dataesr/scientific-paragraphs-categorization huggingface dataset:
In our study, the interstation bias shows little sensitivity, increasing only from 0. 22 to 0. 32 % as spectral resolution decreases. In contrast to  Guerlet et al. (2013) , Butz et al. (  2011 ) also compared XCH 4 results of GOSAT retrievals to TC-CON values. They used the same six TCCON stations as in this study but their database only extended to summer 2010, and their results were obtained with an older version of the RemoTeC software. Nevertheless, their mean scatter of 0. 015 ppm (parts per million; 0. 85 % relative error) and mean bias of -0. 34 % agree well with our study ( σ = 0. 85 % and b = -0. 33 % according to Table  3 ). The increase of interstation bias with lower resolution is statistically significant at a 99. 5 % confidence level for both data filtering methods (Tables  3  and 5 ).
Europe and northern Africa -case study for retrieval biases In Sect. 3. 1, we estimated retrieval biases with respect to water, air mass, and other parameters based on roughly 900 retrieval results, distributed over six TCCON stations. To keep the number of tables and figures reasonable, we limit ourselves in this section to the two extreme cases: GOSAT resolution and 1. 5 cm -1 resolution. Retrieving all available GOSAT data across Europe and northern Africa from May 2009 to May 2011 increases the number of retrievals by a factor of four and it enlarges the parameter space (e. g. SZA and albedo). The improvement in geographic coverage is illustrated in Fig. 7 : the dashed-dotted squares illustrate the ±5 • collocation boxes around the TCCON stations Orléans and Białystok, the coloured pixels show the relative differences in percentage between GOSAT and 1.
The second column shows the retrieval performance if noise is added but a very high SNR of 600 is assumed. The third column shows the synthetic retrieval results with a noise model representative to GOSAT data (SNR = 300). For this nominal synthetic scenario, we observe an increase of the error scatter when the spectral resolution is lowered: σ of XCO 2 increases from 0. 40 to 0. 69 %, σ of XCH 4 almost doubles from 0. 42 to 0. 81 %. The errors are the combination of forward model errors and statistical errors. The right column shows the retrieval accuracy found for GOSAT spectra at original and at lowered resolutions. Contrary to the scatter, the bias of synthetic retrievals also depends on the details of the noise model, i. e. on the functional dependence of SNR with albedo, SZA, and other physical parameters.
Under the assumption of a constant SNR for all synthetic observations, a negative XCH 4 bias at lower spectral resolution occurs only if noise is added to the spectra. At 1. 5 cm -1 , the bias reaches -0. 3 % for SNR = 600 and -0. 5 % for SNR = 300, whereas it remains at +0. 1 % at 0. 25 cm -1 resolution. This behaviour resembles real GOSAT data (see for instance Table  6 ). The absolute XCO 2 bias is smaller than 0. 2 % for any noise level and spectral resolution. All 12 σ values for synthetic spectra can be directly compared with each other, since we culled the results to the same total amount of 1000 filtered retrievals. The filter method was identical to the one employed for GOSAT measurements (cf. Sect. 2. 1) with the following exceptions: The offset criterion was dropped because we did not introduce spectral shifts or offsets or any calibration errors when calculating input spectra.
Acknowledgements. The first author was supported by  ESA  under  ESA  contract number  4000105676/12/NL/AF. S. Guerlet acknowledges funding from  ESA's Climate Change Initiative  on greenhouse gases and the  European Commission 's  Seventh Framework Programme  under grant agreement  218793. A. Butz  is supported by  Deutsche Forschungsgemeinschaft (DFG)  through the  Emmy Noether Programme , grant  BU2599/1-1  (RemoTeC). US funding for TCCON comes from  NASA 's  Terrestrial Ecology Program , grant number  NNX11AG01G , the Orbiting Carbon  A. Galli  et al. : Impact of spectroscopic resolution on retrieval accuracy Observatory Program, the  Atmospheric CO 2 Observations from Space (ACOS) Program  and the  DOE/ARM Program. The Darwin TCCON site was built at  Caltech  with funding from the  OCO  project, and is operated by the  University of Wollongong , with travel funds for maintenance and equipment costs funded by the  OCO-2  project.
We acknowledge funding to support Darwin and Wollongong from the  Australian Research Council , Projects  LE0668470 ,  DP0879468 ,  DP110103118  and  LP0562346. The  University of Bremen  acknowledges financial support of the Białystok and Orléans TCCON sites from the  Senate of Bremen  and  EU projects IMECC, GEOmon, InGOS , and  ICOS-INWIRE , as well as maintenance and logistical work provided by  AeroMeteo Service (Białystok)  and the RAMCES team at  LSCE (Gif-sur-Yvette, France ) and additional operational funding from the  National Institute for Environmental Studies. ECMWF ERA Interim analyses are provided through http://data-portal. ecmwf. int/data/d/interimdaily/. TM4 modelled CH 4 and CO concentration fields have been made available through  J. F. Meirink ,  Royal Netherlands Meteorological Institute (KNMI).
Funding: NE was initially a recipient of a Wellcome Trust PhD training fellowship award and subsequently an  ANR post-doctoral fellow paid by off grant  ( 11 BSV3 01602 ) to GL. This work was supported by a  Wellcome Trust   Special Initiative grant  ( 075820/A/04/Z ) for an integrated approach for the development of sustainable methods to control topical theileriosis to MAD and GL. JPDS acknowledges support from the  Pasteur Institute, JPDS  and GL support from  INSERM , and GL from the  CNRS  and  Labex ParaFrap   ANR-11-LABX-0024. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements. We thank  R Volmer  and  D Ron  ( University of Cambridge ) for the IRE1-deficient and reconstituted cell lines. We thank  D Vucic  ( Genentech ) for compound BV6,  Z Chen  ( University of Texas Southwestern ) for the Flag-RIPK1 construct, and  Sarah Gerlo  ( University of Ghent ) for the EGFP-hIRE1 plasmid. MB has a tenure track position within the Multidisciplinary Research  Program of Ghent University (GROUP-ID). Research in the Vandenabeele unit is supported by  Belgian  grants ( Interuniversity Attraction Poles ,  IAP 7/32 ),  Flemish  grants ( Research Foundation Flanders -G. 0875. 11 , G. 0973. 11 , G. 0A45. 12N, G. 0787. 13N, G. 0172. 12N,  Methusalem grant -BOF09/01M00709 ),  Ghent University  grants (MRP,  GROUP-ID  consortium), grant from the  Foundation against Cancer  ( F94  and  2010-162 ), and grants from the  VIB.
Methods Homozygosity mapping was performed for all family members utilizing the Human Mapping 370 K-Cyto12 SNP genotyping array [Illumina, USA]. For the determination of the homozygosity intervals, data were analyzed by the HomozygosityMapper software  [1]. Sanger sequencing using Big-dye terminator v. 3. 1 cycle sequencing [Applied Biosystems, USA] was performed on an ABI 3730 automatic sequencer [Applied Biosystems, USA] to screen for mutations in candidate genes, perform population frequency studies for variants and determine co-segregation of variants with the disease phenotype within the family. Whole Exome target enrichment Next Generation Sequencing was performed on ABI SOLiD4 platform [Applied Biosystems] according to manufacturer's specifications. DNA library preparation was with TargetSeq™ Exome Enrichment system [Applied Biosystems] as multiplex fragments libraries utilizing both the SOLiD® Fragment Library Construction Kits and SOLiD® Fragment Library Barcoding Kit Module 1-16 for the SOLiD® 4 System.
Bead preparation and enriching was done on an EZ Bead Emulsifier, Amplifier and Enricher utilizing E80 scale. Sequencing modality was with multiplex fragment paired-end. Bioinformatic analyses of whole Exome NGS data was as follows: Raw data files (in a proprietary XSQ file format) were analyzed with the LifeTechnologies LifeScope v2. 5. 4 software running on a dedicated cluster to align the reads produced by the SoLID to a hg19 whole genome reference sequence, sourced from the University of California, Santa Cruz Genome Informatics Group (UCSC). The aligned BAM files were validated, duplicate sequences were identified and removed and incorrectly identified Mate-Pairs were corrected using the Picard v1. 87 software. The Genome Analysis Tool Kit (GATK) v3. 0. 0 was applied to the 'corrected' output files to recalibrate the base quality scores, using machine learning to model any systematic errors in the data; carry out localized realignments around possible insertion/deletion sequences to ensure mapping accuracy; identify viable variants from the sequence reads; and recalibrate the variants to ensure accuracy of the variant calling, in a variant-type specific manner.
Once a suitable list of variants was produced in this manner, the list was filtered using in-house scripts to confirm variant zygosity and identify those variants that conform to the inheritance model. These variants were annotated using an in-house script in conjunction with Annovar to produce an annotated list of variants with the most recent information available on a number of reference websites. As the three-dimensional [3-D] structure of the ZNF407 protein is not available in the Protein Data Bank (PDB), sequence homology methods were utilized to construct 3-D models of the wild type and mutant zinc finger domains 18 and 19 of ZNF407. BLAST-searches (blast. ncbi. nlm. nih. gov/Blast. cgi) were performed within the PDB for crystal structures of homologous proteins. Once these were identified, the CLUSTALW software (www.
ebi. ac. uk) was utilized to insure correct sequence alignments between domains 18 and 19 of ZNF407 and the retrieved zing finger structures found in the PDB. Focus was on complexes with doublestranded DNA only so as to have the proper bioactive conformation of the tandem finger. A high sequence homology with the Aart protein in complex with dsDNA (PDB code 2I13) was identified. The 3-D model of ZNF407 was then generated with the Modeller software (version 9v8)  [2]  based on the crystal structure of Aart in complex with DNA as a template (PDB code 2I13). A cycle of energy minimizations was then performed to obtain a stable conformation of the 3-D model. The quality of the model generated was finally assessed with DOPE score  [3]  and the Ramachandran plot of the best model was calculated by ProCheck  [4]  to discriminate unfavorable amino-acids backbone conformations.
Three-Dimensional ribbon representations of the models were obtained with the molecular graphics program PyMOL (www. pymol. org). To generate the 3-D model of the mutant, Serine 1685 was replaced for a Tryptophan residue in the second finger of the 18-19 tandem finger domain and submitted to the same protocol as that used for the wild type complex. Thereafter both models were compared for an insight into the structural and thermodynamic stability effects of the mutation. To generate the 3-D model of the mutant, Serine 1685 was replaced for a Tryptophan residue in the second finger of the 18-19 tandem finger domain and submitted to the same protocol as that of the wild type complex. Thereafter, both models were compared for an insight into the structural and thermodynamic stability effects of the mutation.
Discussion Homozygosity mapping, screening of positional candidate genes, whole Exome Next Generation Sequencing and data mining were utilized in the effort to identify the disease causing gene and mutation for this apparently novel rare autosomal recessive disease. Out of the five potentially damaging variants identified within the homozygosity intervals, the IGDCC4: c. G727A/p. D243N [rs35223184]; and C15orf39: g. G46293956A/p. R1029K [rs149175372] were immediately excluded since they represent known common polymorphisms with >1% population frequency [1000 Genomes and NCBI dbSNP Build 135]. The third identified variant, TLN2 [c. G6716A/p. R2239H], affects a non-conserved amino acid. The "alternate" aminoacid Histidine is found as the "normal" amino-acid at that position in numerous non-human species.
The fifth variant, a c. C5054G/p. S1685W at the zinc finger transcription factor gene (ZNF407), was found to affect an evolutionarily highly conserved amino acid. It has damaging effects according to PolyPhen and SIFT protein-modeling software  [9, 10] , and it co-segregates with the disease phenotype within the family. It is absent in any of the publically available variant databases [1000 Genomes and NCBI dbSNP Build 135]. Furthermore, it is absent in 400 ethnically matched control chromosomes, making it the only and most likely diseasecausing gene and mutation. Zinc finger genes are found ubiquitously, and categorized into several types based on the structure of their binding residues, and their biological roles within the cell  [11, 12]. Through their ability to bind to DNA, RNA & proteins, zinc finger proteins facilitate numerous cellular functions and processes including, but not limited to, the control of hematopoiesis, regulation of gene expression, cell differentiation, and development  [13].
Their DNAbinding domain is comprised of tandem repeats of two, three or more fingers that can have different binding specificities. These tandem arrays, separated by amino acid linkers, bind through sequence-specific contact to 2-4 bases in the major groove of the DNA, spaced at 3-bp intervals  [14, 15]. Optimal binding is achieved through the sequential binding and wrapping of the zinc finger domains around the DNA  [15]. The genomic sequence of ZNF407, as reported in the Uniprot database of proteins [Uniprot consortium, 2012] is 434,710 bp long and generates a 2,248 amino acid protein product of 247,367 Da. with 22 zinc finger domains. Each of the 22 zinc finger motifs comprises from 23 to 26 amino acid residues. The 22 zinc fingers come in tandems of one (fingers 6, 7, 12 and 13), two (fingers 4-5, 8-9, 10-11), three (1-3, 14-16) and six  (17) (18) (19) (20) (21) (22)  fingers.
Zink fingers 18 and 19 represent amino acid residues F1656-H1680 and F1686-H1708, respectively, and are missing in isoform 3. Perturbation or modification of the linker adjoining two zinc-finger domains possibly result in the loss of DNA binding domains, thus eliminating their functionality and subsequently interfering with the expression of various genes under the zincfinger gene control. Genes under ZNF407 control have not been as of yet identified. The homology modeling approach is justified since the identity rate between the sequence of an unknown structure and a sequence whose experimentally available structure is high. Figure  3  shows the sequence alignment between fingers 18 and 19 of the target sequence (ZNF407), as annotated in UniProt, and the sequence corresponding to fingers 2 and 3 of the high resolution crystal structure (Protein Data Bank code 2I13, subunit A) of a tandem 6-finger zinc finger domain (Aart) bound to a double-stranded DNA and designed to recognize ANN triplets  [17].
ZNF407 mRNA, as well as the corresponding protein are expressed in most normal tissues with moderate nuclear and cytoplasmic positivity. These include adult, embryonic and fetal central and peripheral nervous systems (http://www. nextprot. org/db/entry/NX_Q9C0G0/expression). It is not known if there is tissue specificity for each of the three isoforms or what tissues express the two isoforms affected by the c. C5054G/p. S1685W mutation. To date, twenty polymorphisms for ZNF407 have been reported in the Domain Mapping of Disease Mutations database (DMDM)  [22]. The natural variants reported in the UniProt database entry Q9C0G0 (N69S, G512R, N972T, S1259L, A1913T) are included in DMDM. None of these, though, have been associated with known diseases. However, deletion of a critical region on 18q23 that includes the ZNF407 gene and two other zinc finger genes (ZNF516 and ZNF236), among other genes, is correlated with congenital aural atresia (CAA), which manifests a subset of phenotypes recognized by the 18q deletion (18q-) syndrome  [23].
Thus, deleterious mutations resulting from NGS data need to be supported by additional genomic or functional data whenever possible. The ZNF407 c. C5054G/p. S1685W mutation is the only mutation that lies within one of the homozygosity intervals that is certain to harbor the offending gene that could not be excluded as the disease causing mutation. It cosegregates with the disease phenotype within the family, it is not found as a polymorphic variant in publically available databases or in the ethnic population and it affects an evolutionarily highly conserved amino acid. Normal sequence variants [polymorphisms] have not been reported in ZNF407 linker regions (between zinc fingers) providing further evidence of the sequence specificity required by these regions for normal function and subsequently for the damaging effects of the mutation.
Acknowledgments This publication was made possible in part by  NPRP  grant  09-367-3-087  titled " Genetic Factors In Autosomal Recessive Disorders Among Consanguineous Qatari Families " from the  Qatar National Research Fund  [a member of  Qatar Foundation ] and in part by the  Shafallah Center Foundation (Doha, Qatar). The authors would like to thank  Dr. Vasiliki Chini  for the valuable help with the preparation of this manuscript and the submission logistics, and the  Service de coopération et d'action culturelle of the French Embassy in Doha  for constant support.
9 and 1. 4. Only L2 presents a remaining high response factor of 4. 6. It is linked to the structure of L2 and its high vapor pressure (ca. 5. 5 kPa at 25 ∘ C), which consequently facilitates its volatilization and increases analytical bias. According only to vapor pressure, TMSol must show an important residual overestimation in these conditions as its vapor pressure (ca. 9. 9 kPa at 25 ∘ C) is higher than the one of L2. However, the response factor of TMSol is only around 1. 1 which is linked to Henry's law through the Henry's law constant which is function of temperature, solute and solvent nature. Indeed, this is explained by the hydrogen bonds between the hydroxyl groups of TMSol and those of ethanol. This phenomenon improves the solute/solvent interactions Postanalysis adjustments need to be performed on ICP-OES raw data interpretation as no other technical improvement is available to further decrease the temperature in the nebulization chamber and avoid L2 desorption from mist.
PDL1 expression and copy number alterations in breast cancer PDL1 expression was measured by using probe sets whose identity and specificity showed 100% accuracy (Supplementary Table  1 ). We evaluated PDL1 expression in 45 breast cancer cell lines. Luminal cell lines (N=16) showed lower PDL1 expression level than basal cell lines (N=11; p=1. 46E-03, Tukey test) and mesenchymal cell lines (N=8; p=2. 0E-04, Tukey test). PDL1 expression was similar between basal and mesenchymal cell lines (p=0. 34, Turkey test; Figure  1  and Supplementary Figure  1 ). PDL1 expression was analyzed in 5,454 clinical breast cancer samples pooled from 18 data sets (Supplementary Tables  2 3 ): 1076 tumors (20%) showed PDL1 upregulation when compared to normal breast (ratio T/NB ≥2; "PDL1up" group), and 4378 (80%) did not show upregulation (ratio <2; "PDL1-no up" group).
PDL1 expression and metastasis-free survival We assessed the prognostic value of PDL1 expression in terms of MFS and OSS. MFS data were available for 1,080 patients, including 642 who remained metastasis-free during a median follow-up of 85 months (median MFS not reached) and 438 who displayed metastatic relapse. The 5-year MFS rate was 61% [95CI, 0. 58-0. 64]. In univariate analysis applied to the whole population (Table  2 ), axillary lymph node involvement, large tumor size, high grade, negative ER status, and negative PR status were associated with poor MFS, whereas PDL1 expression was not (p=0. 57, Wald test; HR=0. 94 [0. 75-1. 17], and p=0. 576, log-rank test, Figure  2A ). The same analysis was done in each molecular subtype separately. As shown in Figure  2B , PDL1 expression influenced MFS in the basal subtype with 63% 5-year MFS (CI95 55-73) in the "PDL1-up" group and 44% (CI95 36-54) in the "PDL1-no up" group (p=5.
PDL1 expression and associated biological processes Supervised analysis identified 359 genes differentially expressed in the Guedj's dataset between the tumors with versus without PDL1 upregulation, including 287 genes upregulated and 72 genes downregulated in the "PDL1-up" samples (Supplementary Table  5 , Supplementary Figure  3 ). The robustness of this gene list was confirmed in two large independent sets of respectively 448 and 533 tumors by using a metagene approach and ROC curves with "PDL1-up" genes (Supplementary Figure  3 ) with respective accuracy rates of 90% (p=2. 6E-49, Fisher's exact test) and 80% (p=1. 4E-50, Fisher's exact test). Ontology analysis of these 359 genes (Supplementary Table  6 ) revealed that "PDL1-up" tumors overexpressed genes involved in the regulation of the local immune response.
Breast cancer samples Breast cancer cell lines and clinical samples were profiled using DNA microarrays. Our own data set of breast cancer cell lines included 45 cell lines: BT-20, BT-474, BT-483, CAMA-1, HBL100, HCC38, HCC202, HCC1395, HCC1500, HCC1569, HCC1806, HCC1937, HCC1954, HME-1, carcinosarcoma-derived Hs578T, MCF-7, MCF-10A, MDA-MB-134, MDA-MB-157, MDA-MB-175, MDA-MB-231, MDA-MB-361, MDA-MB-415, MDA-MB-436, MDA-MB-453, SK-BR-3, SK-BR-7, T47D, UACC-812, ZR-75-1, ZR-75-30 (http:// www. atcc. org/), HMEC-derived 184A1 and 184B5 (ATCC, http://www. atcc. org/), BrCa-MZ-01, SUM44, SUM-52, SUM102, SUM-149, SUM159, SUM-185, SUM-190, SUM206, SUM-225, SUM229 http://www. cancer. med. umich. edu/breast_cell/production), and S68 (a kind gift from V. Catros, Cell Biology Department, CHU Rennes, France).
All cell lines are derived from carcinomas except MCF-10A, which is derived from a fibrocystic disease, and HME-1 and 184B5, which represent normal mammary tissue. Our own data set of clinical samples included 286 cases representing pre-treatment invasive carcinomas from patients with non-metastatic and non-inflammatory disease at diagnosis. The study was approved by our institutional review board (the Institut Paoli Calmettes (IPC) "Comité d'Orientation Stratégique") and each patient had given a written informed consent for research use. We pooled it with 17 available data sets comprising at least one probe set representing PDL1. These sets were collected from the National Center for Biotechnology Information (NCBI)/ Genbank GEO and ArrayExpress databases, and authors' website (Supplementary Table  2 ).
Gene expression data analysis Our own gene expression data set (cell lines, clinical normal and cancer samples) had been generated using Affymetrix U133 Plus 2. 0 human microarrays (Affymetrix®, Santa Clara, CA, USA) as previously described  [56]. All data were MIAME compliant and deposited in the Array-Express and GEO databases (E-MTAB-1693 and GSE31448). The breast cancer cell lines and clinical samples were analyzed separately. Cell lines were analyzed as previously described  [57]. PDL1 expression was measured by analyzing different probe sets whose identity and specificity were verified using the NCBI program BLASTN 2. 2. 29+ (Supplementary Table  1 ). The molecular subtypes (luminal, basal, and mesenchymal) were defined as previously reported  [58]. Seventeen cell lines were classified as luminal, 11 as basal, and 8 as mesenchymal, whereas 9 could not be classified in any subtype.