dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analysis Nucleotide sequences of groEL, msp4 and 16S RNA loci were downloaded from GenBank, and analyzed using the program MEGA6 (Molecular Evolutionary Genetics Analysis Version 6. 0)  [78]. Sequences of each gene were aligned by ClustalW applying the IUB matrix. Alignments have been deposited in TreeBase (http://treebase. org/ treebaseweb/home. html). Tree construction was achieved using the Neighbor-Joining method  [79]. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches  [80]. In silico prediction of Tat and Sec substrates Potential Tat substrates were detected as described previously  [57]. Within the protein set coded by the annotated genomes of A. phagocytophilum, available at NCBI, type I signal peptides and their cleavage positions, corresponding to sequences specific to potential Sec substrates, were sought using three existing programs for sec signal prediction: SignalP 4.
Acknowledgements The authors thank  Prof. Stephen Dumler  (The  Johns Hopkins University School of Medicine, Baltimore, USA. );  Prof. Philippe Brouqui  and  Prof. Didier Raoult  ( Faculty of medicine, URMIT Joint Unit, Marseille ) for providing the Webster Strain;  Elisabeth Petit  for her technical support. This work was performed within the context of the  Laboratory of Excellence (Labex) of Integrative Biology  of.
Data We created about 350 interferograms using images from eight Envisat tracks  (006, 028, 049, 071, 235, 278, 300, and 321)  in both descending and ascending orbits, in image and wide-swath modes, spanning the period between 2005 and 2010 and covering the whole Afar region (Figure  1b  and Figure  S1  in the supporting information). The data cover ~350,000 km 2 , which is the largest area ever imaged continuously with InSAR. We used the Jet Propulsion Laboratory (JPL)/California Institute of Technology ROI_PAC software  [Rosen et al. , 2004]  and a 3 arc sec Shuttle Radar Topography Mission digital elevation model (SRTM DEM) to create all the interferograms. We used a standard 20 azimuth and 4 range looks and filtered interferograms using a power spectrum filter before phase unwrapping with the branch-cut method.
Phase unwrapping errors were initially identified in each interferogram using the phase closure technique on minimum spanning trees  [Kruskal, 1956]. They were then manually corrected when the absolute sum of the closure was equal to or larger than 2π. The data were geocoded to the 3 arc sec SRTM DEM (~90 m resolution). The GPS data set analyzed here includes a total of 112 sites. The GPS sites near Dabbahu-Manda Hararo and in central Afar were measured between 2007 and 2010 as a part of the Afar Rift Consortium and National Science Foundation (NSF) projects  [Nooner et al. , 2009] , while some GPS velocities from the Red Sea coast, the Gulf of Aden, and the Main Ethiopian Rift are from other sources  [Kogan et al. , 2012; McClusky et al. , 2010; Saria et al. , 2013; Vigny et al. , 2006 Vigny et al.
InSAR Time Series Analysis We obtained time series of cumulative displacements, average velocities, and their associated uncertainties for each InSAR track using the π-rate software. The π-rate software uses a network-based approach on the geocoded interferograms to mitigate common sources of errors in the interferograms, such as orbital and atmospheric delay errors  [Biggs et al. ,2 0 0 7 ;  Elliott et al. , 2008]. In all our time series analyses, we removed contributions from sudden events (see description in the following) such as those caused by dike intrusions and eruptions. In this study, the focus is on the regional-scale deformation pattern in Afar. Therefore, we averaged 10 geocoded neighboring pixels to ~900 m resolution in all interferograms, which reduced both the noise and the number of data points.
A similarly broad accommodation zone is observed farther south with spreading distributed across Manda Gargori and Dobi in a PBZ about 150 km wide (Figures  6e  and 8a ). Strain is distributed over a much narrower area in the southeastern part of the plate boundary, where extension is mainly localized in Asal-Ghoubbet (Figures  6f  and 8a ) with only minor extension in Hanle and Derele-Gaggade and a PBZ of about 50 km width. Furthermore, no significant strain is observed at the Tendaho fault (Figures  7 8 9 ), suggesting that this structure is not currently active. However, motions of only a few mm/yr would not be resolved by our strain maps as the InSAR data spans a limited time period, 2007-2010. The row of volcanoes between Nabro and Oyma caldera also lacks significant strain (Figures  7  and 8 ), suggesting that these volcanoes do not currently mark an active portion of the plate boundary.
Acknowledgments C. P. thanks  Peng Li  and  Zhenhong Li  for their support with the wide swath p r o c e s s i n gs o f t w a r e. C. P. gr a t e f u l l y acknowledges the support she receives through her  Rita Levi Montalcini  fellowship (Nota  MIUR  Montalcini  26259_21/12/ 2013 ). This work was supported by the  UK.
To this aim, images are cut in small tiles for which we proved that the pushbroom geometry is very accurately approximated by the pinhole model. These tiles are then processed with standard stereo image rectification and stereo matching tools. The specifics of satellite imaging such as pointing accuracy refinement, estimation of the initial elevation from SRTM data, and geodetic coordinate systems are handled transparently by s2p. We demonstrate the robustness of our approach on a large database of satellite images and by providing an online demo of s2p. Figure  1 : 3D point clouds automatically generated from Pléiades stereo datasets, without any manual intervention, with the s2p stereo pipeline. Its implementation can be tested online through a web browser.
, 2014c) , improving the state of the art by one order of magnitude  (Oh et al. , 2010). Each rectified tile is then processed using off-the-shelf stereo matching algorithms. The pipeline deals transparently with inaccuracies of the sensor attitude  (Hanley et al. , 2002 , Grodecki and Dial, 2003 , Fraser and Hanley, 2005) , by estimating relative corrections for each tile without needing ground control points  (de Franchis et al. , 2014b). These local corrections are then combined in a global correction for the entire image, which is used to perform a consistent 3D triangulation. The SRTM information (Shuttle Radar Topography Mission, see section 3. 3) is automatically incorporated to identify corresponding regions in both images. The s2p pipeline also handles three-view stereo datasets. In this case two stereo pairs are processed independently, then the resulting elevation models are merged to increase the coverage (see figure  12  for an example).
S2P OVERVIEW The s2p pipeline deals with pairs or triplets of images. Pairs and triplets are the standard stereo products proposed by the main commercial providers of satellite images such as DigitalGlobe and Airbus Defense and Space (formerly Astrium). In case of a stereo triplet, each pair out of the six possible pairs is processed independently, and the resulting 3D point clouds are then merged. The merging procedure is not discussed in this paper. Figure  2  gives an overview of the processing pipeline for a stereo pair of images. The input images are cut in small tiles, to allow a very precise stereo image rectification. The optimal size of the tiles is discussed in section 3. Then for each tile the calibration data is refined (section 4) and the images are stereorectified (section 3).
Let denote by Γ = RPC -1 (Ω × [hm, hM ]) ⊂ R 3 the back-projected domain, and by (Xi)i=1,. ,N a regular sampling of Γ. Each 3-space point Xi is projected on the two images using the associated RPCs, leading to a virtual correspondence (xi, x i ). The images contents at locations xi and x i may not correspond, but x i is located on the epipolar curve of xi, and that is enough to estimate a fundamental matrix. Extensive experiments, presented in section 6, were carried out on numerous Pléiades datasets. They show that with a tile size of 1000 × 1000 pixels the epipolar error is always less than 0. 05 pixels. This precision fits all the stereo matching algorithms, thus our pipeline uses a tile size fixed to 1000 × 1000 pixels for all Pléiades images. In case of satellites with a different behaviour, the epipolar error can be computed as a preliminary step, and the optimal tile size is automatically selected accordingly.
STEREO MATCHING AND TRIANGULATION For each rectified tile we compute the disparity by applying an off-the-shelf stereo matching algorithm. Because of its performance, we use here the implementation of SGM  (Hirschmüller, 2008)  included in OpenCV 1. However, any other stereo matching algorithm can be used instead. The disparities are then interpreted as point correspondences with the coordinates of the original (non rectified) images, as illustrated in figure  9. From these correspondences the 3D position of the point is triangulated 1 StereoSGBM module in OpenCV 2. 4. 8 (http://opencv. org/), with default parameters. To filter more outliers we compute a second disparity map reversing the reference and secondary images and enforce the consistency of both maps  (Hirschmüller, 2008)   Triangulation with RPC functions.
From a geometric viewpoint, the locally affine rectification method described in section 3 amounts to approximate the two pushbroom sensors with affine camera models. The validity of this approximation relies on the dimensions of the 3-space domain on which it is used. These dimensions are given by the tile size and the altitude range. To understand the influence of these two parameters, we measured the epipolar error on the Pléiades datasets listed in Table  2. Figure  11  shows the error measured on each dataset by varying the tile size up to 5000 × 5000 pixels, while the altitude range was estimated using SRTM data. These results show that on a  Pléiades dataset it is always possible to stereo-rectify tiles of size 1000 × 1000 pixels with an epipolar error lower than 0. 05 pixels.
Pipeline validation As a validation of the s2p pipeline we processed a region from a three-view stereo dataset of Melbourne. Our validation does not include ground control points. Thus we evaluated the relative precision by measuring the height of a known building. The Eureka Tower is a 297. 3-metre skyscraper located in the Southbank precinct of Melbourne, which has been highlighted in figure 12(a-c). The altitude estimates were computed by averaging the heights at the street level (yielding 16. 15 ± 0. 23 meters) and on the roof (yielding 312. 97 ± 0. 49 meters). Thus our estimated height of the Eureka Tower is 296. 82 ± 0. 72 meters. Figure  12(d-f ) shows the elevation models obtained from the nadir-left and nadir-right pairs for the Melbourne dataset. Note that both images contain significant occluded regions in the vicinity of tall structures, however these regions are complementary.
CODE AND ONLINE DEMO The s2p pipeline described here is completely implemented and will be released as open source software. It can be tested online  (de Franchis et al. , 2014a)  thanks to the demo framework of the IPOL journal (IPOL, 2010). Several stereo datasets from Pléiades and a stereo pair from WorldView-1 are available for testing. The implementation is compatible with all the stereo datasets provided by Airbus DS and DigitalGlobe, and thus could be tested on images from WorldView-2, QuickBird-1 and Spot-6.
CONCLUSIONS Thorough experimentation on numerous Pléiades datasets has shown that using tiles of size 1000 × 1000 pixels allows a standard stereo rectification of pushbroom images with a precision of 0. 05 pixel, regardless of the altitude range of the scene. The rectification is performed thanks to the RPC data, whose accuracy is locally refined on each tile. This enables standard stereo matching algorithms to be used and tested. An online demo implements the whole s2p pipeline for one-click testing.
ACKNOWLEDGEMENTS Work partially supported by  Centre National d'Etudes Spatiales  ( MISS  Project),  European Research Council  ( Advanced Grant Twelve Labours ),  Office of Naval Research  (under Grant  N00014-97-1-0839 ),  Direction Générale de l'Armement, Fondation Mathématique Jacques Hadamard  and  Agence Nationale de la Recherche  ( Stereo  project).
The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www. comp-sysbio. org/cmi2ni.
CMI2NI: GRN inference method based on CMI2 Given an expression dataset with n genes and m samples, we developed a novel algorithm, called CMI2NI, to infer its underlying GRN. In CMI2NI, after obtaining MI and CMI2 with Equations (  2 ) and (  10 ), the PC algorithm was then used to remove the (conditional) indirect regulations from the clique/complete graph. GRN inference will be performed by removing those edges without strong causal regulations recursively until there is no change in the network topology, e. g. at Lth-order CMI2. CMI2NI improves the resulted GRN through accurately quantifying the causal regulation strength with CMI2. Figure  2  depicts the diagram of CMI2NI with details described as follows. Firstly, we generated a complete connected graph according to the number of genes.
,. |n -2), which clearly is not available for most of real cases. However, if the algorithm is converged to an Lth-order network, it actually requires only L + 2 independent samples due to CMI2(. ,. |L). In other words, CMI2NI can infer the network without any approximation in the above process even with a small number of samples, i. e. L + 2 independent samples when converged to an Lth-order network, where L is usually between 3 and 5 for many real networks or datasets, far less than n. Thus, comparing to the traditional requirement of n independent samples for computing all statistic dependency among variables, our algorithm can obtain the network without the approximation with a small number of samples L + 2.
Simulation study For simulation expression data, the widely used benchmark networks along with expression datasets from DREAM challenge were adopted here to evaluate our method. The gold standard networks were selected from source networks of real species. The expression data were generated with the nonlinear ordinary differential equation (ODE) systems in which the network structures were determined with detailed dynamics of both transcriptional and translational processes  (50). In this work, the DREAM3 datasets about Yeast knock-out gene expression data with sizes 10, 50 and 100 were used (4). Firstly, we tested CMI2NI on the Yeast gene expression data with network size 10 and sample number 10. We chose 0. 03 as the threshold value of mutual information and conditional mutual inclusive information to decide independence, and the order index was not constrained, i.
Secondly, we tested CMI2NI on the Yeast gene expression data with network size 50 and sample number 50. The network containing 50 nodes with 77 edges was selected from real and experimental verified networks. We set the threshold value 0. 05 for MI and CMI2 to decide independence and the order index was not constrained. As shown in Figure  3B , CMI2NI outperforms other reference methods with the highest AUC score of 0. 834. From Table  1 , we can see that CMI2NI performs better than the other methods with respect to all the metrics listed. For example, CMI2NI achieved 0. 492, 0. 396, 0. 397 and 0. 834 for PPV, ACC, MCC and AUC, respectively. Thirdly, we tested CMI2NI on the Yeast gene expression data with network size 100 and sample number 100. The reference network contains 100 nodes with 166 edges.
We set the threshold value 0. 03 of MI and CMI2 to decide independence and the order index was set to the first order which is a usually adopted order for large-scale networks to reduce the computational burden. As shown in Figure  3C , CMI2NI performs better than other reference methods with the highest AUC value of 0. 856. The detailed results of different approaches can be found in Table  1  with respect to various metrics. From Table  1 , we can see that CMI2NI performs better than the other two methods with the highest values 0. 628, 0. 972, 0. 479 and 0. 856 for PPV, ACC, MCC and AUC, respectively. In the 100-gene network from DREAM dataset, 14 edges were detected by CMI2 but missed by CMI. Furthermore, CMI2 successfully silenced 20 edges overestimated by CMI without reducing the true positive rate in the 100-gene network.
Reconstruction of SOS network in Escherichia coli Besides the above simulation datasets, CMI2NI was also applied to reconstruct gene networks from real gene expression data. We evaluated our CMI2NI on the well-known SOS DNA repair network which is an experimentally verified network in E. coli with real gene expression data  (47, 48). The network is a 9-gene sub-network of SOS pathway in E. coli. The SOS pathway, which regulates cell survival and repair after DNA damage, involves the lexA and recA genes. There are more than 30 genes that are directly regulated by lexA and recA, while tens or even hundreds of other genes that are indirectly regulated by the two genes. Here, the nine transcripts in the test network include the principle mediators of the SOS response (lexA and recA), four other regulatory genes with known involvement in the SOS response (ssb, recF, dinI and umuDC), and three sigma factor genes (rpoD, rpoH and rpoS) whose regulations play important roles in the SOS response.
For the expression data, we chose the perturbation data, which were obtained after perturbations were applied to the test network in E. coli. In the perturbation, each of the nine genes in the test network is overexpressed with arabinose-controlled episomal expression plasmid, and the change in expression of each transcript relative to the unperturbed cells was accordingly measured using quantitative real-time polymerase chain reaction (qPCR). The performance of CMI2NI was evaluated with the network of size 9 and the expression dataset generated under perturbations. We chose 0. 01 as the threshold of both MI and CMI2. To show the performances of CMI2NI and other methods, the true network and inferred networks were visualized with Cytoscape  (51). Figure  4A  shows the true network with 24 edges.
Performance of CMI2NI The theoretical analysis of CMI2 has proven that CMI2 can be decomposed into CMI and a non-negative term. This implies that CMI2 value is bigger than CMI. Hence, CMI2 can  address the underestimation problem of CMI. To further test this theoretical conclusion, we investigated the value of CMI2 with both simulation and real expression datasets. Moreover, we compared values of CMI2 and CMI. For simulation dataset, we used a random dataset as well as the dataset from DREAM challenge. For real gene expression dataset, we used the expression data of SOS network in E. coli. Firstly, we generated random variables with different samples 5, 10, 100 and 500, we took one or more of them as conditional variable(s). We computed their CMI2 and CMI values using these datasets. The calculation was performed 100 times on each dataset, and the mean of the CMI2 and CMI values were used for comparison.
Methods Analytical solutions to periodic rectangular pulse forcing were computed for RL(t) in the receptor binding model (Fig. 2 ) and for a(t) in the ab-model (Figs. 6A). The mean output for all models is defined as the time average of the output variable (e. g. RL(t)). Due to the periodic input and the globally stable nature of feed-forward systems, the output variable approaches a stable steady-state periodic solution. Thus, we consider the mean output to be the mean over one period T of the steady-state periodic solution x(t): vx(t)w ? ~1 T ð T 0 x(t)dt: When the analytical solution was available, we computed the mean output at each frequency using adaptive Lobatto quadra-ture, quadl in MatLab (R2010a, The Mathworks, Natick, MA), with tolerance 1e-6 (Fig. 3A  for receptor binding, and Figs.
6 7 8 9 for the ab-model, respectively). The results for the dimerization model and the phosphorylation model (Figs. 3C, 10) were obtained by numerical integration using the ode15s routine in MatLab. Successive on and off phases of the pulsatile input were sequentially simulated. The steady state mean value was approximated by discarding the first portion of the simulation (initial transient behavior) so the system was close to the periodic solution. The mean was computed using ode15s by adding an auxiliary equation, dx/dt = x, to the system, where x = D or x = W * for the dimer and phosphorylation models, respectively. The MatLab code is available at www. math. fsu. edu. /,bertram/ software/pituitary.
These subjects have previously been described and used as controls in case-control studies  [32] [33] [34]. Data from patients and controls regarding sex, age, ethnic origin, date of birth, anatomoclinical data, pigmentation characteristics (hair, eye, and skin colours), skin type (Fitzpatrick classification I to IV), nevus count (<10, 10-50, 51-100, and >100), and heavy freckles (yes/no) were collected in a standard document. Ancestry was investigated through birth location of parents and grandparents, and only those with a Caucasian ancestry were retained for the study. The pigmentation characteristics of patients and controls are summarized in Supplementary PCR products were verified on a 2% agarose gel and purified by EXOSAP-IT (USB Corporation, OH, USA). Sequencing reaction was performed on 8900 Fast Thermal Cycler (Applied Biosystems), using 10 ng of purified PCR products and the Big-Dye Terminator Cycle Kit (Applied Biosystems).
Acknowledgments The authors would like to thank the  Société Francaise de Dermatologie  for supporting their research. They also thank the dermatological departments of  Ambroise Paré, Bichat, Cochin ,  Henri Mondor , and Saint Louis hospitals for participating in the MelanCohort project and the  Etablissement Francais du Sang  for providing control blood samples. They thank the  Centre de Recherche Biologique of Bichat Hospital  for sample preparation. Importantly, they would like to thank the patients and control subjects who participated in this study.
Identification and cloning of emplk1 The E. multilocularis genome sequence assembly  [28]  was used as available in GeneDB under http://www. genedb. org/Homepage/ Emultilocularis. Published sequences of human Plk1 (GenBank accession number: P53350) and SmPlk1 (AY747306) were employed in extensive BLASTP searches on the genome sequence. Amino acid sequence and domain predictions were carried out using UniProt (http://www. uniprot. org/) and SMART (http:// smart. embl-heidelberg. de/) software. Based on the genomic sequence information, primers were designed to amplify the full EmPlk1 coding sequence. Total RNA was isolated from metacestode vesicles with TRIzol reagent and reverse transcribed into cDNA as previously described  [30]. The cDNA then served as a template for gene amplification by PCR using primers AKO-053 (59-GAC TTC TGC CCG GGT ATG GAT A-39) and AKO-054 (59-GGA AGA CGG CAA ACA TGT GAT-39).