dataesr/scientific-paragraphs-categorization huggingface dataset:
Considering suboptimal reconciliations by altering the elementary event costs The choice of the cost for the elementary events may have a strong impact on the event set inferred by parsimonious reconciliation methods. These costs are usually derived from evolutionary event rates inferred by probabilistic methods on biological datasets  [11]. In the case of simulated datasets, exact event rates are known and can be directly used to derive elementary event costs (see  [5, 27]  and Equation  4 ). A standard strategy to estimate the reliability of an inference is to consider its stability with respect to fluctuations of the method parameters, i. e. here the costs of the elementary events (see  [28]  for an example in the sequence alignment context). Since optimal solutions for slight variations of parameter values are near-optimal solutions for the original parameter values, this strategy can also be viewed as a sampling of suboptimal solutions.
Generating data Experiments were conducted on the basis of a phylogeny of 37 proteobacteria. Along this species tree, 1000 evolutionary histories, composed of D, T, L and S and events (E True ), were simulated according to a birth and death process, leading to 1000 simulated gene trees (G True ). Rates for macro-evolutionary events were chosen using the same scheme as  [14] : (a) the loss rate was randomly chosen in the [0. 001, 0. 0018] interval, where the units are events per gene per million years. Moreover, the ratio between the ''birth'' rate (sum of the duplication and transfer rates) and the loss rate was randomly chosen in the [0. 5,1. 1] interval, while the proportion of the duplication rate to the birth rate was randomly chosen in the [0. 7,1] interval. The Seq-Gen program  [29]  has been used to simulate the evolution of DNA sequences of 1500-3000 bp length along each G True under the Generalised timereversible (GTR) model  [30] , the sequences in turn have been given as input to RAxML  [31]  to infer a maximum likelihood gene tree (G ML ).
Thus, the simulation protocol delivered a dataset of 1000 gene trees to reconcile with the proteobacteria phylogeny. ML trees contain on average 29 leaves and have an average Robinson-Foulds distance of 17. 7% with respect to the true gene trees. The species tree is reconciled with G ML trees instead of G True trees to take the fact that gene trees are only an indirect estimation of the true gene histories into account. For more details on the simulation protocol please refer to  [14]. As done in  [14] , the initial elementary cost for a duplication was chosen as follows: Cost~log DD(E True )DzDT(E True )DzDL(E True ) DD(E True )D ð4Þ where D(E True ), respectively T(E True ) and L(E True ), stands for the sets of duplication, respectively transfer and loss events, in the simulated history E True.
For G ML , only the type of an event (S, D, T, or L) and its predicted location on the species tree are taken into account in the computation of TP, FP, TN and FN values (i. e. the location in the gene tree is disregarded). As done in previous papers  [5, 14] , the reconciliation error was measured on T, D and L events, i. e. events causing a gene tree to differ from the species tree. The error of a predicted set Ê E estimating an event set E True is then measured by the symmetric evolutionary distance between these sets: d s ( Ê E,E True ) : ~DÊ E\E True D DTL zDE True \ Ê ED DTL where the first and second term respectively correspond to FP and FN, and where the DTL subscript recalls the events taken into account. This simple measure was used on the dataset composed of 1000 G ML families to compare the competing strategies to estimate a gene true history, a strategy being all the more accurate when its average error is low.
To obtain a more detailed comparison between competing strategies, one often resorts to a Receiver Operating Characteristic curve (ROC-curve), allowing us to represent the performances of alternative methods on a number of datasets in a single 2D graphic. As we currently do not have any practical solution to compute the number of true negatives (TN) for the problem considered here, we considered Precision-Recall curves (PRcurves) instead. PR-curves are very similar to ROC-curves  [32]  and can be drawn while disregarding TN. Precision and Recall values are defined as follows: Precision : ~TP TPzFP ð5Þ Recall : ~TP TPzFN ð6Þ Each competing strategy gives rise to a PR curve, which are then compared on a single common plot. According to (  5 ) and (  6 ), the higher the PR curve is for a given recall level, the better accuracy the corresponding method displays.
Indeed, relying on half of the dataset to explain an average gap of 2 events (20. 2-18. 2) between a full reconciliation and one containing only events of maximum support, shows that even in cases where several MPRs are available, they share at least 11 events over the 18 they contain on average. The significant reduction in the distance between filtered events and the true reconciliation observed in Table  3  is then obtained thanks to only half of the considered gene families. This shows that this approach is very powerful but also indicates that there is room for further improvement. The low average variability of events among different MPRs leads to many events having maximum support; together with the fact that for T = 100% filtered event sets still contain too many events (18. 2 compared to 13.
Remarks on the dataset of the true gene trees Although considering only randomly chosen reconciliations, Doyon et al. [5]  showed that parsimony reconciliation methods can correctly recover large parts of the true evolutionary histories of gene families when the true gene trees are given as input. This is confirmed on our dataset where the existence of multiple optimal solutions is also taken into account. In fact, among <44 events predicted per gene family when using G True instead of G ML , 94% of them are present in all MPRs, i. e. having 100% support, and 96% of these latter events are correct. Hence, inferring event supports from a set of nearly-optimal reconciliations will likely introduce wrong events. In our opinion, our proposed approach should be applied only when the performance of reconciliation methods is degraded due to erroneously constructed gene or species trees.
This work was funded by the  Languedoc-Roussillon Chercheur d'Avenir program , by the  French Agence Nationale de la Recherche :  Investissements d'avenir/Bioinformatique  ( ANR-10-BINF-01-02 , Ancestrome),  Programme 6\e ' me Extinction  ( ANR-09-PEXT-000 , Phylospace), and by the  Institut de Biologie Computationnelle. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Genome-wide molecular markers are often being used to evaluate genetic diversity in germplasm collections and for making genomic selections in breeding programs. To accurately predict phenotypes and assay genetic diversity, molecular markers should assay a representative sample of the polymorphisms in the population under study. Ascertainment bias arises when marker data is not obtained from a random sample of the polymorphisms in the population of interest. Genotyping-by-sequencing (GBS) is rapidly emerging as a low-cost genotyping platform, even for the large, complex, and polyploid wheat (Triticum aestivum L. ) genome. With GBS, marker discovery and genotyping occur simultaneously, resulting in minimal ascertainment bias. The previous platform of choice for whole-genome genotyping in many species such as wheat was DArT (Diversity Array Technology) and has formed the basis of most of our knowledge about cereals genetic diversity.
Diversity Analysis A population of 365 soft winter wheat varieties and F 5 -derived advanced breeding lines originating from multiple crosses in the Cornell University Wheat Breeding Program (Ithaca, NY) was analyzed in this study. Lines were genotyped with 5,000 Diversity Array Technology (DArT) markers resulting in 1,544 polymorphic markers. All lines were also genotyped using GBS as described in  [18]. The DArT markers had 3. 1% missing datapoints and the GBS data had 43. 9% missing datapoints for 38,412 markers, where a data point refers to one cell in the marker data matrix. The impact of imputation on the DArT given its low level of missing data was assumed to be marginal  [19]. Missing marker data were imputed using random forest  [20]  as described in  [19]  separately for the DArT markers and the GBS markers.
GS Analysis Phenotypic data for four traits were analyzed: grain yield, plant height, heading date, and preharvest sprouting (PHS) as described in  [15]. Preharvest sprouting is the premature germination of seeds while still attached to the mother plant. As was done previously, a bootstrap approach was used to test for the significance of the difference in cross-validated accuracy between DArT and GBS markers for an equal number of markers. A simple ridge regression Best linear unbiased predictor (BLUP) was used with a 10-fold cross-validation. The crossvalidation partition was identical for all analyses. When using the same number of markers, redundant or not, the difference in accuracy was not significant for three out of four traits (based on bootstrap p-values) (Table  3 ). When using all the GBS markers available, accuracy was higher than with the DArT markers.
To demonstrate that inclusion of GBS markers with high levels of missing data was appropriate, subsets of GBS markers were also selected based on a missing data threshold per marker and GS accuracies were computed (Table  S2 ). With variation between traits, accuracies reached a plateau when including markers with a high level of missing data. (Between 15% missing data for heading date and 80% for plant height). This corresponds to a minimum of 4787 GBS markers compared to 1,544 DArT markers available.
Discussion Our analyses tested for significant differences between DArT and GBS markers when the number of markers was the same. That is, whether the DArT could have been drawn from the same distribution as the GBS markers. Our results indicated that the DArT and GBS marker data yielded significantly different results for several statistics related to diversity. For a number of metrics, it was very clear that the DArT markers were not drawn from the same distribution as the GBS markers. This difference was likely due the ascertainment bias inherent in the DArT markers because DArT markers were discovered and validated on a screening panel independent from the genotyped population while with GBS the marker discovery and genotyping took place at the same time. The analyses showed that the diversity image was distorted using DArT compared to GBS markers for an equal number of markers, even though a largely similar first principle component was captured by both platforms.
Data A population of 365 soft winter wheat varieties and F 5 -derived advanced breeding lines originating from multiple crosses in the Cornell University Wheat Breeding Program (Ithaca, NY) was analyzed in this study. Lines were genotyped with 5,000 Diversity Array Technology (DArT) markers (Triticarte Pty. Ltd. , Yaralumla, ACT, Australia), resulting in 1,544 polymorphic markers. The DArT technology for wheat assayed a reduced representation library of the genome; built on a small subset of genotypes using PstI and TaqI restriction enzymes. PstI-PstI fragments were cloned and the fragments polymorphic between a set of 13 Australian wheat genotypes were printed on an array. Each clone was further validated on a large panel of genotypes for quality and polymorphism  [12, 13, 14]. All lines were genotyped using GBS as described in  [18].
Diversity Analysis As a first approach, principal component analysis (PCA) was used to analyze all DArT or GBS data available to look for differences in the representation of the lines. To quantify the differences between the DArT and the GBS platforms, a bootstrap procedure was used. A total of 1,544 imputed GBS markers (same as the number of DArT markers) were sampled and used to compute population genetics statistics. Based on that sample of GBS markers, lines were clustered using the R package mclust  [32]  to identify subpopulations by hierarchical clustering using a parameterized Gaussian mixture model. The Bayesian information criterion (BIC) was used to identify the optimal number of subpopulations as well as the optimal clustering model to use. Based on that subpopulation structure, F st values were computed to measure the genetic differentiation between subpopulations.
F st measures the fraction of the variance in allele frequencies due to population differentiation. The F st estimator of  [33]  which is insensitive to differences in subpopulation sizes was used. The overall gene diversity was computed using the R package hierfstat  [34]. To measure the information lost when the relationship matrix was calculated based on sampled GBS markers or DArT markers instead of using all GBS markers, the Kullback-Leibler divergence was used  [21]. The Kullback-Leibler divergence is a measure of the difference between two probability distributions. It measured the information lost when the relationship based on sampled GBS markers or DArT markers are used to approximate a reference covariance matrix. The relationship matrix based on all GBS markers was used as a reference.
GS Analysis A bootstrap p-value was used to compare the differences in GS accuracy. For each bootstrap, 1,544 imputed GBS markers were sampled and used to compute a realized relationship matrix. A GS model was built using genomic BLUP with the R package rrBLUP  [35]. In genomic BLUP the covariance of the lines is constrained by the realized relationship matrix based on markers. A 10-fold cross validation procedure was used keeping the same partition of the folds for every bootstrap. The procedure was repeated for each of the four traits studied. This provided a cross-validated accuracy for each trait and each bootstrap sample. Cross-validated accuracy was also computed using the DArT markers and a P-value derived using 1000 bootstrap samples. The procedure was repeated after applying the tag SNP selection procedure to the DArT markers using an R 2 threshold of 0.
8. This reduced the number of sampled markers to 787. Tag SNPs for the GBS markers were selected in the same manner, reducing the total number of sampled markers to 31,605. The 10fold cross-validated accuracies were computed for 1000 samples of 787 GBS markers to obtain a bootstrap distribution of accuracies. The 10-fold cross-validated accuracies were also computed using the 787 non-redundant DArT markers and compared to the GBS accuracy distribution to derive a P-value. analysis. (YLD: yield, HT: height, HD: heading date, PHS: preharvest sprouting). For each missing data threshold, the corresponding number of markers is indicated. (DOC) Data S1 Molecular marker and phenotype data. (ZIP).
fMRI data was acquired with a 1. 5T system (Siemens CTI) at the Imaging Center of Lyon (CERMEP "Imagerie du vivant"). Brain scans involved assessment of the BOLD fMRI signal. The anatomical images of the brain (3D MPR) were recorded using a T1-weighted sequence. For each run, whole brain coverage was obtained with Echo Planar Imaging (EPI) images (repetition time TR = 2500 ms, echo time TE = 60 ms, and flip angle 90 • ). Twenty-six brain sections were acquired in an interlaced mode parallel to the AC-PC plane. Slices had a thickness of 4. 4 mm [matrix 64 × 64; and field of view (FOV) = 230 mm, voxel size, 3. 4 × 3. 4 × 4 mm]. Following functional image acquisition, a high-resolution T1-weighted anatomical image was acquired (TR = 1880 ms; TE = 3. 93; lip angle 15 • ; matrix 256 × 256; and slice thickness 1 mm).
Processing of the fMRI data involved the Statistical Parametric Mapping software (SPM 2, Wellcome Department of Imaging Neuroscience, London UK; http://www. fil. ion. ucl. ac. uk/spm) running under Matlab (The Mathworks, Inc. , Natick, StateMA, USA). On the basis of the GLM model  (Friston et al. , 1994) , the task related BOLD changes were estimated as linear combinations of the individual regressors and stored as participant specific contrast images. Several contrasts were realized using the different regressors in order to study the differences between the three sequences (Pause = P, No-Pause = N, Control = C) i. e. , P > C, N > C, P > N, and N > P. These contrasts were selected to extract the activated neural structures specifically involved in processing sequences with and without the imposed pause.
However, in contrast to the canonical base-to-loop precursors, these MIR169 precursors suffered three dicing events, and the miRNA is released by the second and third cuts (Fig. 3C ). In agreement with this possibility, small RNAs have been cloned that correspond to the fragments cleaved by the first and second cleavage sites (Fig. 3C ;  Zhang et al. 2010). The secondary structure of MIR394 precursors and their cleavage pattern suggest that the MIR394 family is also processed by a long baseto-loop mechanism (Supplemental Fig. 3B ; Table  1 ). Interestingly, there is a third set of MIR169 precursors (MIR169h-n) that contains a lower stem but also a structured upper stem (Table  1 ; Fig. 3C ; Supplemental Fig. 3A ). These members of the MIR169 family are detected at low frequency in small RNA databases and in our library (Fig.
Bioinformatic analysis The secondary structure of the precursors was calculated using the program MFOLD  (Zuker 2003)  with default parameters to a temperature of 37°C. The proximal end of the miRNA/miRNA* duplex was defined as position +1. We analyzed the secondary structure, and we considered positions that were matched as 0, while those unpaired were considered as +1, and an average for all the precursors was made. In the precursors' schemes, some large bulges and secondary hairpins were simplified and/or indicated by a straight line. We have constructed and implemented a bioinformatic pipeline using in-house scripts and publicly available data from miRBase to assist in the analysis of the deep-sequencing libraries. We have developed a web-tool for the analysis of SPARE data using MySQL as a database.
Acknowledgments We thank  Julieta Mateos  for the plasmids with the MIR171a and MIR171b precursors, and  Carla Schommer ,  Alexis Maizel ,  Christopher Brosnan , and members of the J. F. P. laboratory for comments on the manuscript. This work was supported by grants from  HFSP  to J. F. P. and J. B. ,  HHMI  and  Agencia Nacional de Promocion Cientifica y Tecnica  to J. F. P. , and fellowships from  CONICET  to N. G. B. , U. C. , and A. L. S. J. F. P. is a member of the same institution. Research in the Meyers' lab was supported by the  U. S. National Science Foundation  award no. 0701745.
Despite having a suitable accuracy when based on markers like in pioneer works  (Campbell and Bobick, 1995) , focused on specific body parts in  (Just et al. , 2004)  or being adapted to dedicated applications such as human sign language recognition  (Bashir et al. , 2007) , skeleton based methods were first not suited to the recognition of various natural actions. But, recent devices like low-cost accelerometers  (Parsa et al. , 2004)  or KINECT allow the use of skeleton information to characterize general activities. In this work, we take advantage of these recent Figure  1 : RGBD-HuDaAct dataset  (Ni et al. , 2011). progresses and present an algorithm for human-action sequence recognition. This algorithm is based on skeleton data, and on trajectory descriptors. Trajectory of each skeleton articulation is associated with a descriptor.
This descriptor is invariant to rotations (toward the vertical axis) and translations of the body, and, captures both trajectory short-term information (throw an extension to 3D of  (Ballas et al. , 2011)  state-of-the-art descriptors) and trajectory middle-term information (throw elementary gesture recognition). These descriptors (one for each skeleton articulation) form the sequence signatures which are analysed by a Multiple-Kernel Learning (MKL). In addition, we study the effect of missing skeleton on our pipeline: despite large academic efforts to deal with general pose estimation  (Baak et al. , 2011; Girshick et al. , 2011; Shotton et al. , 2011) , local failures in skeleton extraction are common in real-life color-depth-videos (RGBD-video). Hence, we extend works of  (Yao et al. , 2011)  where effects of noise on joint positions on detection results is studied, by focusing on our classification results according to the degree of failures in skeleton extraction on the daily-life sequences provided in the RGBD-HuDaAct dataset (figure  1 ).
Finally in  (Barnachon et al. , 2012) , activities are represented as words on a gesture alphabet, this allows to use an automaton to recognize activity languages. Those previous works have proven the relevancy of the skeletonbased features for human activity recognition. In this work, we apply trajectory descriptors to skeleton data to form sequence signatures, invariant both in rotation and translation. These signatures are then used to classify sequences throw a machine learning algorithm. The offered algorithm is tested on both CUHA dataset  (Sung et al. , 2011)  and TUM dataset  (Tenorth et al. , 2009). In addition, we study the impact of skeleton extraction failure on our classification results. In every previous works on human action recognition based on skeleton data, skeleton extraction failure is rarely considered.
Pose Extraction and Normalization In order to be independent of the skeleton data across datasets and existing systems (such as NITE), we choose to use only trajectories of feet, knees, hips, shoulders, head, elbows and hands articulations as input trajectories. In these trajectories, the 3D coordinates of the skeleton articulations are expressed in a system of coordinates which is linked to the camera position. To recognize activity under various viewpoints, a normalization scheme has to be applied resulting in articulation coordinates invariant to geometric transformations. For instance, translation, and rotation invariance is achieved in  (Raptis et al. , 2011; Yao et al. , 2011). In  (Yao et al. , 2011) , skeleton information is reduced to a set of features based on distances between articulations.
Datasets The CUHA dataset (for Cornell University Human Activity) is presented in  (Sung et al. , 2011). It deals with 14 classes of daily-life activities performed by four people (figure  2 ). It contains 68 sequences around 30 seconds each. The evaluation process consists in a leave-one-subject-out cross validation in precision-recall terms. The TUM dataset is presented in  (Tenorth et al. , 2009). It deals with 10 action classes occurring when setting a table (figure  2 ). It contains 19 realistic sequences around 2 minutes each, performed by 5 people. Evaluation from  (Yao et al. , 2011)  consists to split data between training and testing, and to output result in correct-classification rate term. In this dataset, each frame is associated to one action label. As our system expects segmented data (contrary to those from  (Tenorth et al.
Results The evaluation of our trajectory signatures are presented in table 1 (in percentage). These results are completed by the results of 3 well-known methods of the literature: • The first one (MEMM) proposed by  (Sung et al. , 2011)  uses a two-layered maximum entropy Markov Model on pose-based features. • The second one  (Yao et al. , 2010; Yao et al. , 2011)  uses the Hough forests to classify actions associated to weak classifiers based on distances between skeleton or visual feature. • Finally, a Dynamic Time Wrapping (DTW) algorithm combined with the nearest neighbour classification approach  (Fengjun et al. , 2005; Müller and Röder, 2006)  has also been evaluated on these datasets. Only the four best short-term descriptors are presented (Short Term Position, Short Term Motion, Short Term Acceleration, Short Term Markov Motion).