dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients and samples Tumor samples were obtained from patients with breast adenocarcinoma treated in our three institutions: the Institut Paoli-Calmettes (IPC, Marseille, France: 71 IBC and 139 nIBC), the MD Anderson Cancer Center (MDA, Houston, TX, USA: 25 IBC and 58 nIBC), and the General Hospital Sint-Augustinus (TCRU, Antwerp, Belgium: 41 IBC and 55 nIBC). Each patient gave written informed consent and this study was approved by the institutional review boards of all 3 participating centers. The present data set includes 137 samples from patients with IBC and 252 samples from patients with nIBC. Patients with IBC were selected by strictly adhering to the consensus diagnostic criteria published by Dawood and colleagues  (11). IBC cases included locally advanced (AJCC Stage III) and metastatic cases (AJCC Stage IV).
nIBC cases included both early stage disease (AJCC Stages I and II) and advanced stage disease (locally advanced: AJCC Stage III, and metastatic: AJCC Stage IV). ER and ErbB2 expression were defined using probe sets 205225_at (ESR1) and 216836_s_at (ERBB2) as described previously  (12). As a control, we verified the correlation between mRNA status and immunohistochemistry (IHC)-based protein status as discrete variables. The positive or negative mRNA status was defined using a 2-component Gaussian mixture distribution model as described previously  (13). Data are provided in the supplementary data file (Supplementary Figure  1  and Supplementary Table  1 ). Tumor grade was determined using the genomic grade index (GGI)  (14). All patients were treated using a multidisciplinary approach according to standard guidelines.
Data processing and normalization RNA extraction from the 389 samples and hybridization onto Affymetrix GeneChips (HGU133-series) was performed as described before  (6, 8, 9, 15). Expression data were normalized by GCRMA  (16). For each of the 3 data sets separately, probe sets with expression values above log 2 (100) in at least 1% of the arrays were filtered in. Next, the list of common informative probe sets (N=9. 926) was identified. This list was used to merge the distinct data sets. Therefore, we performed regression normalization, using the Limmapackage in BioConductor, to remove technical, lab-specific, variation in gene expression between the distinct data sets. A principal component analysis (PCA) was done on the merged data set prior and after the regression normalization to verify the accuracy of the regression normalization in removing the lab-specific variation in gene expression.
Analysis of publicly available nIBC data Given the higher metastatic risk of IBC compared to nIBC, we tested the hypothesis that our 79-gene IBC/nIBC signature, if biologically relevant, is associated with metastatic relapse in nIBC. Therefore, we collected expression data from 6 publicly available series of adjuvant chemotherapy-and hormone therapy-naive lymph nodenegative early breast cancer patients. These series have been used for similar purposes in earlier studies and follow-up data in terms of metastatic relapse are available. An overview of included data sets is provided in Supplementary Table  3. Redundancy related to patient samples included in multiple data sets was eliminated, yielding 871 samples available for analysis. Before analysis, we mapped hybridization probes for the differentially expressed genes across the two oligonucleotide-based platforms used across the series.
When multiple probes were mapped to the same GeneID (EntrezGene identification number), the one with the highest variance in a particular dataset was selected to represent the GeneID. Analysis of each data set was done separately to guarantee a larger number of genes common with our gene list. After having identified the common genes, we used Distance Weighted Discrimination (DWD)  (23)  to normalize each public dataset in order to be comparable to our present pooled Affymetrix data set. The samples were classified as "IBC-like" or "nIBC-like" using a Support Vector Machine (SVM) function based on the list of 79 differentially expressed genes. The initial outcome of interest for this SVM classifier was the separation of IBC from nIBC. The SVM model was established in our original training set (84 IBC and 166 nIBC samples) with a linear kernel as the parameter.
Statistical analyses In the results section, only nominal P-values are reported; however in case of multiple comparisons, P-values were corrected and were considered significant only if the false discovery rate (FDR) was smaller than 0. 05  (9). To compare the distribution according to categorical variables, Chi-square testing or the Fisher Exact test were used when appropriate. To compare continuous data between two or more groups, respectively the Mann-Whitney U-test and the Kruskal-Wallis test were used. To compare continuous data with one reference value, the Wilcoxon signed rank test was used. To compare distributions of continuous variables, Spearman correlation analysis was performed. The distant metastasis-free survival (DMFS) for nIBC patients included in the public data set was calculated from the date of diagnosis until the date of first metastatic relapse.
Our data also show that the differential distribution pattern of the molecular subtypes between IBC and nIBC should be considered when doing IBC research (e. g. in the present study, up to 82% of the differentially expressed genes between IBC and nIBC are not IBC-specific), not only in translational studies but also in basic research. For example, the SUM149 cell line, although derived from a patient with IBC, remains a Basal-like cell line and therefore observations made using this model system might not be IBC-specific. In fact, application of the 79 gene classifier onto a collection of 7 IBC cell lines obtained through the World IBC Consortium revealed that out of multiple replicates for each cell line, only the SUM149, KPL4 and FC-IBC-01 cell lines were reliably classified as IBC (data not shown).
Acknowledgements The  Institut National du Cancer (INCa)   Translational Research Grant   2007  (FB). The  National Institute of Health   R01CA138239-01  and  R01 CA138239  ARRA supplement (WAW, JMR); The  State of Texas Grant for Rare and Aggressive Cancers  ( NU, JMR, AL, WAW ); The  American Airlines Komen Foundation Promise  Grant  KGO81287  (WAW, JMR, AL),  Komen Foundation  Grant  KG101478  (WAW).
The development of specific monitoring (e. g. continuous monitoring in field pressure pumps, laboratory monitored devices) was proposed to get both field and laboratory data. Finally, these specific cathodic protection data are used in a cathodic protection modelling software called PROCOR TM. The modeling from PROCOR TM is validated from field measurements and can be used to design the CP of any other specific applications. In the present paper, a brief review of corrosion behavior and corrosion cases from seawater desalination units operating at ambient temperature are presented and discussed, for both stainless steels and copper-based alloys. The issues related to the efficiency of cathodic protection in desalination units are reviewed and some results from the "cathodic protection" study are presented together with some modeling work.
Pump Movitec Counter electrode A stainless steel butterfly valve made of UNS S31603 (316L) was also tested. The butterfly valve was mounted in a similar seawater loop as the one described for tube testing, allowing flow rate control and implementation of cathodic protection and monitoring (potential and current) systems. Photograph of the valve before exposure is given in Figure  15. A commercial software called PROCOR TM was used for the modeling of the cathodic protection. This software, initially developed to design cathodic protection systems for immersed or buried structures is an appropriate numerical modeling based on a Boundary Element Method which computes electrochemical potentials and current densities on the structure and everywhere inside the electrolyte  [58, 59]. Using direct derivation of the electrical potential, the software calculates the Underwater Electric Potential field (UEP), the DC electric field related to corrosion within the electrolyte.
Acknowledgements RISE (Sweden) and  Veolia Water  (France) are gratefully acknowledged for financial support. Ifremer is thanked for precious technical support in this study. Gulnara Omarbekova  from  Lubriziol Advance Material  is also gratefully acknowledged for the supply and advices of all pressure bearing plastic elements used in the laboratory set-up. Jean-Michel Hamoignon  and  Charles Leballeur  are acknowledged for their help in the experimental set-ups.
Investigating mechanisms and regional details strongly benefits from the "zoom" capacity of the LMDZ AGCM, and from analyzing the results at timescales ranging from yearly averages down to high frequency time series (6 h in our case). For our future simulations we will consider two main changes: forcing the AGCM with sea surface conditions issued by MIS 3 simulations with a coupled ocean-atmosphere model, instead of the GLAMAP2000 dataset for the LGM, and imposing a vegetation distribution consistent with the www. clim-past. net/9/1385/2013/ Clim. Past, 9, 1385-1402, 2013 simulated glacial climates instead of the present-day distribution. Also, the effect of precipitation will be taken into account along with that of temperature in computing the vegetation changes. We also plan to simulate the entire dust cycle (emission, transport and deposition), in view of a more quantitative comparison to European loess data.
Acknowledgements. This study was supported by the  French Agence Nationale pour la Recherche (ANR)  through the  ACTES  project  ANR-08-BLAN-0227/CSD-6. The simulations were performed using High Performace Computing resources of the  Commissariat à l'Energie Atomique, France. The manuscript has benefited from the comments of three anonymous reviewers. This is LSCE contribution no. 5000  and LDEO contribution no. 7694. Edited by:  V. Rath The publication of this article is financed by  CNRS-INSU.
PCC ring per cell distribution The distributions of PCC rings per cell were evaluated using the DoseEstimate software  [14]. The normalized unit u of the dispersion index (σ 2 /Y) was analyzed for each dose and each proportion of irradiated blood, assuming Poisson distribution if u ≤ 1. 96, and overdispersion if u > 1. 96. The percentage of correct SPBI identifications (%SPBI ID ) was calculated as follows: %SPBI ID ¼ #SPBI u. 1:96 #SPBI T †100; where #SPBI u>1. 96 is the number of rings' distribution by cell that do not follow the Poisson distribution, and #SPBI T is the total number of rings' distributed by the analyzed cell. The surviving fraction (S) was calculated using the equations reported by Lloyd and coworkers  [15]  and Matsubara and coworkers  [16]. The frequencies of rings per cell necessary for the calculation of S were obtained from simulated whole-and partial-body irradiation.
ACCESSION NUMBERS The atomic coordinates have been deposited in the Protein Data Bank under ID codes 4IW0 (TBK1 active, ligand BX795), 4IWO (TBK1 inactive, ligand MRT67215), 4IWP (TBK1 inactive, ligand BX795), and 4IWQ (TBK1 inactive, ligand MRT67307).
ACKNOWLEDGMENTS We thank  Xinping Lin  of  EMBL Heidelberg  and  Luca Signor  of the  Institut de Biologie Structurale Grenoble  for mass spectrometry and  Marc Jamin ,  UVHCI Grenoble  for support with MALLS. We thank  Aline Le Roy  and  Christine Ebel , from the  IBS platform of the Partnership for Structural Biology  and the  Institut de Biologie Structurale in Grenoble (PSB/IBS) , for the assistance and access to the instrument of Analytical Ultracentrifugation. We thank the  EMBL Grenoble Crystallization Facility  for screening of crystallization conditions. We thank  Claude Cochet ,  CEA Grenoble  for support with ATPase assays. We thank  Joanne Hough  and  Ed McIver  from  MRC Technology, Centre for Therapeutics Discovery  for synthesizing and providing MRT67215. We thank  Natalia Shpiro ,  MRC Protein Phosphorylation Unit, University of Dundee, Scotland  for synthesizing MRT67307 and  Philip Cohen  for providing this compound.
VISUALISED TRANSITION MATRICES: ARROW PLOT For the visualization of the eye movements we use the transition matrix. Transition matrix cells contain frequencies of direct transitions between AOIs. A transition is a saccade from one AOI to another one  (Holmqvist et al. , 2011, page 190/191). For this study we construct transition matrices with self-written matlab software, but there is also commercial software available for transition matrices production (BeGaze from SMI and Noldus observer that works with Tobii). To explain how we visualize transition matrices, we use an example with three AOIs (Figure  2 ). A transition matrix describing all transition between these three AOIs has nine cells (3 × 3). The cells on the diagonals are empty (a transition goes from one AOI to another). The cell in the first row and third column describes the number of transitions from AOI nr 1 to AOI nr 3.
AREA OF INTEREST PRODUCTION Eye movements were analyzed with 2 different sets of AOIs. In the clustered condition, AOIs were hand drawn in Adobe Photoshop and based on the heat map. This is a data driven approach and all clusters of fixation received an AOI. In the gridded condition, the stimulus area was divided in 12 equal rectangles. Horizontally oriented displays were divided in a 3 × 4 grid and vertically oriented stimuli were divided in a 4 × 3 grid. Examples of clustered and gridded AOIs can be found in Figure  3.
FIXATION DETECTION We performed fixation detection by a computer program that marked fixations by an adaptive velocity threshold method. First, velocities were obtained by fitting a parabola through three subsequent data points of the position signal. We used the derivative of this parabola to estimate the value of the velocity of the second (center) data point. This procedure was repeated for all data points (except the first and the last point). In the present analysis, everything that is not a saccade is called a fixation. To remove the saccades from the signal we calculated average ) is coded with "t". This manuscript is not about AOI-production. We used these two extreme methods to show the robustness of our measures, not to show any preference for one of the two methods. and standard deviation from the absolute velocity signal (composed from the vertical and horizontal velocity signal).
From the original data set of 54 observers we removed 6 observers due to high values for mean combined (horizontal and vertical) RMS noise (>30 pixels/±0. 8 • ). T 50 To estimate attention drawing power of brand logos we determined T 50 of the brand logo in 39 different ads. To rule out the possibility that our results are due to the choice of AOIs we choose to make our AOIs in two very different ways. We refer to the two methods as gridded and clustered AOIs. Figure  4  shows values for T 50. Each dot in the figure represents data for one advertisement; the x-value is T 50 obtained from the clustered AOIs and the y-value is the T 50 obtained from the gridded AOIs. T 50 ranges from 0. 42 to 7. 8 s for the clustered AOIs and from 0. 52 to 4. 66 s for the gridded AOIs. T 50 reaction times are shorter for gridded AOIs than for clustered AOIs.
DISCUSSION DESIGNING A TEST What to measure and how to test eye movement behavior with the new quantitative measures? In case of ad enhancement we suggest to build a database from quantitative eye tracking measures (including T 50 , entropy and (visualized) transition matrices). New ads should be measured each week with a large number of observers (±50-100). However, such an approach is too expensive for most usability and marketing research companies. Interpreting quantitative measures is also possible based on a simple differential measurement instead of a large database. This is of course much cheaper and easier to carry out. Imagine a client that provides a researcher with an ad and the question whether the design is good enough (it may sound strange, but this is usual practice). The researcher should be provided with (slightly) different versions of the ad, to be able to carry out a differential measurement to allow for interpreting the quantitative eye tracking measures.
Short read sequences and read mapping We used a dataset by Rubin and colleagues available at the European Nucleotide Archive (http://www. ebi. ac. uk/ ena/) under the study accession number SRP001870  [25]. This dataset is composed of 35 bp reads obtained by SOLiD sequencing technology of genomic DNA pools of unrelated chicken from the red jungle fowl (RJF; 8 males and 1 female) and two domesticated populations, broiler (24 males and 18 females) and layer (29 males) (accession numbers SRR035386, SRR035383 and SRR035384 for RJF, SRR035377, SRR035378, SRR035387, SRR035381, SRR035382, SRR035379 and SRR035380 for broiler and SRR035375, SRR035376, SRR035389, SRR035390 and SRR035385 for layer). The reads were mapped against the chicken reference genome (WUGSC 2. 1, May 2006 version;  [27] ) downloaded at the UCSC Genome Browser website (http://genome.
ucsc. edu/) [53]. The mapping was performed using the software BWA  [46]  allowing for a maximum of four mismatched bases and not allowing for insertions/deletions. Reads that mapped at several locations in the genome were excluded. Further, a genomic position had to fulfill four criteria in order to be included for downstream diversity level computation: (i) be covered by more than 4 and less than 50 reads; (ii) be outside of repeat sequences (based on the UCSC Genome Browser chicken repeat annotations); (iii) not correspond to a CpG prone site and (iv) be outside exons and untranslated regions (UTRs) that are likely to be affected by natural selection. In order to obtain data on synonymous and non-synonymous polymorphisms, we separately considered the corresponding positions within exons, still fulfilling criteria i) to iii).
Exons and UTRs coordinates were obtained through the BioMart query interface (http://www. ensembl. org/ biomart/martview)  [54]. When no UTR was annotated for a transcript, we excluded 77 bp upstream of the transcript (i. e. in 5' direction) and 372 bp downstream of the transcript (i. e. in 3' direction), sizes corresponding to the mean lengths of annotated 5' and 3' UTRs in chicken, respectively. A CpG prone site was defined as any C followed by a G or any G preceded by a C, as well as any C/T polymorphism followed by a G or any G/A polymorphism preceded by a C, following  [55].
SNP calling and estimates of diversity level To be called a SNP, we followed the approach by Rubin et al. [25] , that is we applied the criterion that the alternative nucleotide state, i. e. the non-reference allele, must be supported by at least three reads different to the nucleotide state found in the reference genome. While diversity estimates were obtained for RJF, broiler and layer populations separately, the support of the alternative nucleotide state was based on combined data of multiple populations. For example, consider a hypothetical position with a C in the reference genome. If the broiler population had two reads with A and one read with C at this position, and the layer population had one with A and two with C, the position was called a SNP in both layer and broiler populations, because the number of non-reference alleles (i.
e. , A) summed up to 3. Once the SNPs were called, we validated our SNP calls with those SNPs called by Rubin et al. [25] , and only used consistently called SNPs. After the validation step, we computed the number of SNPs per non-overlapping window (SNP density) for 1 Mb, 500 kb, 250 kb and 100 kb windows. Additionally, we computed the mean coverage per window as the average read depth per validated genomic position. These tasks were performed using in-house perl scripts. We determined the number of synonymous and nonsynonymous SNPs per synonymous and non-synonymous sites by in-house C++ scripts incorporating the Bio++ library  [56]. In order to correct SNP density estimates for variation in coverage, we estimated diversity level following an approach by Cutter and Moses  [32] : θ ¼ SNP ½ = log n À 1 ð Þ where [SNP] represents the SNP density and n denotes the mean coverage.
Sequence data Sequence alignments of orthologous intergenic regions for chicken, turkey (Meleagris gallopavo) and zebra finch (Taeniopygia guttata) were retrieved from whole-genome alignments from the Ensembl database release 61 via the Ensembl perl Application Programme Interfaces (APIs). We partitioned the whole-genome alignments into the four window sizes stated above, respectively, each with reference to the chicken genome. Then positions of transcribed regions including UTRs were established and masked with reference to the chicken genome. For each dataset, we restricted the data to windows with a minimum of 10,000 unambiguous sites, of which there were 1,038 windows of size 1 Mb, 2,040 of size 500 kb, 3,986 of size 250 kb and 9,205 of size 100 kb. Coding sequence (CDS) alignments of orthologous genes in chicken, turkey and zebra finch were retrieved through the protein trees from the Ensembl database release 61 via Ensembl perl APIs.
Orthologous genes were restricted to one-to-one orthologs, as defined in the Ensembl database. Alignments of one-to-one orthologs were then concatenated based on the windows defined for intergenic regions; for genes spanning more than one window, the different parts were assigned to the respective window. Windows containing no one-to-one orthologs were discarded for downstream analysis. Sequence alignments were cleaned for possible misaligned sites running Gblocks with default parameter settings  [57].
Estimation of divergence, d N , d S , gene density and recombination rate We estimated chicken-specific divergence for intergenic regions as the branch length between chicken and its common ancestor with turkey after all sites showing a CpG in any of chicken, turkey and zebra finch had been masked from the 3-way alignments. Estimation of branch length was based on the PAML software package version 4. 1 and the general time-reversible substitution model implemented in baseml. CpG sites were masked from the alignments in order to avoid substitution rate variation caused by hypermutability of CpG sites and thus divergence being affected by the local CpG content. We estimated chicken-specific rates of non-synonymous (d N ) and synonymous (d S ) substitution for the concatenated CDS alignments using PAML software package version 4.
A treat-to-target strategy guided by DAS28 results in better outcomes than traditional non-quantitative care  [14]. A recent report compared results of treatment in two populations of early RA patients: the GUEPARD (Guerir la Polyarthrite Rhumatoide Debutante) clinical trial with treat-to-target tight control based on DAS28 vs the ESPOIR (Etude et Suivi des Polyarthrites Indifferenciees Recentes) cohort with routine care  [15]. Improvement appeared greater in GUEPARD vs ESPOIR patients, at higher levels according to patient and global RA Core Data Set measures than according to joint counts and laboratory tests. These observations suggested that further formal comparisons of each RA Core Data Set measure and DAS28, CDAI and RAPID3 to distinguish results of GUEPARD tight control vs ESPOIR routine care would be of value, as presented in this report.