dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting log-transformed odds ratio was used to cluster the phenotype classes and the immune cell types (twodimensional hierarchical clustering, Euclidean distance, average linkage). Clustering and visualization were done with the software Genesis  [58]. The relative number of immune cells, I c , which is proportional to the absolute number of cells of a specific immune cell type c in a heterogeneous tumor sample, was estimated by calculating in each cell type: I c ¼ X n c i¼1 log 10 x c i þ 1 À Á w c i ; where n c is the number of immune metagenes per cell type c, x c i are the normalized counts per gene i (TPM), and w c i is the weight defined as median log2-intensity of gene i in cell type c from microarray expression data, which was used to identify immune related genes.
Characterization of colorectal cancer cancer-germline antigens The CTDatabase  [59]  was queried for tumor-specific antigens: 92 localized on chromosome X and 124 in the remaining chromosomes. The genes that showed downregulation in the tumor samples compared with normal tissue (n = 50) were removed (both from TCGA and Genentech cohort, statistical significance by at least one of the EBSeq and DESeq analyses). Using the normal tissue samples, a baseline for cancer-germline antigen expression was set as three standard deviations from the median normal expression  [60]. Positive expression is considered above the baseline. Additionally, zero standard deviation was excluded, resulting with 178 cancergermline antigens. K-means clustering was applied to the RNA-sequencing data for each patient group to identify clusters of highly expressed cancer-germline antigens.
Characterization of the molecular phenotypes from TCGA data Molecular phenotypes were defined using three features: MSI status, mutation rates, and methylation subtypes. For all three molecular phenotypes we used the most recent update of the raw sequencing TCGA data set. The MSI status was experimentally determined and provided with the clinical dataset (n = 590). The tumors assigned with MSI-low status (n = 93) were subsumed to the MSS group (n = 427). In the original publication, mutation rates were provided for 224 tumors  [10]. Adopting the same approach, mutation rates were then calculated for a total of 470 patients using raw exomesequencing data and MSI status as available by the time of query. To define the hypermutability of the tumors, we calculated the non-silent mutation rate and the number of nonsynonymous coding mutations per 1 million bases and normalized by the sequencing coverage of the experiment.
Characterization of colorectal cancer neo-antigens From the 222,169 somatic mutations, we selected mutations that reside within exons and do not span exon borders. The DNA changes were mapped on the protein level. Start codon changes and splicing mutations were excluded. Along the missense, nonstop, frameshift, and non-frameshift mutations, we slid windows with lengths 8 of 11 nucleotides. To estimate the HLA haplotype of each patient, the raw RNA-seq data were analyzed using HLAminer's method for prediction of HLA class I alleles  [61]. The HLA sequences were updated according to the IMGT/HLA public repository  [62]. To reduce the ambiguity, we considered the matching normal samples, the Allele Frequency Net Database  [63]  and the prediction scores. The predicted four-digit HLA class I alleles and the mutated peptides were used as input for netMHCpan  [64]  to estimate their binding affinities.
Both strong and weak binding peptides were analyzed for the selection of neo-antigens (prediction rank ≤2). The selected neo-antigens were then compared with human protein sequences and the matching peptides were distinguished from the novel peptides. Epitope databases were screened in order to evaluate the predicted neo-antigens (last update Dec 2014). Deposited peptidic epitopes, T-cell assays and MHC ligand assays were downloaded from the Immune Epitope Database  [65] , Dana Farber repository  [66] , and CIG-DB  [67]. Automatized HTTP-mediated download and HTML processing retrieved all records from the following databases: MHCBN  [68] , PeptideDatabase  [69] , SYFPEITHI  [70] , and TANTIGEN  [71]. The entries were filtered to select for HLA class I-binding epitopes. The total number of reported peptide-MHC affinities (55,000) was five times smaller than the predicted CRC antigenome, and therefore only a small fraction of the MHC-peptide combinations were experimentally covered.
Acknowledgments The results shown here are in part based upon data generated by the  TCGA Research Network : http://cancergenome. nih. gov. We thank  Matthias Baldauf  and  Dietmar Rieder  for maintaining the high-performance computing infrastructure. This work was supported by the  Austrian Science Fund (FWF) :  W1101-B18  ( DK Molecular Cell Biology and Oncology ) and SFB021 Cell proliferation and cell death in tumors, and the  Tiroler Standortagentur (Bioinformatics Tyrol ).
The polypeptide linker between POTRA1 and helix H1 in FhaC DIS was found to be well ordered. Tracing of the linker revealed that in the current crystal structure two adjacent FhaC DIS molecules form an intermolecularly swapped dimer via exchange of their helices H1 (Fig. 1b, c ). The FhaC DIS structure triggered a reanalysis of the previous 3. 15 Å resolution WTFhaC data, which were reprocessed from raw images to 2. 9 Å resolution using state-of-the-art techniques. Reinterpretation of the WTFhaC diffraction data on the basis of the FhaC DIS structure clearly revealed modelling ambiguities in the previous WTFhaC structure (PDB entry 2QDZ  9  , referred to as 'WTFhaC old ') and yielded a revised model of WTFhaC (referred to as 'WTFhaC new '). Comparison of FhaC structural models: H1 linker and L6 loop.
(iii) The positioning and structure of loop L6 and the adjacent strand is well-resolved in FhaC DIS in contrast to WTFhaC old. FhaC DIS differs from WTFhaC old by a massive register shift in loop L6 and the following strand 12 and a concomitant change in the position of the tip of loop L6 by 17 Å (Figs 2 and 3, Supplementary Fig. 1 ). In WTFhaC old , loop L6 was depicted in a conformation, where its tip touches the periplasmic rim of the barrel (cyan conformation in Fig. 3a ), but in the 2. 5 Å resolution FhaC DIS structure the tip of loop L6 touches the inner side of the barrel wall at strand 13 (magenta conformation in Fig. 3a , Supplementary Fig. 2 ). In particular, this 'lid lock' conformation of loop L6 describes the WTFhaC data unambiguously better than the periplasmic conformation in WTFhaC old , as directly evidenced by omit maps calculated for these two conformations against the respective data sets (Fig.
Arg29 of the terminal winding of H1 may contribute to this interaction by stacking to the other face of Tyr177. In WTFhaC new , the polypeptide region 36-56 is disordered and thus remains structurally unresolved (Fig. 6a, b ). In contrast, in FhaC DIS carrying the (V169T,I176N) double mutation, the full linker is ordered. In fact, Leu38, the third residue of the segment that is disordered in WTFhaC new , directly interacts in FhaC DIS with the mutated residue Thr169 by formation of a hydrogen bond between the Leu38 backbone-N and the Thr169 side chain-OH (Fig. 6d ). Apparently, this interaction triggers ordering also of the entire segment, where residues 39 to 42 are involved in further interactions with the same POTRA2 domain. Biochemical data clearly indicate that the observed interactions around residues 33 and 38 (Fig.
The second extract was subjected to chromatography onto a cation-exchange column Poros HS20 (Perkin-Elmer) equilibrated in 20 mM sodium phosphate (pH 7. 0) with 1% b-octyl glucoside. FhaC was eluted with a linear 0-1 M gradient of NaCl. The FhaC-containing fractions were pooled and applied onto a 1 ml HiTrap chelating column (Amersham Biosciences) equilibrated in 20 mM sodium phosphate (pH 7. 0), 1% b-octyl glucoside. FhaC was eluted by a pulse of 500 mM imidazole (pH 6. 5) in the equilibration buffer. For crystallization, FhaC was concentrated to 26 mg ml À 1 by using Vivaspin centrifugal devices with a 50 kDa cut-off (Vivascience). Crystallization and data collection. Crystals were obtained at 20 °C using the hanging drop vapour diffusion method. The protein and precipitant solutions were  mixed in a 1:1 ratio.
Crystals were grown at a protein concentration of 26 mg ml À 1 in 34% PEG1000, 1% b-octyl glucoside and 500 mM imidazole (pH 6. 5). Diffraction data were collected at 100 K on beamline ID14-4 at the European Synchrotron Radiation Facility (Grenoble, France). All diffraction data were processed with XDS  22. Data collection and refinement statistics are summarized in Table  1. Whereas the crystal packing remained isomorphous to the old crystal form, the diffraction limit increased to 2. 5 Å. Model building was accomplished manually with Coot (Crystallographic Object Oriented Toolkit)  23  from the CCP4 suite  24. The refinement with Buster 25 led to an R work of 21. 7% and an R free of 25. 9% using data to 2. 5 Å for FhaC DIS and R work /R free of 22. 2%/27. 9% using data to 2. 9 Å for the reprocessed WTFhaC data  9.
All structural differences between the FhaC DIS model and the superseded WTFhaC old model (PDB entry 2QDZ  9  ) are summarized in Supplementary Table  1. Composite omit maps after simulated annealing of the FhaC DIS structure and the superseded WTFhaC old structure against the FhaC DIS and the WTFhaC diffraction data (PDB entry 2QDZ 9 ) were generated using PHENIX  26. Sequence alignments. Eleven TamA orthologs, ten BamA orthologs and ten TpsB proteins were selected from the NCBI database, showing pairwise sequence ). In addition, the H. ducreyi BamA sequence was added (NCBI accession code 4K3C_A) and a TpsB sequence (WP_004649222. 1). Alignments, starting with the second last POTRA domains, were performed with Clustal Omega  27, 28  and further edited taking into consideration available structural data (Supplementary Fig.
Acknowledgements Crystallographic experiments were performed at the  European Synchrotron Radiation Facility, Grenoble, France. We thank  R. Ravelli  for his support at beamline ID14. This work was supported by the  French National Research Agency  (Grant  ANR-2010-BLANC 1306 DYN FHAC  to F. J. -D), the  Swiss National Science Foundation  (Grant  PP00P3_128419  to S. H. ) and the  European Research Council  ( FP7  contract  MOMP 281764  to S. H. ). F. G. acknowledges a fellowship by the  Werner-Siemens Foundation.
A statistical method trained and optimized to retrieve seven-layer relative humidity (RH) profiles is presented and evaluated with measurements from radiosondes. The method makes use of the microwave payload of the Megha-Tropiques platform, namely the SAPHIR sounder and the MADRAS imager. The approach, based on a generalized additive model (GAM), embeds both the physical and statistical characteristics of the inverse problem in the training phase, and no explicit thermodynamical constraint -such as a temperature profile or an integrated water vapor content -is provided to the model at the stage of retrieval. The model is built for cloud-free conditions in order to avoid the cases of scattering of the microwave radiation in the 18. 7-183. 31 GHz range covered by the payload. Two instrumental configurations are tested: a SAPHIR-MADRAS scheme and a SAPHIR-only scheme to deal with the stop of data acquisition of MADRAS in January 2013 for technical reasons.
Indeed, the Microwave Sounder (MWS) and the Microwave Imager (MWI) of MetOp-SG have channels very close to those of SAPHIR and MADRAS. The second objective is to demonstrate the potential of purely statistical methods in the following problem: given a set of brightness temperatures (BTs) provided by a spaceborne radiometer, what is the vertical distribution of RH and what are the expected limits of such an approach? Many retrieval approaches exist; however, to our knowledge, a few of them estimate the RH profile from a simple input data set restricted to the BTs. Indeed, most of the approaches are physically based iterative techniques such as a n-dimensional variational algorithm that converges to the least biased profile using other inputs as prior knowledge of the system under study (such as surface emissivity, temperature profile and sometimes a prior water vapor profile for BT simulations).
In the following the term RH will refer to the relative humidity computed with respect to the liquid phase of water only. In-orbit instrumental (name) (GHz) (MHz) (along × across track) Noise (NE T (K)) S1 f 0 ± 0. 2 ±200 1. 44 SAPHIR S2 f 0 ± 1. Only clear-sky conditions are considered. Indeed, as underlined by  Brogniez et al. (2013) , the representation of the cloudy conditions in a training database still presents a limit because reference profiles of cloudy situations with known uncertainties are difficult to gather, which could introduce unwanted additional errors in the methodology. Moreover, we chose to restrict the main part of the current work to a full description of the retrieval models dedicated to oceanic situations. The retrieval models for land surfaces have a similar design and we will mainly discuss the approach followed to consider the extremely variable continental emissivity  (Karbou et al.
5 •. The simulations also make use of the instrumental noise to have a realistic base of work. The radiometric sensitivity is often considered as the instrumental noise since it gives the minimum variation in the measured upwelling radiation that a specific channel can detect (noise-equivalent T : NE T , in K). This noise may be considered as additive and modeled as realizations of a random variable following a normal distribution with a zero mean and a standard deviation equal to the NE T value for each channel. The simulated data sets are re-built by aggregating 10 noisy samples for each original sample. With the conclusion of MADRAS after almost 15 months of measurements, two configurations of the RH retrieval method have been considered: a SAPHIR-only scheme and a SAPHIR-MADRAS scheme, the latter being associated to a selection of the optimal channels.
While such normalization does not affect the estimation provided by GAM (but only the relative weight of each predictor in the fit), the normalized input data set is the same for all models in order to simplify the process. A principal component analysis (PCA) is also implemented on the BT to feed each statistical model with uncorrelated and linearly independent data. Indeed, the weighting functions of the six channels of SAPHIR slightly overlap each other to cover the entire absorption line. As a result, while each channel receives mainly the radiation emitted by a given layer of the atmosphere, contributions from layers above and below are not negligible, yielding to some interdependencies between the channels. Finally, in order to account for the known exponential relationship between the BT in the 183.
8 %RH. When the RH retrieval is based on the MLP approach, the SD increases from 2. 8 to 3 %RH. In these two cases the difference is relatively small. In fact, an indepth study of the relevancy of the channels reveals that the selected inputs are only weakly dependent on the retrieval model but are highly dependent on the atmospheric layer. For the SAPHIR-only scheme, all channels are used. An impact study of the pre-processing of the data on the accuracy of RH retrieval shows that, whatever the atmospheric layer or algorithm considered, the improvement obtained with PCA is negligible (< 3 % of the error variance). The use of uncorrelated inputs is thus not necessarily required for the considered models. Finally, the linearization of the problem with the exponential function is beneficial only for the MLP: in this case it leads to a decrease of the error variance of about 50 %, while no significant improvement is observed for LS-SVM and GAM (< 3 % of the error variance).
Here, the GAMs have been optimized using the nominal viewing angle of MADRAS (53. 5 • ) and limited to the nadir geometry of SAPHIR. In fact, the observed relationship between the BT and the viewing angle can be accurately approximated by a multi-variate linear function, as noticed by  Goldberg et al. (2000)  and  Buehler et al. (2004). Knowing the means and variances of this relationship for each angle is enough to assimilate this function in the normalization method, which is based on standard scores. These have been computed every 2 • from nadir to 52 • (the maximum viewing angle of SAPHIR is 50. 7 • ) using the training database. processes involved in the initiation of the Madden-Julian Oscillation and to improve its simulation and prediction (Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011/Dynamics of the Madden-Julian Oscillation/ARM Madden-Julian Oscillation Investigation Experiment, hereafter C/D/A).
The 99. 9 % level of confidence is indicated on Fig. 7  and t values below this level are not given. Box-and-whiskers diagrams are used to represent the distributions of the differences and show the similarity/differences of the estimations when using both SAPHIR and MADRAS or only SAPHIR. As expected from the syn-thetic data analysis, the mid-tropospheric layers 2 to 5 are very well retrieved, with quite good correlations (0. 6-0. 92) when SAPHIR and MADRAS are combined. Additional analyses show that the SD of the differences reach a maximum of 10 %RH (layer 4). The removal of MADRAS clearly affects the estimation of RH for most layers, while for layer 3 there is no significant effect. This is expected from the distribution of the weighting functions that present a large overlap around 300 hPa (see also Fig.
Conclusions Microwave observations from the SAPHIR and MADRAS microwave radiometers of the Megha-Tropiques satellite are used to retrieve seven-layer RH profiles. For this purpose, optimized GAMs were trained for each atmospheric layer over a realistic set of synthetic observations. This set is composed of 18 years of radiosonde profiles covering the tropical belt (±30 • ), sampled from the ARSA database, used in combination with a radiative transfer model  (RTTOV v9. 3)  to get the associated synthetic BTs. Our approach consists of using only the satellite measurements as inputs of the retrieval method. The training phase of the model considers implicitly the role of temperature, humidity and surface characteristics of the tropical atmosphere. To assess the performance of GAM, two other algorithms based on supervised learning, namely a MLP and a LS-SVM, have been also trained and optimized using adapted validation methods.
31 GHz line, such as AMSU-B or MHS, shows that the current approach gives similar performance (root mean square errors of about 10 %RH in the mid-troposphere). It is also consistent with the layer-averaged RH profiles estimated by the Indian team involved in the Megha-Tropiques mission, although further constraining the retrieval by NCEP/NCAR outputs (Venkat  Ratnam et al. , 2013; Gohil et al. , 2013). A 1-D-variational technique exploring SAPHIR data should further improve the estimation of RH. Following this work, our current efforts focus on the estimation of the conditional error associated to the retrieval itself. Indeed, because the widths and altitudes of the weighting functions of SAPHIR are strongly dependent on the thermodynamical state of the atmosphere (the drier the atmosphere, the wider the layer; the maximum of sensitiv-ity shifting from the upper troposphere towards the midtroposphere), it is clearly expected that the robustness of the RH estimation will be conditioned by the state of the atmosphere.
Acknowledgements. The authors thanks the  LMD/ABC (t)/ ARA  group for producing and making available to the community their "ARSA" radiosounding database (available from http://ara. abct. lmd. polytechnique. fr/index. php?page=arsa), and more specifically  Noelle Scott  for her advice on the use of ARSA for training purposes. Gaelle Clain  is also acknowledged for her work on the CINDY/DYNAMO/AMIE profiles and their collocation with Megha-Tropiques. The help of  R. Johnson ,  P. Ciesielski  ( CSU ) and  J. Wang  ( NCAR ) with the CINDY/DYNAMO/AMIE radiosounding data set was greatly appreciated. The advice of P. Eriksson were welcomed and helped to improve the manuscript. Finally, the two anonymous reviewers are thanked for their comments and questions that improved the description of the work.
A narrow jet of ACC water also flows across the plateau in the narrow, mid-depth (∼ 1000 m) channel just to the south of Kerguelen Island (Fig. 1 ). This feature corresponds with the northernmost presence of a subsurface temperature minimum formed by winter cooling (near 200 m depth) and thus defines the Successive images of surface chlorophyll distributions (ocean colour images from NASA MODIS-Aqua imaging spectroradiometer; presented as daily unified data product SSALTO/DUACS 1 km) show the bloom development. Image date 28 October: most stations of the initial survey downstream of Kerguelen Island (TNS 1-10, TEW 1-6), the HNLC reference station (R2, upstream), and the KEOSP1 plateau reference station (A3-1 at the southern end of the TNS transect) were sampled before any significant biomass accumulation had occurred.
9 % CaCO 3 , δ 13 C= +2. 9), was weighed into smooth wall tin capsules (5 × 5. 5 mm SC1190, Sercon) and lowered into the Exetainer tubes and purged; then 1mL of DIC free seawater was added before proceeding as for the samples. Blank, standard, and sample headspaces (one standard after each of the five samples) were sampled using a Finnigan Gas-Bench2 (Thermoscientific) fitted with a 100 µL sample loop. The headspace gases from the gas bench were analysed (continuous flow) by the DeltaV Plus isotope ratio mass spectrometer and Isodat 3 software at CSIRO Marine and Atmospheric Research.
Acknowledgements. The  Institute Polaire Paul-Emile Victor , the captain and crew of the  Marion Dufresne , the  Australian Commonwealth Cooperative Research Centre Program , and  CSIRO Marine and Atmospheric Research (CMAR)  provided logistic and financial support. Special thanks go to  Pierre Sangiardi  ( IPEV ) for implementing the underway seawater supply for our high-volume particle sampling and underway sensor mapping;  Louise Oriole  ( Laboratoire d'Océanographie Microbienne, Banyuls sur mer, France ) for shipboard nutrient analyses;  Abraham Passmore  ( ACE CRC ) for BSi analyses;  Peter Jansen  ( IMOS ) for the logging system for the underway ISUS ultraviolet nitrate sensor;  Ben Weeding  ( IMOS ) for advancing ISUS calibration;  Thomas Rodemann  for CHN analyses in the  University of Tasmania Central Sciences Laboratory ; the  VUB  for nitrate isotope analyses;  Clair Lo Monoco Nicolas Metzl  ( LOCEAN, UPMC-CNRS ) for access to pCO 2 results; and  Andy Bowie  ( ACE CRC/UTAS ),  Pier van der Merwe (ACE CRC) , and  Fabien Queroue  ( UTAS/UBO ) for access to iron results.
This work was supported by the  French Research program  of  INSU-CNRS LEFE-CYBER  (Les enveloppes fluides et l'environnement-Cycles biogéochimiques, environnement et ressources), the  French ANR (Agence Nationale de la Recherche ,  SIMI-6 program ,  ANR-10-BLAN-0614 ), and the  French CNES  ( Centre National d'Etudes Spatiales ). Edited by:  I. Obernosterer.
Some of the ITS2 sequences were obtained by direct bi-directional sequencing, but most of the ITS2 PCR products were cloned with the TOPO10 kit from Invitrogen. Between one and four clones per specimen were sequenced. Sequence editing and generation of consensus sequences were accomplished using Codon-Code Aligner (CodonCode Corporation). Alignments were automatically generated using Muscle  [44]  as implemented in Seaview program  [45]  and verified manually. Phylogenetic trees were reconstructed using PhyML  [46]  or NJ as implemented in Seaview  [45] , with 100 and 1000 bootstrap replicates, respectively. The illustrated trees were constructed with PhyML. Only the bootstrap values (BV) higher than 80% are shown on the trees. We used GTR + G + I model as indicated by MEGA5  [47]. Our COI sequences were compared to Genbank (NCBI) sequences using BLAST (www.