dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper introduces a bi-modal face recognition approach. The objective is to study how combining depth and intensity information can increase face recognition precision. In the proposed approach, local features based on LBP (Local Binary Pattern) and DLBP (Depth Local Binary Pattern) are extracted from intensity and depth images respectively. Our approach combines the results of classifiers trained on extracted intensity and depth cues in order to identify faces. Experiments are performed on three datasets: Texas 3D face dataset, BOSPHORUS 3D face dataset and FRGC 3D face dataset. The obtained results demonstrate the enhanced performance of the proposed method compared to mono-modal (2D or 3D) face recognition. Most processes of the proposed system are performed automatically. It leads to a potential prototype of face recognition using the latest RGB-D sensors, such as Microsoft Kinect or Intel RealSense 3D Camera.
EXPERIMENTS In order to demonstrate the benefits from combining 2D and 3D modalities, we evaluate in this section the proposed bi-modal approach on three 3D face datasets: TEXAS  (Gupta et al. , 2010) , FRGC  (Phillips et al. , 2005)  and BOSPHORUS  (Savran et al. , 2008). The TEXAS 3D dataset consists of 1149 images of 118 persons. All faces are frontal with different expressions and illumination changes. The FRGC dataset contains 4007 images of 466 person with frontal views, minor pose variations and major expression and illumination variations. A number of images is removed from this dataset since the depth maps were unusable because of large holes due to the scanner artifacts. The BOSPHORUS dataset includes 4666 images of 105 persons with different expressions and pose variations. Images with large pose variations were not used from this dataset.
g. ,  Mai and Beroza 2002). Even for a same event, the co-seismic slip of the 2010, M w 8. 8, Maule earthquake, imaged using different inverse methodologies and dataset presents also strong spatial variability (e. g. ,  Lay et al. 2010; Vigny et al. 2011). The resulting variability in these slip models can be interpreted as epistemic uncertainty due to modeling errors when using different dataset and methods. Similar observations can be pointed out for the 2011, M w 9. 0, Tohoku-Oki earthquake (e. g. ,  Simons et al. 2011; Yagi and Fukahata 2011; Lay et al. 2011; Yoshida et al. 2011) , where the peak-slip amplitude retrieved lies in the order of 40-50 m. Under this perspective, uniform slip over an extended seismic source is an oversimplification for modeling hypothetical future earthquakes.
Stochastic complex earthquake rupture model The earthquake rupture is a complex process, which from a kinematic modeling point of view requires a complete and detailed description of the spatial and temporal history of the slip. For instance, the co-seismic slip of earthquakes imaged using kinematic inversion, and different kinds of seismological datasets present a strong spatial-temporal variability at different scales (e. g. ,  Mai and Beroza 2002). For local tsunami, the spatially heterogeneous static co-seismic slip on the fault controls tsunami wave amplitudes  (Geist and Dmowska 1999; Geist 2002). Motivated by these works, our study describes static heterogeneous slip distributions using a stochastic k -2 earthquake source model for near-shore tsunami modeling. Andrews (1980)  showed that if the slip spectrum amplitudes falloff as k -2 in the wavenumber domain, then, the source radiates a far-field displacement spectrum that follows the classical x -2 model proposed by  Aki (1967).
Initial condition for tsunami modeling In this work, the so-called passive generation of tsunami is used. Under this approach, one neglects the dynamic seabed displacement resulting from the earthquake rupture process. It means that the initial condition for tsunami propagation is obtained by translating the final (static) vertical seafloor deformation field to the free surface, and the hydrodynamics in space and time are computed. Because the vertical co-seismic displacement is an input for NEOWAVE, the numerical code recomputes the initial bathymetry by adding up the vertical deformation. This assumption is rather acceptable following the hypothesis that the total rupture time for a regular tsunamigenic earthquake is shorter than the period of propagating water waves.
Acknowledgments Some figures were drawn with Generic Mapping Tools (GMT) version 4. 0 (Wessel and Smith 1998). This work was partially funded by  Conicyt (Comision Nacional de Ciencia y Tecnologı ´a)  under Grant Fondecyt #  1130636. We thank the Editor and two anonymous reviewers for their comments and suggestions that improved the manuscript. We also thank  Dr. Yoshiki Yamazaki  who kindly help us with the use of his numerical code, NEOWAVE, and for the comments and suggestions made on the draft of this manuscript. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
A preliminary analysis revealed that some of the TFs of the core genome also control genes of the accessory genome  [23]. This allowed to propose that, when comparing the same regulon in different strains, we can define a panregulon, including a set of core (shared) target genes and an accessory (variable) regulon fraction  [23]. It should be noticed that while the core regulon is necessarily formed by genes belonging to the core genome, the opposite can also be true (i. e. that a gene belonging to the core genome belongs to the accessory regulon). However, the dynamics of the panregulon in relation to the evolutionary rules controlling the variability of the accessory regulon fraction are still not understood. We have therefore constructed the regulatory network of the S. meliloti species, using the PSSMs of 41 TFs collected from the literature and public databases.
We have applied a combination of TFBS prediction methods, combining their output with information about the core and accessory gene families. We have also predicted the presence of the same TFBSs in five other closely related rhizobial species (termed 'outgroups': Rhizobium leguminosarum bv. viciae, Rhizobium etli, Mesorhizobium loti, Sinorhizobium fredii and Sinorhizobium medicae). This regulatory network has been used to highlight the different behaviours that are present within and between species. Our predictions and other comparative genomics observations are publicly available (https://github. com/combogenomics/rhizoreg/).
Consequently, we cannot a priori exclude that the regulatory functions of FixJ may be carried on by homologs (as for instance orthologs of SMa1686) in strains A0643DD and C0438LL. Indeed, previous works have indicated that several target genes of FixJ lack a direct symbiotic function, suggesting the presence of functional redundancy in the genome  [47]. Sixteen TFs were absent in at least one of the outgroups. Of these, 6 are encoded by pSymA, the symbiotic megaplasmid, including two copies of NodD, FixJ, RctR, SyrM and RhrA (S1 Fig). Such difference between intraspecific and interspecific TF gene content may anticipate a similar difference at the downstream regulatory network, for the absence of cross-regulatory links. To minimize the number of false positives in our predictions, we selected PSSMs with relatively high information content (over the reference strain minimum information content, see Materials and Methods) A wide range of information gain for PSSMs was observed; of the starting 83 TFBSs retrieved from literature and databases, 41 have been found to have enough information content to reliably predict their TFBSs (Fig 1a, S1 Table ).
An example of a known regulatory interaction predicted by our approach is rem (SMc03046), a putative transcriptional regulator involved in the control of motility in S. meliloti Rm1021  [49] , which was predicted to be under the control of MucR in our analysis (S1 Material). To provide additional validation to our predictions, we used a compendium of S. meliloti gene expression data from the Colombos database  [50]  (see Materials and Methods). The full compendium contained 424 conditions and was used to calculate average correlation coefficients among the genes of i) the same predicted regulons, ii) the regulons reported in the literature and iii) random groups of genes sampled from the genome (Fig 1d and S2 Material). We have selected the conditions maximising the average correlation for a group of genes using a genetic algorithm (see Materials and Methods).
We then concluded that the patterns of regulatory network variation are paralleled, at the species level, by changes in promoter sequences and by the variation in the accessory genome composition, at least in S. meliloti. These two fractions of the pangenome could then be used as bona fide predictors of the extent of rewiring in regulatory networks. However, from these data we cannot confirm a direct causative explanation for the observed regulatory network variation, as this analysis has been focused on the whole pangenome. The striking difference between the slow rate of coding sequence evolution versus the much larger difference in the regulatory networks is however worth noting.
Evolutionary dynamics of regulatory networks Regulatory network evolutionary dynamics showed interesting differences within and between species. Each observed regulatory interaction in the two datasets (S. meliloti and the outgroups) and its state across all strains was used to build a hidden markov model to infer the preferred state transitions in our predictions (see Materials and methods), that corresponds to the ways the gene regulatory network can grow and shrink. The possible states of a target gene depend on the presence of the TF, the target gene itself and the upstream TFBS. Therefore, each target gene can be found in one of six different states (Fig  4a ). The "plugged" state being the only functional one, which corresponds to a target gene with a TFBS in its promoter region when the TF is present in the genome.
Regulatory motifs collection The 83 regulators whose PSSM has been extracted from the various sources are listed in S1 Table. For those PSSMs retrieved from the literature, we collected the upstream regions of the regulated genes and (when available), the consensus binding sites from bibliographical records; the upstream regions have then been analysed with the meme program  [7] (version 4. 9. 0), using the model that retrieved the PSMM with higher similarity to literature. Twenty-two motif files have been generated using the information retrieved from the RhizoRegNet database  [27]. Fifteen motif files have been generated using the information retrieved from the RegTransBase database  [57]. For the 5 regulators having more than one predicted motif, for instance those having a variable length (FixJ, RpoD, RpoE2, RpoH1 and RpoH2), one motif file for each motif length has been generated.
Search of regulatory motifs occurrences For each genome, background k-mers frequencies have been calculated using the fasta-getmarkov program from the MEME suite (version 4. 9. 0) [7] , using 3 as the maximum value for k. Each regulatory motif has been searched inside each genomic sequence using four scanning algorithms. The mast program from the MEME suite (version 4. 9. 0) [7]  has been used with an E-value threshold of 100 and the use of a genome-specific background file. The matrix-scan program from the RSAT suite  [8] [9] [10]  has been used with a P-value threshold of 0. 001, the background file and a pseudocount of 0. 01, as suggested by Nishida et al. [59]. The Bio. motifs package from the Biopython library (version 1. 62b)  [11]  has been used with a false negative rate threshold of 0.
05 and a pseudocount of 0. 01, as suggested by Nishida et al. [59]. The nHMMer program from the HMMer suite (version 3. 1b1)  [12] [13] [14]  has been used with an E-value threshold of 100 and with all the heuristic filters turned off. Each regulatory motif hit has been parsed, separating the hits being present in the upstream region of a gene from the others. The upstream region has been defined as the intergenic region (not overlapping any coding sequence) in front of the first codon with a maximum size of 600 bp. In the case of a palindrome motif, the motif orientation has been ignored. The distributions of the raw scores has been tested using a normality test, as implemented in the SciPy library (version 0. 13. 3) [60]   [61]. The score threshold has been determined through the calculation of the raw scores quartiles (Q1 and Q3) and defining the score threshold (τ S in Eq 1) in order to consider only the upper outliers  [62].
t S ¼ Q3 þ ð1:5ðQ3 À Q1ÞÞ: ð1Þ For the Biopython method the bit score has been used, while for the RSAT, HMMer and MEME methods the negative base 10 logarithm of the E-value has been considered. The regulatory motifs predicted by at least three methods have been considered for further analysis.
Validation of the predictions The compendium of gene expression data for S. meliloti str. Rm1021 from the Colombos database  [50]  was used to calculate correlation coefficients among genes in the regulons reported in the literature, our predictions and random sets of genes. Random regulons were produced by random sampling groups of genes of size 5, 10 and 15, for which 500 sets were produced. Correlation was quantified by the squared uncentered correlation coefficient, which was calculated using Matlab, as the square of 1cos distance. Values plotted in Fig 1d are averages over the entire set of genes under analysis. We have implemented a strategy allowing to select the conditions maximizing the average squared correlation within a group of genes, since many of the conditions of the compendium are likely not related to our predictions.
Selection of the conditions was performed using the genetic algorithm implemented in the GA Matlab function, with default tolerances (TolCon = 10 -6 , TolFun = 10 -6 ). We let the algorithm select the conditions minimizing 1 R 2 where R is the uncentered correlation averaged over all pairwise comparisons made within the group of genes under analysis. Since we noticed that correlations are strongly and inversely correlated with the number N of included conditions, especially when N 20, we discarded all cases where the number of conditions was less than 20 (final N = 950). All conditions containing missing data in at least one of the genes under analysis were discarded before starting the procedure. For some of the known and predicted regulons, correlations were not calculated as the available number of conditions after removing missing data was less than 30 before the optimization.
2. 0) [72]. The distance matrix for both distance categories has been computed from the phylogenetic tree using the textitBio. Phylo package inside the Biopython library (version 1. 62b)  [73]. The "accessory" distance has been calculated through the construction of a presence/absence binary matrix for all the accessory genome OGs; the distance between each strain has been then calculated using the Jaccard distance measure, as implemented in the SciPy library (version 0. 13. 3) [61].
As basal-like tumors are enriched for mesenchymal features and cell lines have been grouped according to the intrinsic classification, we focused on basal-like and luminal cell lines  (Perou et al, 2000; Neve et al, 2006; Prat et al, 2010). Sensitivity varied dramatically across the cell line panel (Fig 2A, Supplementary Dataset S1): Several cell lines were refractory to PKC412 even at the highest concentration tested (50 lM), whereas others were strongly inhibited. A low passage cell line derived from a TNBC patient (PDC44) with progressive disease despite treatment with various chemotherapeutics, including doxorubicin and cyclophosphamide, was also highly sensitive to PKC412 (Fig  2B ). Dividing the cell lines according to their HER2/ER/PR status revealed that the mean EC 50 for PKC412 in TNBC cell lines was an order of magnitude lower as compared to receptor-positive cells (Supplementary Fig S4, P < 0.
Chemical proteomics identifies candidate kinase targets implicated in PKC412 specificity PKC412 has been shown to be a potent inhibitor of several kinases, including FLT3, c-KIT, VEGFR and several PKC family members. Therefore, we investigated whether six structurally unrelated compounds that inhibit various combinations of these 'canonical' targets could specifically induce cell death in the basal-like breast cancer cell lines. Only the PKC412 structural analog Go6976 recapitulated the profile, suggesting that a non-canonical target may be responsible for, or at least contribute to, the specificity for TNBC cells  (Fig 3A, Supplementary Fig S3 and Supplementary Dataset S2). Of note, the PKC inhibitor LY333531, a compound structurally related to PKC412, did not display the PKC412 cell line specificity pattern, indicating that this small molecule does not inhibit the critical PKC412 target(s)  (Fig 3A, Supplementary Fig S4).
Subsequently, we constructed protein-protein interaction networks based on public databases but focusing only on the genes expressed in the cells under investigation  (Burkard et al, 2010). Both networks were of similar size comprising some 9,000 genes and 104,000 edges representing protein-protein interactions and contained all 11 of our candidate PKC412 targets identified by chemical proteomics, suggesting that the presence or absence of the putative target(s) does not explain selectivity  (Supplementary Fig S6). Assuming that concerted expression changes in the network are more likely to represent functional effects  (Lage et al, 2008) , we added edge weights representing gene expression co-regulation upon PKC412 treatment. For all proteins in the network, we used an algorithm mimicking a diffusion process  (Kohler et al, 2008) , starting at the experimentally determined chemical proteomics interactors, to computationally predict the probability of individual nodes being affected by PKC412 treatment over the entire network.
Tyr705 phosphorylation of STAT3 is required for its dimerization, nuclear translocation and DNA binding  (Bromberg et al, 1999) , indicating that SYK inhibitor-treated cells have reduced activity of STAT3. Inhibition of STAT3 phosphorylation upon Together, we reveal an unexpected role for the tyrosine kinase SYK in maintaining STAT3 phosphorylation in basal-like breast cancer and provide a rationale for clinically testing SYK small-molecule inhibitors in this subclass of tumors. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the dataset identifier PXD001680.
RNA sequencing MDA-MB-468 cells and ZR75-1 cells were treated with DMSO or PKC412 (1 lM) in triplicate for 6 h. After treatment, RNA was extracted and sequencing libraries for HiSeq2000 were generated using the Truseq 2. 0 kit (Illumina). Raw sequencing data were aligned to the human genome, and FPKM values were calculated using a custom analysis pipeline established in-house. Subsequent KEGG pathway enrichment analysis of genes displaying greater than threefold change was performed with the online tool DAVID (http:// david. abcc. ncifcrf. gov/). Raw data are accessible at Gene Expression Omnibus (accession number GSE63721).
Protein-protein interaction network analysis A network of all known human protein-protein interactions was assembled using available online data from several databases (IntAct, MINT, HPRD, BioGRID, DIP, InnateDB, CORUM). Database versions were the most recent available at the time of generating this integrated network (July 24 th , 2012). The obtained network was comprised of 13,101 proteins and 127,064 interactions (edges). Basal and luminal-specific networks were obtained by intersecting this generic network with expressed genes (FPKM > 0. 1) in the respective cell line RNA-seq data. In basal MDA-MB-468 cells, we found 14,721 genes to be expressed, which resulted in a basal network comprised of 9,096 proteins connected by 103,937 edges after intersection with the generic network. Luminal ZR75-1 cells (14,314 genes expressed) yielded a luminal network comprised of 8,966 proteins linked by 103,733 edges.
Molecular docking The X-ray structure of SYK co-crystallized with staurosporine (PDB code: 1XBC;  Atwell et al, 2004)  was downloaded from the RCSB protein data bank  (Bernstein et al, 1977)  and prepared using the pdb2 receptor tool of Open Eye's OEDocking suite (Ref. OEDocking version 3. 0. 1. OpenEye Scientific Software, Santa Fe, NM, http://www. eyesopen. com). Potential 3D conformations of the ligands, staurosporine aglycon and its flexible derivative, were calculated using the program OMEGA  (Hawkins et al, 2010; Hawkins & Nicholls, 2012) (Ref. OMEGA version 2. 5. 1. 4. Open-Eye Scientific Software, Santa Fe, NM, http://www. eyesopen. com) and stored in a multi-conformer database. Because of the availability of the co-crystal structure of SYK with staurosporine, the docking program HYBRID, implemented in the OEDocking suite, was selected for the molecular docking study.
Lentivirally mediated depletion and overexpression of SYK cDNA Targeting shRNA sequences for STAT3 (targeting sequence: GCACAATCTACGAAGAATCAA), SYK (1:GCAGGCCATCATCAGTC AGAA, 2:CGACAAAGACAAGACAGGGAA), and FER (1:CAAACATT CCTCAACTTATAG, 2:CAGAACAACTTAGTAGGATAA) were obtained from the TRCN database (http://www. broadinstitute. org/rnai/ public/gene/search) and cloned into the lentiviral vector pLKO. 1 using AgeI and EcoRI restriction sites. Identity of cloned vectors was verified by sequencing, and lentiviral particles were produced by calcium phosphate transfection of the vectors along with the packaging plasmids into HEK293T cells. GFP was used as a transfection control. Two days after transfection, virus-containing supernatant was harvested, centrifuged at approximately 140 g for 10 min, and filtered to remove HEK293T cells from the supernatant.
2015 The Authors Molecular Systems Biology 11: 789 | 2015 Markus K Muellner et al Targeting a breast cancer cell state Molecular Systems Biology using a top 10 hybrid method with a collision-induced dissociation (CID) MS 2 event followed by a high-energy C-trap dissociation (HCD) MS 2 event for each selected precursor ion. Acquired RAW data were analyzed by the Proteome Discoverer 1. 4. 0. 288 platform integrating MASCOT, phosphoRS, and target decoy PSM validator software modules. Database search was performed against the human SwissProt database (sp_human_v2013. 1) with the following parameters: fixed modification of carbamidomethylation (C); variable modification of oxidation (M) and phosphorylation (S, T and Y); two miscleavage sites; search tolerance of 10 ppm on precursor; and 0. 6 or 0.
Acknowledgements We are grateful to  Robert Weinberg  for the pBABE-puro-Twist1 plasmid,  William Hahn  and  Jean Zhao  for providing the SYK cDNA,  Tomasz Konopka  for his help with RNA-seq analysis, and to  Georg Winter  for providing immobilized PKC412. We thank  Helen Pickersgill  and  Thijn Brummelkamp  for critical reading of the manuscript and members of the  Nijman lab  for helpful discussions. This work was supported by grants from the  Vienna Science and Technology Fund  ( WWTF LS09-009 ), the  Austrian Science Fund (FWF ,  P21768-B13 ,  SFB-F47 ), the  Austrian Federal Ministry for Science and Research (BMWF)  under the  GEN-AU program  ( GZ 200. 142/1-VI/I/2006  and  GZ BMWF-70. 081/0018-II/1a/2008 ), and an  ERC   Starting grant  ( 311166-Gene. Drugs. Cancer ).