dataesr/scientific-paragraphs-categorization huggingface dataset:
Though no correlation has been measured between survival difference and microarray platform or the number of evaluated CINSARC genes, clear evidence of cohort size effect has been observed: the larger cohort, the higher survival difference (Wilcoxon rank sum test P = 1. 7e-5). Among the 83 tested datasets, 46 contain at least 100 tumors and CINSARC classification is significantly associated with the outcome in 26 of them (57%). There are 30 datasets with at least 150 tumors and CINSARC classification is significantly associated with the outcome in 19 of them (63%). At 200 tumors threshold, CINSARC classification is significantly associated with the outcome in 11 out of 16 (69%) datasets. In these 11 datasets, we wondered how would perform CINSARC if less tumors were taken into account. In fact, we previously reported CINSARC robustness as it was a clinical marker in independent sarcoma datasets  29  but we did not evaluate its robustness in term of cohort subsampling  36.
To firmly evaluate CINSARC robustness regarding cohort size, we investigated the 11 datasets with a least 200 tumors where CINSARC is a prognostic factor. For each dataset, we randomly generated 10,000 sub-cohorts at 75% and 50% of the total cohort size with similar death rates (±10%; Supplementary Figure  4 ). All of the 11 datasets demonstrate that better prognoses are obtained with 75% compared to 50% subsampling. In eight datasets, CINSARC still classify tumors according to their aggressiveness in most of random trials (>50% at P < 0. 05). However, due to the decrease in statistical power, CINSARC mostly produces non-significant prognoses in three datasets (PMID: 17410195, 21720365 and 17023574, cancer types are: B-cell acute lymphoblastic leukemia, multiple myeloma and ovarian tumors, respectively).
Moreover, AURKA alone has been identified as a prognostic marker in gastrointestinal stromal tumors  26. Originally, CINSARC is a metastatic marker to predict which sarcomas with complex genetics have high relapse risk (on average 50% at 5-year)  24. The results we present demonstrate its usefulness as a generic tumor aggressiveness predictor since overall/disease-specific survivals were considered in PRECOG. Importantly, several PRECOG factors could restrict signature prognostic values, CINSARC included, in the different cancer types. First: it is questionable that a single tumor dataset represents the classical cancer-specific transcriptomic profile and therefore another dataset could produce different results. Second: some studies comprise very high or very low (typically > 90% or < 10%, respectively) death rates which could be a limiting factor in PRECOG survival evaluation since cohorts are split by median gene expression.
Third: several cancer types, due to microarray models used in the different studies, may lack important genes so prognostic values were limited to a restricted number of genes (i. e. GPL80: Affymetrix Hu6800, GPL91: Affymetrix HG-U95A, non-commercial: GPL257 and GPL3906, etc. ). Fourth: it has been described that cohort size is a parameter affecting survival estimation  39. To bypass these limitations and confirm GSEA results, we performed survival analyses using CINSARC classification. Most datasets (>57%) with at least 100 samples presented a significantly survival difference whereas smaller datasets performed worst. This can be explained as statistical power depends on sample size so the larger dataset, the more statistical power. Moreover, the classification method (leave-one-out cross-validation) computes more robust gene expression patterns with large datasets.
Finally, we assessed CINSARC robustness in terms of cohort subsampling and, previously reported, dataset independence  29. Of note, 483 signatures (out of 15,499; 3%) were investigated at pan-cancer level with unexpected small intersections (<90%) with known genes in PRECOG, corresponding to 10,004 unique genes not reported in PRECOG. A large proportion is punctual: 92% of them only appear in maximum two signatures. As for the recurring genes (806), majority are uncharacterized and/or non-coding genes: 414 are genes predicted by open reading frame analyses, 92 are genes with unknown functions and unidentified orthologs, 60 are L and R ribosomal protein pseudogenes and 28 are long intergenic non-protein coding RNA. Considering the recurrent and curated genes, only 212 of them were not evaluated in PRECOG resource, suggesting high coverage of annotated genes.
Other statistical methods. Biological pathways were determined using GOseq (v1. 26. 0) package from Bioconductor on the top 200 genes ranked by decreasing meta-z-scores or z-scores  47. P are given by the Wallenius noncentral hypergeometric distribution method and adjusted with Benjamini-Hochberg correction for multiple comparisons. Protein-protein interactions were determined using expected/existing interaction ratios from STRINGdb (1. 14. 0) package from Bioconductor with database version 10 and score threshold set to 400 (removes low-confidence interactions) on the top 200 genes ranked by decreasing meta-z-scores or z-scores. The lower expected/existing interaction ratios, the higher are the number of protein-protein interactions  48. The leave-one-out cross-validation classification was performed as follows: each sample was categorized using the nearest centroid method according to outcomes and transcriptomic profiles of all other samples.
: sheets 24 to 40). Several other signatures performed equally: BREAST THORNER09 217GENES, BREAST TROESTER06 134GENES UP DOXTREATED-HME-CC, BREAST TROESTER06 81GENES UP SHAM HME-CC, CAIRO HEPATOBLASTOMA CLASSES UP, GSE18893 TCONV VS TREG 24H TNF STIM UP, NAKAYAMA SOFT TISSUE TUMORS PCA2 UP, RODRIGUES THYROID CARCINOMA POORLY DIFFERENTIATED UP, STEMCELL SHATS10 100GENES CONSENSUS STEMNESS RANKING, WHITEFORD PEDIATRIC CANCER MARKERS and ZHOU CELL CYCLE GENES IN IR RESPONSE 24HR. These 12 signatures are predominantly enriched in the same tumor types: bladder cancer, astrocytoma, glioma, neuroblastoma, breast cancer, germ cell tumor, Mantle cell lymphoma, multiple myeloma, lung adenocarcinoma, ovarian cancer and prostate cancer.
Acknowledgements Computer time for this study was provided by the computing facilities  MCIA (Mésocentre de Calcul Intensif Aquitain) of the Université de Bordeaux  and of the  Université de Pau et des Pays de l' Adour. The authors thank  Dr. Ravi Nookala  for the medical writing service. The authors thank  Candice Merle  for critically reading the manuscript. This work was supported by  Fondation ARC  and euroSARC ( FP7-HEALTH-2011 ).
In consequence, the selection of stars analog to the Sun will benefit from the inclusion of the detection of solar-like oscillations as an additional selection criterium. This is what we called a seismic solaranalog star  [9] , thus extending the classical definition  [1]. Among the Kepler sample of oscillating solar-like stars, we identified 18 seismic solar analogs  [9]. However, calibration issues 1 present since the launch of Kepler were discovered in the Q2-Q16 Data Release 24 (DR24) short-cadence (SC) data, affecting approximately e-mail: david. salabert@cea. fr 1 See detailed explanations at http://archive. stsci. edu/kepler/. half of all Kepler SC targets. We note that the longcadence (LC) data are not affected. The origin of the problems was traced to an accounting error which scrambled much of the SC collateral smear data used to correct for the effects of Kepler's shutterless readout.
Global seismic and background parameters Out of the 18 seismic solar analogs observed with Kepler  [9] , nine of them were observed in short cadence from Q5 to Q17: KIC 3656476, KIC 4914923, KIC 5774694, KIC 6116048, KIC 7296438, KIC 7680114, KIC 9098294, KIC 10644253, and KIC 11127479. We checked that all these targets were affected by the smear calibration issues from the list of affected stars available at the MAST archive  2. We note that as the two stars KIC 5774694 and KIC 11127479 were observed only during one and three quarters respectively, they were disregarded for the following analysis. A total of seven solar analogs were then analyzed. DR25 and previous data releases (DR21 or DR23 depending on the star, denoted in the rest of the text DR23 -) were corrected for the usual issues found in the Kepler light curves following the procedures explained in  [10].
Conclusions Calibration problems associated to collateral smear were detected in the Kepler DR24 and reprocessed corrected light curves were provided in DR25. These issues were actually found to affect all data releases since launch. We performed here a preliminary study to evaluate the impact on the global seismic parameters and on the background parameters between the two data releases. We analyzed EPJ Web of Conferences 160, 01007 (2017) the sample of seven seismic solar analogs observed by Kepler in short cadence continuously between Q5 and Q17. We started with this set of stars as it constitutes the best sample to put the Sun into context along its evolution, and any significant differences on the seismic and background parameters need to be investigated before any further studies of this sample take place.
40, 44, 45 Based on this, the risk of inadvertent germline transmission can also be deemed minimal in females. [48]  Figure  3. AAV2/8. TBG. hARSB Shedding in the Semen of Male Rabbits AAV2/8. TBG. hARSB DNA was analyzed by qPCR in the semen of adult New Zealand males rabbits injected with either AAV2/8. TBG. hARSB vector at a dose of 2 Â 10 12 GC/kg (n = 3) or with the excipient as the control (n = 2). AAV DNA was undetectable in control rabbits receiving the excipient. Vector DNA is reported as genome copies (GC) per 100 mL of semen. The limit of quantification (LOQ) of qPCR was 30 GC/well; the limit of detection (LOD) was 9 GC/well. D, days postinjection. Importantly, data from clinical trials for hemophilia B based on AAV systemic administration further confirm the low risk of germline transmission in humans.
ACKNOWLEDGMENTS We gratefully acknowledge  REGENXBIO  for the production and characterization of the AAV2/8. TBG. hARSBvector lot used in this study. We also thank  Enrico M. Surace  and  Bianca Fontanella  ( TIGEM ) for help with study design and  Annamaria Carissimo  ( TIGEM Bioinformatics Core ) for analytical support. This work was supported by  Fondazione Telethon   TGM11MT6 , the  European Commission Seventh Framework Programme  ( FP7/2007-2013 ) (MeuSIX grant  304999 ) and the  Isaac Foundation.
The Models In the following we describe our HMM to model individual genomes as mixtures of IBD and non-IBD segments. We first consider a model with only two states (one IBD or autozygous class and one non-IBD class) and then describe the extension of the model to combine several IBD classes with varying time to the common ancestor (age measured in generations). To deal with the specificities of Next-Generation Sequencing (NGS) data (whole genome sequencing, low-fold sequencing, genotype-by-sequencing) that may provide less accurate genotype call than SNP chip arrays, we also propose alternative emission probabilities functions that integrate over the uncertainties of each possible genotype.
Emission probabilities and extension to NGS data. To complete the specification of the HMM we need to specify the emission probabilities, i. e. , the probabilities of the data Y l observed at each marker M l given the underlying state S l of the two individual chromosomes that might either be IBD (S l K) or non-IBD (S l = K). Let I l represent the number of alleles observed for marker M l (in the rest of the study we only considered bi-allelic SNP i. e. , I l = 2 for all l) and A li the corresponding alleles (i ∈ (1, I l )). Depending on the technology and the analyses performed, Y l then either consists of i) a genotype A li A l j (where i ∈ (1, I l ) and j ∈ (1, I l )) among the J l = I l (I l +1) 2 possible genotypes; or ii) a vector of likelihoods P Y l | A li A l j for each possible genotypes as provided by a genotype calling model as implemented within standard and popular softwares such as GATK  (McKenna et al, 2010)  or SAMTOOLS  (Li et al, 2009).
The program ZooRoH implementing the algorithm for the different models is freely available at https://github. com/tdruet/ZooRoH. Unless otherwise stated, model parameters were estimated with 1000 iterations of the EM algorithm and setting to 0. 001. Estimation of the realized local (locus-specific) inbreeding (φ l ). The Baum-Welch algorithm allows to estimate the local state probabilities that correspond in our case to the K probabilities P S l = c | Θ, Y that the two chromosome segments belong to the IBD class c (c ∈ (1, K IBD )) or to the non-IBD class (c = K) at the marker M l position given the estimated parameter set Θ and the observed genetic data Y. These probabilities can be used to estimate both the realized genome-wide (over all the markers) and local (for each and every marker) inbreeding.
Indeed, genetic data allows to directly infer the realized IBD status of an individual for each locus in the genome as opposed to pedigree-based inbreeding estimates that infer the expected IBD status for all the loci. More precisely, the local estimate φl of the realized inbreeding at marker M l is defined as the probability that this marker lies in an IBD segment and may thus be computed by summing over all its local IBD state probabilities (i. e. , excluding the non-IBD class): φ l = K IBD c=1 P S l = c | Θ, Y (6) Estimation of the realized inbreeding associated to each IBD age-based classes (F (c) g ) and the genome-wide inbreeding (F g ). As above, the inbreeding F (c) g associated to IBD class c (c ∈ (1, K IBD )) can be defined as the proportion of the genome belonging to the class c and is estimated as the average of the corresponding local state probabilities over all the L locus: F (c) g = 1 L L l=1 P S l = c | Θ, Y (7) Finally, the genome-wide estimate of the realized individual inbreeding F G is simply the average over the genome of the local estimates obtained for the L markers: F g = 1 L L l=1 φ l = K IBD c=1 F (c) g (8) Model assessment.
The process stops when the cumulative length of the simulated segments was greater than 100 cM (the last simulated segment being trimmed to obtain a chromosome length exactly equal to 100 cM). Under the multi-states model with several IBD classes, simulations were performed sequentially, with successive waves of inbreeding starting with the most ancient. We started by simulating the most ancient IBD class with the process described above. Then, each new IBD class was simulated similarly (with its own G i and ρ i ) except that new inbreeding (IBD) masked previous classes whereas non-IBD segments did not change previously simulated states. To simulate genotyping data, we first randomly sampled for each SNP the population frequency of an arbitrarily chosen reference allele either i) from an empirical distribution derived from real cattle genotyping SNP assay and WGS data (Figure  S1 ), or ii) from a (U-Shaped) distribution β (0.
) commonly used and that have been proven to work well (e. g. ,  Leutenegger et al, 2003; Vieira et al, 2016). Still, we performed additional simulations relying on population genetics models to obtain simulated data less dependent on these assumptions. To that end we used the program ARGON  (Palamara, 2016)  that simulates data under a discrete time Wright-Fischer process. With constant and large effective population size N e , inbreeding is expected to be low and to be spread over many generations. To concentrate inbreeding in specific age classes we simulated bottlenecks keeping large N e outside these events to reduce the noise due to inbreeding coming from other generations. In the first scenario WF1, we considered an ancestral population P 0 with a constant haploid effective population size equal to N e0 =20,000 that split in two populations P 1 and P 2 at generation time T s in the past with respective population sizes N e1 =10,000 or 100,000 (according to the scenario) and N e2 =10,000.
Human, dog and sheep real data sets For illustration purposes, we used publicly available genotyping data from i) the Human Genome Diversity Panel (HGDP)  (Jakobsson et al, 2008)  as downloaded from ftp://ftp. cephb. fr/hgdp_supp10/Harvard_HGDP-CEPH; ii) the dog LUPA project  (Vaysse et al, 2011)  as downloaded from http://dogs. genouest. org/SWEEP. dir/ Supplemental. html; and iii) the Sheep Diversity panel  (Kijas et al, 2012)  as downloaded from the WIDDE database  (Sempere et al, 2015). We then used the software plink  (Purcell et al, 2007)  to process and filter the genotyping data by removing individuals with a genotyping call rate below 90% and only keeping autosomal SNPs that had call rate > 95% and a MAF > 0. 01 (in the original data set). As a result, the final data sets consisted of  304,406, 152,151 and 48,872  SNPs in human, dog and sheep respectively.
Analyzing simulated data with several underlying IBD classes. We further evaluated the performances of the different models on simulated data sets with more than one class for the underlying IBD segments, i. e. for which inbreeding originated from several sources of different ages G k and contributions F (k) g to the overall inbreeding. We detail hereafter the analyses of individual genomes of 2,500 cM (with a marker density of 10 SNPs per cM) that were simulated under the 3G inference model, i. e. , assuming two different classes for IBD segments and one non-IBD class. Each simulation scenario was thus defined by the ages of inbreeding (G 1 and G 2 ) and the mixing proportions (ρ 1 and ρ 2 ) of the two classes of IBD segments. It should be noticed that the simulated mixing proportions (ρ 1 and ρ 2 ) directly controlled (and are generally close to) the amount of inbreeding originating from their corresponding IBD class.
[Table  2  about here. ] To provide insights on the behavior of our model to a misspecification of the underlying number of IBD classes, we also analyzed these data simulated under the 3G model with the 1G, the 2G and 4G models. As expected, when considering the 1G and 2G models, the estimated age of the single assumed IBD class was intermediate between the two simulated G 1 and G 2 actual values (Table  S3 ). In agreement with previous findings, the 1G and 2G lead to highly similar estimates except for large G 1 and G 2 for which the estimated G tended to be higher with the 2G than the 1G model (e. g. , med( G) = 181 and med( G) = 201 respectively for the scenario with G 1 = 128 and G 2 = 256). More interestingly, using the 1G and 2G models (i. e. , with a single IBD class) to analyze these data resulted in an underestimation of F g for scenarios with a marked differences between G 1 and G 2 (Table  S4 ).
For each model, the predefined ages of the K -1 IBD-classes always ranged from 2 to 2 K-1 (with G k = 2 k for each class k ∈ (1, K -1)) while the age of the unique non-IBD class was the same as the older IBD class (i. e. , G K = G K-1 = 8192). Application of these mixKG models to the various data sets previously generated under the 1G, the 3G and the 4G inference models proved highly efficient (Table  S7  and S8 ). For instance and in agreement with above results, the mix10G model provided accurate estimation of the overall inbreeding F g (MAE always lower than 0. 005 irrespective of the simulated scenarios) but also of the local inbreeding as indicated by MAE's that were always as good as the best alternative model (e. g. , compare Table  S7  and Table  S5 ). Moreover, such models with pre-defined ages for the IBD classes allowed to provide indications on the actual ages of inbreeding G k.
We indeed observed that the estimated inbreeding contributions (F (k) g ) for the K -1 IBD classes were mainly concentrated in those IBD-classes with pre-defined ages close to the true simulated ones as shown in Figure  1  for a dense SNP data sets (1000 SNPs per Mb) analyzed under the mix14G models and in Figures S4 to S8 for additional simulated data sets with smaller SNP density (either 10 or 100 SNPs per Mb) that were analyzed under mix10G or mix12G models. [Figure  1  about here. ] Model comparisons and selection. We finally evaluated the BIC criteria to compare the models. When comparing different KG models (from 1G to 6G) applied to various simulation scenarios (ranging from 1 to 4 simulated IBD-distributions), we observed that the BIC criterion tended to support the correct underlying models and never provided support for models with a number of classes K higher than the simulated ones (Tables  S9  and S10 ).
Simulations under a discrete time Wright-Fischer process To evaluate the robustness of the model to departure from model assumptions, we analyzed data simulated under a discrete-time Wright-Fisher process using the recently developed program ARGON  (Palamara, 2016). For our purposes, a decisive advantage of ARGON is that it allowed to identify all the IBD segments (here we only considered those 10 kb) and to obtain their age (i. e. , time to most recent ancestor or TMRCA). Inbreeding was generated by assuming population histories with either i) a strong bottleneck in the recent past followed by a rapid expansion as might be observed in invasive populations (WF1 scenarios) or ii) a reduced effective population size in the last twenty generations as might be observed in some domestic populations (WF2 scenarios).
Accordingly, in WF1 scenarios, this proportion increased with lower effective population size (Ne 1 ), older split time (T s ) and to a lesser extent higher bottleneck population size (N eb ) and timing (T b ) (Figures  S9  and S10 ). We analyzed all these simulated data sets with a mix10G model that consisted of 9 IBD-classes with predefined ages ranging from 2 to 512 (with G k = 2 k for each class k) and one non-IBD class that had the same age as the older IBD class (i. e. , G 10 = G 9 = 512). The choice for a MixKG model was motivated by our previous findings that demonstrated it was informative to date the origin of inbreeding and performed as well as other models in estimating local and overall inbreeding. In addition, it allowed to compare all the simulated individuals according to the same age-based partitioning of inbreeding.
Discussion In this study, we developed and evaluated HMM models that use genomic data to estimate and to partition individual inbreeding into classes of different ages. There actually exist a wide variety of methods to estimate individual inbreeding and these have different properties. Pedigree-based methods rely on a genealogy (the inbreeding can only result from individuals within the genealogy) and predict the expected IBD status at a locus whereas genomic measures estimate realized inbreeding (the observed level of inbreeding). Genomic estimates can either be global, giving a unique measure per individual, or local. Obviously, these latter measures provide more information but require a higher marker density. Assessing the distribution of ROH within individual genome have recently become popular to characterize global and local inbreeding  (Kirin et al, 2010; McQuillan et al, 2008; Pemberton et al, 2012).
As shown by our application in real data sets, even in domestic populations for which inbreeding might be expected to result from a limited number of founder individuals, individual inbreeding generally result from ancestors in different generations back in time probably due to the subsequent intense use of some key (selected) breeders. Hence, extending the model to several IBD-classes is highly valuable and might be viewed as defining multiple reference populations instead of a single one. Inbreeding is then captured as distantly in the past as made possible by the available marker density and informativeness. There is thus no need to arbitrarily define any base population with unrelated ancestors nor to select an arbitrary threshold below which stretches of homozygous markers are considered non-IBD.