dataesr/scientific-paragraphs-categorization huggingface dataset:
Population structure On the entire dataset and for both dates, gene diversity was assessed using expected heterozygosity (H E ) and the number of alleles (N A ) using FSTAT 2. 9. 3  (Goudet 1995). We used the nonparametric Wilcoxon-Mann-Whitney U-test to detect differences in heterozygosity between dates. Linkage disequilibrium between all pairs of loci was tested using Fisher's exact tests implemented in GENEPOP  (Rousset 2008). The false discovery rate (FDR) procedure implemented in the R package QVALUE  (Storey 2002 ) allowed controlling for multiple testing. Genetic differentiation between clusters was estimated through F ST using GENEPOP. Likelihood ratio G test allowed estimating the significance of F ST values.
Cline fitting tool To fit genetic clines, we used the software CFit7  (Gay et al. 2008 , available at http://www. cefe. cnrs. fr/genetique-et-ecologie-evolutive/ cfit), which we modified to account for 2D data. It allows fitting biallelic allele count data to geographical distance using various cline shapes. Several clines can be fitted to compare their slopes (concordance) and position (coincidence) by constraining shape parameters. Estimation is performed by maximum likelihood. Likelihood is computed considering that allelic counts in a site are drawn from a binomial distribution. It is maximised using a simulated annealing algorithm. Here, most genetic markers had only two alleles (Appendix S3). The others were converted to two-allele systems by assigning each allele to a cluster-specific compound allele according to its coordinates on the first axis of a multiple correspondence analysis (MCA)  (Daguin et al.
As detailed in Appendix S6, we reparameterised eqn 4 using the scaled Logit shape and integrated this equation in a new version of CFit7 (available online) thus allowing a maximum likelihood estimation of dispersal (jointly with cline angle, cline centre and centre shift) from the full dataset. Loci without any cline pattern at T 1 were included in the likelihood computation assuming a constant allele frequency over all sites at T 1. Likelihood was corrected for overdispersal of genetic data using the variance inflation factor  (Lebreton et al. 1992) , estimated from the residual deviance of the complete model divided by its residual degrees of freedom. Model selection was done using Akaike criterion. Support limits for r and h were estimated within 2 units around the maximum likelihood value.
Delineating the genetic discontinuity In both datasets (T 1 and T 2 ), GENELAND and TESS clearly detected two genetic groups (Appendix S2). In T 1 , applying an assignment threshold of 0. 80, GENELAND assigned 86 individuals to the first cluster and 56 to the second one. Only two individuals were not assigned. Applying the same assignment threshold in T 2 , only seven individuals were not assigned as 158 individuals were assigned to another first cluster and 79 individuals to a second one. As expected under the admixture model, differences in membership profiles were found with TESS (Appendix S2). In T 1 , TESS assigned 30 and 70 individuals to the first and second parental populations, respectively, the other 44 individuals, that is ~30% of the population, being admixed. In T 2 , the proportion of admixed individuals increased to ~40% (i.
e. 100 individuals). The remaining 93 and 51 individuals were assigned to two other parental populations. The existence of a break in allelic frequencies was more conspicuous from GENELAND map but both algorithms exhibited consistent spatial genetic structures. Finally, the multilocus genetic discontinuity was located at approximately the same latitude (~4. 90°, Fig. 2 ) in both datasets. Indices of genetic diversity for T 1 and T 2 samples are given in Appendix S3. Expected heterozygosity did not significantly vary between the two samplings (U-test P = 0. 815). No pair of loci showed significant linkage disequilibrium after FDR control (Q-value < 0. 05). Genetic differentiation (F ST ) between the two clusters detected in GENELAND was significant in both samplings (F ST = 0. 091 at T 1 and F ST = 0.
Influence of kernel on dispersal estimation Figure  4c  shows the effects of the family and kurtosis of underlying dispersal kernels as well as time of first sampling on r estimation. All estimates felt within the support limits obtained on the real dataset, indicating that the departure from diffusion approximation causes biases well below measurement error in our case. Only the kernel Δ 4 , a mixture of Binomial distributions with extremely high kurtosis, combined with a very early sampling date (T 1 < 8 generations) could generate a detectable bias (underestimation of r). Under these conditions, clines showed non-logit shapes with discontinuities in allele frequencies (Fig. 4b ). Interestingly, an exponential power distribution with the same kurtosis value induced smaller biases (kernel Δ 11 ).
Testing cline coincidence, similarity in cline vanishing rates between loci and isotropy. A. Compared models. Parameters constrained and type of constraint operated are highlighted in bold. r is the standard deviation of parentoffspring axial dispersal distances, b a cline slope parameter, h 1 lower asymptotic frequency, h p the step in allelic frequency between the two populations, h cline angle, c cline centre, and d centre shift between T 1 and T 2. B. Model comparison on the whole dataset (both samplings and 15 loci). LL is log likelihood and k the number of parameters. Q AIC corrects Akaike Information Criterion (AIC) for overdispersed frequency data and the best model is highlighted in bold /c, b, h1 , h p , d & r6 ¼between locus B hT 1 =hT 2 /c, b, h 1 , h p , d & r6 ¼between locus C hT 1 =hT 2 /c, b, h 1 , h p & d6 ¼between locus/r=0 D hT 1 =hT 2 /c, b, h 1 , h p & d6 ¼between locus/s same r for each locus E hT 1 =hT 2 /b,h 1 , h p & d6 ¼between locus/same r & c for each locus F hT 1 =hT 2 /b, h 1 & h p 6 ¼between locus/same r, c & d for each locus G hT 1 =hT 2 /b, h 1 & h p 6 ¼between locus/same r & c for each locus/d=0 H hT l =hT 2 /c, b, h 1 & h p 6 ¼between locus/same r & d for each locus I hT l =hT 2 /c, b, h 1 & h p 6 ¼between locus/same r for each locus/d=0.
ACKNOWLEDGEMENTS We thank  F. Halkett ,  J. E. Ngando  and  O. Nguidjo  for technical and logistical support during sampling in Cameroon;  L. Pignolet  and M. -F. Zapater for isolation of fungal strains;  F. Halkett ,  E. Klein ,  D. Bourguet ,  T. Guillemaud ,  A. S. Walker ,  F. Balloux ,  F. Palero ,  B. Barr  es and  E. Bazin  for helpful discussions; two anonymous referees  & Adam Porter  for very useful comments on a previous version of this manuscript;  H. Rothnie  for her attentive reading and careful English language checking. Data used in this work were produced partly through the molecular genetic analysis technical facilities of the  IFR119 'Montpellier Environnement Biodiversit e'. Adrien Rieux  conducted this work during his PhD which was supported by a  CIFRE doctoral fellowship  from  Bayer CropScience.
PET METHODOLOGY In the morning of day 2, all the patients underwent a resting PET examination using [ 18 F] Fluoro-2-deoxy-d-glucose ( 18 F-FDG). Data were collected using the high-resolution PET device ECAT Exact HR+ with isotropic resolution of 4. 6 mm × 4. 2 mm × 4. 2 mm (field of view = 158 mm). The patients were fasted for at least 4 h before scanning. The head was positioned on a headrest according to the cantho-meatal line and gently restrained with straps. 18 F-FDG uptake was measured in the resting condition, with eyes closed, in a quiet and dark environment. A catheter was introduced in a vein of the arm for radiotracer administration. Following 68 Ga transmission scans, 3-5 mCi of 18 F-FDG were injected as a bolus at time 0, and a 10-min PET data acquisition period was begun at 50 min post-injection.
Sixty-three planes were acquired with septa out (volume acquisition), using a voxel size of 2. 2 mm × 2. 2 mm × 2. 43 mm (x, y, z). During PET data acquisition, head motion was monitored continuously with laser beams. Preprocessing of FDG-PET data included (1) voxel-wise correction for partial volume effects (PVE) using the corresponding structural T1 MRI with the PMOD software (PMOD Technologies Ltd. , Adliswil, Switzerland), (2) coregistration onto corresponding T1 MRI and spatial normalization to the MNI space using the parameters estimated from the corresponding T1-weighted MRI using the voxel-based morphometry 5. 1 (VBM) toolbox (http://dbm. neuro. uni-jena. de) implemented in statistical parametric mapping 5 (SPM 5) software (Wellcome Trust Centre for Neuroimaging, London, UK), (3) quantitative scaling using the cerebellum gray matter as a reference to obtain standardized uptake value ratio (SUVr) images (cerebellum gray matter values were obtained for each participant using the cerebellum defined in the Automated Anatomical Labeling (AAL) atlas  (Tzourio-Mazoyer et al.
DATA ANALYSES Analyses focused on the recent past (P4), the three other periods being used as control data for behavioral analyses. Memory scores obtained with the TEMPau task were analyzed using analyses of variance (ANOVA) with group (AD vs. controls) as between-subject factor and periods (P1, P2, P3, and P4) as within-subject factor. These analyses were followed by post hoc tests (HSD Tukey) when applicable. As the two memories of the most recent period (P4) were experienced on two different days, separated by a night during which sleep was recorded, we conducted similar analyses dividing the period P4 in two subperiods: yesterday and today. These analyses will allow to determine whether sleep has a beneficial effect on consolidation of autobiographical in AD patients and older adults. In addition, we also looked for correlations between sleep parameters (such as the percentage of time spent in each sleep stage) measured during the night and the memory score for the event experienced the previous-day.
Motivation: Array-CGH can be used to determine DNA copy number, imbalances in which are a fundamental factor in the genesis and progression of tumors. The discovery of classes with similar patterns of array-CGH profiles therefore adds to our understanding of cancer and the treatment of patients. Various input data representations for array-CGH, dissimilarity measures between tumor samples and clustering algorithms may be used for this purpose. The choice between procedures is often difficult. An evaluation procedure is therefore required to select the best class discovery method (combination of one input data representation, one dissimilarity measure and one clustering algorithm) for array-CGH. Robustness of the resulting classes is a common requirement, but no stability-based comparison of class discovery methods for array-CGH profiles has ever been reported.
Results: We applied several class discovery methods and evaluated the stability of their solutions, with a modified version of Bertoni's x 2 -based test  [1]. Our version relaxes the assumption of independency required by original Bertoni's x 2 -based test. We conclude that Minimal Regions of alteration (a concept introduced by [2]) for input data representation, sim [3] or agree  [4]  for dissimilarity measure and the use of average group distance in the clustering algorithm produce the most robust classes of array-CGH profiles.
For example, chromosome 3 monosomy and chromosome 8q gain is used as an indicator of high metastatic risk in uveal melanoma  [19] , whereas EGFR amplification is an indication for trastuzumab treatment in breast cancer  [20]. However, array-CGH data have specific features differentiating them from expression array data. First, the logratio signals calculated have a small range, which may be discretized into different classes: loss, normal, gain and amplification. Second, neighboring genomic segments are likely to be altered in the same way. Due to these particular features, class discovery for array-CGH data merits a separate analysis, and this constitutes the scope of our work. Only a few studies dedicated to class discovery for CGH or array-CGH data have been published. [21] ,  [3]  and  [22]  examined chromosomal CGH data whereas  [4]  explored array-CGH data.
[3] stressed the unusual nature of CGH data and recommended the use of particular dissimilarity measures. They proposed several different dissimilarity measures, the most original of which is sim, which measures the number of contiguous genomic intervals of alterations of the same type overlapping between pairs of samples. [22]  presented an algorithm for identifying small sets of important genomic intervals called markers. They showed that markers distinguished effectively between different histological cancer types, thereby improving the quality of clustering. [4]  proposed the WECCA algorithm (weighted clustering of called array-CGH data), a method including a dissimilarity measure and a clustering algorithm devoted to array-CGH data. They defined two dissimilarity measures based on the concepts of agreement (agree) and concordance (conc).
Agree is defined as the probability of alterations being identical at the same location in two different samples, under a null model. Conc reflects the similarity in ordering of the types of alteration in two different samples. The clustering algorithm functions as an agglomerative linkage adapted to these two dissimilarity measures and is called total. [4]  demonstrated that total linkage is likely to produce tight clusters. Moreover, WECCA produces clusters strongly associated with survival. Continuing on from these studies, we compared several class discovery methods with a view to identifying the method most appropriate for array-CGH data. We define a class discovery method as the combination of an input data representation, a dissimilarity measure and a clustering algorithm. In many fields, biology and cancer research in particular, it is important for the classes identified to be statistically stable.
However, the stability of the classes obtained has never before been estimated for array-CGH data. We therefore tried to determine the best way to obtain stable classes of tumors. Stability is defined as follows: if the class discovery method is applied repeatedly to independent samples and generates similar solutions in each case, then it may be considered statistically stable. This paper is structured as follows. First, we discuss several possibilities for representing the input data of an array-CGH experiment and we provide a description of array-CGH data preprocessing. Next, we present the dissimilarity measures and clustering algorithms used in this article and the stability-based validation method applied. Then, we show results for several public data sets. Finaly, we present and discuss our results.
Input Data Representation Strategies In an array-CGH experiment, a signal intensity is measured for each probe, for the tumor sample and the reference. The logratio of the signal for the sample to the signal for the reference is calculated and denoted signal logratio. These logratios may be used directly or further processed before their use as input data for classification. It remains unclear which input data representation is optimal for class discovery. Below, we consider several strategies for input data representation for array-CGH classification.
Strategies using ''All probes'' These strategies are straightforward, as they make use of all probes. The input data representation for each probe may be: N logratio -data are expressed on the base 2 logarithmic scale. This representation is the most common in array-CGH data analysis. N smoothed logratio -the logratio of the probe is smoothed using its neighbors in the genome. In algorithms such as GLAD  [23] , the smoothed logratio values are calculated by estimating a piecewise constant function of the raw logratios, using a segmentation procedure. GLAD uses an adaptive weight smoothing algorithm, ensuring that only neighboring probes with similar DNA copy numbers are smoothed together. Several algorithms for the segmentation of array-CGH data have been described (see  [24] , for a review).
Strategies using ''Data compression'' In array-CGH data, some probes may be redundant because neighboring genomic segments are likely to be altered in the same way. Data compression strategies involve reducing the number of dimensions so that only a few relevant variables are handled. Statistical compression. The number of dimensions is reduced by Principal Components Analysis (PCA). PCA computes a linear combination of probes that jointly account for most of the variability in the data. PCA is carried out on logratio values and the first components identified constitute the input data representation associated with this strategy. Biological compression. Variable compression is based on the concept of Minimal Regions  [2]. A Minimal Region (MR) is defined as the largest sequence of altered probes (contiguous probes with identical, and not normal, calls) common to a subset of array-CGH profiles, called support.
Data sets We used five array-CGH data sets publicly available from ACTuDB  [6]. Table  1  provides a brief description of each data set, with all datasets identified by the name of the first author. For all data sets, logratios, smoothed logratios and calls were downloaded from ACTuDB. The sex chromosomes were excluded from the analysis. All data sets presented missing values (between 3 and 13% of the data), which were imputed with the procedure presented in Material S1 (section Missing values). We performed PCA on logratios and retained the principal components jointly accounting for at least 90% of data variability. MR were obtained with VAMP  [5] , with support ranging from 5 to 50% of the tumors, using increments of 5%.
Dissimilarity measures The objects studied here are tumor samples. As it is not possible to devise a general formula for identifying the best dissimilarity measure for each individual situation, we consider some of the most frequently used methods  [32]. We use the general notation dissimilarity measure to refer to a distance or a similarity or a dissimilarity. To convert a distance or dissimilarity measure into a similarity measure, or vice versa, the value is simply subtracted from the maximum value obtained. For each input data representation strategy, we calculated different pairwise dissimilarity measures: Euclidean, Manhattan and Pearson correlation. We also calculated the dissimilarity measures proposed by Liu, and by van Wieringen: sim, agree and conc. All three were applied only to calls and biological compression strategies.
These coefficients require a partition to be calculated. To avoid the triky selection of the number of classes, we calculated the coefficients for several partitions. Several ways of perturbing the data have been proposed. We decided to resample the data by repeatedly drawing overlapping subsets of samples from the same dataset without replacement  [35, 36]. Assessing the significance of solutions. We assessed the statistical significance of the stability of the structure discovered by the class discovery method, using a modified version of the x 2based test proposed by  [1]. This test was initially designed to determine the number of clusters in a stability framework, but can easily be transposed for class discovery method selection in the same framework, as described below. A perturbation procedure was applied 2M times to the data set X, building M pairs of subsets of X.
If the null hypothesis is rejected, we exclude the least stable method, according to the sorting of G, and repeat the test. P-values were adjusted for multiple testing by Bonferroni-Holm correction  [37]. This x 2 -based test is repeated until no significant difference is detected or until only one class discovery method is left. The set of methods remaining represents the set of stable methods discovered. All methods are implemented within the R programming language http://www. r-project. org. We used cluster and hybridHclust R packages available from http://www. r-project. org, clusterv and mosclust R packages available from http://homes. dsi. unimi. it/valenti/software. html and WECCA available from http:// www. few. vu. nl/wvanwie/software/WECCA/WECCA. html.
Discussion and Conclusion We investigated the application of several input data representations, dissimilarity measures and clustering algorithms for array-CGH data. We compared the resulting class discovery methods in terms of the stability of their solutions. The two dissimilarities sim and agree appeared to be an efficient choice for array-CGH data in association with MR input data representation. Their superiority can be explained by the fact that they were built taking into account the specificities of array-CGH data. We conclude that the characterization of array-CGH data by MR  [2]  is a good choice for class discovery purposes, as our experiments demonstrate that stable partitions are generally achieved with this method. As these solutions are reached by reducing the number of data dimensions, the data are characterized in a parsimonious manner.
Secondly, this method facilitates data interpretation because it allows biologists to study a limited number of alterations rather than having to study all the probes to account for differences. Finally, data representation based on MR reduces the amount of data required for class discovery. This feature is particularly useful for high-density array-CGH technologies. To conclude, we recommend the use of hierarchical agglomerative average linkage and sim or agree similarity measures associated with MR for a stable class discovery framework.
Most recently, Fierer et al. [4] , Delmont et al. [26]  and Mackelprang et al. [27]  applied shotgun metagenomics to grassland, agricultural soils and permafrost to characterise the impact of fertilisation, seasonal changes, the vertical distribution and the response to thaw on the soil microbial communities. These shotgun post-analyses were made possible by the development of several bioinformatics tools that can be used to (i) manage the flow of generated data, (ii) compare data with specialised databases (i. e. , CAZYmes  [28] ; MG-RAST  [29] ; RDP  [30] ), (iii) extract relevant information and (iv) open new perspectives for our understanding of the soil microbiome. The main objectives of this study were to determine how the structure and functional abilities of the soil microbial communities are impacted by the soil forest stratification.
MG-RAST Analysis Raw sequences for each horizon along with the corresponding quality values were uploaded to the MG-RAST server  [29]. For the pyrosequencing sequences, the taxonomic analysis was performed using two databases: Ribosomal Data Project (RDP) (an e-value threshold of 0. 01 and a minimum percentage identity of 80%) for the 16S rRNA fragments identified, and SEED (an evalue cut-off of 1e205) for all the other sequences. The default parameters of the MG-RAST were used for the taxonomic and functional assignation of the Illumina sequences. All of the Illumina reads that were shorter than 35 bases or had a median quality score below 20 were removed.
Carbohydrate-active Enzymes: Annotation and Analysis Raw sequences of the Breuil-Chenue metagenomes (pyrosequencing reads) were searched for carbohydrate active enzymes (CAZymes) using the CAZY database  [28]  reference dataset found at the CAT (CAZymes Analysis Toolkit) web service  [44]. The assignation was searched against this reference dataset using protein BLAST with an e-value cut-off of 1e205.