dataesr/scientific-paragraphs-categorization huggingface dataset:
(A) First bar chart: the percentage coverage of cancer functional events (CFEs) in the pan-cancer dataset occurring in at least one cell line. Coverage for each class of CFEs individually and when combined is shown. Second bar chart: the median coverage by cancer type of frequently occurring (>5% of tumor samples) cancer-specific CFEs in at least one cell line. The solid line indicates coverage of CFEs occurring in >2 cell lines. Third bar chart: coverage in each cancer type of frequently occurring cancer genes (CGs). Missing cancer genes are grouped by the level of evidence supporting their classification as a cancer gene. The number of cell lines for each cancer-type and the full name of each cancer-type and associated acronym are shown. (B) Matrix of Pearson correlations of CFE frequency between cell lines and patient tumors for each cancer-type and class of CFEs.
Introduction and motivation This paper presents experimental results on event-by-event fluctuations of multiplicities and transverse momenta of charged particles produced in inelastic p+p interactions at 20, 31, 40, 80 and 158GeV/c. The measurements were performed by the multi-purpose NA61/SHINE [1, 2] experiment at the CERN Super Proton Synchrotron (SPS). They are part of the strong interaction programme devoted to the study of the properties of the onset of deconfinement and search for the critical point of strongly interacting matter. Within this program a two dimensional scan in collision energy and size of colliding nuclei is in progress. Data on p+p, Be+Be and Ar+Sc collisions were already recorded and data on p+Pb and Xe+La collisions will be registered within the coming years. The expected signal of a critical point is a non-monotonic dependence of various fluctuation measures in such a scan, for recent review see Ref.
Data-based correction for off-target interactions The event quantities used to derive final fluctuation measures were calculated for events recorded in the LH filled (target inserted, I) and removed (target removed, R) configurations. The latter data set represents interactions with material downstream and upstream of the liquid hydrogen (off-target interactions). Then, in the absence of other corrections, the corrected mean value of the distribution of any quantity (denoted as X) was calculated as: where N ev denotes the number of events and ε is a normalization factor. The value of ε was derived based on the distribution of the fitted z coordinate of the interaction vertex. All vertices far away from the target originate from interactions with the beam line and detector materials. Neglecting the beam attenuation in the target one gets: X = 1 N I ev -ε • N R ev   N I ev ∑ i=1 X I i -ε • N R ev ∑ j=1 X R j   , (7) ε = N I ev N R ev z>-450 cm.
Acknowledgements We would like to thank the  CERN PH, BE  and  EN Departments  for the strong support of  NA61. This work was supported by the  Hungarian Scientific Research Fund  (grants  OTKA 68506  and  71989 ), the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , the  Polish Ministry of Science and Higher Education  (grants  667/N-CERN/2010/0 ,  NN 202 48 4339  and  NN 202 23 1837 ), the  Polish National Center for Science  (grants  2011/03/N/ST2/03691 ,  2013/11/N/ST2/03879 ,  2014/ 13/N/ST2/02565 ,  2014/14/E/ST2/00018  and  2015/18/M/ST2/00125 ), the  Foundation for Polish Science -MPD program , co-financed by the  European Union  within the  European Regional Development Fund , the  Federal Agency of Education of the Ministry of Education and Science of the Russian Federation  ( SPbSU research  grant  11.
38. 242. 2015 ), the  Russian Academy of Science  and the  Russian Foundation for Basic Research  (grants  08-02-00018 ,  09-02-00664  and  12-02-91503-CERN ), the  Ministry of Education, Culture, Sports, Science and Technology, Japan ,  Grant-in-Aid for Scientific Research  (grants  18071005 , 19034011, 19740162, 20740160 and 20039012), the  German Research Foundation  (grant  GA 1480/2-2 ), the  EU-funded Marie Curie Outgoing Fellowship , Grant  PIOF-GA-2013-624803 , the  Bulgarian Nuclear Regulatory Agency  and the  Joint Institute for Nuclear Research, Dubna  (bilateral contract No. 4418-1-15/17 ),  Ministry of Education and Science of the Republic of Serbia  (grant  OI171002 ),  Swiss Nationalfonds Foundation  (grant  200020117913/1 ),  ETH   Research Grant   TH-01 07-3  and the  U. S.
For each redMaPPer cluster, member galaxies are identified according to their photometric redshift, color and their cluster-centric distance. To reduce the contamination induced by fake member galaxies, we only use satellite galaxies with membership probability Pmem > 0. 8. The redMaPPer cluster finder identifies 5 central galaxy candidates for each cluster, each with an estimate of the probability Pcen that the galaxy in question is the central galaxy of the cluster. We remove all central galaxy candidates from our lens sample. For more details about redMaPPer cluster catalog, we refer the readers to  Rykoff et al. (2014) ;  Rozo & Rykoff (2014). Stellar masses are estimated for member galaxies in the redMaPPer catalog using the Bayesian spectral energy distribution (SED) modeling code iSEDfit  (Moustakas et al.
Dependence on the projected halo-centric radius To derive the subhalo parameters, we calculate the χ 2 defined as: χ 2 = ij (∆Σ(Ri) -∆Σ(Ri) obs )( C -1 ij )(∆Σ(Rj) -∆Σ(Rj) obs ) , (19) where ∆Σ(Ri) and ∆Σ(Ri) obs are the model and the observed lensing signal in the ith radial bin. The matrix Cij is the covariance matrix of data error between different radius bins. Even if the ellipticity of different sources are independent, the cross term of the covariance matrix could still be non-zero, due to some source galaxies are used more than once(e. g. Han et al. 2015). The covariance matrix can be reasonably calculated with bootstrap method using the survey data themselves  (Mandelbaum et al. 2005 ). In the paper, We divide the CS82 survey area into 45 equal area sub-regions. We then generate 3000 bootstrap samples by resampling the 45 sub-regions of CS82 observation data sets and calculate the covariance matrix using the bootstrap sample.
Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT) , which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. The Brazilian partnership on CFHT is managed by the  Laboratório Nacional de Astrofìsica (LNA). This work made use of the CHE cluster, managed and funded by  ICRA/CBPF/MCTI , with financial support from  FINEP  and  FAPERJ. We thank the support of the  Laboratrio Interinstitucional de e-Astronomia (LIneA). We thank the  CFHTLenS team  for their pipeline development and verification upon which much of this surveys pipeline was built.
LR acknowledges the  NSFC (grant No. 11303033 , 11133003 ), the support from  Youth Innovation Promotion Association of CAS. HYS acknowledges the support from  Marie-Curie International Fellowship  ( FP7-PEOPLE-2012-IIF/327561 ),  Swiss National Science Foundation (SNSF)  and  NSFC of China  under grants  11103011. HJM acknowledges support of  NSF   AST-0908334 ,  NSF   AST-1109354  and  NSF   AST-1517528. JPK acknowledges support from the  ERC  advanced grant LIDA and from  CNRS. TE is supported by the  Deutsche Forschungs-gemeinschaft  through the  Transregional Collaborative Research Centre TR 33 -The Dark Universe. AL is supported by  World Premier International Research Center Initiative  ( WPI Initiative ),  MEXT, Japan. BM acknowledges financial support from the  CAPES Foundation  grant  12174-13-0.
Reconstruction of the genome-scale metabolic network and a macromolecule network involved in R. solanacearum virulence We generated a metabolic reconstruction consisting of the chemical reactions that transport and interconvert metabolites in R. solanacearum strain GMI1000. This network reconstruction was achieved through the development of a bioinformatic pipeline (see Material and Methods and S1 Material for details) based on the functional annotation of the genome  [30] , literature and database searches, and a manual curation protocol  [31]. The reconstructed genome-scale metabolic network of strain GMI1000 encompasses 1825 biochemical reactions as well as 280 exchange reactions with the environment linking 1203 unique metabolites localized in three distinct compartments (cytoplasm, periplasm, extracellular).
Data were analyzed and statistic calculated using the R software package OPM  [61]. NMR analysis of bacterial culture supernatants. Exocellular compounds were collected from cultivation broth in shake flasks. A volume between 3 and 10 ml of culture supernatants was collected depending on bacterial concentration, filtered on 0. 22 μm filter (Millex syringe filters) and frozen at -20°C until analysis. Metabolites in culture supernatants were quantified by Nuclear Magnetic Resonance (NMR) spectroscopy. 1D 1 H spectra were recorded on an Avance II 500-MHz NMR spectrometer using a 5-mm z-gradient BBI probe head (Bruker, Rheinstatten, Germany) at a temperature of 298°K. NMR measurements were acquired by using a 30°pulse, 5. 000-Hz sweep width, and 3. 27-s acquisition times and recording 16 scans with a relaxation delay between scans of 10 seconds.
For accelerating this step, we have developed SAMIR (Semi Automatic Metabolic Identifier Reconciliation), a web tool that allows reconciling the identifiers of reactions and metabolites between the different draft models in a semi automatic way (see Section I. C in S1 Material). iii) At last, the four standardized draft metabolic models were merged in a single one following an expert evaluation of the best annotation reactions based on the homology analysis and the evidence of the reactions. This step was facilitated by using a relational database in Access linking common identifiers through the four draft networks (see Section I. D in S1 Material). All the methods and tools used in these three steps are detailed in S1 Material. Reaction candidates of the high-quality draft reconstruction and genes annotated to be involved in metabolic processes were curated one by one following an expert evaluation of their information.
Thus, curation of the reactions was done by seeking for information in metabolic databases KEGG  [65]  and MetaCyc  [66]  as well as the transporter database TCDB  [67]. On purpose, a R. solanacearum GMI1000 specific pathway/genome database was generated using the Pathway-tools software  [68]  to assess their metabolic pathway context, and concomitantly, the MicroScope platform  [69]  was used to evaluate the genomic context (e. g. shared synteny). In addition, it was used to access easily the various information provided on the platform on genes, like blast through various databases and links to the bibliography for the blast hits, prediction of gene product localization, prediction of EC number. Dead-end reactions were computed thanks to MetExplore  [70]  and the gap filling was performed by reassessment of pathway context through the previously listed tools and databases.
Transport reactions as well as assimilation pathways of various substrates were curated thanks to the analysis of the phenotype microarrays performed with the Biolog technology (see below for detailed protocol). The mass and balance of the network were computed and corrected. Stoichiometrically balanced cycles were computed by running flux balance analysis without open exchange fluxes and those leading to free conversion between NADPH and NADH were curated; those which had no consequences on flux simulation results were kept. Finally, the model was converted into Systems Biology Markup Language (SBML) format (S2 Material) suitable for constraint-based computing and was documented following MIRIAM Registry recommendation, with assignment of a Systems Biology Ontology references at each component of the model  [71] , assignment of metadata like bibliography information and confidence level information.
Acknowledgments We thank  Timothy Denny  for kindly sharing his knowledge on the EPS quantification and  Jessica Genin  for contribution to the competition experiment. MetaToul (Metabolomics & Fluxomics Facitilies, Toulouse, France , www. metatoul. fr)and its staff members ( Lindsay Peyriga  and  Jean-Charles Portais ) are gratefully acknowledged for access to NMR facilities. MetaToul is part of the  French National  infrastructure for metabolomics and fluxomics, www. metabohub. fr. We would also like to thank  Pierre Boistard ,  Nathalie Poupin ,  Patrick Kiefer , and  Nemo Peeters  for reading the manuscript and providing helpful comments.
RP was supported by  EMBO (Long-Term Fellowship   ALTF 1627-2011 ),  Marie Curie Actions  ( EMBOCOFUND2010 ,  GA-2010-267146 ), and  European Research Council  ( ERC-StG336808  project  VariWhim ). We acknowledge funding from the  Institut National de la Recherche Agronomique (Plant Health Division  grant  AAP SPE 2012 ) and the  French Laboratory of Excellence  project  TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
X-ray diffraction Single crystals of 3, 5 and 6 suitable for X-ray crystal analyses were obtained by slow diffusion of vapors of pentane into dichloromethane solutions. Single crystal data collection was performed at 150 K with an APEX II Bruker-AXS (Centre de Diffractométrie, Université de Rennes 1, France) with Mo-Kα radiation (λ = 0. 71073 Å). Reflections were indexed, Lorentzpolarization corrected and integrated by the DENZO program of the KappaCCD software package. The data merging process was performed using the SCALEPACK program. 22 Structure determinations were performed by direct methods with the solving program SIR97,  23  that revealed all the non-hydrogen atoms. SHELXL program  24  was used to refine the structures by full-matrix least-squares based on F 2. All non-hydrogen atoms were refined with anisotropic displacement parameters.
Hydrogen atoms were included in idealised positions and refined with isotropic displacement parameters. Single crystals of all these derivatives were always coated in Paratone oil once removed from the mother solution, mounted at low temperature on the diffractometer goniometer and X-ray data collection was performed at low temperature. Atomic scattering factors for all atoms were taken from International Tables for X-ray Crystallography. 25 CCDC reference numbers 1430204, 1430205 and 1430206 contain the supplementary crystallographic data for 3, 5 and 6.
Acknowledgements This work is supported by the  Ministère de la Recherche et de l'Enseignement Supérieur , the  CNRS , the  Région Bretagne, China-French associated international laboratory in "Functional Organophosphorus Materials", Balaton PHC  ( 830386 K) -TÉT_12_FR-1-2013-0017 , the  French National Research Agency (ANR) / Research Grants Council (RGC) Joint Research Scheme (ANR MOLMAT ) and  COST CM10302  (SIPS). The authors are grateful to  Dr T. Roisnel  ( Centre de Diffractométrie, ISCR Université de Rennes 1, France ) and  Dr C. Lescop  ( ISCR ) for X-ray diffraction studies.
Intracellular analysis of BAFF Peripheral blood samples from CLL patients were stained for intracellular BAFF expression as previously described  [8]. Briefly, analysis of intracellular BAFF expression by CD19+ cells was performed on fresh PB samples. Monoclonal antibodies (MoAbs) used for analyses included anti-CD19 PE (Clone HIB 19, IgG 1 , BD Pharmingen, San Diego, CA, USA) and fluorescein isothiocyanate (FITC)-conjugated anti-BAFF (Clone 137314, IgG 1 , R&D Systems, Minneapolis, MN USA). The samples were analyzed by flow cytometry directly after preparation. For data acquisition and analysis, a FACSCalibur instrument with CellQuest software (Becton Dickinson, Franklin Lakes, NJ, USA) was used. The percentage of positive cells was measured from a cutoff set using an isotype-matched nonspecific control antibody.
Deviation from the Hardy-Weinberg equilibrium (HWE) was estimated with the chi-square test and measured as f ¼ p CC -p 2 C p C 1-p C ð Þ , where p C and p CC are allele C and genotype CC frequencies while f < 0 and f > 0 correspond to deficiency and excess of homozygotes, respectively, and f = 0 in the case of HWE. Haplotype frequencies (HFs) among SNPs were estimated with the maximum likelihood function  [20]. Differences in genotype distributions between two groups of cases and between combined group of cases and controls were tested with the χ 2 statistic, and the distribution of χ 2 was estimated numerically. Analysis was performed using GNU Octave software version 3. 8. 2.
We confirmed our previous finding showing the difference in genotype distribution between CLL patients and controls for rs9514828. Based on obtained OR values, we assumed the overdominant model in which heterozygotes had protective effect. As was mentioned earlier, rs9514828 is located within the MZF1-binding site  [9]. We ran an in silico analysis using ConSite software  [28]  and found that the binding of MZF1 is predicted to be less strong for the C allele (score 8. 510) than for the T allele (score 9. 085). MZF1 has been shown to be involved in hematopoietic malignancies. MZF1 is a promoter/ enhancer binding-type transcription factor and has been shown to act as a trans activator as well as a trans repressor, and it has been suggested that its oncogenic activity may be related to combined values of increased and decreased expression of target genes  [29].
Moreover, ConSite software predicted the presence of a potential binding site for Sp1 transcription factor but only for the C allele (score 6. 115). One of the mechanisms allowing neoplastic cells to survive and develop is the ability to overcome the intrinsic and extrinsic signals which in normal conditions lead to apoptosis. Sp1 transcription factor has been shown to be involved in regulation of numerous proand anti-apoptotic proteins such as BCL-2 or MCL1. It has also been reported that Sp1 regulation may act in two opposite directions. It may promote neoplastic cell resistance to apoptosis but may also promote these cells' sensitivity to induction of apoptosis  [30]. Taking into consideration the results of the in silico analysis, one may speculate that rs9514828 polymorphism may influence the regulation of the BAFF gene by affecting the binding sites of the above-mentioned transcription factors.
Acknowledgments This work was supported by the  National Science Centre Poland , grant number  N N402 680940. Part of patients' DNA samples and clinical data was obtained in cooperation supported by the  National Science Centre Poland , grant number  N N402 682040. Intracellular analysis of BAFF was supported by research grant number  2 PO5B 116 27  from the  State Committee for Scientific Research (Poland). We wish to thank  Izabela Nowak, PhD , and  Andrzej Wiśniewski, PhD , from the  Laboratory of Immunogenetics  and  Tissue Immunology  for great help in collecting DNA samples from healthy volunteers. We are also very grateful to our patients and healthy volunteers for blood donation and agreement to participate in this study and the following MD doctors for help in collecting patients' samples and clinical data:  Prof.
We developed an interactive electronic weed identification tool, AFROweeds, and an online network, Weedsbook, for agricultural change agents to aid communication and offer assistance to rice farmers with specific weed problems. We collected quantitative and qualitative data to assess effectiveness and usefulness of these products with potential users. With the online version of AFROweeds, used on an electronic tablet, average weed identification time in the field was 7 min 6 s with 44% successful identifications. Poor mobile network coverage and slow internet were the main reasons for the relative long identification time and low success rate. A second trial was done with the offline version, preinstalled on a tablet. The average identification time was 6 min 34 s, with a success rate of 75%.
To assist current and future agricultural change agents in communicating and helping rice farmers with specific weed problems in their fields, we initiated the development of electronic means of support. Weed identification is considered a critical first step for targeted weed management  (Zimdahl, 2007) , but accurate and timely identification requires specialized knowledge and skills rarely available among agricultural change agents working in the field. For that reason, a user-friendly support tool for the identification of rice weeds was one of our primary development aims. We coupled this identification tool, called AFROweeds, to a database with information on local names, biology, ecology and management and an online collaborative network, called Weedsbook, enabling users to find more information on aspects like weed ecology and management and to exchange with experts.
The AFROweeds tool focuses on weeds in rice and covers all relevant African regions. The tool is available in two languages (English and French) and provides a userfriendly identification aid, leading to factsheets containing additional information on local names, ecology, biology, local uses, and control rather than just the usual photos, drawings, and taxonomic descriptions in the aforementioned books. Inclusion of local names in the database will enhance the communication between agricultural change agents and farmers. The network Weedsbook was set up to enable those users to exchange with peers and experts to get guidelines and answers to additional queries or to exchange experiences and observations. The products generated by this initiative, i. e. the identification tool, database and network, may not necessarily be accessible and useful by small-scale farmers in SSA, as their use requires a certain level of education and access to smartphones and internet.
The intended users are rather the current and future actors that work in farmer support systems. This actor group primarily includes staff of the national agricultural research and extension systems (NARES), like extension services, public crop protection agents and field technicians and researchers of national research organizations and universities. Lecturers and researchers can use these tools to train students at agricultural colleges and universities. The objective of this study was to evaluate the performance and usefulness of the tool, database, and network, and to assess whether and how these products should be improved. To this end we put them to test among a group of potential users.
AFROweeds, an electronic identification tool and database on rice weeds We developed an interactive electronic tool and database for computers, electronic tablets, and smartphones called AFROweeds. The idea of electronic tools for identification of pests or diseases in crops is not new. Pertot et al. (2012) , for instance, developed a tool to identify plant diseases, while several previous tools have been developed for weed identification. Examples of such tools are Adventrop, for general weeds in the Sudano-sahelian zone of Africa  (Grard et al. , 2010) ; AdventOI, for weeds in Indian Ocean islands (Le  Bourgeois et al. , 2008) ; and OSWALD, for major weeds in rice paddies of Cambodia and Lao People's Democratic Republic  (Grard et al. , 2006). Like Adventrop, AdventOI, and OSWALD, the AFROweeds identification tool is a computer program (also called identikit), based on IDAO, an open-source plant identification software with an interactive graphical interface and supported by a database.