dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA expression analysis A GTF file containing ensEMBL mouse transcript information (release 67) was used to generate a count table with HTSeq-count  [65]  with the following parameters: --stranded yesmode union -type exon. These options summarise unambiguous read counts, on the sense strand, over entire gene units using all exons. Genes differentially expressed upon doxorubicin treatment in wt ES cells were identified using the Bioconductor package DESeq  [66]. Genes with adjusted p-value < 0. 05 and fold change ≥ 2. 5 were identified as differentially expressed. Genes with a base Mean count < 200 (empirically determined) were removed from the list as they correspond to genes weakly expressed in both conditions. The corresponding differential expression heatmap (Figure  2A ) was generated using Java Treeview  [67].
Average profile and heatmap analysis of ChIP-seq Occupancy All read depths were normalised to the total number of uniquely mapped reads in each experiment. ChIP-seq density was determined by calculating the average number of hits per base in 100 bp windows with a 20 bp slide using normalised bigwig files over a 6 kb interval centred on the TSS (for promoters) or a 3 kb interval over the centre of each peak (for nonpromoter regions) using custom Perl and R scripts interfaced with the Galaxy server  [68, 69] , which are available upon request. Average profiles were generated by plotting the median value in each window for each sample. For heat maps, the sliding window file obtained from wt ES cells was subjected to k-means clustering using Cluster 3. 0 [70]. Results from clustering were used to rank sliding windows files for all other samples accordingly.
Bags abstraction The main interest of splitting the global tuple-space into several bags is that each bag may define its own semantic associated to the rd, get and put and the tuples themselves. As a result, bags can encapsulate software or hardware components. a database: each table of the database can be associated to a bag; rd() and put() corresponds to the reads and writes on the database. a service: This concerns remote services as well as local services directly embedded in the bag. The partially instantiated tuple composed of the input parameters is passed to the rd(). The concerned service is invoked from the bag, using the legacy protocol imposed by the remote service (e. g. SOAP for a Webservice based approach or a native Remote Procedure Call). The output parameters obtained as the result of the service is added to the input parameters defining the fully instantiated tuple returned by the rd().
an event system: Tuples contain the topic, the ID, the timestamp and a payload; the rd() and put() operations correspond to subscribe and publish of the event system. a sensor: Data are collected from the gateway and inserted into different bags storing the various relevant information. For instance, for a very simple approach, we can consider the three bags associating the id of the sensor to the sensed value, the type of sensor and its location. Successive values can be obtained through the rd(), changing the location of the sensor is as simple as modifying the resource by consuming it with a get() and inserting the updated information with a put(). The sampling frequency, the type of bag (e. g. set, multiset, FIFO,. ) or the precision can be adapted at this stage. The behaviour of the rd() may also be adapted to replace a simple tuple matching by a more complex matching: e.
EgoSpaces adds the View concept to limit the data seen by an agent (e. g. cost of the communication, physical location, thresholds on data). The view concept is interesting because it allows the developer to define when an agent should react. The MobiGATE Coordination Language (MCL)  [33]  insists on the separation between computation and coordination which is a shared approach with LINC. Their approach seems well suited for distributing a stream or a known service in a mobile environment. However, in MCL it seems difficult to focus on the coordination when a very complex and dynamic system is considered. UbiCoMo  [7]  proposes a coordination model that mainly focuses on accessing data in ubiquitous environments. However, this limits too much UbiCoMo expressiveness to fit into this specific paradigm.
MCL and UbiCoMo share with LINC the importance of separation between coordination and computation. In  [11]  the authors propose a process-based methodology to design eventbased mobile applications. They aim to translate UML activity diagram to event based models offering more flexibility. As outlined in their conclusion, eventbased approaches does not make synchronisation easy. We believe this issue could be overcome by the transactional guaranties of LINC. We also believe that LINC rules could be generated from the activity diagram. This could be an interesting track to consider. In  [17]  the authors present a programming model for concurrent coordination patterns targeting highly parallel and distributed applications. Similarly to LINC, they provide a language to focus on the coordination in complex environment.
We illustrated this with several real world case studies implemented on top of LINC. This demonstrated the ability of LINC to be used in contexts such as building automation, power efficiency or monitoring network systems. Future work will focus on providing a high-level language such as automata in order to prove the correctness of the coordination and then to automatically generate the corresponding coordination rules. We envisage to use this mechanism for self-evolving system where we can prove that the evolution will not break the running application. Acknowledgement. This work has been partially funded by the  FP7   SCUBA  project under grant nb  288079.
The standard ISOLDE target and converter configuration has been studied in detail using the Monte Carlo code FLUKA  [6, 7]  and the cross-section codes TALYS  [8]  and ABRABLA  [9, 10]. Due to the fact that the present converter layout results in reduced ion beam intensities when compared to the direct beam configuration, alternative designs for the target system have been proposed  [11]. At the same time this optimization aimed at decreasing the production of neutron-deficient species in order to improve the purity of neutron-rich-nuclear beams. To test the performance of such systems and the accuracy of theoretical modeling, a prototype target and converter system, inspired by one of the proposed configurations in  [11] , has been built and characterized at ISOLDE with a 1. 4 GeV proton beam.
Numerical simulations The two configurations shown in Fig. 2  have been proposed to improve the purity of neutron-rich isotopes beams, e. g. 80 Zn and 130 Cd, by reducing the production of neutron-deficient isobars 80 Rb and 130 Cs  [11]. For both approaches an increase by more than one order of magnitude in the ratios 80 Zn/ 80 Rb and 130 Cd/ 130 Cs has been predicted. The Monte Carlo code FLUKA is used to run the simulations, with two different sets of cross-sections: FLUKA's own cross-sections and a combination of cross-sections calculated with TALYS and ABRABLA. In the later case, the average fluences (proton and neutron track-length fluence spectra) over the UC x target volume is calculated with FLUKA; TALYS is used to compute cross-sections for energies up to 200 MeV and ABRABLA to calculate the cross-section from 200 MeV to 2 GeV, an energy range for a possible future energy upgrade connected to the HIE-ISOLDE project.
Thermo-electrical simulations using the ANSYS code  [12]  have been carried out in order to investigate the thermal behavior of the final target prototype (Fig. 3 ) unit under both normal operation conditions and in case of defective electrical contacts, which is a possible incident scenario. In the standard ISOLDE target units, the heating of the target container is arranged by a DC current coupled from copper connection blocks to the tantalum container via tantalum sheets, connected with stainless steel M5 screws. Fig. 4  shows the temperature distribution in the target container for an applied current of 950 A. The first simulation, shown in the upper panel of Fig. 4 , is related to the normal operation conditions where no faulty contacts are found and both thermal and electrical resistance are low.
Acknowledgements The authors would like to acknowledge the support from  E. Barbero  in the construction of the unit. A. Gottberg acknowledges the financial support of the  European Community  under the ENSAR-JRA2 ActiLab (Contract Number  262010 ). R. Luis acknowledges the  FCT  for PhD Grant No. SFRH/BD/60255/2009. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by  CENTRA/IST. Computations were performed at the cluster '' Baltasar-Sete-Sóis '', supported by the  DyBHo256667 ERC Starting Grant.
Tree inference and sequence analysis The relationship between DmelNV (GenBank NC_007919. 3; [57] ), DsubNV, DimmNV and other Nora-like sequences was inferred from VP4 (capsid protein), which is the most conserved gene and the one with the most coverage in the non-Drosophila sequences. The other Nora-like sequences included Nasonia vitripennis Nora-like virus (GenBank FJ790488;  [60] ), Haematobia irritans Noralike virus (GenBank HO004689, HO000459, and HO000794;  [59] ), and two Nora-like sequences newly identified here in the transcriptomes of Spodoptera exigua (GenBank GAOR01000957;  [79] ) and Chrysopa pallens (GenBank GAGF01018485;  [80] ). We excluded sequences virtually identical to DmelNV that appear in the transcriptomes of Leptopilina boulardi and Leptopilina heterotoma (GenBank GAJA01006738, GAJC01010128 and GAJA01017939;  [81] ), as these species are widely cultured on D.
melanogaster in the laboratory. For protein alignment, see text S1. For the N. vitripennis Nora-like virus we selected the longest sequence (FJ790488) for analysis. Two approaches to phylogenetic inference were used. First, MrBayes (v3. 2. 1, ref. [82] ) with discrete gamma-distributed rate variation and model-jumping between amino acid substitution models. Two parallel runs of four heated chains were used, and convergence was assessed by examination of the potential scale reduction factor (PSRF) and the variance in split-frequencies between runs (PSRF ,1 for all parameters; variance in split-frequencies ,0. 001). Second, a maximumlikelihood analysis was run using PhyML  [83]  under a WAG amino-acid substitution model  [84]  with discrete gamma-distributed rate variation. Data were bootstrapped 1000 times to infer bootstrap node-support.
Cloning The following constructs were described previously: all DmelNV VP1 constructs  [14] , pAFW-AGO1 and pAFW-AGO2  [86] , pAFW (Drosophila Genomics Resource Center, https://dgrc. cgb. indiana. edu), pMT-Luc  [38] , pMT-Rluc  [38] , pRmHa-Renilla-hairpin  [87] , pAc5-V5-His-A (Invitrogen), and pAc5-V5-His-Ntag  [14]. cDNA of D. immigrans and D. subobscura was made using Promega MMLV-RT in the presence of Promega RNasin Plus according to manufacturer's instructions. Subsequently, DimmNV VP1 and DsubNV VP1 sequences were PCR amplified from D. immigrans and D. subobscura cDNA and cloned as full-length and deletion constructs downstream of the V5-His tag in pAc5-V5-His-Ntag (details available upon request). The D. immigrans AGO2 cDNA sequence (GenBank KF362118), including partial 59 and 39 UTRs, was PCR amplified using the primer pair 59-TGCAGCAAAAATTAGAAGCAAA-39 and 59-AGCCGTACCTAGAACCAGCA-39.
Acknowledgments We thank members of the  Van Rij laboratory  and  Martijn Huynen  for discussions. We thank  Marco Heestermans  for technical support, and  Sam Lewis  for help with fly collections. Furthermore, we would like to thank  M. Siomi  for providing AGO2 antibody;  Y. Tomari  for AGO expression plasmids;  G. J. Hannon  for the Renilla hairpin vector; and  C. Antoniewski  for the pAFW plasmid.
This work was financially supported by a  VIDI fellowship  (project number  864. 08. 003 ) and the  Open Program of the Division for Earth and Life Sciences  (project number  821. 02. 028 ) from the  Netherlands Organization for Scientific Research , and a fellowship from the  Nijmegen Centre for Molecular Life Sciences of Radboud University Nijmegen Medical Centre  to  RPvR. Work  in DJO's laboratory is funded by a  Wellcome Trust   RCD Fellowship  ( 085064/Z/08/Z ). Work in MCS's laboratory is funded by the  European Research Council  ( FP7/2007-2013 ERC 242703 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Correlation between PhoP binding and transcriptional regulation To explore the relationship between binding of PhoP and transcriptional control on the target genes, we performed deep transcriptomic analysis by RNA-seq under the same in vitro conditions employed for ChIP-seq. We compared exponentially growing H37Rv wild type to the isogenic phoP mutant and quantified gene expression according to the functional categories in the TubercuList database (http://tuberculist. epfl. ch), generating results reported in Worksheet 1 in Table  S2. An arbitrary 3fold threshold was applied to the dataset for further analysis. Integration of the ChIP-seq and RNA-seq data revealed that 19 PhoP binding sites were associated with altered expression of the flanking gene(s) (Table  1 ). Since some of these ORFs are part of predicted operons  [17, 18, 19] , the total number of genes under direct control of PhoP was found to be at least 30 in the experimental conditions tested (Table  1 ).
56 elution buffer with 50 mg Proteinase K (Eurogentec). DNA was purified by phenol-chloroform extraction and quantified by Nanodrop and Qubit fluorometer according to the manufacturer's recommendations (Invitrogen). Library preparation for ChIP-seq analysis and Illumina high-throughput sequencing DNA fragments obtained from the immunoprecipitation procedure were used for library construction and sequencing with the ChIP-Seq Sample Preparation Kit (Illumina), according to the protocol provided by the manufacturer. One lane per library was sequenced on the Illumina Genome Analyzer IIx at the Lausanne Genomics Technologies Facility using the SR Cluster Generation Kit v2 and SBS 36 Cycle Kit v2. Data were processed with the Illumina Pipeline Software v1. 40.
Genome annotation All analyses in this study were carried out using the M. tuberculosis H37Rv annotation from the TubercuList database (http://tuberculist. epfl. ch/), which includes 4019 protein coding sequences (CDS), 73 genes encoding for stable RNAs, small RNAs and tRNAs. In order to quantify protein occupancy and transcription across the entire genome, 3080 intergenic regions (regions flanked by two non-overlapping CDS) were included, resulting in a total of 7172 features.
ChIP-seq data analysis ChIP-seq analysis was performed using the HTSstation pipeline at EPFL (http://htsstation. epfl. ch/). Briefly, the single-ended sequence reads generated from ChIP-seq experiments were aligned to the M. tuberculosis H37Rv genome (NCBI accession NC_000962. 2) using Bowtie  [50]  with options ''-l 28 -best -strata''. Peaks were analysed using MACS v. 1. 4 [51]  with parameters ''-bw 200 -m 10100''. Alignment files were converted to bigWig format for visualization in the UCSC genome browser Mycobacterium tuberculosis H37Rv 06/20/1998 Assembly  [52]. To determine the level of ChIP-seq enrichment for each feature, an enrichment ratio (ER) was calculated by dividing the read count for the ChIP-seq sample in the wild type strain by the read count for the mutant (control) sample.
Twelve cycles of PCR amplification using Phusion DNA polymerase were then performed and the library was finally purified with AMPure beads (Beckman Coulter) as per the manufacturer's instructions. A small aliquot (2. 5 ml) was analyzed on Invitrogen Qubit and Agilent Bioanalyzer prior to sequencing on Illumina HiSeq 2000 using the TruSeq SR Cluster Generation Kit v3 and TruSeq SBS Kit v3. Data were processed with the Illumina Pipeline Software v1. 82.
RNA-seq data analysis The single-ended sequence reads generated from RNA-seq experiments were aligned to the M. tuberculosis H37Rv genome (NCBI accession NC_000962. 2) using Bowtie2 with default parameters  [54]. Read counts for all annotated features were obtained with htseq-count program (http://www-huber. embl. de/ users/anders/HTSeq/doc/count. html). Regions where genes overlapped were excluded from counting. Reads spanning more than one feature were counted for each feature. Since the RNA library was strand-specific, the orientation of sequence reads had to correspond to the orientation of annotated features to be counted. Analysis of differential gene expression was carried out using the DESeq package  [55]. Quantitative PCR (qRT-PCR) for ChIP-seq and RNA-seq data validation One microgram of M.
tuberculosis RNA was converted to cDNA using SuperScript III Reverse Transcriptase (Invitrogen) according to the manufacturer's recommendations. All PCR primers were designed using Primer Express software (Applied Biosystems). The 10 ml PCR reaction consisted of 16 Sybr Green PCR Master Mix (Applied Biosystems), 0. 25 mM of each primer and 1 ml of 1:10 diluted cDNA or IP DNA from immunoprecipitation reactions. Reactions were carried out in triplicate in an Applied Biosystems StepOnePlus Sequence Detection System (Applied Biosystems) according to the manufacturer's instructions. Melting curves were constructed to ensure that only one amplification product was obtained. In the case of qRT-PCR for RNA-seq data confirmation, normalization was obtained to the number of sigA molecules in each sample.
Mass spectrometry and data analysis Each SAX fraction was resuspended in 2% acetonitrile, 0. 1% FA for LC-MS/MS injections and then loaded on a homemade capillary pre-column (Magic AQ C18; 3 mm by 200 A ˚; 2 cm6100 mm ID) and separated on a C18 tip-capillary column (Nikkyo Technos Co; Magic AQ C18; 3 mm by 100 A ˚; 15 cm675 mm). MS/MS data was acquired in data-dependent mode (over a 4 hr acetonitrile 2-42% gradient) on an Orbitrap Q exactive Mass spectrometer equipped with a Dionex Ultimate 3000 RSLC nano UPLC system and homemade nanoESI source. Acquired RAW files were processed using MaxQuant version 1. 3. 0. 5  [58]  and its internal search engine Andromeda  [59]. MS/ Ms spectra were searched against M. tuberculosis strain H37Rv database R23 (http://tuberculist. epfl. ch/) [60]. MaxQuant default identification settings were used in combination with dimethyllabeling parameters.
Data access The ChIP-seq and RNA-seq datasets have been deposited in NCBI's Gene Expression Omnibus  [63]  under accession number GSE54241. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://www. proteomexchange. org) via the PRIDE (Proteomics Identification Database) partner repository  [64]  with the dataset identifier PXD000698.
(PDF) Table  S1  ChIP-seq analysis of the PhoP binding sites in M. tuberculosis H37Rv. Worksheet 1 in Table  S1. List of PhoP binding sites detected by ChIP-seq experiments on the wild type strain H37Rv. The phoP mutant strain was used as a negative control to exclude false positive signals. For each peak we provide the following details: peak coordinates (columns B and C), peak length (D), summit coordinate (F), sequence coverage (G), fold enrichment (I), overlap with  [15]  (L), peak annotation (O). In addition, the summit coordinate for RNApol ChIP-seq  [17]  is detailed in column M. The motifs identified within each binding site are reported together with the distance from peak summit and the motif orientation. Worksheet 2 in Table  S1  and Worksheet 3 in Table  S1. They include the lists of signals obtained by Galagan and co-workers  [15]  as they can be downloaded from the TBDB database (http://www.
tbdb. org/). (XLSX) Table  S2  Differential gene expression analysis between M. tuberculosis H37Rv phoP isogenic mutant and the wild type. Worksheet 1 in Table  S2. This worksheet includes the results obtained upon performing RNA-seq experiments in the wild type strain H37Rv vs. the phoP mutant. Sequence coverage is provided in columns B and C for both of the strains for all of the open reading frames (ORF) in the TubercuList database. FoldChange and log2(FoldChange) expression levels are detailed in columns G and H. Worksheet 2 in Table  S2. This includes the head-to-head comparison between RNA-seq data presented in this manuscript and previous microarray-based transcriptomic analyses by Walters and colleagues  [5].
(XLSX) Table  S3  In-depth proteomic analysis of the secretome of M. tuberculosis H37Rv wild type, phoP mutant and complemented strains. Worksheet 1 in Table  S3. This includes the results obtained upon performing in-depth proteomic analysis of the secreted protein fractions from the wild type strain H37Rv, the phoP mutant and the complemented strains. Proteins were identified and labeled according to the annotation in the TubercuList database. Significance B values are detailed in columns I, J, K and L. Details of labeling are provided in the Materials and Methods section and in the Excel workbook. (XLSX).
Acknowledgments The authors gratefully acknowledge  Alberto Cebollada  for his help with the statistical analysis,  Santiago Uranga  and  Nacho Aguilo  ´for scientific discussions and work with the mouse model of infection,  Ainhoa Arbue  ´s for kindly providing pAZ31 plasmid,  Jan Rybniker  and  Rita Szekely  for advice on macrophage infection,  Keith Harshman  ( Lausanne Genomics Technologies Facility ) for high-throughput sequencing,  Romain Hamelin ,  Diego Chiappe  and  Florence Armand  of the  EPFL Proteomics Facility , the  PRIDE Team  for their assistance with proteomic dataset submission.
This work was supported by grant  BIO2011-23555  from  Spanish Ministry of Economy and Competitiveness , grant  FP7 European   NEWTBVAC 241745 ,  Swiss National Science Foundation  under grant  31003A-140778  and SystemsX. LS was supported by grant  BES-2009-013037  from  Spanish Ministry of Economy and Competitiveness. JGA was supported by  Juan de la Cierva Programme  (reference  JCI-2009-03799 ) from  Spanish Ministry of Science and Innovation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgment This paper is supported by the National Basic Research Program of China (973Green, no. 2012CB316000), the Key (Key grant) Project of Chinese Ministry of Education (no. 313053), the Key Technologies R&D Program of China (no. 2012BAH75F01) and the grant of 'Investing for the Future' program of France ANR to the CominLabs excellence laboratory (ANR-10-LABX-07-01).
Acknowledgements. Comments by reviewers  S. Carter  and  N. Ross  led to great improvement the manuscript. We are also thankful to  R. Rosenau ,  T. Scambos  and  S. Ligtenberg  for sharing data and/or discussion. T. Flament's PhD grant is funded by the  French Space Agency (CNES)  and the  Centre National de la Recherche Scientifique (CNRS). E. Berthier  acknowledges support from the  CNES  through the TOSCA and from the  Programme National de Télédétection Spatiale. SPOT5 images and DEMs were available at reduced cost thanks to support from  CNES  through  ISIS  project  639. This study was partially funded by the  Agence Nationale pour la Recherche (ANR)  through the  SUMER  project no. ANR-12-BS06-0018  and the  ADAGe  project no. ANR-09-SYSC-001. Edited by:  A. Kääb The publication of this article is financed by  CNRS-INSU.