dataesr/scientific-paragraphs-categorization huggingface dataset:
Another way to monitor LAI through LDASs is to assimilate observations of LAI. Satellite derived LAI products have benefited from recent advances in remote sensing  (Fang et al. , 2013; Baret et al. , 2013; Xiao et al. , 2013)  and datasets are now available at the global scale and high resolution. While assimilating LAI has been done very often with crop models to estimate crop yields (see e. g. Pauwels et al. , 2007; Ines et al. , 2013; Jin et al. , 2018) , such assimilation has been, to our knowledge, seldom performed by LDASs. Jarlan et al. (2008)  and  Sabater et al. (2008)  have succeeded introducing such approach in LDASs. The latter study has notably shown that assimilating jointly observations of SSM and LAI can improve the quality of root-zone SM estimates for one location in South West France.
, 2011; Martens et al. , 2017) , (ii) upscaled groundbased observations of gross primary production (GPP) from the FLUXCOM project  (Tramontana et al. , 2016; Jung et al. , 2017) , and (iii) river discharges from the Global Runoff Data Centre (GRDC). The paper is organised as follows: Sect. 2 details the various components involved in LDAS-Monde including the data assimilation schemes. Sect. 3 describes the experimental setup and the different datasets used in the experiment as atmospheric forcing, assimilated observations or for evaluation. The impact of the EnSRF on LSVs is then assessed in Sect. 4, including the comparison with the SEKF. Finally the paper discusses the issues encountered during the experiment and provides prospects in Sect. 5, before concluding in Sect. 6.
From a practical point of view, ISBA is run in this paper at a regular 0. 25 o spatial resolution. Each ISBA grid cell is divided into 12 generic patches: 9 representing different types of vegetations (deciduous forests, coniferous forests, evergreen forests, C3 crops, C4 crops, C4 irrigated crops, grasslands, tropical herbaceous and wetlands), and three others depicting bare soils, bare rocks and permanent snow or ice surfaces. Each patch covers a varying percentage of grid cells. Denoted α [p] for patch p of a given grid cell, this percentage is also known as the patch fraction. Vegetation and soil parameters for each patch and grid cell of ISBA are derived from the ECOCLIMAP II land cover database  (Faroux et al. , 2013)  that is fully integrated in SURFEX.
Observations for assimilation In this paper we assimilate observations from the SWI-001 and GEOV1 LAI data sets, both being distributed by the Copernicus Global Land Service. These satellite-derived products have been already successfully assimilated in LDAS-Monde (e. g. Leroux et al. , 2018; Albergel et al. , 2019). The SWI-001 product consists of Soil Water Indices (SWI) obtained through a recursive exponential filter  (Albergel et al. , 2008)  using backscatter observations from the ASCAT C-band radar  (Wagner et al. , 1999; Bartalis et al. , 2007). A one-day time scale is used in the recursive filter in order to measure the wetness of the first centimetres of the soil. This product is available daily at a 0. 1 o spatial resolution. The raw SWI-001 averaged over the 2008-2017 period can be seen in Figure  1  (a).
Validation data sets We consider independent datasets of evapotranspiration, gross primary production and river discharges to assess the validity of our approach and measure the influence of the EnSRF on the improvement of LSV reanalyses. Satellite-derived estimates of evapotranspiration come from the GLEAM v3. 3b product  (Miralles et al. , 2011; Martens et al. , 2017). Daily estimates available for the period 1980 -2018 at a 0. 25 o spatial resolution are fully driven by satellite observations and, as such, are independent from LDAS-Monde estimates.
We focus on SM in layer 4 (10-20 cm depth, SM4) and layer 6 (40-60 cm depth, SM6) as SM in layer 3 (4-10 cm depth) exhibits the same behaviour as SM4 and soil moisture in layer 5 (20-40 cm depth) and layer 7 (60-80 cm depth) have a similar response to SM6 (not shown). Potential improvements on EnSRF and SEKF estimates for evapotranspiration and GPP are measured using the same metrics as for SSM and LAI. Finally the influence on river discharges for both DA approaches is measured by the Nash-Sutcliffe efficiency (NSE) score: LAI reaching its maximum early August instead of late June -early July for observations. The senescence phase subsequently takes place later in the autumn compared to observations. Both data assimilation systems correct efficiently model simulations for that latter phase with EnSRF estimates getting closer to observations than SEKF ones.
Impact of assimilation on soil moisture in deeper layers Except in arid areas, SM4 estimates and analysis increments for SEKF and EnSRF tend to be similar, thus, making our SM4 estimates less dependant on the data assimilation method. Regarding SM6 estimates, both SEKF and EnSRF produce a drier soil layer than the model for most of the domain as shown in Figure  10. We identify these patterns for every month without any seasonality (not shown). For SEKF drier estimates are obtained through cycling as analysis increments are close to zero. For EnSRF, cycling is also responsible to this drying but analysis increments are not negligible (-0. 01 m 3. m -  for biggest values) and compensate the wet bias from model error in SM6 (not shown). As for SM4, SM6 EnSRF estimates are larger than SEKF and model estimates in Northern Africa, but this time only for places where bare soil dominates as for places where bare rocks dominates the soil dries due to cycling.
Evaluation using Evapotranspiration and Gross Primary Production We now evaluate the performance of our data assimilation systems using independent satellite-based datasets of evapotranspiration (ET) and gross primary production (GPP). The model tends to underestimate ET leading to an averaged negative bias of -0. 328 kg. m -2. day -  reaching -0. 8 kg. m -2. day - in June and July. Both SEKF and EnSRF reduce this bias to -0. 114 kg. m -2. day -  and -0. 059 kg. m -2. day -  , respectively. for almost everywhere, it improves correlation (between 0. 05 and 0. 1) especially over Spain, Northern Africa or around the Caspian Sea where correlations between the model and GLEAM were poorer than for the rest of the domain, showing its positive impact on ET. Similar conclusions can be drawn from geographical patterns observed for RMSD and nRMSD (not shown, see Table  1  for averaged results).
For deeper layers (20-80 cm depth), SEKF and EnSRF estimates of soil moisture are similar but are obtained through different mechanisms. While drier soil moisture in SEKF is obtained through the model by transfering information from updated soil moisture in (near-)surface, the EnSRF produces those estimates partly because of the data assimilation routine itself, acting like a bias correction procedure for those layers to compensate for the wet model bias via the correlations between soil moisture in deeper layers and surface soil moisture and LAI. Finally, validation of our approach has been carried out using datasets of evapotranspiration, gross primary production and river discharges, showing a moderate positive impact for the two previous but a marked positive one for the latter. While involving a crude model error, this paper shows the potential of the EnSRF within LDAS-Monde and constitutes a good basis for further developments.
transforming observations of reflectance from SPOT-VGT and PROBA-V satellites into LAI. This dataset is available every 10 days at best at a one kilometre spatial resolution. The GEOV1 LAI averaged over the 2008-2017 period can be seen in Figure 1 (b). https://doi. org/10. 5194/hess-2019-391 Preprint. Discussion started: 20 August 2019 c Author(s) 2019. CC BY 4. 0 License. Following Barbu et al. (2014), both observation datasets are interpolated on the model grid (0. 25 o spatial resolution) where and when at least half of observation grid points are available. As in previous LDAS-Monde studies, we use a 24h assimilation window and observations are assimilated at 9:00 UTC.
Figure13depicts correlation between GPP from the FLUXCOM dataset and model estimates (a) and the difference between correlations for the model and the estimates produced with SEKF (b) and EnSRF (c). As for ET, the EnSRF provides GPP estimates that are more correlated to the FLUXCOM dataset than model and SEKF estimates for almost everywhere, on average 0. 817 compared to 0. 784 for the model and 0. 786 for the SEKF. The best improvements are noticeable on around the.
We thus tested the association between OXPHOS and genomic alterations, i. e. , DNA copy number alterations (CNAs). While ETC mRNA levels were higher in high-versus low-OXPHOS HGSOCs (Figure  S1D ), no gene amplification was found in ETC genes or in ETC-regulated transcription factors (Figures  S3H  and S3I ). Moreover, there was no association between OXPHOS stratification and global mutation counts or CNA per tumor (Figure  S3J ). We also tested the link between OXPHOS and HR status by Pathway enrichments were defined from Gene Ontology (GO), Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, using DAVID web software (https://david. ncifcrf. gov/). The 50 most differential proteins (from Mann-Whitney test), between the 2 metabolic subgroups of HGSOCs identified in Figure  1A , were used for the enrichment analysis.
FDR was computed using the Benjamini-Hochberg procedure to account for multiple testing. REViGO software was used to summarize information by removing redundant GO terms. using the LST (large-scale state transitions) signature, a robust indicator of HRD  (Popova et al. , 2012; Goundiam et al. , 2015) , as confirmed by two other published signatures  (Abkevich et al. , 2012; Birkbak et al. , 2012)  (Figure  S3K ). OXPHOS signature was significantly associated with HRD in Curie cohort, with a similar tendency, but not significant, in TCGA (Figures  3A  and 3B ). As HRD is known to be associated with chronic oxidative stress  (Martinez-Outschoorn et al. , 2012; Gorrini et al. , 2013) , we next evaluated features of oxidative stress in HGSOCs. We first observed that the NFE2L2 (NRF2)-dependent antioxidant response (list in Table  S3 ) was upregulated in high-OXPHOS tumors in both Curie and TCGA cohorts  (Figure 3C)  in the absence of any deleterious mutations of KEAP1 (Kelch-like ECH-associated protein 1).
Their referring oncologist informed all patients included in our study that their biological samples could be used for research purposes and patients signed an informed consent of non-opposition. In case of patient refusal, expressed either orally or written, residual tumor samples were excluded from our study. The Institutional Review Board and Ethics committee of the Institut Curie Hospital Group approved all analyses realized in this study, as well as the National Commission for Data Processing and Liberties (N approval: 1674356 delivered on March 30, 2013).
The mass spectrometer operated in positive ion mode at a 1. 8-kV needle voltage. Data were acquired using Xcalibur 2. 2 software in a data-dependent mode. Mass spectrometry scans (m/z 350-1600) were recorded at a resolution of R = 70 000 (@ m/z 200) and an automatic gain control (AGC) target of 3 3 10 6 ions collected within 100 ms. Dynamic exclusion was set to 30 s and the top 12 ions were selected from fragmentation in higher energy collisional dissociation (HCD) mode. MS/MS scans with a target value of 1 3 10 5 ions were collected with a maximum fill time of 100 ms and a resolution of R = 17500. Additionally, only +2 and +3 charged ions were selected for fragmentation. Others settings were as follows: no sheath nor auxiliary gas flow, heated capillary temperature, 250 C; normalized HCD collision energy of 27%, and an isolation width of 2 m/z.
nLC-MS/MS Analysis Data were searched by SEQUEST through Proteome Discoverer 1. 4 (Thermo Fisher Scientific) against a subset of the 2016. 01 version of UniProt database restricted to Homo sapiens Reference Proteome Set (70,671 entries). Spectra from peptides higher than 5000 Dalton (Da) or lower than 350 Da were rejected. The search parameters were as follows: mass accuracy of the monoisotopic peptide precursor and peptide fragments were set to 10 ppm and 0. 02 Da, respectively. Only b-and y-ions were considered for mass calculation. Peptide validation was performed using the Percolator algorithm and only ''high confidence'' peptides were retained corresponding to 1% false positive rate at peptide level. Raw LC-MS/MS data were imported in Progenesis QI for Proteomics 2. 0 (Nonlinear Dynamics Ltd, Newcastle, U.
K). Data processing included the following steps: (i) Features detection, (ii) Features alignment across all samples, (iii) Volume integration for 2-6 charge-state ions, (iv) Normalization on total protein abundance, (v) Import of sequence information, (vi) Calculation of protein abundance (sum of the volume of corresponding peptides). Only non-conflicting features and unique peptides were considered for calculation at the protein level. Quantitative data were considered for peptides with a signal > 10,000 for at least 5% of samples. A minimum of 2 unique peptides was required to identify a given protein within the full dataset. Data was standardized per run. All detected peptides in low-and high-OXPHOS HGSOC are listed in Table  S4. Proteomic Data from the TCGA Cohort Proteomic data from the TCGA cohort was extracted from Table  mmc3  in  Zhang et al.
Metabolomic Analysis of HGSOC Samples Metabolomic analysis of 45 HGSOC from the Curie cohort was performed by Metabolon. (Morrisville, USA). Briefly, samples enriched in at least 50% of epithelial cancer cells were extracted and prepared for analysis using Metabolon's standard solvent extraction method. The extracted samples were split into equal parts for analysis on the gas chromatography mass spectrometry (GC/MS) and liquid chromatography tandem-mass spectrometry (LC/MS/MS) platforms to allow the detection of 374 biochemicals. Quality controls (QC) were performed using internal standards and Metabolon QC samples. All detected metabolites in low-and high-OXPHOS HGSOC are listed in Table  S5. For sparse partial least square discriminant analysis (sPLS-DA), data were filtered by interquartile range, normalized to the median and Log-transformed.
CNA and Mutations Genomic data from the TCGA were extracted from the NIH Genomic Data Common (GDC) data portal (https://portal. gdc. cancer. gov). For DNA copy number alteration, data were available for 88 low-OXPHOS and 79 high-OXPHOS HGSOC. Mutation status was extracted for 46 low-OXPHOS and 43 high-OXPHOS HGSOC. BRCA1 and RAD51C promoter methylation status was defined in the TCGA cohort, as described in  Manieet al. (2016). Transcriptomic Analysis from HGSOC Trancriptomic analysis from Curie Cohort was described in  Mateescu et al. (2011). Data are freely accessible in the Gene Expression Omibus under the accession number GEO: GSE26193. For the TCGA cohort, freely available transcriptomic data (Cancer Genome Atlas Research Network, 2011) have been downloaded from the following portal: https://cancergenome.
After electrophoresis, proteins were transferred to a 0. 45 mm nitrocellulose transfer membrane (Sigma Aldrich # Z741975) and incubated overnight at 4 C with the appropriate primary antibodies: a cocktail of 5 ETC proteins (listed above, Proteomic Data from the TCGA Cohort) (1:2000; ABCAM #ab110411), PML (1:2000; Santa Cruz #SC-5621), PGC-1a (1:500; Santa Cruz #sc-13067), K-Acetylated (1:2000; Cell Signaling #9441) and Actin (1:10,000; Sigma Aldrich #A5441). Specific binding of antibodies was detected using appropriate peroxidase-conjugated secondary antibodies (Jackson ImmunoResearch Laboratories #115-035-003 and 115-035-045) and visualized by enhanced chemiluminescence detection (Western Lightning Plus-ECL, PerkinElmer). Densitometric analyses of immunoblots were performed using ImageJ software.
After a 3 min wait, 3 response measurements were taken after each addition. ECAR and OCR values were normalized to the total amount of protein per well. ECAR and OCR data points refer to the average rates during the measurement cycles and were reported as absolute rates (mpH / min for ECAR, pMoles / min for OCR). For experiment testing carbon source preference, cells were incubated overnight in DMEM ± 10 mM Glucose, ± 2 mM Glutamine. Basal ECAR/OCR measurement was performed the following day. For fatty acid oxidation or glutaminolysis inhibition experiments, cells were incubated overnight in DMEM ± 10 mM Glucose, ± 2 mM Glutamine. OCR was measured 30 min after Etomoxir treatment (40 mM, Sigma Aldrich #E1905) or 1 hour after CB-839 treatment (10 mM, Selleckchem #S7655), respectively. For modulating OCR experiment, cells were incubated 48 hours in N-acetyl-L-Cystein (NAC at 5mM, Sigma Aldrich #A7250) or Rosiglitazone (at 20 mM, Sigma Aldrich #R2408) before OCR measurement.
Features of Oxidative Stress Briefly, cells were seeded onto six-well plates and grown up to 70% confluency, incubated directly with fluorescent probes for basal conditions. Then excess reagent was removed by washing the cells with PBS, trypsinized and resuspended in PBS solution containing 1% FBS for flow cytometric analysis. Flow cytometry data were acquired using an LSR FORTESSA analyzer (BD biosciences). For ROS quantification upon treatment, cells were treated for 24 hr with Carboplatin (at [5. 10 À5 M] ACCORD, 10 mg / ml) and Paclitaxel (at [10 À6 M], KABI, 6 mg / ml) or Ironomycin (at 6 mM, synthesis is described in  Mai et al. , 2017) , and then processed, as described above. Cellular ROS Cells were incubated with 2 mM CellRox Reagent (Life Technologies, #C10422) for 30 min at 37 C in the dark.
ACKNOWLEDGMENTS We are grateful to  S. Claverol  from the proteomic platform at  Bordeaux University and Renaud Leclere and AndreNicolas from Pathex at Institut Curie  for help and advice. F. Bellvert  and  N. Buttin  from  Metatoul  ( MetaboHUB - ANR-11-INBS-0010 ) are gratefully acknowledged for fluxome analyses. We thank  O. Mariani  from  CRB  for help in preparing proteins from HGSOCs,  O. Renaud  and the  PICT platform at Curie  for technical assistance with microscopy, and  M. Baraibar  and  B. Friguet  from  OxiProteomics  for their expertise. We acknowledge the  PICT-IBiSA  ( CelTisPhyBio Labex ,  ANR-10-LBX-0038 ;  France-BioImaging ,  ANR-10-INSB-04 ) for electron microscopy. We are grateful to the  Christelle Bouillot Association  for the Seahorse apparatus funding. The results are in part based upon data from TCGA: https://cancergenome.
nih. gov/. G. G. was supported by funding from the  Foundation ARC. Y. K. was supported by the  Foundation for Medical Research (FRM)  and the  Si-RIC-Curie program  ( INCa-DGOS-4654 ). F. M. -G. , C. B. , and V. M. were supported by the  Institut National de la Santeet de la Recherche Medicale (Inserm). O. G. was funded by  Institut Curie. R. Rodriguez  is supported by  ERC  (grant number  647973 ). The experimental work was supported by grants from the  Ligue Nationale Contre le Cancer (Labelisation) ,  Institut Curie ,  Inserm  ( PC201317 ),  INCa  ( 2011-1-PLBIO-12-IC-1  and  2015-1-RT-04-ICR-1 ), and the  Simone and Cino del Duca Foundation  for the Grand Prix attributed to F. M. -G. F. M. -G is very grateful to her funders for providing support these last years.
NAS-99 spectrophotometer (ACTGene) was used to analyze the RNA purity. Total RNA (1 μg) was used to synthesize the cDNA using the PrimeScript ® RT Reagent Kit with the gDNA Eraser (Takara, Dalian, China). Real-time qPCR was performed using the SYBR ® Premix Ex Taq ™ (Tli RNaseH Plus) (Takara, Dalian, China). Primers for qPCR were synthe- sized using PRIMER 3. 0 (http://bioinfo. ut. ee/primer3-0. 4. 0/) based on the conserved sequence of Soybean aphid (Aphis glycines) CH1 gene (GenBank No. JQ246352. 1) (Table  4 ). Elongation factor 1 alpha (EF1α), beta-actin (β-ACT) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal reference genes  76. The reaction volume for qPCR was 20 μL including 10 μL of the SYBR ® Premix Ex Taq, 1 μL of cDNA template, 0. 4 μL of each primer, 0.
4 μL of ROX Reference Dye II, and 7. 8 μL of RNase-free water. The thermal cycling condition was initiated at 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s and 60 °C for 34 s, and a dissociation stage at 95 °C for 15 s, 60 °C for 1 min and 95 °C for 30 s, and 60 °C for 15 s. Three independent biological replicates with three technical replications were carried out for each qRT-PCR. To check the amplification efficiencies and cycle threshold (Ct), the standard curve was established with serial dilutions of cDNA (1, 1/10, 1/100, 1/1000, 1/10,000, and 1/100,000). Quantification of gene transcription was calculated using 2 -∆∆Ct method  77. Data analysis. The LC 15 , LC 30 and LC 50 of buprofezin were analyzed using a log-probit model 78 as commonly used in various studies  42, 48. The demographical (r, λ, R 0 , T, and GRR) and basic life table parameters (s xj , v xj , l x , m x , l x m x, and e xj ) were calculated using TWOSEX-MSChart program  54, 79, 80.
Phase I: Review of the quality assessment literature Seeking to integrate the Task Force work within existing quality appraisal activities, a number of publications on quality parameters for non-RCT CER were first identified and reviewed  [9] [10] [11] [12] [13] [14]. The principles of these publications were chiefly incorporated within two key papers published in 2014-Berger et al. [14]  and Roche et al. [13] -which jointly formalize the CER nomenclature and offer recommendations for systematic quality appraisal of observational database studies. Roche et al. [13] -Quality criteria for observational database comparative studieswas a literature-based proposal published by members of REG offering suggestions for the quality appraisal of observational database studies with a particular focus on respiratory medicine.
Acknowledgements The authors would like to thank  Katy Gallop ,  Sarah Acaster  and  Zoe Mitchel  for their role in the literature review. The authors would like to thank the project management contributions in the later phases of this work by  Naomi Launders. Finally , the authors would also like to thank all those who participated in the review and revision of the RELEVANT tool:  Arzu Bakirtas ,  Turkey ;  David Halpin , UK;  Bernardino Alcazar Navarrette, Spain ;  Enrico Heffler , Italy;  Helen Reddell , Australia;  Joergen Vestbo ,  Denmark, UK ;  Juan José Soler Cataluna. Spain ;  Kostas Kostikas , Switzerland; Laurent Laforest, France;  Ludger Klimek ,  Germany ;  Luis Caraballo , Colombia;  Manon Belhassen , France;  Matteo Bonini , Italy;  Ömer Kalayci ,  Turkey ;  Paraskevi Maggina , Greece; Piyameth Dilokthomsakul, Thailand;  Sinthia Bosnic-Anticevich , Australia;  Yee Vern Yong , Malaysia.
Cloning, Expression and Purification Sequence-optimised synthetic DNA encoding a N-terminal his-tag, a TEV protease recognition site and the HTNV-NP (NCBI accession code NC_005218) was synthetised (Geneart) and cloned into a pFastBac1 vector between NdeI and NotI restriction sites. The HTNV-NP-expressing baculovirus was generated via the standard Bac-to-Bac method (Invitrogen). For large scale expression, Sf21 cells at 0. 5 Â 10 6 cells/mL concentration were infected by adding 0. 1% of virus. Expression was stopped 72 hr after the day of proliferation arrest. The cells were disrupted by sonication 3 min (10 s ON, 20 s OFF, 40% amplitude) on ice in lysis buffer (20 mM Tris-HCl pH 8, 300 mM NaCl, 10 mM MgCl 2 , 2 mM b-mercaptoethanol and 20 mM Imidazole) with EDTA free protease inhibitor complex (Roche).