dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012; Peylin et al. , 2016; Schürmann et al. , 2016). Atmospheric CO 2 concentration observations are one of the most accurate, long-term datasets in environmental science, and they provide important information about the global CO 2 sink capacity by the land and ocean. Many of the aforementioned studies reported that adding extra data streams helped to constrain unresolved sub-spaces of the total parameter space. Richardson et al. (2010)  and  Keenan et al. (2012)  concluded that using ancillary information (e. g. woody biomass increment, field-based LAI and chamber measurements of soil respiration), in addition to NEE data, provided a valuable extra constraint on many model parameters, which improved both the bias in model predictions and reduced the associated uncertainties. The results of the REFLEX model-data fusion inter-comparison project also indicated that observations of the different carbon pools would help to constrain parameters such as root allocation and woody turnover that were not well resolved using NEE and LAI data alone  (Fox et al.
, 2005; Richardson et al. , 2010; Kato et al. , 2013; Keenan et al. , 2013; Bacour et al. , 2015; Schürmann et al. , 2016). Kato et al. (2013)  and  Bacour et al. (2015)  both evaluated the complementarity of eddy covariance and FA-PAR data streams at site level, i. e. the impact of assimilating one individual data stream on the other model state variable, as well as when both data streams were included in the optimisation (see discussion in Sect. 2. 2). The study of  Keenan et al. (2013)  was particularly notable in its aim to quantify which data streams provide the most information (in terms of model-data mismatch) and how many data streams are actually needed to constrain the problem. They reported that of the 17 field-based data streams available, projections of future carbon dynamics were well-constrained with only 5 of the data sources and, crucially, not with eddy covariance NEE measurements alone.
This was due to the fact the total stocks take into account losses related to disturbance and management (e. g. canopy thinning) -processes that were not included in that version of the model. Kato et al. (2013)  assimilated SeaWiFS FAPAR  (Gobron et al. , 2006)  and eddy covariance LE measurements at the FLUXNET site in Maun, Botswana. They showed that the individual assimilation of each the two data streams resulted in a perfect (i. e. within the observational uncertainty) fit to the assimilated dataset, but a considerable degradation of the fit to the non-assimilated dataset compared to the prior. A comparison against eddy covariance measurements of gross carbon uptake (gross primary production -GPP) pointed to a bias in the FAPAR data because the fit to the independent GPP data was degraded after assimilating FAPAR data only, while the fit improved after assimilating the LE data only.
Nevertheless, the simultaneous assimilation of both data streams achieved a compromise between the two suboptimal results achieved after assimilating only one data stream. The calibration further limited the number of parameters with correlated errors and yielded a higher theoretical reduction in parameter uncertainty and a decrease in the RMSD by 16 % for the GPP data compared to the prior. Bacour et al. (2015)  also noted that when assimilating in situ and satellite-derived FAPAR data and in situ NEE and LE flux data from two French FLUXNET sites into the ORCHIDEE LSM, both separately and together, the posterior parameter values changed significantly for the photosynthesis-and phenology-related parameters, depending on the bias between the model and the observations and the correlation between the parameter errors.
When NEE data were assimilated alone there was an even stronger positive bias (model-observations) in the start of leaf onset in the FAPAR data than in the prior simulations, and no improvement in the maximum value. This was likely due to the fact that there were enough degrees of freedom to fit the NEE without changing the phenology-related parameters. Similarly, the fit to the NEE was degraded when the model was only optimized with FAPAR data. The model was able to fit the maximum FAPAR, but this resulted in an adverse effect on the carbon assimilation capacity of the vegetation. The authors argued this was related to incompatibilities between the FAPAR and both the model and NEE measurements, possibly due to the larger spatial footprint of the satellite-derived FAPAR data and/or inaccuracies in the retrieval algorithm.
However, given that assimilating in situ FAPAR also degraded the fit to the NEE, they also speculated that the culprit may be an inconsistency between the model and the data due to the different characterisation of FAPAR or LAI in the model compared to the satellite retrieval algorithm. For example, satellite-derived greenness measures (FAPAR/NDVI) also contain information on the non-green elements of vegetation, but the model only simulates green LAI. Furthermore parameters and processes in models have been developed at certain temporal and spatial scales; vegetation is often simply represented as a "big leaf" model in LSMs, taking no account of vertical canopy structure or the spatial heterogeneity in a scene, thus presenting an additional source of inconsistency compared to what is measured.
The joint (simultaneous) assimilation of all three data streams in  Bacour et al. (2015)  reconciled the different sources of information, with an improvement in the model-data fit for NEE, LE and FAPAR. However, the compromise achieved in the joint assimilation was only possible when the FAPAR data were normalised to their maximum and minimum values, which partially accounted for any bias in the magnitude of the FAPAR or inconsistency with the model. The story of biases and apparent inconsistencies in FAPAR data does not end there. A bias correction was also necessary in the study by  Kaminski et al. (2012)  with CCDAS-BETHY using the MERIS FAPAR product in addition to atmospheric CO 2 data (see above). They found that optimisation procedure failed when using the original FAPAR product because the FAPAR data were biased towards higher values.
Only after applying a bias correction on the FAPAR data prior to assimilation was the optimisation successful. Schürmann et al. (2016)  also reported the need to reduce a prior model bias in FAPAR. Even though the assimilation successfully corrected for this FAPAR bias, the impact of the prior bias was evident in the spatial patterns of the modelled heterotrophic respiration. Assimilating FAPAR data alone therefore resulted in a slight degradation in the net C flux and consequently led to incorrect simulations of the atmospheric CO 2 growth rate. The addition of CO 2 as a constraint prevented this degradation and resulted in a compromise in which FA-PAR helped to disentangle these processes and find different parameter values compared to the CO 2 -only case, thus improving the fit to both data streams.
Forkel et al. (2014)  discovered an apparent inconsistency between satellite-derived FAPAR and GPP data in tundra regions when using these data (plus satellite-derived albedo) to optimise the LPJmL LSM. They too speculated that the data might be positively biased, in this case due to issues with satellite measurements taken at high sun zenith angles. However, they gave alternative suggestions, one being that an inadequate model structure may be at fault -for example, LPJmL does not include vegetation classes corresponding to shrub, moss and lichen species that are dominant in these ecosystems. They also noted that the GPP product they used, which is based on a model tree ensemble upscaling of FLUXNET data  (Jung et al. , 2011) , might contain representation-related biases, given that there are very few FLUXNET stations in tundra regions.
Acknowledgements. We acknowledge the support from the  International Space Science Institute (ISSI). This publication is an outcome of the ISSI's Working Group on " Carbon Cycle Data Assimilation: How to Consistently Assimilate Multiple Data Streams ". Natasha MacBean  was also funded by the  GEOCAR-BON Project  ( ENV. 2011. 4. 1. 1-1-283080 )within the European Union's 7th  Framework Programme for Research and Development. The authors wish to thank collaborators in the atmospheric inversion and carbon cycle DA communities with whom they have had numerous past conversations that have led to an improvement in their understanding of the issues presented here. Finally we thank the two anonymous referees whose comments have helped to improve the clarity and breadth of the manuscript. Edited by: C.
Driver reactions in cancer We analyze four cancer networks (i. e. , breast, lung, renal, and urothelial cancer) and networks of the corresponding healthy tissues (glandular breast, pneumocytes, kidney, and urothelial cells)  (Gatto et al. 2014 ). To determine the reactions which are associated with genes known to cause cancer, we obtain 547 census genes from the Catalog of Somatic Mutations in Cancer (COSMIC)  (Futreal et al. 2004 ) and an additional 1453 genes from high-throughput mutational screenings, whole-exome sequencing, and whole-genome sequencing of cancer samples from the Network of Cancer Genes (NCG) (D  'Antonio et al. 2012 ) (March 2015 database versions). Next, we map the reactions to genes through their gene-enzymereaction relationship (which excludes nonenzymatic reactions, such as transport and diffusion reactions), yielding a total of 2363 reactions from the four cancer networks.
Of these, 112 are associated with cancer causing genes from COSMIC (4. 7%) and 614 to genes from the NCG data set (26. 0%). We identify 620 reactions as critical drivers in any of the four cancer networks (26. 2%). Out of these, 45 are associated with the census genes in COSMIC (7. 3%) and 191 to genes from the NCG data set (30. 8%), yielding P values of 6. 8 × 10 -4 (COSMIC) and 9. 6 × 10 -4 (NCG) (hypergeometric test) for the overrepresentation of these genes. To identify reactions which may have a key role in (de)regulation of cancer metabolism and tumor development, we focus on reactions which are critical drivers in cancer networks and redundant in the corresponding healthy tissues (metabolic switches). There is no reaction which is a metabolic switch for each of the four cancer types.
Acknowledgments We thank  Alisdair Fernie ,  Tino Krell ,  Jan Lisec , and  Clemens A. Schmitt  for critical comments on the manuscript, and  Francesco Gatto  and  Jens Nielsen  for kindly providing the cancer networks. This work was supported by a  Marie Curie Intra European Fellowship  within the European Union's  Seventh Framework Programme  (FP7/2007-2013),  European Commission (ERC)  grant agreement number  329682 , the  Max Kade Foundation , the  National Institutes of Health (NIH) Grants: Centers of Excellence of Genomic Science (CEGS)   1P50HG004233  and  1R01HL118455-01A1 , and the  John Templeton Foundation  Awards  PFI-777  and  51977.
24  and Supplementary Table  32 ). Under-represented (  13 ) and overrepresented (8) proteins were identified during early infection (11-17% infected cells at 3 days post-inoculation). Most were also represented as infection advanced when the number of underand over-represented proteins increased to 50 and 31, respectively (56-61% infected cells; 10 days post-inoculation). Analysis of protein ontology demonstrated differences between under-and over-represented proteins in both early and late infections for cell growth (adducin, spectrin and b-tubulin) and transport (Na þ /K þ ATPase, voltage-dependent anion-selective channel or mitochondrial porin and fatty acid-binding protein; Supplementary Tables  32 33 34 ). The genome of a Rickettsia (Alphaproteobacteria: Rickettsiales) species, Rickettsia endosymbiont of Ixodes scapularis (REIS), was assembled from both bacterial artificial chromosome clones and recruited whole-genome shotgun reads (available at GenBank, NZ_ACLC00000000).
The samples were fractionated by isoelectric focusing and each fraction analysed by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) using a Surveyor LC system coupled to a linear ion trap mass spectrometer model LTQ (Thermo Finnigan, San Jose, CA, USA) and protein identification was carried out using SEQUEST algorithm (Bioworks 3. 2 package, Thermo Finnigan), allowing optional (Methionine oxidation) and fixed modifications (Cysteine carboxamidomethylation, Lysine and N-terminal modification of þ 144. 1020 Da). The MS/MS raw files were searched against the alphaproteobacteria combined with the arachnida Swissprot database (Uniprot release 15. 5, 7 July 2009) supplemented with porcine trypsin and human keratins. This joint database contains 638,408 protein sequences. False discovery rate of identification was controlled by searching the same collections of MS/MS spectra against inverted databases constructed from the same target databases.
The alphaproteobacteria Swissprot database was used to identify Anaplasma and discard possible symbiotic bacterial sequences from further analyses. Ixodes scapularis genetic diversity and population structure. 74 RADseq libraries were produced from female I. scapularis representing nine 'populations' from the states of Florida, Indiana, Maine, Massachusetts, North Carolina, New Hampshire, Virginia and Wisconsin and the Wikel reference colony. RADseq libraries were constructed using 1 mg genomic DNA from individual ticks, separately digested with the SbfI restriction enzyme. Adaptor ligated libraries were pooled and sequenced at the Purdue Genomics Core Facility on the Illumina HiSeq 2500 in Rapid run mode. Further analysis was performed by the Bioinformatics Core at Purdue University. Illumina reads were corrected for restriction site, clustered and de-multiplexed (sorted by barcode) using the 'process_radtags.
pl' script of STACKS. For SNP identification, reads from each sample were separately aligned to the IscaW1 assembly using the end-to-end mode and default parameters of Bowtie2 v 2. 1. 0. Genetic diversity within and between I. scapularis populations was calculated using 745,760 SNPs across 35,460 polymorphic loci. F-statistics were used to assess genetic distance or differentiation as evidence of selection where F IS is the inbreeding coefficient of an individual (I) relative to the subpopulation (S) and F ST is the difference in allele frequency between subpopulations (S) compared with the total population (T). The population structure of I. scapularis across North America was separately analysed using a subset of 34,693 representative SNPs (1 SNP per polymorphic locus). The 'population' step from STACKS was used to analyse genetic diversity and fastStructure (beta release) was used to analyse population structure.
Hamden, CT) genome was sequenced to approximately 3. 8-fold coverage using Sanger sequencing and assembled using the Celera Assembler configured to accommodate high repeat content within the genome and heterozygosity in the donor population (Supplementary Table1). The assembly and raw reads are available at GenBank under the project accession ABJB010000000, consisting of contig accessions ABJB010000001-ABJB011141594 and VectorBase as IscaW1, 3 May 2012. The annotation of the I. scapularis genome was performed via a joint effort between the JCVI and VectorBase. The genome annotation release (IscaW1. 4) is available at VectorBase (https://www. vectorbase. org/)andGenBank (accession ID: ABJB010000000). Forty-five bacterial artificial chromosome clones, B183,834 ESTs and 45 microRNAs were also sequenced and annotated (Supplementary Figs4-6and Supplementary Tables.
Acknowledgements This project has been funded in part with federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services (NIAID, NIH, DHHS)  under contract numbers  N01-AI30071 ,  HHSN272200900007C ,  HHSN266200400001C  and  5R01GM77117-5. Its contents are solely the responsibility of the authors and do not represent the official views of the  NIH. Additional grants and contracts supporting work described in this manuscript were from the  NIH-NIAID  ( HHSN266200400039C  and  HHSN272200900039C ) to F. H. C. , and a subcontract under  HHSN272200900039C  to C. A. H. and J. M. M. , the  Australian Research Council Discovery Project  ( DP120100240 ) to S. C. B. and R. S. , the  Ministerio de Ciencia e Innovacion of Spain  ( BFU2007-6292 ;  BFU2010-15484 ) to J.
R. ,  BIO2009-07990  and  BIO2012-37926  to J. V. NIH-1R01AI090062  to Y. P. , L. S. , and J. K. ,  NIH   1R21AI096268  and  NSF IOS-0949194  to R. M. R. , the  Xunta de Galicia of Spain  ( 10PXIB918057PR ) to J. M. C. T. and M. T. ,  BFU2011-23896  and  EU FP7 ANTIGONE (278976 ) to J. F. , the  USDA-NRI/CREES  ( 2008-35302-18820 ) and  Texas AgriLife Research Vector Biology  grant to P. V. P. and  European Research Council   Starting Independent Researcher Grant  ( 205202 ) to R. B. , J. M. R was supported by the intramural program of the  NIAID , R. M. W. by a  Marie Curie International Outgoing Fellowship   PIOF-GA-2011-303312 , E. M. Z. by  Swiss National Science Foundation  awards  31003A-125350  and  31003A-143936 , J. M. G. by an  NIH-NCATS  award  TL1 TR000162  and  NSF   Graduate Research Fellowship  ( DGE 1333468 ), V.
Hamden, CT) genome was sequenced to approximately 3. 8-fold coverage using Sanger sequencing and assembled using the Celera Assembler configured to accommodate high repeat content within the genome and heterozygosity in the donor population (Supplementary Table1). The assembly and raw reads are available at GenBank under the project accession ABJB010000000, consisting of contig accessions ABJB010000001-ABJB011141594 and VectorBase as IscaW1, 3 May 2012. The annotation of the I. scapularis genome was performed via a joint effort between the JCVI and VectorBase. The genome annotation release (IscaW1. 4) is available at VectorBase (https://www. vectorbase. org/)andGenBank (accession ID: ABJB010000000). Forty-five bacterial artificial chromosome clones, B183,834 ESTs and 45 microRNAs were also sequenced and annotated (Supplementary Figs4-6and Supplementary Tables The genome of I.
Acknowledgements J. H. was supported by the  Labex SIGNALIFE  ( ANR-11-LABX-0028-01 ) and K. M. was supported by the  European Regional Development Fund  through the  BioMedIT  project, F. S. was supported by  ATIP-Avenir (CNRS/INSERM) ,  Becas Chile ,  CONICYT/ FONDECYT (3150156 ) and MN-FISB ( NC120043 ) postdoctoral fellowships. This study was funded by the  Swedish Research Council  ( 325-2014-6547  and  621-2014-4605 ), the  Research Council of Norway  ( 222364/F20 ) to J. W. ; by a  Marie Curie International Outgoing Fellowship , the  Wellcome Trust , and  Estonian Research Council  ( IUT34-4 ) to L. P. ;  ATIP-Avenir (CNRS/INSERM) ,  ARC  (grant number  PJA20151203273 ),  FP7-PEOPLE-2012-CIG  (grant number  322035 ),  ANR  ( ANR-13-BSV6-0006-01  and  Labex   SIGNALIFE ANR-11-LABX-0028-01 ),  Canceropole PACA  (AAP emergence 2015) and  DuPont Young Professor Award  to G.
Long-term fellowships The longer term research-based ESMO fellowships have durations between 1 and 2 years and are available to undertake both clinical and translational oncology research projects. The application for long-term fellowships also requires a detailed research project proposal including timelines, an acceptance letter from the mentor (ie, the person responsible for the project at the host institute) and a letter of recommendation from the candidate's department head at the home institute. The ESMO Fellowship and Award Committee that reviews the applications focuses on the overall quality of the application, as well as on the project proposed in terms of overall strategy, methodology and the suggested analyses of the research data as well as project feasibility (ie, to accomplish the specific aims within the grant term).
There are a number of online resources offered by ESMO that are of great benefit in an oncology practice. One of these resources is OncologyPRO, which is free and unlimited to all ESMO members. Through this website, members can download all of the ESMO publications, including handbooks and guidelines, watch oncology webcasts, download slides from previous ESMO meetings and have the opportunity to watch E-learning and V-learning modules from leading oncology experts and accrue CME points. ESMO members also have complete access to Annals of Oncology, both online and printed versions. A valuable tool for everyday practice is OncoAssist. This application offers the user adjuvant tools, prognostic scores, formulas, an AJCC staging manual and a CTCAE score. OncoAssist is free of cost to ESMO members and can be downloaded to desktops, tablets or smartphones.
Acknowledgements The authors and the  European Society for Medical Oncology  would like to extend their deepest gratitude to  Katharine Fumasoli  and  Lone Kristoffersen  for their instrumental role of aiding in the advancement of the career development of many young oncologists worldwide. The authors would also like to thank the following contributors and active ESMO YO members who shared their ESMO YO experiences to give a global view of how ESMO can contribute to medical development and in a medical practice. The authors would also like to acknowledge:  Surendra Pal Chaudhary, India Institute of Medical Sciences (New Delhi, India). Franklin Fernandes Pimentel, HCRP-USP (Ribeirão Preto-SP, Brazil). Karima Oualla ,  Centre Hospitalier Universitaire Hassan II (Fes, Morocco). Carmen Herrero Vicent ,  Instituto Valenciano de Oncologia (Valencia, Spain).
Ultimately, the magnitude of aboveground carbon losses will depend on the identity of tree species that replace the individuals lost in defaunated forests. For example, although small-seeded species are considerably shorter on average as adults than large-  (c) Wood densities do not consistently differ with seed sizes of animal-dispersed species and are lowest among abiotically dispersed species (Supplementary Table  3 ). Bars represent means and error bars 95% confidence intervals. The numbers of species belonging to each category are reported within bars. Species' maximum diameters are obtained from the plot data set while maximum attainable height and wood density are collated from secondary sources (Methods section). seeded ones (Fig. 3b ), the tallest species of both groups have similar heights, resulting in the characteristic wedge shape of seed size-maximum height relationships (Fig.
Methods Vegetation plot data. Tree community data consisting of measurements of all trees Z10 cm diameter at breast height (DBH) were obtained from 58 vegetation plots with a total sampled area of 55 ha from 10 sites spanning the humid tropics (Supplementary Table  4 ). Data were obtained from three sources: (a)   41. For multi-temporal data sets containing repeated measures, only data from single surveys conducted during 2011-2013 were considered. Species names were checked and standardized in accordance with currently accepted taxonomies  42. Tree DBH values were crosschecked against measurements from previous survey periods, when available, to detect and correct obvious errors. Entries for conifers (0. 03% of all individuals recorded in plots) and woody vegetation other than trees, such as lianas (0.