dataesr/scientific-paragraphs-categorization huggingface dataset:
Since this metric quantifies the proportional presence of large trees, it can be used to estimate AGB and monitor changes associated with the disturbance of large trees from mortality events and selective logging. We first explore the properties of LCA across a range of landscapes in the Neotropics. Next, we hypothesize that LCA is a good predictive metric of the spatial variations of AGB over a wide range of old growth forests. To this end, we assembled a collection of airborne Lidar measurements and ground inventory data at nine sites in old growth Neotropical forests. The Lidar data provide variations in canopy height and distribution of large trees that allow us to address the following questions: 1) is there 2 Materials and Methods.
25 ha each), La Selva (11 plots of 1 ha each), Manaus (10 plots of 0. 25 ha each), Nouragues (7 plots of 1 ha each) and Tapajós (10 plots of 0. 25 ha each). In these plots, all trees with a diameter at breast height (DBH) ≥10 cm have been mapped, measured and identified to the species. Trees with irregularities or buttresses were measured higher on the bole. Total tree height measurements were available for a subset of these trees. The method for calculating AGB inv from forest inventories at 1 ha scale is reported in S. 1 of the supplementary information. Stand averaged wood density of each site was calculated and is reported in level data (AGB inv and mean wood density) were provided for Antimary (50 plots of 0. 25 ha each), Nouragues (27 plots of 1 ha each) and Paracou (85 plots of 1 ha each).
Lidar data Lidar sensors scan the vegetation vertical structure and return a three dimensional point cloud derived from the time it took each pulse to return to the instrument. The Lidar datasets acquired over the study sites come from discrete return Lidar instruments and were gridded horizontally at a 1m resolution using the echoes classified as either vegetation or ground. They yield three products: digital surface model (DSM) corresponding to the top canopy elevation, digital terrain model (DTM) corresponding to the ground elevation, and canopy height model (CHM), which is the height difference between the DSM and the DTM. DTMs were interpolated from a Delaunay triangulation or comparable interpolation methods, after outliers have been removed. DSMs were created using the highest return within a cell.
Lidar data over Paracou were acquired in last return mode, causing a bias of 50 cm on the CHM  (Vincent et al. , 2012). This bias is not addressed in this study because our height increment for the determination of optimal height thresholding is larger (1m) (see  Sect. 4. 3). Data were acquired between 2009 and 2013, using relatively similar sensors and acquisition configurations (Table  2 ). The potential differences between the Lidar datasets and their impact on the results are addressed in the Discussion. For each site, we selected a 1x1 km (100 ha) area of old growth forest, oriented north-south, without any human disturbance to the extent possible. Topography derived from Lidar data within the selected 1 km 2 subset images provides information on landscape variations that may impact the forest structure.
For Chocó and Antimary, we developed models based on 0. 25 ha plots and 50 m x 50 m pixels of Lidar data and after estimating AGB Lidar , aggregated the image to 1 ha or 100 m pixels. For the remaining sites of the Central Amazon (Cotriguaçu, Manaus and Tapajós), we used a model based on existing data derived from airborne and spaceborne Lidar  (Lefsky et al. , 2007). This model may have larger uncertainty in estimating biomass compared to our site specific model, but we here assume that all 1 ha scale AGB Lidar estimates have approximately similar uncertainties.
Pixel clustering was based on the similarity of the four nearest neighbors (similar results were obtained with an eight neighbor model, results not shown here). Figure  S2  summarizes the steps taken to go from the Lidar canopy height model to the final LCA map. Processing was conducted using the IDL software (Interface Description Language, Exelis). We determined the optimal minimum crown size and canopy height threshold calculating the coefficient of correlation between AGB Lidar and LCA. We also performed the same analysis using AGB inv and LCA at the four calibration sites. This step allowed us to examine if optimal height thresholds differed from one site to the other. The goal was to find a single optimal height threshold and crown size that could be applied for LCA retrieval across closed canopy Neotropical forests.
Detecting Changes of Selectively Logging Forest degradation due to selective logging is difficult to detect with conventional remote sensing techniques due to small scale and minor impacts on the forest canopy and biomass compared to severe forest disturbances (e. g. fires, storms, or clearing). However, selective logging targets large trees  (Pearson et al. , 2014)  and thus may be detectable using LCA. Here, we use the Antimary study site that was selectively logged after the 2010 Lidar acquisition to examine the use of LCA for detecting logging impacts on the forest canopy and AGB. We apply the large tree segmentation approach on both the 2010 and on a 2011 post-logging Lidar data (see  Andersen et al. , 2014  for details) to quantify the logging impacts in terms of the distribution of large trees removed from the forest and the loss of aboveground biomass.
For AGB estimation, the model based on LCA weighted by WD gives the best result by bringing R 2 up to 0. 78 and RMSE down to 46. 02 Mg ha -1 (Fig. 4b , Fig. 5 ,   S3 ). These findings show that relying on a fraction of the Lidar information gives comparable results as using metrics depending on information from all pixels, such as MCH, highlighting the importance of large canopy trees to estimate biomass. The relationship between LCA and other metrics derived from ground data, such as Lorey's height or basal area, are presented in Table  S4. respectively in Antimary (Fig. 6 ). Difference in LCA between the two dates (2010-2011) (Fig. 6a ) at 1 ha grid cell captured the areas of largest changes in the few months following logging (logging took place between June and November 2011, Lidar data were collected in late November 2011).
Future Applications of LCA LCA definition in our study relies on the high resolution information on forest height, allowing for the detection of crown area of large canopy trees. Can a similar measure be derived from large footprint Lidar observations such as the future NASA spaceborne Lidar mission GEDI (Global Ecosystem Dynamic Investigation)? GEDI will not provide spatially continuous data Similarly, future spaceborne radar missions could also provide useful information to retrieve large canopy areas. The synthetic aperture radar (SAR) tomographical observations of the European Space Agency (ESA) BIOMASS mission will provide wall-to-wall imagery of canopy profile that could be converted to LCA over the landscape  (Le Toan et al. , 2011). Preliminary research based on airborne TomoSAR measurements has already shown that backscatter power at about 30 m above the ground, with sensitivity to the distribution of large trees, explained the variation of AGB over Nouragues and Paracou plots better than the backscatter power related to the lower part of the canopy (0-15 m)  (Minh et al.
Conclusions We introduce LCA as a new Lidar derived index to capture the variations of large trees and total volume and biomass across landscapes that remain spatially and regionally invariant. The importance of LCA is in its relevance to the structure and ecological characteristics of large trees in filling the canopy space and their unique contribution in determining the total volume and   (Condit, 1998; Hubbell et al. , 1999 Hubbell et al. , , 2005))        Normalizing AGB by averaged wood density brings the data from different sites closer to a common fit.
Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-547 Manuscript under review for journal Biogeosciences Discussion started: 5 January 2018 c Author(s) 2018. CC BY 4. 0 License. The impacts of logging on the distribution of large trees and changes of AGB was detected by simply deriving the LCA index from pre and post-logging Lidar data acquired in 2010 and 2011.
Acknowledgements The work described in this paper was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. This work has benefited from " Investissement d'Avenir " grants managed by the  French Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01  and  TULIP, ref. ANR-10-LABX-0041 ;  ANAEE-France :  ANR-11-INBS-0001 ) and from  CNES  ( TOSCA  project;  PI T Le Toan ). Field and Lidar data from the Brazilian sites were acquired by the  Sustainable Landscapes Brazil  project supported by the  Brazilian Agricultural Research Corporation  (EMBRAPA), the  US Forest Service , and  USAID , and the  US Department of State. La Selva  field work was supported by the  U. S. National Science Foundation LTREB Program NSF   LTREB 1357177.
Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-547 Manuscript under review for journal Biogeosciences Discussion started: 5 January 2018 c Author(s) 2018. CC BY 4. 0 License. 769 , Chocó: (bioredd. org), La Selva: Carbono project (Clark 770 and Clark, 2000), Manaus and Tapajós: Espírito-Santo (unpublished results), Nouragues: Réjou-Méchain et al. , 771 2015, Paracou: Gourlet-Fleury et al. , 2004; Vincent et al. , 2012. Site Data Plots Size N plots Year Mean WD Mean AGB Annual rainfall (ha) (g cm -3 ) (Mg ha -1 ) (mm yr -1 ) Antimary Plot level 0. 25 50 2010 0. 61 234 2000 (Brazil) BCI * Tree level 1 50 2010 0. 56 235 2600 (Panama) Chocó Tree level 0. 25 42 2013 0. 60 224 10000 (Colombia) Cotriguaçu Not - - - 0. 60 - 2000 (Brazil) available La Selva * Tree level 1 11 2009 0. 45 178 4000 (Costa Rica) Manaus Tree level 0.
Fatigue results The fatigue tests under load control and constant amplitude were carried out up to the total failure of the specimens. The number of cycles to failure was therefore taken (as a first approximate) as the fatigue life. scale, correspond to the results obtained for 2 aging conditions: as received specimen and aged specimen (in this case, time exposure is 13 hours corresponding to an energy of 1497 mJ/mm²). As clearly shown, the data are quite scattered. Even scattering is generally observed when dealing with fatigue, one can note that in this case, it is relatively important. This could be the consequence of the selfheating induced by cyclic loading, the material being viscoelastic. Nevertheless, when fitting the two set of data separately with straight lines (in the logarithmic scale), the effect of aging is clearly highlighted.
Conclusion In this paper, we have investigated the fatigue life behaviour of bulk low-density polyethylene submitted to UV-radiations. UV radiations generally modify the molecular structure inducing chain scission mechanism in a thin layer of the irradiated surface which thickness depends on the absorbed energy. We have found that UV aging does not affect the mechanical response of the bulk material but the fracture properties. Indeed, stresses and strains at break decreases with increasing UV absorbed dose. Fatigue tests were performed under load control to avoid compression loading induced by creep during fatigue cycling. We have noted an important scattering of the fatigue life data partly induced to self-heating. Nevertheless, the whole data be unified using a master curve which is obtained by applying an equivalence principle based upon the radiation dose undergone by the specimen.
NO - 3 + NO - 2 concentration and δ 15 N, and PN sink δ 15 N Samples collected in the upper 70 m at the LD stations had ≤ 0. 1 µM NO - 3 + NO - 2  (Caffin et al. , 2017)  and increased with depth, consistent with prior regional observations (Garcia et al. , 2014) (Fig. 1c ). All nutrient concentration data are available at http://www. obs-vlfr. fr/proof/php/outpace/ outpace. php (last access: 10 April 2018). Water column NO - 3 + NO - 2 δ 15 N data  (Knapp et al. , 2018 ) are available at https://www. bco-dmo. org/dataset/733237/data (last access: 11 April 2018) and show similar trends at the LD stations, with 650 m NO - 3 + NO - the range of previous regional measurements  (Yoshikawa et al. , 2015). The elevation of thermocline NO - 3 + NO - 2 δ 15 N relative to the mean ocean NO - 3 + NO - 2 δ 15 N of 5 ‰ is attributed to denitrification and/or anammox occurring in the ODZs of the ETSP, where thermocline NO - 3 δ 15 N can exceed 20 ‰ (e.
Acknowledgements. This is a contribution of the  OUTPACE (Oligotrophy to Ultra-oligoTrophy PACific Experiment ) project (https://outpace. mio. univ-amu. fr/)funded by the  French national research agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CyBER programme (CNRS-INSU) , the  GOPS programme (IRD)  and the  CNES. Funding for the contribution of  Angela N. Knapp  and  Kelly M. McCabe  was provided by the  U. S. National Science Foundation OCE # 1537314. The OUTPACE cruise (https://doi. org/10. 17600/15000900) was managed by the  MIO (OSU Institut Pytheas, AMU)  from  Marseilles (France). The authors thank the crew of the  R/V L'Atalante  for outstanding shipboard operation. Gilles Rougier  and  Marc Picheral  are warmly thanked for their efficient help in CTD rosette management and data processing, as is  Catherine Schmechtig  for the LEFE CYBER database management and  Carlos Miranda  for analytical assistance.
Data availability. All data and metadata are available at the French INSU/CNRS LEFE CYBER database (scientific coordinator: Hervé Claustre; data manager and webmaster: Catherine Schmechtig) at the following web address: http://www. obs-vlfr. fr/ proof/php/outpace/outpace. php. The data supporting the conclusions of this paper may be obtained at the BCO-DMO database https://www. bco-dmo. org/dataset/733237 (Knapp et al. , 2018).
Porcine epidemic diarrhoea virus, strain CV777, was initially characterized in 1978 as the causative agent of a disease first identified in the UK in 1971. This coronavirus has been widely distributed among laboratories and has been passaged both within pigs and in cell culture. To determine the variability between different stocks of the PEDV strain CV777, sequencing of the full-length genome (ca. 28kb) has been performed in 6 different laboratories, using different protocols. Not surprisingly, each of the different full genome sequences were distinct from each other and from the reference sequence (Accession number AF353511) but they are >99% identical. Unique and shared differences between sequences were identified. The coding region for the surface-exposed spike protein showed the highest proportion of variability including both point mutations and small deletions.
These two strains of PEDV are also referred to as "INDEL" and "non-INDEL" respectively, reflecting the presence or absence of insertions and deletions within the S gene sequence (prototypic strains are: USA/OH851/2014, GenBank accession no. KJ399978,  [9]  and USA/ Kansas29/2013, GenBank accession no. KJ645637. 1, [10]  respectively). However, neither form of nomenclature is entirely satisfactory. Indeed, both of these US variants of PEDV have been classified as being within genotype 2, based on complete genome sequences  [5] , while the early European strains, e. g. CV777, are in genotype 1. Recently, new outbreaks of PED have occurred in various European countries including Germany, Italy, France, Portugal, Austria and Slovenia (see  [11] [12] [13] [14] [15] [16] ). These new cases have nearly all been caused by viruses closely related to just one of the two US variants (closely related to OH851/2014).
Results All the sequences determined were very similar (>99% identical) to the reference CV777 sequence but each was unique (n. b. , for the complete viral genome a 1% difference corresponds to 280 nt changes). Accession numbers for the sequences have been submitted to the ENA under the study accession no. PRJEB20818. Throughout the description of changes in the sequence, the numbering system used is that from the reference sequence of 28033 nt for CV777  [2]  (Accession no. AF353511). The genome organization of PEDV is shown in Fig 1. The nt sequence changes for each of the different strains and the amino acid substitutions that result (when applicable) are listed in Table  1. Each of these six sequences lacked nt 72 and nts 82-85 compared to the reference sequence. The key features of the individual strains/ sequences and for the separate regions of the genome (see Fig  1 ) are described separately below for each laboratory.
Samples were sequenced at the DTU Multi-Assay Core (DMAC, Kgs. Lyngby, Denmark) using the Nextera-XT DNA library Preparation kit and a MiSeq with Reagent Kit v3 600 bp (Illumina Inc, San Diego, CA, USA). The sequence reads were analysed using CLC Genomics (Qiagen) and Geneious software (v. 6. 1. 8; Biomatters). b) IZSLER. The virus isolate used at IZSLER is designated CV777. A lyophilized virus sample, 5 th passage in Vero cells, was provided, in 2003, from the University of Gent that had received it from Switzerland. The CV777 was then grown in Vero cells until the 9 th passage that was used for the NGS. There is no information available about the virulence of this strain in pigs. RNA was extracted from 200 μl of virus culture supernatant using the TRIzol method (QIAzol Lysis Reagent, QIAGEN, Hilden, Germany) according to the manufacturer's instructions and was resuspended in 10 mM Tris-HCl, 1 mM EDTA, pH 8.
The resulting cDNAs were amplified by 18 cycles of PCR with IonXpress RNA-Seq Barcode Primers (Life Technologies). The size distribution of the library was assessed for quality with the High Sensitivity DNA kit (Agilent). 30-60% of fragments were >160 bp, thus validating the quality of the library. Sequencing of the cDNA library started with 10pmol of the amplified library using the Ion One Touch 2 system (Life Technologies). Sequencing was performed using the Ion Proton Sequencer and an Ion PI Chip v2 (Life Technologies). Sample reads were cleaned with Trimmomatic software  [31]  and aligned to the PEDV CV777 (Accession no. AF353511) complete genome using tmap (Torrent Suit 4. 0. 2, https:// github. com/iontorrent/TMAP) to evaluate coverage depth for each sample, Mira assembly  [32]  was ran on uncleaned, downsampled reads to a mean coverage depth of 80 after best kmer size estimation by kmergenie  [33].
These libraries were then quality and quantity checked using a Bioanalyzer 2100 (Agilent Technologies, Bo ¨blingen, Germany) and Kapa Library Quantification Kit for Illumina (Kapa Biosystems, Cape Town, South Africa) on a Bio-Rad CFX96 Real-Time System (Bio-Rad Laboratories, Hercules, USA). Sequencing was performed with an Illumina MiSeq instrument with MiSeq reagent Kit v3 for 600 cycles (Illumina, San Diego, USA). Sequence assembly, the subsequent mapping of the raw sequence data, and the analysis of the resulting sequences were achieved with the Genome Sequencer software suite (v. 3. 0; Roche) and the Geneious software suite (v. 8. 1. 3; Biomatters). e) APHA. The virus strain used at APHA is also designated CV777. This strain was initially obtained from Dr M. B. Pensaert  [1]  and was passaged in pigs and later in Vero cells.
There is no information available about the virulence of this strain in pigs. For the NGS, RNA was isolated from CV777-infected Vero cells when CPE was apparent using TRIzol™ (Invitrogen). The extracted PEDV RNA was subjected to DNase digestion and used as the template for cDNA generation using the cDNA Synthesis System (Roche) which was subsequently used for the preparation of sequencing libraries using the Nextera XT kit (Illumina Inc. ). Paired end sequencing was performed on an Illumina MiSeq. The consensus sequence was obtained by de novo assembly using Velvet 1. 2. 10 and re-evaluated using the templated genome assembly function of the SeqMan NGen v13 software (DNASTAR Inc. Madison, USA) and the consensus sequence was generated using the SeqMan Pro v13 software. f) WBR. The virus strain CV777 was obtained from Pensaert & de Bouck in the late 1970's and induced diarrhea in experimental pigs  [1]  but there is no information about the virulence in pigs of the cell-grown material.
Fragments under 200 bp in length were removed and the integrity and size distribution of the cDNA library was determined with an Agilent 2100 Bioanalyzer using the High Sensitivity DNA Kit (Agilent) and shown to meet the quality criteria for MiSeq sequencing recommended by Illumina. PEDV-specific reads were extracted from MiSeq sequence data files by alignment to the reference CV777 sequence (AF353511. 1) using blastn analysis. PEDV-reads were assembled to contigs and sequence differences between the de novo PEDV-sequence and the reference CV777 sequence (AF353511. 1) were visualized using the ''Tablet" program  [39].
Acknowledgments We gratefully acknowledge financial support from the  CoVet Lab  (a partnership of national veterinary public health institutes from  Denmark, France , The  Netherlands, Sweden  and the  United Kingdom). At FLI  and  ANSES , the study has been funded, in part, by the  EU   Horizon 2020 program  ( European Commission  Grant Agreement No. 643476  " COMPARE "). In addition, we are grateful for excellent technical assistance from  Preben Normann  ( DTU ),  Jane Borch  ( DTU ),  Marlene D. Dalgaard  ( DMAC, DTU ),  Patrick Zitzow (FLI) ,  Julie Peake  (APHA),  Richard Ellis  (APHA) and  Frank Harders  (WBVR).
Results Canonical PI3Kγ signaling protects from T. cruzi infection. We initially examined the expression and activation of PI3Kγ signaling in the heart tissue of T. cruzi-infected mice. For this purpose, we performed analysis of the cardiac tissue transcriptome from T. cruzi Y strain-infected C57BL/6 mice previously obtained by our group  15  (GSE41089). Data revealed that the expression of Pik3cg (P = 4. 0E-3; unpaired t-test) and Pik3cd (P = 7. 4E-6; unpaired t-test) genes, but not Pik3ca, are upregulated in the heart tissue of T. cruzi-infected mice 18 days post infection (dpi) (Fig. 1a ). Corroborating our transcriptome analysis, heart tissue of infected C57BL/6 mice showed an increased mRNA expression of Pik3cg and Pik3cd genes, but not Pik3ca, compared to heart samples from uninfected mice (Fig.
cruzi infection and ultimately avoid heart damage and host death. PI3Kγ signaling in human chagasic myocarditis. To translate our findings in the mouse model to the human disease, we examined the expression and activation of PI3Kγ signaling in the heart samples of chagasic disease patients. Human heart tissue transcriptome (GSE84796) revealed that the expression of PIK3CG gene was upregulated in the samples from patients with CCC when compared to heart samples from healthy control individuals (Ctl) (relative expression = 5. 37; P = 1. 0E-5; Student's t-test with the Benjamini-Hochberg method) or even samples from patients with non-chagasic cardiomyopathy (idiopathic dilated cardiomyopathy, DCM, GSE111544, Relative expression = 6. 60; P = 2. 1E-8; Student's t-test with the Benjamini-Hochberg method) (Fig.
9a ). None of the other members of the class 1 PI3K family (α, β, and δ subunits) appeared to be modulated as dramatically as the PIK3CG gene (Fig. 9a ). To validate the transcriptome data, samples from human hearts were used for the evaluation of PIK3CG expression by real-time PCR. Corroborating our transcriptome analyses, PIK3CG and PIK3CD expression were upregulated (Fig. 9b ), whereas the expression of PIK3CA and PIK3CB was similar in the heart samples from CCC patients in comparison to those from healthy control individuals or DCM patients (Fig. 9b ). Next, we verified the activation of PI3Kγ signaling in the chagasic human samples. Although there was no change in AKT1 and AKT2 mRNA (GSE84796 and validation-Supplementary Fig. 1c, d ) and protein expression (Fig. 9c, d ), the activated forms of AKT1 and AKT2 (pAKT1 and pAKT2) were increased in the heart tissue from CCC patients compared to those from DCM patients or healthy controls (Fig.