dataesr/scientific-paragraphs-categorization huggingface dataset:
To demonstrate the difference for a relevant example, we calculated the estimated emissions from deforestation in Brazil from 2009-2011, after the reported date of either RS map, using the PRODES dataset (INPE, 2012). We found significantly higher carbon estimates with KDHρ than with either RS map (Table  2 ). It is possible that estimates from the RS maps are lower because these areas were already degraded at the time the maps were made, with this perhaps explaining the lower value for RS2 than RS1, as RS2 is produced for a date c. 5 years later.
In SW Amazonia, the difference is caused by the same two factors in the opposite direction: using pan-Amazonian wood density and D:H relationships here results in an overestimate of AGB. In SW Amazonia the two factors are not equal in magnitude, with wood density causing approximately three times more overestimation than the D:H relationship (Table  1 ). Wood density and D:H relationships alone, however, cannot explain all the differences between RS1/RS2 and K DHρ. In both the Guiana Shield and the SW Amazon, the difference between KDHρ and KD is smaller than the difference between KDHρ and the RS maps. The unexplained difference is over 10% for the Guiana Shield, and 3-5% for the SW Amazon. There must therefore either be further factors in the processing chains involved in developing RS1 and RS2 from their input datasets that contribute to the over-and under-estimation in these regions, or the non-random nature of the input field datasets must be causing this additional difference.
We believe the latter explanation is unlikely, as any bias towards pristine forests would tend to cause an overestimate in the plot-based estimate, but instead the field plots estimate lower AGB in SW Amazonia. Possible explanations for the former include incorrect or saturating relationships between GLAS footprints and AGB, or explanatory variables not fully capturing the spatial variability in forest structure, causing the resulting maps to tend towards the mean. The differences between RS1 and RS2 themselves are caused in part by the use of different remotesensing datasets, different methods of processing the GLAS data, and different extrapolation approaches, but also by the choice of allometric equation to convert their field plot data into AGB estimates. RS2 uses a two-parameter equation excluding height, whereas RS1 uses the three-parameter equation we used to estimate AGB from our plots, albeit with effectively a pan-Amazonian D:H relationship.
CONCLUSIONS The two remote-sensing maps, RS1  (Saatchi et al. , 2011)  and RS2  (Baccini et al. , 2012) , show very different spatial patterns of AGB distribution across Amazonia, compared to each other and compared to field plots distributed across the region (Figs 2 & 3). In particular, the strong gradient of increasing AGB from SW to NE Amazonia that we observe in the field data is not replicated in the RS datasets. RS1 and RS2 do have associated uncertainty estimates, but the differences observed between the maps and field plots considerably exceed the reported uncertainties over Amazonia in both cases. Our analysis shows that this is mostly due to neither study accounting for the known regional variations in wood density and D:H relationships. Specifically, they do not use any spatial layers corresponding to wood density, and use only continental  (Saatchi et al.
algorithms  (Asner et al. , 2012). There is a clear need for the ecological community to provide regional and pantropical maps of basal-area-weighted wood density based on plot data, potentially extrapolated using climate or other layers, as inputs for the biomass mapping communities. Second, our study demonstrates the importance of creating and sustaining large networks of field plots. This analysis was only possible because a sufficient number of plots have been located across the basin using a standard methodology, with the data included in a single database allowing identical processing chains to be applied to stem data. It is important that such networks are maintained and improved across the tropics, and vital that current spatial data gaps are filled (Fig. 1b ). Third, measuring tree height and identifying the thousands of Amazon tree species (at least to genus level, enabling stems to be matched to wood density information), were essential components of the field data.
Recording tree diameters alone would not have allowed us to identify these important regional gradients in AGB: variation in biodiversity can matter greatly for determining carbon stocks. Forests in the Changing Earth System'), and Natural Environment Research Council (NERC) Urgency Grant and NERC Consortium Grants 'AMAZONICA' (NE/F005806/1) and 'TROBIT' (NE/D005590/1). Additional data were included from the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partly funded by these institutions, the Gordon and Betty Moore Foundation, and.
ACKNOWLEDGEMENTS This paper is a product of the RAINFOR network, supported by a  Gordon and Betty Moore Foundation  grant, the  European Union 's  Seventh Framework Programme  ( 283080 , 'GEOCARBON';  282664, 'AMAZALERT' ;  ERC  grant 'Tropical other donors. The PPBio network was supported by  PRONEX -FAPEAM/CNPq  ( 1600/2006 ), Hidroveg  FAPESP/FAPEAM, Universal/CNPq  ( 473308/2009-6 ) and INCT-CENBAM. This work was partly supported by  Investissement d'Avenir grants of the French ANR  ( CEBA :  ANR-10-LABX-0025 ;  TULIP :  ANR-10-LABX-0041 ) and  CNPq/PELD  ( Proc. 558069/2009 -6 ). E. M. and R. J. W. B. are funded independently by research fellowships from  NERC  (grant ref:  NE/I021217/1  to E. M. and grant ref:  NE/I021160/1  to R. J. W. B. ). S. L. L. is funded by a  Royal Society Fellowship.
We thank  Sue Grahame  and  Georgia Pickavance  for their support with the ForestPlots. netdatabase and  Joana Ricardo  for work supporting RAINFOR collaborators. We dedicate this work to the memory of  Alwyn Gentry ,  Jean Pierre Veillon ,  Samuel Almeida  and  Sandra Patiño , whose pioneering efforts to understand Neotropical forest composition and structure from the ground up have been an inspiration for succeeding generations of ecologists.
Antigen-presenting cells produce type I IFN and chemokines in response to MOPV, but not LASV Our findings confirm previous reports  [24, 25]  that MP and, to a lesser extent DC, produce significant amounts of IFNb, IFNa1 and IFNa2 mRNA in response to MOPV infection. By contrast, LASV-infected DC did not synthesize large amounts of these mRNAs and in LASV-infected MP, only moderate levels are produced (figure  1A ). We then quantified the mRNAs encoding CC and CXC chemokines in infected DC and MP. The infection of DC with LASV or MOPV did not lead to synthesis of the CCL2, 3, 4, 5, 7, 13, and 17 mRNAs (data not shown), whereas an increase in the levels of the mRNAs encoding CXCL9, 10, and 11 was observed in MOPV-infected DC, but not in LASV-infected DC (figure  1B ). The infection of MP with MOPV induced robust expression of the CCL2, 3, 4, 5, 7, and 13 genes, and of the CXCL9, 10, and 11 genes, in particular (figure  1C ).
Macrophages release large amounts of chemokines in response to MOPV The amounts of chemokines released into the culture supernatants were then assessed by ELISA. The results were consistent with mRNA findings, as large amounts of the CC chemokines CCL3, 4, 5, and 7 and, particularly, of the CXC chemokines CXCL9, 10, and 11 were observed 24 and/or 72 h after the infection of MP with MOPV (figure  2A ). By contrast, MOPVinfected DC produced only moderate amounts (although significantly higher than those for mock-infected cells) of CXCL10 (data not shown and figure  2B ). LASV-infected DC and MP produced only modest amounts of CXCL10, and LASV-infected MP produced only small amounts of CCL4.
Acknowledgments We thank  M. Bouloy  for developing the reverse genetics system for LASV,  C. Clegg  and  G. Lloyd  ( both from the Centre for Applied Microbiology and Research, Porton Down, Salisbury, United Kingdom ) for providing MOPV,  S. Becker  ( Institut fu ¨r Virologie, Philipps-Universita ¨t Marburg, Marburg, Germany ) for providing the AV strain of LASV, and  T. G. Ksiazek  ( Department of Pathology, The University of Texas Medical Branch, Galveston, TX ),  P. E. Rollin  ( Special Pathogens Branch, Centers for Disease Control and Prevention, Atlanta, GA ), and  P. Jahrling  ( Integrated Research Facility at Fort Detrick, Division of Clinical Research, NIAID, NIH, National Interagency Biodefense Campus, Fort Detrick, Frederick, MD ) for providing us with LASV-specific antibodies. We thank the  Etablissement Francais du Sang  for providing blood samples and the  Laboratoire P4 Inserm-Jean Merieux team  for their assistance.
Background: Patients with HER2-positive early breast cancer (EBC) preferred subcutaneous (s. c. ) trastuzumab, delivered via single-use injection device (SID), over the intravenous (i. v. ) formulation (Cohort 1 of the PrefHer study: NCT01401166). Here, we report patient preference, healthcare professional satisfaction, and safety data pooled from Cohort 1 and also Cohort 2, where s. c. trastuzumab was delivered via hand-held syringe. Patients and methods: Patients were randomized to receive four adjuvant cycles of 600 mg fixed-dose s. c. trastuzumab followed by four cycles of standard i. v. trastuzumab, or vice versa. The primary endpoint was overall preference proportions for s. c. or i. v. , assessed by patient interviews in the evaluable ITT population. Results: A total of 245 patients were randomized to receive s.
introduction Trastuzumab-containing regimens are standard of care for HER2-positive early breast cancer (EBC) and metastatic breast cancer (MBC)  [1] [2] [3]. A 600 mg fixed-dose manual injection of subcutaneous (s. c. ) trastuzumab (Herceptin® SC, F. Hoffmann-La Roche Ltd, Basel, Switzerland) given via hand-held syringe is approved by the European Medicines Agency for EBC and MBC as an alternative to conventional intravenous (i. v. ) infusion, based on noninferiority of trough serum concentration (geometric mean ratio of 1. 33) and pathologic complete response (40. 7% and 45. 4% in the i. v. and s. c. groups, respectively) in the phase III HannaH study (NCT00950300)  [4]. An s. c. single-use injection device (SID), which automatically delivers a pre-inserted s. c. dose via a button press when attached to the thigh, has comparable pharmacokinetics and safety to the handheld syringe  [5].
Intuitively, s. c. trastuzumab should be more convenient for patients as administration requires only 2-5 min  [6]. Objectively, reductions in patients' infusion chair time, healthcare professionals' time, and other hospital resources have been demonstrated  [7, 8]. The international, open-label, randomized, PrefHer study (NCT01401166) examined patients' preferences in the adjuvant breast cancer setting for i. v. or s. c. delivery via two cohorts using both methods of s. c. trastuzumab administration (SID or hand-held syringe)  [9]. We present additional and final results of patient preferences in the overall study population (data pooled from both cohorts). methods patients Patient eligibility criteria have been described previously  [9]  and are available in the supplementary material at Annals of Oncology online.
HannaH will assess immunogenicity up to 60 months post-treatment  [17]  and SafeHer will further assess immunogenicity with the SID  [18]. Interpretation of safety analyses should also take into account the limitations of having a short period of time during which the events were recorded for this analysis (eight 3-weekly cycles). Future analyses will assess data from the continuation periods once all patients have completed follow-up. The apparent discrepancy between increased clinicianreported AEs during the s. c. period and patients' reports of s. c. producing less pain, bruising, and irritation may have resulted from a more conservative approach to reporting due to inexperience with the s. c. formulation  [4]. Healthcare professionals were more satisfied with s. c. regardless of administration method.
acknowledgements We thank the individuals who contributed to the design of the study instruments, the patients, their families, the nurses, the interviewers, and the investigators who participated in this study. We also thank Rebecca Elliot of Genentech, Inc. (South San Francisco, CA) for immunogenicity analyses. Support for third-party writing assistance for this manuscript, furnished by Daniel Clyde, PhD, was provided by F. Hoffmann-La Roche Ltd. funding This work was supported by F. Hoffmann-La Roche Ltd, Basel, Switzerland.
Primary reasons for patients' preferences (evaluable intention-to-treat population)Responses to the question 'What are the two main reasons for your preference?' were recorded verbatim by the interviewer. Four experienced researchers independently scrutinized the dataset and provided overarching themes or core categories for coding. When broad consensus about these had been reached, each researcher independently coded every patient's response; the researchers then reconciled codings with each other and determined if any thematic categories could reasonably be collapsed together or if a new category was required. a Some patients gave only one reason or no reason. Percentages were calculated on a per-patient basis (N = 467).
Classification and features 16S rRNA gene analysis Figure  1  shows the phylogenetic neighborhood of R. elongatum DSM 19469 T in a 16S rRNA gene based tree. The sequences of the two identical 16S rRNA gene copies in the genome do not differ from the previously published 16S rRNA gene sequence (AB601471). A representative genomic 16S rRNA gene sequence of R. elongatum DSM 19469 T was compared with the Greengenes database  [22]  for determining the weighted relative frequencies of taxa and (truncated  [23] ) keywords as previously described  [5]. The most frequently occurring genera were Rhodovulum (35. 1%), Jannaschia (13. 5%), Dinoroseobacter (10. 6%), Rhodobacter (9. 6%) and Roseobacter (8. 5%) (89 hits in total). Regarding the two hits to sequences from members of the species, the average identity within HSPs was 100.
In this study the utilization of carbon compounds by R. elongatum DSM 19469 T grown at 28°C was also determined using Generation-III microplates in an OmniLog phenotyping device (BIOLOG Inc. , Hayward, CA, USA). The microplates were inoculated with a cell suspension at a cell density of 95-96% turbidity and dye IF-A. Further additives were vitamin, micronutrient and sea-salt solutions, which had to be added for dealing with such marine bacteria  [24]. The plates were sealed with parafilm to avoid a loss of fluid. The measurement data were exported and further analyzed with the opm package for R  [7, 25] , using its functionality for statistically estimating parameters from the respiration curves such as the maximum height, and automatically translating these values into negative, ambiguous, and positive reactions.
Genome project history The genome of strain DSM 19469 T was sequenced within the DFG funded project "Ecology, Physiology and Molecular Biology of the Roseobacter group: Towards a Systems Biology Understanding of a Globally Important Clade of Marine Bacteria". The strain was chosen for genome sequencing ac-cording the Genomic Encyclopedia of Bacteria and Archaea (GEBA) criteria  [27, 28]. Project information can found in the Genomes OnLine Database  [29]. The Whole Genome Shotgun (WGS) sequence is deposited in GenBank and the Integrated Microbial Genomes database (IMG)  [30]. A summary of the project information is shown in Table  2.
Genome sequencing and assembly The genome was sequenced using a combination of two libraries (Table  2 ). Illumina sequencing was performed on a GA IIx platform with 150 cycles. The paired-end library contained inserts of an average of 441 bp in length. The first run delivered 2. 7 million reads. To increase the sequencing depth, a second Illumina run was performed, providing another 1. 2 million reads. After error correction and clipping by fastq-mcf  [33]  and quake  [34] , the data was assembled using Velvet  [35]. The first draft assembly from 1,753,098 fil-tered reads with an average read length of 89 bp resulted in 97 contigs. To gain information on the contig arrangement an additional 454 run was performed. The pairedend jumping library of 3kb insert size was sequenced on a 1/8 lane.
Genome annotation Genes were identified using Prodigal  [39]  as part of the JGI genome annotation pipeline. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) nonredundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Identification of RNA genes was carried out by using HMMER 3. 0rc1  [40]  (rRNAs) and tRNAscan-SE 1. 23  [41]  (tRNAs). Other non-coding genes were predicted using INFERNAL 1. 0. 2 [42]  Additional gene prediction analysis and functional annotation was performed within the Integrated Microbial Genomes -Expert Review (IMG-ER) platform  [43]  CRISPR elements were detected using CRT  [44]  and PILERCR  [45].
Gene cloning Limnoporus total RNA was extracted from different embryonic and nymphal stages. First strand cDNA synthesis was then performed, using total RNA as a template, according to Invitrogen manual instructions. To clone Limnoporus dll, hh, and wg, degenerate primers were designed based on sequence alignments of Tribolium, Acyrthosiphon, and Pediculus. Specific primers for dac, dpp, egfr, hth, and Ubx were designed based on sequences obtained from a whole transcriptome of Limnoporus. Primer sequences and Genbank accession numbers are presented in Table  S1.
Quantification of expression using comparative transcriptomics Limnoporus T2-and T3-legs from wild type and a sample of pooled Ubx RNAi treatments with a 2 μg/μl ds-Ubx concentration, spanning mid-and late stages, were dissected and used for total RNA extraction. The RNA from these legs was used by ProfilExperts (Lyon-France) to conduct deep sequencing using Illumina technology. A total of 50 million reads were generated per sample. These reads were aligned against a pre-existing full Limnoporus transcriptome, using BLASTn, to quantify Ubx transcript levels by determining the number of Reads Per Kilobase per Million mapped reads (RPKM) in each sample  (Mortazavi et al. , 2008).
Statistical analyses Statistical significance in leg length between each RNAi group and controls was determined by performing an analysis of variance (ANOVA) with the mean value of each pair of leg segments corrected to the egg length to account for embryo size variations. Statistical analyses were performed using the SPSS (Statistical Package for the Social Sciences) software package (IBM Corporation). In the scatter plots in Fig. 4 , the centered second order polynomial (quadratic) model, and the linear regression fit model were plotted using GraphPad Prism (version 6. 01). R² values for the models are presented in Table  S3.
We therefore analyzed Ubx transcript levels using the comparative transcriptomics data between legs dissected from wild type (WT) and Ubx RNAi dataset generated from a pool of samples injected with 2 μg/μl concentration (Fig. 3D ). In this dataset, the number of Ubx Reads Per Kilobase per Million (RPKM) is reduced from 32 in WT T2-legs to 17 in Ubx RNAi T2-legs, and from 162 in WT T3legs to 92 in Ubx RNAi T3-legs (Fig. 3D ). Therefore, RNAi depletes Ubx with equal efficiency in T2-and T3-legs, by 46. 8% and 43. 2% respectively. Combined, these experiments demonstrate that: (i) Ubx RNAi is specific and does not affect Abd-A; (ii) the efficiency of Ubx depletion generally increases with increased dose of injected ds-Ubx; and (iii) Ubx is depleted with equal efficiency in T2-and T3-legs. To assess the effect of Ubx RNAi on the length of T2-and T3-legs, we dissected and measured each leg across a sample of embryos.
Ubx dose-dependent effect on leg length is linear in walking but nonlinear in surface rowing semi-aquatic insects To determine the correlation between the responses of T2-and T3-legs to Ubx RNAi, we analyzed the association between T2-and T3-leg length in each individual using a larger dataset, composed of all injected concentrations and additional samples (N ¼92). This analysis completes the previous, average-based, method because it considers the values of leg length of each embryo rather than a mean, it controls for the variation in phenotype penetrance observed (Table  S4 ) and that is characteristic to the RNAi technique  (Jaubert-Possamai et al. , 2007; Kitzmann et al. , 2013) , and finally it reveals how the lengths of the two legs change with respect to each other within each embryo.
If Ubx operates to increase or decrease leg length in a dose-dependent manner, we should expect a non-linear association between the responses of T2-and T3-legs to Ubx RNAi. Testing this prediction is based on three consistent observations: (i) control legs express higher levels of Ubx protein relative to their Ubx RNAi-treated counterparts (Fig. 3A-D ); (ii) RNAi depletes Ubx transcripts equally in T2-and T3-legs (Fig. 3D ); and (iii) the length of T2-legs consistently decreases with increased depletion of Ubx protein (Fig. 3E ). Therefore, we can use the length of T2-legs as a proxy to infer Ubx levels, such that the shorter T2-legs are the lower Ubx protein levels are. Based on this, we plotted T2-leg length by T3-leg length using a dataset including a larger sample size than the sample used in Fig.
The spatial expression of leg patterning genes is unaffected in Ubx RNAi treatments Ubx is known to regulate a set of important leg patterning genes, including decapentaplegic (dpp), hedgehog (hh), epidermal growth factor receptor (egfr), and wingless (wg) in insects  (Estella and Mann, 2008; Estella et al. , 2008; Grossmann and Prpic, 2012). Because of the changes in the spatial pattern and the levels of Ubx expression as well as its opposite effect on leg length throughout the evolution of the semi-aquatic insects, we wanted to know whether or not these changes in Ubx affected the expression of a set of important leg patterning genes. First, we interrogated our comparative leg transcriptome datasets generated from normal and Ubx RNAi treatments to determine if the levels of expression of these key patterning genes are affected by Ubx depletion.
Acknowledgments We thank  Ehab Abouheif  for hosting AK and PR in his lab during earlier steps of this project,  Michalis Averof ,  Samir Merabet ,  Dali Ma , and  Emilia Santos , and three anonymous reviewers for comments on the manuscript. We thank  Kelly Langlais  for technical support and  E. Furlong ,  R. White , and  Developmental Studies Hybridoma Bank  for providing the anti-Mef2, anti-UbdA and Exd antibodies respectively, all of which cross-react in various semi-aquatic insect species. We thank  John Colbourne  for help with generating Limnoporus full transcriptome used as a reference, and  Lois Taulelle  and  Hervé Gilquin  for providing access to computing resources in the  Pôle Scientifique de Modélisation Numérique (PSMN) at the ENS Lyon. Scanning electron microscopy was performed in the facility of the  Centre Technologique des Microstructures at the Université Claude Bernard Lyon1.
More than 1,300 eukaryotic genome sequences are archived in NCBI as of April 2014 (ftp://ftp. ncbi. nlm. nih. gov/ genomes/GENOME_REPORTS/). Clearly, the number of genome data submissions has steadily risen over recent years, notably between 2009 and 2010 (Figure  1 ). This trend will persist and accelerate as the cost of sequencing continues to plummet. However, many genome sequencing projects concern species that are closely related to already well-characterized model organisms; of 2,401 listed project at NCBI, only 991 different species from 653 genera are represented thereby illustrating a focus on sequencing genomes from related strains or populations of model species, which benefit from mature genome structure annotations and a wealth of other functional genetic information generated by closely-related model organisms.