dataesr/scientific-paragraphs-categorization huggingface dataset:
A well-known drawback of using exact word matches in sequence comparison is that word matches at neighbouring sequence positions are statistically far from independent. In database searching, exact word matches have therefore been replaced by so-called spaced seeds defined by patterns of match and don't care positions  (12). It has been shown in many studies that such spaced seeds are far superior to contiguous (exact) word matches in detecting local homologies  (12) (13) (14). Motivated by this approach, we recently proposed two methods for alignment-free sequence comparison that are based on inexact word matches, spaced words  (15)  and kmacs  (16). In this paper, we present a user-friendly web interface for these two methods.
Generalized friendship paradox in complex networks We consider two coauthorship networks constructed from the bibliographic information of Physical Review (PR) journals and Google Scholar (GS) profile dataset of network scientists (See Method Section). Each node of a network denotes an author of papers and a link is established between two authors if they wrote a paper together. The number of nodes, denoted by N , is 242592 for the PR network and 29968 for the GS network. For the node characteristics in the PR network, we consider the number of coauthors, the number of citations, the number of publications, and the average number of citations per publication. As for the GS network, the number of coauthors and the number of citations are considered. The characteristic of node i will be denoted by x i , and for the degree we denote it by k i.
Data description We describe how the data for coauthorship networks have been collected and prepared. For the Physical Review (PR) network, the bibliographic data containing all papers published in Physical Review journals from 1893 to 2009 was downloaded from American Physical Society. The number of papers is 463348, and each paper has the title, the list of authors, the date of publication, and citation information. By using author identification algorithm proposed by  [39] , we identified each author by his/her last name and initials of first and middle names if available. The number of identified authors is 242592. Combined with the numbers of citations and the list of authors of papers, we obtained for each author the number of coauthors, the number of citations, the number of publications, and the average number of citations per publication.
Google Scholar (GS) service (scholar. google. com) provides profiles of academic authors. Each profile of the author contains information of the total number of citations and coauthor list of the author. Using snowball sampling  [26]  starting from "Albert-László Barabási" (one of the leading network scientists), the coauthor relations and their citation information are collected. The number of authors in the dataset is 29968. Here we note that not all scientists have profile in the GS and not all coauthor relations are accessible.
Acknowledgements The authors thank  Daniel Kim  and  Hawoong Jeong  for providing  Google Scholar data  and  American Physical Society  for providing Physical Review bibliographic data. Y. -H. E. acknowledges support from the  EC FET Open  project " New tools and algorithms for directed network analysis " (NADINE number  288956 ). H. -H. J. acknowledges financial support by the  Aalto University postdoctoral programme.
Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subject images, parcellation approaches use brain activity (e. g. , found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and k-means clustering algorithms.
We denote W the diagonal matrix that contains the sum of the rows of W. Then, let (ξ 1 ,. , ξ m ) the first m solutions of Wξ = λ W ξ. The spectral clustering of the dataset is defined as: u spectral = k-means ([ξ 1 ,. , ξ m ]) , (5) m = 100 in our experiments. We also tried different (larger or smaller) values, but those did not yield significantly better solutions.
Reproducibility by bootstrap The two previous metrics only address the fit of the data by the model. Another important criterion in neuroimaging is reproducibility  (LaConte et al. , 2003) , which we define in this context as the consistency of two clustering solutions across repeats on bootstrap samples taken from the data, measured by assignment statistics of voxels to clusters. To estimate reproducibility, we repeated the clustering by bootstrapping over subjects and assessed the stability of the clustering between pairs of bootstrap samples using two standard metrics: adjusted mutual information or adjusted rand index. The adjusted Rand index (ARI) is comprised between -1 and 1, and measures the consistency of the two labelings while being invariant to a permutation of the labels  (Vinh et al.
IMPLEMENTATION We use the algorithms and metrics from the scikit-learn toolbox  (Pedregosa et al. , 2011). In particular, Ward's algorithm is very efficient on data size of typical brain images. The following version of the software were used:Matlab R2013A, version 8. 1. 0. 64, SPM8 v. 5242, scikit learn v. 0. 14. The code used in this work is available at https://github. com/bthirion/frontiers_2014.
SIMULATED DATA Data are simulated according to model (Equation  6 ): on a 2D grid of shape 20 × 25 pixels, five random clusters are generated with a hierarchical clustering approach, by using Ward's parcellation on a set of random signals; 10 individual datasets are sampled using the generative model: for each parcel the parameters μ are sampled from N (0, 1), σ 1 = 1 and the random subject effect β are drawn from N (0, 1), σ 2 = 1. Note that the βs are kept constant across parcels. Data corresponding to a sample of 10 subjects are generated. To make the data more realistic, we add a deformation to each individual dataset that has a magnitude of 0, 1, or 2 pixels in each direction and smooth it-or not-with a kernel of full width at half maximum (fwhm) of 1. 17 pixel. Note, however, that this breaks (on purpose) the hypotheses of the generative model and makes the simulations more realistic.
An example is shown in Figure  1. The question that we address is whether we can hope to recover the true number of clusters from the simulation; to do so, we can use one of the three selection criteria: BIC, cross-validation and bootstrap reproducibility (we use B-AMI by default, but B-ARI yields similar results on this dataset). The recovery is quantified through the adjusted rand index between the true labeling of voxels and the obtained one. The results are based on 200 replications of the experiment, and the optimal number K of parcels is searched in the {2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30} set.
A session comprised 130 scans. The first four functional scans were discarded to allow the MR signal to reach steady state. Anatomical T1 images were acquired on the same scanner, with a spatial resolution of (1. 0 × 1. 0 × 1. 2) mm 3. The data were subject to a pre-processing procedure that includes the correction of the difference in slice timing, motion estimation and correction, co-registration of the EPI volumes to the T1 image, non-linear spatial normalization of T1 images, then of the fMRI scans to the SPM T1 template. All of these steps were performed using the SPM8 software. Optionally, we considered a 5 mm isotropic smoothing the normalized images. In parallel, an average mask of the gray matter was obtained from the individual normalized anatomies, subsampled at the fMRI resolution, and used to mask the volume of interest in the functional dataset.
This procedure yields approximately Q = 57, 000 voxels at 3 mm resolution. A General Linear Model (GLM) analysis was applied for the volume using the Nipy package http://nipy. sourceforge. net/. The model included the 10 conditions of the experiments convolved with a standard hemodynamic filter and its time derivative, a high-pass filter (cutoff:128s); the procedure included an estimation of the noise auto-correlation using an AR(1) model. Activation maps were derived for six functional contrasts, that display the activations related to left versus right button presses, motor versus non-motor tasks, sentence listening versus sentence reading, computation versus sentence reading, reading versus passive checkerboard viewing, vertical versus horizontal checkerboard viewing. We consider that these six contrasts give the most usable summary of the topographic information conveyed by the initial 10 conditions, without obvious redundancies, while avoiding non-specific effects.
HCP DATA A set of N = 67 subjects of the Human Connectome Project (HCP) dataset was also used in our experiments. These subjects are part of the Q2 release; we used the task-fMRI dataset, that comprises seven different sessions (see  Barch et al. , 2013  for details), all of which are used here. Starting from the preprocessed volume data provided by the HCP consortium, these dataset were analyzed similarly to the Localizer dataset, using the Nipy software for the GLM analysis, that was carried out using the paradigm information provided with the data. The same gray matter mask was used as for the Localizer dataset was used to facilitate comparisons between the two datasets. In order to reduce computation time, a subset of F = 9 functional contrasts were used: the faces-shape contrast of the emotional protocol, the punish-reward contrast of the gambling protocol, the math-story contrast of the language protocol, the left foot-average and left hand-average contrasts of the motor protocol, the match-relation contrast of the relational protocol, the theory of mind-random contrast of the social protocol and the two back-zero back contrast of the working memory protocol.
Comparison with an anatomical atlas. As a basis for comparison with anatomical atlases, we evaluated the loglikelihood of the data with the most detailed atlas that we could find. We used the Harvard-Oxford atlas both cortical and subcortical http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/Atlases together with the cerebellar atlas  (Diedrichsen et al. , 2009). The version used was that of FSL 4. 1. The regions were systematically divided into left and right hemispheres by taking the sign of the x MNI coordinate of the voxels. Using this procedure, we obtained 158 regions. This atlas was resampled at the resolution of the test fMRI data, These parameters are those for the computation-sentence reading functional contrast. μ: mean intensity, σ 1 : within subject variance, and σ 2 : between subject variance.
and the likelihood of the data summed over parcels was evaluated and compared with that of data-driven parcellations with 158 parcels, obtained either from the Localizer dataset itself or from the HCP dataset. Standard deviation of the log-likelihood are obtained by drawing B = 30 bootstrap samples. The results are shown in Table  1. We show the corresponding results on the HCP dataset in Table  2. It can be seen that the anatomical atlas achieves the poorest fit: summarizing the fMRI data on the corresponding set of parcels loses a lot of information. Even a purely geometric parcellation performs better, which can be understood given that it tends to create parcels with equal size, hence achieves a more regular sampling of the volume of interest. For K = 158 the best performing parcellation on the training set is obtained from k-means, but these parcellations do not generalize well from a dataset to another.
Atlas Summed Stdv log-likelihood Harvard-Oxford atlas -6. 642 10 7 1. 9 10 5 Geometric parcellation -6. 589 10 7 1. 9 10 5 k-means parcellation -6. 463 10 7 1. 8 10 5 Ward parcellation -6. 513 10 7 1. 9 10 5 Spectral clustering parcellation -6. 591 10 7 1. 8 10 5 (HCP) k-means parcellation -6. 710 10 7 1. 9 10 5 (HCP) Ward parcellation -6. 522 10 7 1. 9 10 5 (HCP) Spectral clustering parcellation -6. 613 10 7 1. 9 10 5 The number of parcels used is K = 158 for all methods. The standard deviation is obtained by drawing 30 bootstrap samples. bootstrap variability of these results is typically small with respect to between-method difference for the Localizer dataset, ensuring that the differences are significant. This is less so with the HCP dataset, for two reasons: the number of subjects is smaller, and the per-subject SNR seems relatively lower in that dataset (see  Barch et al.
Analysis of the goodness of fit the models (localizer dataset) The goodness of fit of the model is given by the log-likelihood, which can be compared across methods for a fixed value of K in Figure  6A. The main observations are: • For all methods, the curve achieves an optimum value for a very large number of parcels (3000 ≤ K ≤ 7000), which is much more that the number typically expected and used in neuroimaging experiments. • k-means and Ward's clustering achieve the lowest distortioni. e. , loss of information from the original signal-with k-means performing better for low number of parcels and Ward's clustering performing better for large number of clusters. Spectral clustering is inferior in terms of goodness of fit. It is even lower than a purely geometric parcellation of the brain volume for some values of K.
Accuracy-reproducibility compromise (localizer dataset) The reproducibility of the clustering estimated by bootstrapping the data can be studied as a function of the number of clusters, or as a function of the likelihood. Both representations are presented in Figure  7. The number of parcels used is K = 158 for all methods. The standard deviation is obtained by drawing 30 bootstrap samples. The reproducibility index displays a clear optimum value at K 200 parcels. For larger values, the reproducibility index decreases slowly, but increases again for very large number of parcels K > 4000. This late increase can readily be interpreted as an artifact due to the fact that we are now observing a very large number of very small clusters, and that the reproducibility indexes are not well suited in this case.
MODEL SELECTION RESULTS ON THE HCP DATASET A summary of the results obtained by doing the same experiments on the HCP dataset is provided in Figure  8. In spite of weak changes of the optimal values K ≤ 3000 for accuracy, K ∈  [200, 500]  for reproducibility, this dataset reproduces exactly the trends observed with the Localizer dataset: Ward's method outperforms the others in terms of accuracy and for high K values, spectral clustering yields a poor fit and a high reproducibility, k-means a good fit, especially for small K, yet very low reproducibility.
Our simulations show that cross-validation almost systematically outperforms BIC, but we did not notice systematic differences in the real dataset. The other important aspect of a brain description is its stability, and we also investigated other criteria that the selected number of clusters based on the consistency of parcellations. This approach behaved similarly as the others on synthetic data, but provided a much more conservative selection on real data (K ∼ 200-500 parcels according to the dataset and method). The fact that reproducibility and accuracy yield different decisions for model selection is well known, and has been illustrated in neuroimaging by  LaConte et al. (2003) ; this effect is tightly related to the classical bias /variance in statistics.
Which algorithm to prefer? Regarding the clustering algorithms themselves, our general finding is that Ward's algorithm should be preferred, unless a small number of parcels is required. Indeed, spatially-constrained Ward's clustering outperforms the other techniques in the large K regime (say, K ≥ 500) in terms of goodness of fit, while having fair results in terms of reproducibility. With respect to k-means, it offers the additional advantage of providing spatially connected parcels. In theory, k-means algorithm should do better in terms of accuracy, but the optimization problem solved by k-means is hard (non-convex) and thus bound to sub-optimal solutions; as a consequence the greedy approach in Ward's algorithm outperforms it. Moreover, k-means based parcellations tend to fit data idiosyncrasies and thus do not generalize well across datasets, as shown in Tables  1, 2.
Limitations of this experiment Our experiments are based on two datasets, with a pre-defined set of contrasts. We have been able to check that using any of the contrasts or all of them yields qualitatively similar results (data not shown). The power of the experiment is that it is based on a relatively large number of subjects (67-128), so that one can at least conjecture that the between-subject variability observed in functional neuroimaging is correctly sampled. Note that the Localizer data come from two different scanners, resulting in an un-modeled latent factor. We observed, however, that our conclusions were unaltered when performed on a subset of subjects coming from the same scanner (data not shown). The model that we use has several limitations: • The parcellation itself is fixed across subjects.
While a relaxation to individual dataset has been proposed in  Thirion et al. (2006) , such a procedure loses some of the properties of clustering, and make model selection much harder. • Our model (Equation  6 ) does not account for spatial effects in the within-parcel covariance, which would probably make it more robust to data smoothness and possibly to cross-subject spatial jitter, but the computational price to pay for these models is high. • It assumes that the true activation signal is piecewise constant. A smooth interpolation scheme between parcels might make it more powerful, hence reducing the requirement of large K values. Again, this would increase the complexity of the model fitting.
Results: Transcriptomic changes observed in BaP-exposed Xenopus were further characterized using a time-dependent enrichment analysis, which revealed the pollutant-dependent gene regulation of important biochemical pathways, such as cholesterol biosynthesis, insulin signaling, adipocytokines signaling, glycolysis/ gluconeogenesis and MAPK signaling. These results were substantiated at the physiological level with the detection of a pronounced metabolic disorder resulting in a possible insulin resistance-like syndrome phenotype. Hepatotoxicity induced by lipid and cholesterol metabolism impairments was clearly identified in BaP-exposed individuals. Conclusions: Our data suggested that BaP may disrupt overall liver physiology, and carbohydrate and cholesterol metabolism in particular, even after short-term exposure.
X. tropicalis would therefore appear to be an excellent amphibian model for in-depth studies on the more hidden effects of chemical contaminants, particularly BaP, on the female liver at different biological levels. The objective of this study was to reveal in vivo hepatic responses in X. tropicalis females exposed to an environmentally relevant concentration of BaP (10 μg. L -1 ) at the transcriptomic, cellular and physiological levels. Due to the dynamic nature of cellular responses, a kinetic approach was applied over a 24-hour exposure period by sequencing mRNA at 6, 12, 18 and 24 hours postexposure. Transcriptomic data collected from exposed and non-exposed animals were compared in order to identify hepatic metabolic pathways significantly affected by BaP treatment. The metabolic pathways which were found to be enriched at transcriptomic level were studied in detail as regards their chronological activation by BaP, and were further associated to cytological and physiological liver phenotype changes over the same time period.
In addition, the general transcription pattern clearly indicated overtranscription at 12 hours post-exposure regardless of the gene function considered (Additional file 5: Figure  S3 ). However, a manual annotation only highlights the general functions associated with gene transcription, and does not identify the biochemical pathways in which proteins may be involved. An annotation enrichment analysis was therefore performed in order to determine whether the genes associated with KEGG pathways (Kyoto Encyclopedia of Genes and Genomes) were significantly over-represented in differentially transcribed genes. An initial assignment of the 308 Xenopus genes using their Ensembl identifiers yielded less than 6. 7% of genes recognized by the functional annotation tool DAVID (Database for Annotation, Visualisation and Integrated Discovery).
Among the key proteins which regulate apoptosis/proliferation, MAPKs are closely linked to insulin signaling in the liver. Previous studies have described changes in the liver in response to insulin including glycolysis induction and MAPK-ERK signaling pathway leading to cell proliferation  [55]. However, the MAPK signaling pathway is classically defined as a key regulator of the cellular balance between apoptosis and proliferation  [56]. Our results highlight the transcription of key oncogenes involved in cell proliferation (fos) and apoptosis (jun D). These results suggest a possible balance between cell proliferation induced by hyperinsulinemia, and apoptosis induced by lipotoxicity and ER stress  [39, 47, 55]. However, since we did not observe any hepatocyte proliferation at the histological level, and given the measurement of proliferative cell nuclear antigen (PCNA) (data not shown), our results indicate that the pro-apoptotic branch of the MAPK pathway predominated in BaP-treated Xenopus.
Sequencing of cDNA libraries, mapping reads on the Xenopus genome Each cDNA library was sequenced on a separated flow cell lane on a Genome Analyzer II (Illumina Corporation) at the National Sequencing Center (Genoscope, Evry, France) to obtain 75 bp single-end reads. Quality control of the reads was performed using the R/Bioconductor package Short-Read v. 1. 8. 2 (http://www. bioconductor. org/packages/2. 7/bioc/html/ShortRead. html). The reads were trimmed of adaptor sequences and low-quality 3' ends using functions from the HTSeq python package (v. 0. 4. 7p2, http://www-huber. embl. de/users/anders/HTSeq), and removed completely when their length was less than 35 bp. The cleaned reads were mapped to the Xenopus genome assembly (JGI4. 2) with Ensembl annotations (release 62) using Bowtie-0.
Gene expression quantification and differential analysis The HTSeq-count function (http://www-huber. embl. de/ users/anders/HTSeq/doc/count. html) from the HTSeq package was applied with the option mode = intersec-tion_nonempty to Tophat alignments to enumerate, for each library, the number of short reads overlapping the Ensembl annotated genes. A table containing the counts for each gene in each library was assembled and filtered prior to the next step to ensure only genes represented by at least 20 reads across all libraries were kept. The Bioconductor package DESeq 1. 2. 1  [67]  was then used to normalize short read counts across all libraries and to test for the differential expression of the annotated transcripts between exposed and unexposed animals at each exposure time point. Genes were considered to be differentially transcribed when the transcription ratio TR (BaP-treated versus control) at any time point was >1.