usually at 150ºC, until no changes in the peak intensities were observed in the spectra. Physisorbed molecules were subsequently removed by evacuation at 150°C. Py adsorption-desorption was carried out typically at 150-350 o C. The FTIR spectra were collected at different temperatures, typically between 50 and 350 o C. The obtained spectra were analysed, including integration, subtraction and determination of peak positions, using specialised Thermo software, Omnic. The integration limits were 1565 to 1515 cm -1 for the Py-B peak and 1465 to 1535 cm -1 for the Py-L peak (±3 cm -1 depending on the nature of the sample and the temperature of the FTIR measurements).
Acknowledgments The authors thank ENSICAEN for the overall support for this research programme. We gratefully acknowledge  Johnson Matthey PLC and Keele University  ( ACORN-2015  grant) for their support and funding provided for this work as the PhD studentship for C. F. M. J. thanks  Keele University  ( ACORN-2018  grant), the  Royal Society  ( International Exchanges grant   IE160562 ) and the  Newton Fund  ( Institutional Links grant   261867079 ) for supporting his PhD project. The authors thank  Dr. D. Broom  for providing a sample of  NIST  reference material  RM8852 ,  Prof. A. Y. Khodakov  for providing a sample of alumina and  Dr. A. Vicente  for assistance with the MAS NMR measurements.
Immune classification of STS: Histology-independent microenvironment composition and functional orientation The TME compositions from 4 independent discovery primary STS datasets (TCGA SARC, GSE21050, GSE21122 and GSE30929, Extended Data Table  1 ) with publicly available gene expression profiles were analysed with MCP-counter, a gene-expression-based TME deconvolution tool  9. An immune-based classification of STS was developed from this analysis (Methods, Extended Data Fig. 1 ) and tumours were assigned to one of five Sarcoma Immune Classes (SICs), labelled A, B, C, D and E, with highly distinct profiles (Fig. 1 ). We compared the SIC distribution across histologic subtypes and found that most LMS tumours were classified to SICs A and B (Fig. 1a ). DDLPS accounted for half of SIC C tumours.
SICs are associated with survival of STS patients After verification that the two cohorts with available survival data (TCGA SARC, n=213; GSE21050, n=283) exhibited similar survival patterns (data not shown), these were pooled to study the clinical outcome of the five SICs (Fig. 2a ). SIC A patients exhibited the shortest overall survival (OS) compared to D or E patients (p=0. 048 and p=0. 025, respectively). Similarly, among the other STS histologies from FSG cohort, SIC A patients had a shorter OS compared to SIC E patients (Extended Data Fig. 2b ). In a multivariate model with classical prognostic factors (Fig. 2b ), SICs were found to be significantly associated with prognosis, independent of other clinical parameters (as compared to SIC A, p=0. 011 and p=0. 029, for SIC D and SIC E, respectively).
In situ validation of SIC profiles in tumours To validate in situ the TME profiles of SICs, we analysed an independent cohort of 93 STS cases (NTUH cohort, Extended Data Table  1 ). 73 samples passed quality control for transcriptomic analysis using Nanostring nCounter technology. We classified this cohort into the same 5 SICs (see methods) with the following distribution: A: 16 (21. 9%), B: 19 (26. 0%), C: 10 (13. 7%), D: 17 (23. 3%), E: 11 (15. 1%). The NTUH cohort samples exhibited gene-expression-based TME profiles similar to that of TCGA SARC and GSE21050 cohorts (Extended Data Fig. 7a ). By quantitative immunohistochemistry (IHC), "Immune desert" SIC A was characterized by very low densities of CD3 + , CD8 + or CD20 + cells, whereas "Immune and TLS high" SIC E exhibited high densities of these cells (pairwise comparison, p=4.
Establishing the immune classification of STS To establish a robust immune classification of STS, publicly available transcriptomic data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) repository representing 4 large and independent patient cohorts were included. Only tumours from the most common histologies of genomically complex STS were included: leiomyosarcoma (LMS), undifferentiated pleomorphic sarcoma (UPS) and dedifferentiated liposarcoma (DDLPS). We analysed data from the TCGA SARC 8 (n = 213), GSE21050 31 (n = 283), GSE21122  32  (n = 72) and GSE30929  33  (n = 40) cohorts.
Public transcriptomic data pre-processing Transcriptomic data was downloaded from the TCGA data portal (SARC cohort) and Gene Expression Omnibus (Accession codes: GSE21050, GSE21122 and GSE30929). TCGA SARC was restricted to complex genomics sarcomas (UPS, DDLPS and LMS). Normalized TCGA SARC RNA-Seq data was log2-transformed. Micro-array data was normalized using frozen-RMA method  36  from the R package frma. Batch effect was corrected across series using ComBat  37  , with histology as covariate.