dataesr/scientific-paragraphs-categorization huggingface dataset:
For detection of collagen fibril deposits, tissues sections were incubated 1 h at room temperature with primary antibody anticollagen type I (1:250, CL50151AP1 Cedarlane, Ontario, Canada) and further with the secondary anti-rabbit IgG Dako Envision HRP system (K4002, Dako-Cytomation) during 30 min. Immunological complexes were visualized by the addition of the DAB substrate during 10 min (TA-125-HDX, Thermo Fisher Scientific). Sections were counterstained with hematoxylin and mounted. Kidney sections were scanned using a Nanozoomer 2. 0 RS (Hamamatsu Photonics SARL, Massy, France) and treated with the Morpho-expert image-analysis software (version 1. 00, Explora Nova, La Rochelle, France) for morphometric analyses. Western blot Analyses-Complete details of the procedure can be found in the supplementary Experimental Procedures.
After reaction with ninhydrin, arginine was detected at 570 nm. Data acquisition and calculations were made using the Jeol Work station software version 3. 45. Statistical Analysis of Data from Mouse Model-Data were expressed as mean Ϯ S. D. Results were analyzed by statistical nonparametric Mann-Whitney test. Graphics were generated by Prism 5 GraphPad software. p values less than 0. 05 were considered statistically significant.
urine sample from an healthy individual, we obtained a median coefficient of variation (CV) for the PAI values of 5% for a triplicate consecutive repeat LC-MS measurement, whereas the variability related to a triplicate sample preparation was 14% (supplemental Data S9A). These CVs were at the lower end compared with other urinary proteome studies  (30, 31) , and indicated that the variability of the analytical method is much lower than the inter-individual variability expected in a clinical study (see below). For identification of urinary proteins associated with UPJ obstruction, four different groups of five newborns were studied (Fig. 1  and Materials and Methods for detailed group descriptions): healthy individuals (Healthy), newborns with mild UPJ obstruction evolving to spontaneous resolution (Mild), and severe UPJ obstruction that needed to be surgically corrected (Severe).
In the latter, we analyzed both bladder urine samples and urine samples directly taken from the pelvis during surgery (marked Severe-Bladder and Severe-Pelvis, respectively). All samples were prepared and analyzed randomly. Each of the 20 samples were measured in triplicate, which improved MS/MS sampling and increased the number of identified proteins with 30% (supplemental Data S9B). In the time course of the present clinical study (about 10 days), good chromatographic reproducibility was observed across random triplicate injections (supplemental Data S10) as well as a good analytical repeatability (median PAI CV of 13% for randomly triplicate measurements), whereas the inter-individual variability in a same study group was 50 to 60% (supplemental Data S11). Using the MFPaQ software  (32) , we could identify and quantify 970 unique urinary proteins from these 60 raw files (supplemental Table  S6 ).
05), but that were not significantly modulated in the Mild/Healthy comparison. Among such markers, some had both a modified abundance in bladder urine (Severe-Bladder/Healthy comparison) and in urine collected directly from the pelvis of the pathological kidney (Severe-Pelvis/Healthy comparison), suggesting that the modification was originating specifically from the obstructed kidney (Table  I , supplemental Data S12). On the contrary, markers that were found with different abundance in Severe-Bladder/Healthy comparison but were not significant in Severe-Pelvis/Healthy comparison were classified as proteins potentially modulated in the contralateral kidney (supplemental Data S12-S13). As a first verification of the urinary proteome-based analysis we searched for the presence of proteins known to be modified in UPJ obstruction.
1  and 4 ). Western blot analyses showed a significantly lower urinary ARG1 abundance in newborns with UPJ obstruction compared with controls (Fig. 4A  and 4B ). This modified urinary ARG1 abundance was confirmed by MRM (Fig. 4C , supplemental Table  S4 , supplemental Data S8, MRM chromatograms views of the peptides monitored for ARG1 are provided in supplemental Data S16 -S17). Although we observed a significant difference in age between the "Healthy" and "Severe-Bladder" groups in both the discovery and verification cohorts, we did not observe a correlation between the age of the children and the level of ARG1 excretion, irrespective of the approach (i. e. LC-MS/MS, Western blot, or MRM) we used (supplemental Data S18). However in contrast to the initial LC-MS/MS analysis where ARG1 was decreased only during severe obstruction, here we observed reduced urinary ARG1 abundance compared with controls irrespective of the severity of UPJ obstruction and verification method used.
The table indicates raw fold changes. For ratio values Ͻ1, we performed a 1/x transformation and indicated those as negative fold changes. Reduced urinary abundance of EGF and ARG1 in pathologic samples is highlighted by blue and red characters. of proteins based on peptide ion intensity (30) allowed the identification of ϳ400 urinary proteins per run. We used a similar method based on single-run analysis of individual samples, and label-free quantitative analysis using the MFPaQ software.
We therefore developed an MRM protocol for the detection of proteins in 3 ml of urine. Although the ultrafiltration method (e. g. FASP-based) was reproducible in our hands in the discovery phase using larger volumes of urine (Ն10 ml), it gave less reproducible results with small sample volumes (3 ml). This is most probably caused by loss Concentrations were calculated based on peptide TIGIIGAPFSK, product ion y9 (calibration curve shown in supplemental Data S5) for ARG1, spiked at 10 fmol. For purpose of relative comparison, concentrations were also estimated with QuaSAR using the PEPotec peptide DVDPGEHYILK, product ion y8, although the exact spiked amount of this partially purified peptide could only be approximated (around 50 fmol). Analysis of the samples was performed using 3. 57 g of total urinary proteins and concentrations were calculated in fmol/g of total protein.
ARG1 was verified as a protein with modified abundance among the proteins specifically originating from the obstructed kidney. Modified EGF excretion was also verified by MRM, confirming the previous observation of modified urinary EGF abundance in obstructive nephropathy  (33, 34, 47). According to the study of Li et al. , urinary EGF could allow discrimination between mild or severe obstruction in infants  (47) , however, in our study, this difference, using MRM, between the Severe-Bladder and Mild groups lost significance after correction for multiple testing (supplemental Data S8). Thus, the relation between the urinary EGF abundance and the severity of the pathology should be investigated on a larger cohort of patients. The decrease in urinary abundance of LRCC15 was also confirmed, but this difference lost significance in the comparison "Severe-Bladder/Mild" after BH correction.
Acknowledgments-We thank the skilled technical help of  J. J. Maoret  from the  GET TQ  (www. genotoul. fr)facility for the QPCR analysis. * This work was funded by the  Agence Nationale pour la Recherche  ( Beyond Markers ,  ANR-07-PHYSIO-004-01 ) program. S. D. , F. B. , and J. L. B acknowledge support by  Inserm  and the  "Direction Regional Clinique" (CHU de Toulouse, France)  under the  Interface program. J. P. S. was supported by  Inserm  and the  "Direction de la Recherche Ḿedicale et Innovation" (CHU de Toulouse, France)  under the " Contrat Hospitalier de Recherche Translationnelle" program. The work was supported in part by grants from the  Region Midi-Pyrenees, European funds (FEDER) , and the  French Ministery of Research (Investissements d'avenir, Proteomics French Infrastructure)  to BM and OBS.
Quality images assessment For Veo acquisition, respiratory artefacts were graded as 'negligible' in 24 cases (89%) for reader 1 and 25 cases (93%) for reader 2, 'moderate' in 3 cases (11%) for reader 1 and 2 cases (7%) for reader 2, and no 'salient' artefact was recorded. For FBP acquisition, respiratory artefacts were graded as 'negligible' in 19 cases (70%) for reader 1 and 24 cases (89%) for reader 2, 'moderate' in 7 cases (26%) for reader 1 and 3 cases (11%) for reader 2, and as 'salient' in 1 case for reader 1 and in 0 cases for reader 2. Veo and FBP did not differ in subjective assessment of respiratory artefacts between the two radiologists ( p=0. 16 for reader 1 and p=0. 65 for reader 2). Tables  1  and 2  provide results from subjective image noise assessed by the two radiologists using, average of objective noise data and SNR.
Pleural plaques A total of 297 pleural plaques (figure  2 ) were observed in 20 participants (74%). Detection of plaques did not differ between Veo (259; 87%) and FBP (287; 97%; p=0. 10). Thus, the third simultaneous reading of Veo and FBP resulted in the detection of 10 plaques that were not detected during the first reading of FBP images. The agreement for pleural plaques depiction was 84% with a κ of 0. 05. However, when data were examined only for the presence of pleural plaque (yes or no) in patients, agreement increased to 96% (κ=0. 91). 28 29 Moreover agreement for size measurement (Lin coefficient) was 0. 83 ( p<0. 001) and κ coefficient for calcification detection was 0. 86. For one participant, despite a Veo acquisition considered normal, FBP acquisition was positive for one isolated plaque.
Results and Discussion De Novo Yeast Genome Assemblies from Short-Read Sequencing Data We selected 42 haploid (or homozygous diploid) yeast strains and sequenced their genomes to intermediate-to-high (10-60Â) coverage using paired-end Illumina technology. A subset of six strains were sequenced to very high (400-80 0Â) coverage (table  1 ). Genomes of 14 S. cerevisiae and 13 S. paradoxus strains for which the coverage was higher than    -Strains without values were not de novo assembled. Additional information of the sequenced strains was reported in  Liti, Carter, et al. (2009). The S. cerevisiae Y12 originally reported from Ivory Coast Palm wine subsequently clarified (Fay J, personal communication) that the strain sent was the Sake strain K12 from Japan. a Refers to raw coverage. Bergstro ¨m et al.
(A) Scaffolding de novo assemblies using genetic linkage information from advanced intercross lines dramatically improves assembly connectivity and reveals extensive structural conservation of the core chromosomes in four of the major S. cerevisiae lineages. Displayed is a dot plot of sequence similarity between the assembly scaffolds of the strain YPS128 from the North American phylogenetic lineage and the 16 nuclear chromosomes of the S. cerevisiae reference genome (strain S288c), before and after the incorporation of the genetic linkage data into the scaffolding process. After scaffolding by genetic linkage, the majority of the assembly sequence is contained in 16 large scaffolds that are collinear with the chromosomes of the reference genome. Results are highly similar for the other three strains for which genetic linkage data is available; the West African strain DBVPG6044, the Wine/European strain DBVPG6765 and the sake/ Japanese strain Y12 (the recent sequencing of the sake strain Kyokai no.
All contigs/scaffolds that had a BlastN match to a Staphylococcus species in the top five hits from the NCBI nr database were removed from the assembly. No hybrid contigs or scaffolds containing both Saccharomyces and Staphylococcus were found. Assembly Scaffolding Using Genetic Linkage Data Four of the S. cerevisiae strains have been used as parents for advanced intercross lines as part of projects to study complex traits and recombination, and the genomes of a large number of segregants from these lines have been sequenced: 192 F12 segregants from a two-parent cross between YPS128 and DBVPG6044  (Illingworth et al. 2013 ) and 192 F12 segregants from a four-parent cross between YPS128, DBVPG6044, Y12, and DBVPG6765  (Cubillos et al. 2013) ; in both cases sequenced by paired-end Illumina technology with 100-bp read lengths.
The patterns of linkage disequilibrium (LD) between SNPs in these artificial populations were used to further scaffold the de novo assemblies of the four parental strains. First, for each of the parental genome assemblies, the Illumina reads for the other parental strains were mapped to the assembly and SNPs were called (read mapping and SNP calling performed as described in the section "Read-mapping and variant calling"). For the two strains of the two-parent cross (YPS128 and DBVPG6044), only data from this cross was used, and data from the four-parent cross was used only for the remaining two strains (Y12 and DBVPG6765). The segregant individuals were then genotyped at the resulting lists of SNPs using samtools mpileup 0. 1. 18 (Li et al. 2009) , assigning the corresponding allele if the log-likelihood ratio between the homozygous states of the two alleles was bigger than 10 or smaller than À10, assigning unknown genotype if inbetween.
assembly. To test whether these are assembly artifacts in our assembly or sequence incorrectly left out of the van Overbeek et al. assembly, the 454 reads used to construct the van Overbeek et al. assembly were mapped back to our SK1 assembly using SSAHA2  (Ning et al. 2001) , and the depth of coverage was assayed in these 31 regions. Excluding the 100 bp at the edge of contigs, all positions in these regions were covered by five or more reads with mapping qualities of at least 75. This shows that these sequences are actually present in the strain SK1 but have for some technical reason been left out of the van Overbeek et al. assembly. No false positives and no false negatives of this kind were thus identified in our SK1 de novo assembly. Reference Genomes and Annotation Data Used For S. cerevisiae, the S288c reference genome and corresponding annotation files (Release R64-1-1, downloaded from the Saccharomyces Genome Database [http://yeastgenome.
org/, last accessed January 28, 2014] on February 5, 2011) was used for reference-based analyses. The sequence of the 2-mm circle plasmid was downloaded from NCBI's GenBank (accession NC_001398). For S. paradoxus, the CBS432 reference genome from  Liti, Carter, et al. (2009)  was used, with annotation being obtained from  Scannell et al. (2011). A S. paradoxus CBS432 mitochondrial sequence was obtained from  Prochazka et al. (2012)  (GenBank accession: JQ862335. 1). For analyses assaying the presence of genomic material in the S. paradoxus reference genome, an alternative version of the CBS432 assembly that includes some additional sequence that was incorrectly left out from the original assembly was used (available from  Liti, Carter, et al. [2009] ). We define the subtelomeric regions as the outermost 33 kb of each reference chromosome, following  Brown et al.
ARR Gene Cluster Analysis The haplotypes of the two ARR gene copy clusters in the strain BC187 were phased by first calling SNPs in a 6,400-bp region encompassing the cluster using samtools mpileup on mapped Illumina reads, and then phasing the SNPs using the ReadBackedPhasing program from the Genome Analysis Toolkit  (McKenna et al. 2010 ) with both Illumina and Sanger paired-end reads as input. For the strains Y12 and DBVPG1373, the two ARR cluster copies were not sufficiently diverged for phasing to be possible. Neighbor-joining trees were constructed by Seaview  (Gouy et al. 2010) , and for Y12 a single consensus sequence for the two haplotypes was constructed for phylogenetic analysis by excluding the positions where the two copies differ in sequence (five positions). Data from all the strains on the mitotic growth rate, length of mitotic lag, and mitotic growth efficiency in a medium containing 5 mM sodium arsenite oxide was obtained from  Warringer et al.
Acknowledgments The authors thank all the members of the Sanger Institute sequencing production teams for generating the sequence data. The authors also thank  Conrad Nieduszynski  for constructive feedback on the manuscript and  Magnus Alm Rosenblad  for assistance with computing resources. This work was supported by grants from  ATIP-Avenir (CNRS/ INSERM) ,  ARC  (grant number  SFI20111203947 ), and  FP7-PEOPLE-2012-CIG  (grant number  322035 ) to G. L. , the  Wellcome Trust  (grant number  WT077192/Z/05/Z ) to R. D. , and  Becas Chile  to F. S.
Patient population From all patients that had a [ 18 F]choline PET/CT examination at our two centres from 2011 to 2013, we retrospectively included 60 patients that met the following inclusion criteria: -Males with biochemical recurrence of PSA after curative treatment for prostate cancer including total prostatectomy, external radiotherapy or radium treatment. Biochemical recurrence was defined by PSA level over 0. 2 ng/ml if initial treatment was prostatectomy, or PSA level over 2 ng/ml if initial treatment was conformational radiotherapy  [5, 6]. -[ 18 F]choline PET/CT examination with an acquisition protocol including at least one dynamic examination and a later full body examination. The patient data were anonymized (Table  1 ). All patients provided informed consent for their participation in this study.
Statistical analysis The analyses were performed using the software STATA version 12. 0 (College Station, StataCorp LP, TX, USA). The main assessment criterion was the reproducibility of inter-and intra-observer interpretation of [ 18 F]choline PET/CT examinations, evaluated by the kappa (κ) statistical test  [11]. Cohen's Kappa coefficient (reproducibility between two observers) and the Fleiss' Kappa coefficient (reproducibility between four observers) indicated reproducibility of interpretations: κ > 0. 81 is excellent, 0. 80 > κ > 0. 61 is good, 0. 60 > κ > 0. 41 is moderate, 0. 40 < κ < 0. 00 is average, and κ < 0. 00 is poor  [12]. The kappa coefficients were recorded with their confidence intervals (CI). The null hypothesis was that κ = 0, i. e. no inter-or intra-observer agreement other than pure chance.
The use of a multiple kernels approach allows editing, adding or removing piRNA features that can be heterogeneous in a modular manner according to their relevance in a given species. Our algorithm is based on a combination of the previously identified features [sequence features (k-mer motifs and a uridine at the first position) and piRNAs cluster feature] and a new telomere/centromere vicinity feature. These features are heterogeneous, and the kernels allow to unify their representation. The proposed algorithm, named piRPred, gives promising results on Drosophila and Human data and outscores previously published piRNA identification algorithms.
, 2014). The proTRAC algorithm is based on a statistical probabilistic analysis. It analyzes mapped sequence reads and identifies clusters based on significant deviations from a uniform piRNA distribution, using different types of information, including the density of mapped reads, strand asymmetry, frequency of putative piRNA loci with U at the first position of the sequence, or A at Position 10. On the other hand, the piClust algorithm uses a density-based clustering approach without assuming any parametric distribution and considers the actual interdistance between mapped reads for the determination of clustering, with results that were claimed to outperform the proTRAC algorithm. Taken together, previous piRNA prediction algorithms were based on the following features: (i) transcript length of 24-35 nt, (ii) nucleotide bias at Position 1 of G expression, (iii) localization in clusters and (iv) differential frequencies of certain k-mer sequences.
This implies that the heterogeneous features can all be replaced by appropriately kernel matrices. This allows the elimination of the data heterogeneity. Constructing the same representation for all datasets and integrating these representations is the main intuition behind kernel fusion methods. In recent years, several machine learning methods have been proposed to exploit different information sources using kernels (G € onen and Alpayd, 2011). Research in multiple kernel learning (MKL) has focused on both developing new formulations as well as optimizing them. Different formulations are required to address the needs of different applications. Most of the methods using these formulations propose to learn the combined kernels by tuning automatically the kernel weights  (G € onen and Alpayd, 2011).
Finally, it has been shown in  (Zhang et al. , 2011)  that a set of k-mer motifs have different frequencies in piRNA and non-piRNA sequences. Thus, in the present version of our algorithm, we consider the following features: (1) The frequency of certain k-mer motifs. (2) The presence of a uridine base at the first position of the sequence. (3) The distance to centromeric and telomeric regions of the chromosome. (4) The occurrence of piRNAs in clusters on the genome. We define three kernels: one kernel representing the two first features, and two kernels representing the third and the fourth kernels, i365 piRNA prediction respectively. Each kernel is a square similarity matrix of size N Â N, N being the size of the training dataset (including positive and negative samples), and the building process is performed as following: for each sequence, we built a vector (or a matrix) representing the feature.
Kernel parameter selection Determining the kernel parameters for the SVM is a problem in practice. A popular method for defining the kernel parameters is the grid search method  (Hsu and Lin, 2002). The classifier is trained with different kernel parameters, and the parameters that provide the best results are chosen. This makes the training process time-consuming, especially when there are large datasets. To avoid this problem, the choices concerning parameter settings are often driven by heuristics. An example of a heuristic is to select the value of that should be relative to the variance of the data, but this is rarely the best choice. An alternative to classic grid search is to define the parameter using the between-cluster distances in the feature space  (Wu and Wang, 2009). For each kernel parameter value, we calculate a distance index and choose the value, which leads to the best separation index.
Training datasets We use different sets of positive and negative data to perform the cross-validation and the prediction tests on Human and Drosophila species. Positive data are taken from piRNABank  (Lakshmi and Agrawal, 2008;  (http://pirnabank. ibab. ac. in/). The piRNABank currently contains 23 439 and 22 336 non-redundant piRNA sequences of Human and Drosophila genomes, respectively. We built the negative dataset with non-redundant sequences of several types: Sequences of size between 25 and 33 nt corresponding to portions at 5 0 of transfer RNA (tRNA) sequences, that are taken from tRNA database (http://lowelab. ucsc. edu/ GtRNAdb/). Sequences corresponding to mature miRNAs and taken from miRBase (http://www. mirbase. org/, version 20). Sequences of size between 25 and 33 nt chosen randomly from the exonic regions of protein-coding genes taken from Ensembl Genes 75 database through Biomart (http://www.
ensembl. org/biomart) The Human negative dataset and the Drosophila negative dataset contain, respectively, 59 947 and 16 243 non-redundant sequences, composed of 590 and 301 sequences of tRNA portions, 2576 and 698 sequences of mature miRNAs and 56 781 and 15 244 sequences of exonic regions. For training samples, we randomly selected five positive datasets and five negative datasets each composed of 7500 Human sequences, and five positive datasets and five negative datasets each composed of 5000 Drosophila sequences. We also selected randomly 2500 positive sequences for each Human and Drosophila, other than the ones used in the training step, to test our algorithm on classifying new sequences. Finally, for each sequence, we gathered the following information: the name (id), the nucleotide sequence, the strand ('+' or '-'), the chromosome ID and the position on the chromosome.