dataesr/scientific-paragraphs-categorization huggingface dataset:
We used 50% and 75% bootstrap support thresholds for binning, and estimated the supergene trees using RAxML with fully partitioned analyses. We show results with weighted and unweighted binning (left), and unbinned analyses (right). We used 50% bootstrap support threshold for binning. Supergene trees were estimated using fully partitioned analyses. MP-EST with weighted and unweighted binning returned the same tree. The branches on the binned MP-EST tree are labeled with two support values side by side: the first is for unweighted binning and the second is for weighted binning; branches without designation have 100% support. Branches in red indicate contradictions to known subgroups. Table  S2. Average bootstrap support. We show the average bootstrap support values of the estimated gene trees for the simulated datasets.
Results are shown for fixed number of genes (1000 for avian and 200 for mammalian, 100 for 15-taxon and 100 for 10-taxon datasets). We fixed the level of ILS to 1X for avian, mammalian and 15-taxon datasets; and varied the level of ILS for 10-taxon datasets with 100bp sequence length. We show the species tree branch length error (the ratio of estimated branch length to true branch length for branches of the true tree that appear in the estimated tree; 1 indicates correct estimation). We varied the number of genes from 50 to 2000, and fixed the sequence length to 500bp with default amount of ILS (1X level). We used 50% bootstrap support threshold for binning. Supergene trees were estimated using unpartitioned analyses. We varied the numbers of genes, and gene sequence alignments length with default amount of ILS.
Figure2. Divergence of estimated gene tree (triplet) distributions from true gene tree distributions for MP-EST analyses of simulated avian datasets. In (a), we vary the gene sequence length (250bp genes have the highest error, and 1500bp has the lowest error) and explore 1000 genes under default ILS levels, and in (b) we vary the amount of ILS and fix the number of genes to 1000 and sequence length to 500bp. True triplet frequencies are estimated based on true gene trees for each of the n 3 possible triplets, where n is the number of species. Similarly, triplet frequencies are calculated from estimated gene/supergene trees. For each of these n 3 triplets, we calculate the Jensen-Shannon divergence of the estimated triplet distribution from the true gene tree triplet distribution. We show the empirical cumulative distribution of these divergence scores.
Statistical significance test results for choice of binning method on ASTRAL. We performed ANOVA to test the significance of the choice of methods (unbinned, weighted binned, unweighted binned, WSB-50: weighted statistical binning using 50% bootstrap support threshold and WSB-75: weighted binning using 75% bootstrap support threshold). For weighted vs. unweighted, we compared 50% bootstrap support threshold for avian, 75% for mammalian, and both 50% and 75% for 15and 10-taxon datasets. All p-values are corrected for multiple hypothesis testing using the FDR.
Statistical significance test results for interaction effects (binning and simulation parameter) on ASTRAL. We performed ANOVA to test the significance of whether there is an interaction between the choice of the method (unbinned, weighted binned, unweighted binned, WSB-50: weighted statistical binning using 50% bootstrap support threshold and WSB-75: weighted statistical binning using 75% bootstrap support threshold) and the variable changed in each dataset. For weighted vs. unweighted, we compared 50% bootstrap support threshold for avian, 75% for mammalian, and both 50% and 75% for 15-and 10-taxon datasets. All p-values are corrected for multiple hypothesis testing using the FDR correction (n = 17). "n. a. " stands for "not available".
Species trees estimated by binned (with and without weighting) ASTRAL using MLBS on avian biological datasets. (a) Unweighted binned ASTRAL, and (b) weighted binned ASTRAL. Branches without designation have 100% support. We used 50% bootstrap support threshold for binning. Supergene trees were estimated using fully partitioned analyses. Binned ASTRAL with weighting and binned ASTRAL without weighting differ only in the placement of Opisthocomus hoazin. However, the branches supporting different placements of Opisthocomus hoazin have low support values (47% for unweighted binning and 55% for weighted binning).
Data Availability Most of the datasets used in this study are available through the prior publications. The new datasets generated for this study are available on figshare, with DOI: http://dx. doi. org/10. 6084/m9. figshare. 1411146. (Retrieved May 13, 2015. ) The weighted statistical binning software is available on github at https://github. com/smirarab/binning (Retrieved May 14, 2015. ).
( a )Figure 5. a5 Figure 5. Cumulative distribution of the bootstrap support values (obtained using MLBS) of true positive (TP) and false positive (FP) edges estimated by binned and unbinned MP-EST on avian datasets. In (a) we fix the number of genes to 1000, use default ILS levels, and vary sequence length to control gene tree estimation error, and in (b) we study 1000 genes with 500bp sequence length, and vary ILS levels. To produce the graph, we order the branches in the estimated species tree by their quality, so that the true positives with high support come first, followed by lower support true positives, then by false positives with low support, and finally by false positives with high support. The false positive branches with support above 75% are the most troublesome, and the highly supported false positives are indicated by the grey area.
a. 0. 57 Mammalian ILS level 0. 57 n. a. 0. 0009 Mammalian # of genes 0. 91 n. a. <0. 0001 Dataset Interaction variable Weighted vs Unweighted WSB-50 vs Unbinned WSB-75 vs Unbinned 10-taxon ILS level 0. 99 0. 99 0. 49 15-taxon # of genes, seq length 0. 99 & 0. 99 0. 59 & 0. 99 0. 24 & 0. 17 Avian sequence length 0. 99 <0. 0001 n. a. Avian ILS level 0. 99 <0. 0001 n. a. Avian # of genes 0. 99 <0. 0001 n. a. Mammalian # of genes, seq length 0. 99 & 0. 99 n. a. 0. 99 & 0. 38 Mammalian ILS level 0. 15 n. a. 0. 15 correction (n = 14). "n. a. " stands for "not available". statisticallysignificant;seeTable4). The impact of binning is also significantly impacted by ILS levels (see Table4), with the largest improvements obtained for lower levels of ILS. In general, binning helps both ASTRAL and MP-EST, but MP-EST tends to be helped more than ASTRAL.
Species trees estimated by binned (with and without weighting) ASTRAL using MLBS on avian biological datasets. (a) Unweighted binned ASTRAL, and (b) weighted binned ASTRAL. Branches without designation have 100% support. We used 50% bootstrap support threshold for binning. Supergene trees were estimated using fully partitioned analyses. Binned ASTRAL with weighting and binned ASTRAL without weighting differ only in the placement of Opisthocomus hoazin. However, the branches supporting different placements of Opisthocomus hoazin have low support values (47% for unweighted binning and 55% for weighted binning). 50 85 Cursores Columbea Otidimorphae Australaves 47 50 99 98 51 Calypte anna Chaetura pelagica Antrostomus carolinensis Podiceps cristatus Passeriformes Psittaciformes Falco peregrinus Cariama cristata Coraciimorphae Accipitriformes Tyto alba Pelecanus crispus Egrett agarzetta Nipponia nippon Phalacrocorax carbo Procellariimorphae Gavia stellata Phaethon lepturus Eurypyga helias Balearica regulorum Charadrius vociferus Opisthocomus hoazin Tauraco erythrolophus Struthio camelus Tinamus guttatus Anas platyrhynchos Gallus gallus Meleagris gallopavo Mesitornis unicolor Pterocles gutturalis Columbal ivia Phoenicopterus ruber Cuculus canorus Chlamydotis macqueenii 98 53 55 52 99 83 52 Binned ASTRAL (unweighted) Binned ASTRAL (weighted) Figure S9.
Women with SO were reviewed by a specialized dietician and advised about healthy eating and about how to maintain their weight during pregnancy. Community midwives discussed diet and exercise with lean controls during pregnancy. PPWR was defined as -(postnatal weight loss) = weight at 36 weeks gestationweight at postnatal visit. A greater difference indicates lower PPWR. We evaluated various demographic factors preceding pregnancy (collectively termed maternal factors) and those arising during pregnancy (collectively termed pregnancy factors) which potentially influence maternal mood during pregnancy and may confound the analyses of mood assessment through questionnaire and verified the data with hospital records. Questions about traumatic obstetric history  (Mota et al. , 2010) , reproductive problems  (Stanton et al.
Statistical Analyses Statistical analyses were performed using SPSS 19 (IBM, New York, USA) and figures drawn with SPSS 19 and Graphpad 6 (Graphpad Software Inc. , La Jolla, USA). Prior to any analyses, data distribution was determined by Q-Q Plot and by histogram visualisation. Where required, the data were log-transformed. P≤0. 05 was used as a cut-off of statistical significance for descriptive data. Regression analyses were carried out with BMI as independent variable and each mood assessment scores and other potential confounders as dependent variable for each study time-point. Linear regression analyses were performed to adjust for demographic factors preceding pregnancy (maternal factors, p 1 ), during pregnancy (pregnancy factors, p 2 ), and both (p 3 ). Previous history of mood diagnosis was further considered by: 1) omitting participants with a previous mental health diagnosis (p 4 ) and 2) performing further linear regression adjusting for history of mental health diagnosis (p 5 ).
Mood assessments data were also analysed using clinical cut-offs for each questionnaire using logistic regression. In testing the correlations among maternal mood, GWG, PPWR and serum cortisol levels, maternal mood outcomes were grouped into "anxiety symptoms" and "depression symptoms". Anxiety symptoms were represented by Hospital Anxiety (HA from HADS) and STAI, whilst depression outcomes were represented by Hospital Depression (HD, from HADS) and GHQ. To avoid multiple testing and the need for including a Bonferonni correction, the z-score was calculated for each outcome and we used averaged z-scores for each symptom group in the analysis. We confirmed that the z-scores reflected the general observations that A&D symptoms are highly correlated across week 17 and 28 pregnancy (all Pearson's correlation <0.
7, p ≤0. 0001) and that generally the SO group displayed poorer mood outcomes (S Table  1 ). Linear and quadratic curve-fitting were used to test whether there was any non-linear relationship between z-score anxiety and z-score depression with GWG and PPWR. MEDCURVE SPSS plug-in (http://www. afhayes. com/) was used to test whether there was any non-linear mediation by serum cortisol levels.
Using simulated data with 10% permutation error (4% actual error) in genotyping, the method had a specificity of 0. 96 (4% of correct markers are rejected) and a sensitivity of 0. 99 (1% of wrong markers are accepted) if markers with heritability lower than 0. 975 are discarded. Checking of Mendelian errors resulted in a lower sensitivity (0. 84) for the same simulation. The proposed method is further illustrated with a real data set with genotypes from 3534 animals genotyped for 50,433 markers from the Illumina PorcineSNP60 chip and a pedigree of 6473 individuals; those markers underwent very little quality control. A total of 4099 markers with P-values lower than 0. 01 were discarded based on our method, with associated estimates of heritability as low as 0. 12. Contrary to other techniques, our method uses all information in the population simultaneously, can be used in any population with markers and pedigree recordings, and is simple to implement using standard software for REML estimation.
2012; Wang et al. 2013). The quality of genotypic data in genomic evaluations thus has been carefully considered for some time, and a number of procedures for quality control (QC) have been developed. The QC filtering of SNPs in genomic evaluation can increase accuracy, reduce computational effort, and improve stability of estimates of the effects of the remaining SNPs  (Wiggans et al. 2009). We propose a method based on maximum likelihood to check the quality of marker genotypings in a possibly complex pedigreed population that is partially or completely genotyped for a set of biallelic markers. This method specifically aims at detecting loci for which a large number of individuals are wrongly genotyped. First, we briefly describe current methods. Then the method is presented, and results using publicly available and simulated data are shown.
Tests of the Method Simulations: To ascertain the sensitivity (i. e. , fraction of incorrect markers that are rejected) and the specificity (i. e. , fraction of correct markers that are not rejected) of the test, we simulated data using QmSim  (Sargolzaei and Schenkel 2009)  following Mendelian rules-therefore, the simulated data had no errors. We considered a simplified pig nucleus breeding program scenario with 10 autosomal chromosomes of 160 cM each and 70,000 SNPs. First, a mutation-drift equilibrium was reached in 2500 generations of random mating in a population with effective population size equal to 500 and mutation rate 2 3 10 24 , followed by a severe bottleneck with effective population size of 75 evolving during 30 generations. Then there was selection (not described here) during 5 generations, where 20 boars were mated with 200 sows producing 2000 offspring, with a total of 10,220 animals, with complete pedigree and 5000 randomly chosen animals genotyped for 28,254 markers with MAF.
" A total of 50,433 SNPs were used in this study after filtering genotypes for minor allele frequency (,0. 01) and the SNP call rate (,90%) and excluding SNPs on the sexual chromosomes. The reason to exclude MAF , 0. 01 is that numerical maximization of REML is unreliable in that case. For comparison purposes, the same analysis was carried out in two extreme scenarios by randomly permuting half or all the genotypes for each SNP in the data set.
Statistical analysis: For each SNP in the data set, the heritability and the LRT statistic were calculated to test for zero error variance in genotyping. The maximum residual log-likelihoods under the full (alternative hypothesis without a priori on the values of s 2 u and s 2 e ) and reduced models (null hypothesis assuming s 2 e ¼ 0) were obtained using remlf90  (Misztal et al. 2002). Heritability of gene content was estimated for each SNP under the full model. Estimates can be carried on in parallel if needed. Because this software uses Henderson's mixed-model equations, the reduced model had to be approximated by specifying a very small value for the residual variance, s 2 e ¼ 0:0001. The advantage of computing in this way is that standard REML software can be used. We also used QC checking based on Mendelian errors included in preGSf90  (Aguilar et al.
2014) ; a marker was rejected if its genotypes showed Mendelian inconsistencies in more than 1% of the parent-offspring couples or trios. The complete scripts for QC, the PIC data set, and the three simulated data sets are available at http://genoweb. toulouse. inra. fr/~alegarra/qualitycontrol. tar. gz. A version with the scripts and a small subset of the PIC data set is available as Supporting Information, File S1.
Acknowledgments The editor and reviewers are acknowledged for very useful comments. This work was made possible by a visit of  NS Forneris  to  INRA, Toulouse, France , financed by the  Saint-Exupéry Scholarship Program 2013-2014  ( MinCyT Argentina-French Embassy ). AL and ZGV acknowledge financing from projects  X-Gen  and  GenSSeq of INRA metaprogram SelGen. NSF  and RJCC were partially funded by grants  UBACyT   861/2011  and  PIP CONICET 833/2013. This project was partly supported by the  Toulouse Midi-Pyrénées bioinformatics platform.
Our data reveal that CTLs are endowed with a previously unappreciated ability to control an excess of target cells over prolonged time frames. Individual CTLs were able to eliminate up to 12 target cells in 12 hr. However, the contribution of individual CTLs to the control of target cells was very heterogeneous. A similar variability of individual killing performance was measured in different clones. In-depth statistical analysis revealed that within a clone, only a subset of CTLs displayed a high-rate killing capability. This subset developed upon contact with the target cells, required a strong T cell receptor (TCR)-mediated CTL activation, and displayed a burst kinetic behavior. Rather than being imprinted, such behavior was reset in the progeny of individual cells. Together, our data reveal that the cytotoxic activity of human CTLs is a cumulative process.
The mean number of killed target cells was limited during the first 6 hr, while it progressively increased over the 6-to 12-hr time interval (Figure  4B ). This single-CTL analysis therefore confirms that, in a context of an excess of target cells, the initial killing activity is relatively modest and is followed by an increased multiple killing activity in some CTLs. We then aimed at characterizing the statistical nature of the heterogeneity of multiple killing observed in individual CTLs. Clearly, the killing time appears to follow an exponential law. As a consequence, the distribution of the killing time on the considered time window is a Poisson process whose unknown intensity is closely related to the parameter of the exponential law. With a dataset stemming from the analysis of 259 single CTLs in three independent experiments, we first evaluated with a chi-square test whether the cumulative number of killed target cells over 12 hr would distribute according to a single Poisson distribution (Supplemental Statistical Methods).
Then, we used the test of Azais and Genz  (Azais and Genz, 2013)  to detect changes in the mean by taking into account the correlation structure learned in the time series (see Supplemental Information for a detailed description). Our breakpoint test indicates that the baseline hypothesis, namely that homogeneity in the mean is maintained all along the killing process, is rejected when considering the compiled dataset. The analysis indicates a first type error test lower than p value = 5. 7 3 10 À4 with a breakpoint in the homogeneity likely to occur at about 10 hr (Figure  4D ). Comparable results were obtained when considering the three data subsets separately (see Supplemental Information). The registration of the prolonged killing kinetics of individual CTLs facing an excess of target cells therefore confirms the high heterogeneity in the killing efficiency measured with the end-point assay.
High-Rate Killing Relies on a Late Phase of Burst Killing Preceded by Isolated Killing Events We then investigated which characteristics might distinguish the two subsets of CTLs. Based on the dataset comprising 259 single-CTL killing kinetics, we noticed that for most CTLs, the killing pace was highly irregular over the 12-hr period. In particular, some CTLs appeared to kill multiple target cells in a row with a burst kinetic (Figure  5A ). To appreciate the killing pace of each CTL, we quantified the maximum killing rate reached during the 12-hr recording (considering a series of three consecutive killing events). Most CTLs killed with an irreg-ular pace, as indicated by the fact that we detected killing-rate peaks above the theoretical average killing rate (Figure  5B ). The maximum killing rates increased as a function of individual 12-hr killing performance to reach two to four killing events/hr in the CTLs managing to kill 9-12 targets cells over 12 hr.
On the basis of this analysis, we defined burst killing as a serial killing of at least three targets at a rate of more than one target/hr, which is superior to the average killing rate of the best-performing CTLs (12 targets killed in 12 hr). The analysis of the compiled dataset revealed that 42% of the CTLs exhibited a burst killing phenotype. This CTL subset killed an average of 6. 3 target cells over the 12-hr period. Furthermore, the distribution of the 12-hr killing performance according to the occurrence of burst killing revealed two subsets of CTLs that fitted the mixed Poisson distribution identified by our statistical analysis (Figure  5C ). Together, these data indicate that a hallmark of high-rate killer cells is their capacity to accelerate killing pace to kill multiple target cells over a limited amount of time.
In our model of human CTL clones recognizing a CMV peptide presented by EBV-transformed B cells, a (B) Kinetics of multiple killing events following the first killing event is represented after alignment of the killing records so that time 0 corresponds in each well to the appearance of the first caspase-3 + target cell. (C) Cumulative frequency of CTL displaying burst killing, with or without synchronization of the killing kinetics on the basis of the first killing event. Data represent mean ± SEM from three experiments. reproducible impact on the target cell population was measured at an E:T ratio as low as 1:40. Importantly, near-complete elimination of the target cell population was obtained with a 1:10 E:T ratio. Such a level of killing has not been reported previously for human CTLs.
Clearly, our data show that the killing activity of human CTL clones is a cumulative process, which results from a non-synchronized and nonlinear accumulation of killing events. Following an initial phase of latency during which a relatively low number of target cells activated caspase-3, an accumulation of killing events was measured. Comparable kinetics were observed under bulk and single-CTL settings, implying that they did not result from the initiation of synergistic cooperation among CTLs. We show that the delayed onset of an increased killing activity is preceded by isolated events of low-pace killing. Strikingly, similar burst kinetics have been recently described for natural killer (NK) cell killing activity, using a reporter of granzyme B activity in target cells  (Choi and Mitchison, 2013).
A reference tube consisting of 1 3 10 5 JY cells in 200 ml plus 50-ml bead solution was acquired until reaching 2,000 beads. In the experimental tubes, acquisition was also fixed to 2,000 beads. The absolute number of residual live target cells was calculated by multiplying the number of events collected in the CFMDA À /7-AAD À gate by the ratio set by the reference tube. All data were acquired on a BD FACSCalibur cytometer and analyzed using FlowJo software. Fluorescence-Activated Cell Sorter-Assisted Clonal Killing Assay Ten GFP-expressing target cells either unpulsed or pulsed with 100 nM pp65 were distributed in 96-well U-bottom low-cell-binding plates (Nunc Dishes) using a BD FACSAria II cell sorter operating in single-cell-purity sorting mode. Sequentially, 1 or 20 CD8 + T cell clones prestained with 0.
Measurement of IFN-g and Perforin Loss Target cells were either unpulsed or pulsed with 100 nM pp65 were cocultivated with CD8 + T cell clones prestained with 0. 1 mM CellTracker Green CMFDA (5chloromethylfluorescein diacetate from Invitrogen) at 1:10 E:T ratio during 4 hr. In some experiments, 10 mg/ml brefeldin A (Sigma) and/or 1 mg/ml CsA was added to the culture. Cells were fixed with 2% paraformaldehyde, permeabilized with 0. 1% saponin (in PBS/3% BSA/HEPES), stained with AF700-labeled anti-IFN-g monoclonal antibody (clone XMG1. 2; BD Biosciences), and AF647-labeled anti-human perforin monocolonal antibody (clone dG9; BioLegend). Data were acquired on a BD LSR Fortessa cytometer and analyzed using FlowJo software.