dataesr/scientific-paragraphs-categorization huggingface dataset:
Biomass balance models provide a balance between resources (domestic production plus imports minus exports) and utilisation (food, feed, other) for each region and each agri-food product. The system of balance equations can simulate land use change and greenhouse gas (GHG) emissions induced by changes in the uses of agri-food products, provided hypotheses on the evolution of a set of variables are available. The GlobAgri platform has been used to generate a database and a biomass balance model which are specifically customised for Agrimonde-Terra. It is named GlobAgri-AgT and it considers 32 aggregates of agri-food products (25 plants and 7 animal aggregates) and covers 14 broad regions. GlobAgri-AgT is used to conduct quantitative analysis of the impact of each scenario on food availability and utilisation on a global and regional level.
2D , lane 3) or SINV-WT samples (Fig. 2D , lane 4), SINV-G infection showed a clear accumulation of Dg (Fig. 2D , lane 5). In addition to Dg, which appears to be the main defective genome accumulated during the passages, we were able to recover other truncated genome sequences using a similar cloning technique (data not shown). We then used qPCR to analyze the accumulation of Dg during high-MOI passage. A first primer pair located in the deleted region of Dg was used to measure the amount of full-length genomes. Independently, primers located at the 5= and 3= ends of the Dg breakpoint allowed the specific detection of Dg since the distance between these two primers on a full-length genome was too great to permit full-length genome detection. (Fig. 3A to C ). Dg quickly accumulated during high-MOI passage of SINV-G (Fig.
5A ). We then measured the number of viable, full-length genomes that arose from recombination. Interestingly, we detected full-length virus as soon as 24 h postelectroporation in supernatant of replicon-G-electroporated cells (Fig. 5B ). At 48 h postelectroporation, replicon-G yielded 10 times more infectious virus than its wild-type counterpart. To verify that plaques truly corresponded to full-length virus, naive cells were infected with viral supernatant from these WT or G electroporations. After 16 h, we extracted RNA from cells and probed viral genomes by Northern blotting (Fig. 5C ). As expected, fulllength virus was detected from these infections. Overall, these data demonstrate that the RdRp point mutation conferring mutator status to SINV-G also confers a higher recombination rate.
DNA Sequencing and Bioinformatics Sequencing was carried out on the Illumnina HiSeq2500 platform. DNAstar Seqman NGN software (DNASTAR, Inc. version 12. 2) was used for sequence assembly. Normalization was done with RPM, Qseq was used for peak detection and the wig-files were aligned to genome assembly dm6 on the UCSC genome browser for visualization  [65]. See S1 Text: Extended Experimental Procedures for details. [26] , previous studies demonstrated that when the Type I>0 switch is perturbed, and daughters undergo aberrant divisions, some ectopic daughters may undergo apoptosis  [23]. Therefore, to reveal the full proliferation effect of kuz mutants we combined kuz e29-4 with a programmed cell death (PCD) mutant (Df(3L)ED225). However, this did not result in increased Ap cell numbers beyond that observed in kuz e29-4 alone.
Acknowledgments We are grateful to  S. Henikoff ,  F. Matsuzaki ,  S. Bray ,  R. Holmgren ,  R. S. Howley ,  J. B. Skeath ,  J. Posakony , the  Developmental Studies Hybridoma Bank at the University of Iowa , The Stowers Institute, and the  Bloomington Stock Center  for sharing antibodies, fly lines and DNAs. We thank  Vanessa Roca  and  Lydie Couturier  for injection and generation of the E(spl)-HLH-mγ null line. A. Angel ,  H. Ekman ,  C. Jonsson  and  A. Starkenberg  provided excellent technical assistance.
This work was supported by the  Knut and Alice Wallenberg Foundation ,  Research Grant : ( KAW2012. 0101 ,www. wallenberg. com/kaw/), Swedish Research Council ,  Research Grant : ( VR-NT ) (621-2010-5214, www. vr. se), and by the  Swedish Cancer Foundation ,  Research Grant : (120531, www. cancerfonden. se). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
After microscopic observations of liquid cultures, we inoculated solid media (Petri dishes or Roll-tubes) to obtain pure colonies. Cultures were performed at ambient temperature. DNA from pure colonies was extracted as explained before, 16S rRNA gene was also amplified and sequenced. 16S rRNA genes of pure cultures were determined and analyzed using the RDP database  [3, 4]. The sequences were compared with available sequences in GenBank, using a BLAST search. For a sequence showing only 97% similarity with that of Pectobacterium aroidearum, we built a phylogenetic tree whose topology was determined using the neighbour-joining method (Fig. 2 ). Support for internal nodes was assessed by bootstrap analysis (1500 replicates)  [5]. These analyses were conducted in MEGA6  [6].
Methods The VISAGES study was a prospective, noninterventional cohort study conducted in France in PID patients, who were receiving IgRT for at least 3 months at the time of enrollment and who were planning to pursue IgRT for at least 12 further months. Patients who were participating in a clinical trial could not be enrolled. Patients were recruited by hospital centers highly experienced in the management of PIDs and were followed up under real-life conditions for 12 months after enrollment. Given its observational nature, the study did not modify the usual medical care of the patients. The type, dose, and route of IgRT were entirely left up to the physician's discretion. Collected data included demographics, body mass index, lifestyle, occupation and past and concomitant diseases. The type, number and severity of infectious events within the 12 months preceding enrollment were reported at enrollment and prospectively collected over the follow-up period.
Modalities of IgRT At the time of enrollment 50. 6 % of patients were receiving SCIg at home, 40. 0 % were receiving IVIg at hospital and 9. 4 % were receiving IVIg at home. Similar numbers have been recently reported in a study of 216 PID patients and 84 caregivers recruited in 21 countries  [31] : hospital-based IVIg (47 %), home-based IVIg (7 %), hospital-based SCIg (3 %) and home-based SCIg (43 %). The importance of SCIg in France has already been highlighted. In 2006 the IRIS study group found that 58 % of French PID patients were receiving SCIg  [33]. Conversely, the analysis of the European ESID database in 2008 showed that 76 % of patients were receiving IVIg  [34, 35].
Lung cancer etiology is multifactorial, and growing evidence has indicated that long non-coding RNAs (lncRNAs) are important players in lung carcinogenesis. We performed a large-scale meta-analysis of 690,564 SNPs in 15,531 autosomal lncRNAs by using datasets from six previously published genome-wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium in populations of European ancestry. Previously unreported significant SNPs (P value < 1 × 10 -7 ) were further validated in two additional independent lung cancer GWAS datasets from Harvard University and deCODE. In the final meta-analysis of all eight GWAS datasets with 17,153 cases and 239,337 controls, a novel risk SNP rs114020893 in the lncRNA NEXN-AS1 region at 1p31. 1 remained statistically significant (odds ratio = 1.
Currently, little is known about the associations between genetic variants of lncRNAs and lung cancer risk. In the present study, we re-visited several published GWASs and evaluated the effects of lncRNA SNPs on lung cancer risk by using a large-scale meta-analysis of six previously published lung cancer GWAS datasets from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium and two additional GWAS datasets of independent Caucasian populations from Harvard University and Icelandic lung cancer study  8.
Results The combined dataset of six previously published GWAS datasets used for the initial analysis (discovery) consisted of 12,160 cases and 16,838 controls of European ancestry. The initially identified associations between SNPs in lncRNAs and lung cancer risk are shown in Fig. 1. In brief, the meta-analysis of 690,564 SNPs in lncRNAs from the TRICL consortium showed that 59 SNPs were associated with lung cancer risk with a P value < 1 × 10 -7 , and no heterogeneity among these GWAS datasets was noted, except for one SNP of rs35031105 (Supplementary Table  1 ). Of these 59 SNPs, 53 from 11 lncRNAs were located at the lung cancer risk-related loci 6p21. 33 and 6p22. 1 that have been reported previously  6, 20. The other five SNPs from three lncRNAs were located in 15q25. 1 that was also reported by multiple studies  5, 10, 21.
Therefore, we focused on the remaining previously unreported SNP rs114020893 located in lncRNA NEXN-AS1 (also known as C1orf118) on chromosome 1p31. 1 for the further analysis. For the purposes of illustration, the forest plot of the meta-analysis of rs114020893 using the six GWAS datasets is presented in Supplementary Figure  1. For the rs114020893 C variant allele in the GWAS datasets from the Institute of Cancer Research (ICR), the MD Anderson Cancer Center (MDACC), the International Agency for Research on Cancer (IARC), the National Cancer Institute (NCI), the Samuel Lunenfeld Research Institute study (Toronto), and the German Lung Cancer Study (GLC)  21  , the allele frequencies were 0. 098, 0. 092, 0. 072, 0. 084, 0. 073 and 0. 066, respectively; the additive odds ratio (OR) were 1. 20, 1.
42, 1. 41, 1. 18, 1. 28 and 1. 11, respectively; and its risk effect was significant in the first four datasets with a larger number of observations. There was no heterogeneity observed among these datasets, with I 2 of 0 and the Q-test P value of 0. 672 in the meta-analysis. In the combined results, the per-unit increase of the C allele was associated with1. 23-fold increase of lung cancer risk [95% confidence interval (CI) = 1. 14-1. 33, P = 9. 08 × 10 -8 ]. The regional association plot generated by using LocusZoom  22  for rs114020893 ± 500 KB in the additive genetic model (Fig. 2 ) indicated that there were five other SNPs that showed moderate linkage disequilibrium (LD) with rs114020893. We validated this initial finding by the data for the NEXN-AS1 SNP rs114020893 from two additional independent lung cancer GWASs of Harvard University (984 cases and 970 controls) and deCODE (4,009 cases and 221,529 controls).
As shown in Table  1 , the rs114020893 SNP from both the Harvard and deCODE datasets was also significantly associated with risk of lung cancer (OR = 1. 52, 95%CI = 1. 10-2. 11, P = 0. 012 for Harvard and OR = 1. 10, 95% CI = 1. 01-1. 18, P = 0. 023 for deCODE), which were consistent with those derived from the six TRICL GWAS datasets. After pooling the data from all eight GWAS datasets, the rs114020893C allele was associated with a 1. 17-fold (95% CI = 1. 11-1. 24) increased lung cancer risk, with a P value of 8. 31 × 10 -9 , which remained statistically significant even after a conservative Bonferroni correction for 675,953 tests (Bonferroni-corrected significance cut-off: 7. 24 × 10 -8 from 0. 05/690,564). Further subgroup analyses by tumor histology (Fig. 3 ) indicated that there was no difference in the association of rs114020893 with lung cancer risk between adenocarcinoma (OR = 1.
16, 95% CI = 1. 06-1. 26) and squamous cell carcinoma (OR = 1. 26, 95% CI = 1. 14-1. 39). The reason for this SNP not to be previously reported is because it is an imputed SNP in all the eight GWAS datasets, which suggests that other untyped SNPs could also have been missed by the published GWASs. Considering the fact that rs114020893 is located within the lncRNA of NEXN-AS1, it is biologically plausible that rs114020893 may influence the function of NEXN-AS1 by affecting its folding structure. Using the online tool RNAfold (http://rna. tbi. univie. ac. at/cgi-bin/RNAfold. cgi), it was predicted that rs114020893 could cause a change in the local lncRNA structure of NEXN-AS1 as shown in Supplementary Figure  2 , in which the SNP rs114020893 changes the folding structure of the NEXN-AS1. To determine in which tissues RNA transcripts (including NEXN-AS1) that include the SNP rs114020893 may be expressed, we utilized data from the ENCODE project, which systematically maps functional elements in the genome across a range of cell types  23.
Discussion Several loci including 5p15. 33, 6p21. 33 and 15q25. 1 have been found to be associated with lung cancer risk in previously published GWASs  5, 6, 9, 24. However, these findings could only explain a small fraction of the risk of lung cancer. In the present study with an initial meta-analysis of six previously published GWAS datasets of the TRICL consortium, we systematically evaluated the associations of genetic variants within all lncRNAs that had been reported to date  17  , and we further validated the most promising association in two additional independent GWAS datasets of Harvard and deCODE lung cancer studies. As a result, we found that a novel SNP rs114020893 in the lncRNA gene NEXN-AS1 located at 1p31. 1 was significantly associated with lung cancer risk. This novel SNP was predicted to change the lncRNA structure.
However, there are some limitations that need to be mentioned. Firstly, our list of lncRNAs may not be comprehensive, because we were limited by those that have already been identified to date and those included in the GENCODE database (15,531 autosomal lncRNAs). Secondly, the classification of lncRNAs has not been functionally characterized, and thus little is known about the biological meanings of lncRNAs, which makes explanation of our results difficult. Thirdly, the identified novel SNP was imputed, and thus its real effect size may need to be validated in actual genotyping data in the future. Finally, it is still unclear how such a SNP modifies the formation or effects of lncRNAs. This SNP is also located at the 345 bp upstream of the NEXN gene, but its functional relevance is still not clear.
Materials and Methods Study populations. The meta-analysis first used the combined genotyping and imputation dataset of six previously published GWASs of lung cancer with 12,160 lung cancer cases and 16,838 controls of European ancestry from the TRICL consortium and International Lung Cancer Consortium  8, 21, 24. As shown in Supplementary Table  2 , these six published studies included the Institute of Cancer Research (ICR) GWAS, the MD Anderson Cancer Center (MDACC) GWAS, the International Agency for Research on Cancer (IARC) GWAS, the National Cancer Institute (NCI) GWAS, the Samuel Lunenfeld Research Institute study (Toronto) GWAS, and the German Lung Cancer Study (GLC)  21. Additional two datasets of independent GWASs of Caucasian populations was used: the Harvard Lung Cancer Study (Harvard) which includes 984 cases and 970 controls and the Icelandic Lung Cancer Study (deCODE), which, in addition to using data from chip typed individuals, also allows inclusion of individuals that have not been chip typed, but for which genotype probabilities are imputed using methods of familial imputation.
The effective sample size of deCODE's dataset is 6,612cases and 6,612 controls 8 (Supplementary Table  2 ). A written informed consent was obtained from each participant of these GWASs, and the present study followed the study protocols approved by the institutional review board for each of the participating institutions. Selection of lncRNA genes and SNPs. We selected 15,900 lncRNAs from the publically available database GENCODE Release22 (GRCh38; released in March, 2015)  16, 36. All genotyping was performed by one of Illumina HumanHap 317, 317 + 240S, 370Duo, 550, 610 or 1 M arrays  8, 21. The genotyping data were also used for imputation from all scans for over 10 million SNPs from the 1000 Genomes Project (phase I integrated release 3, March 2012) as the reference by using IMPUTE2 v2.
1. 1, MaCH v1. 0 or minimac (version 2012. 10. 3) software. The quality control process has been detailed in previous reports  8, 21. As a result, the final dataset included 15,531 lncRNA genes located on autosomes, and 690,564 genotyped or imputed common [minor allele frequency (MAF) > 0. 05] SNPs within these lncRNA genes were used for association analyses. SNPs with a P value < 1 × 10 -7 in the TRICL datasets were further tested in the Harvard and deCODE datasets, excluding those SNPs located in the regions that had been reported to contribute to lung cancer risk in previously published studies  5, 6, 9, 24. The detailed work-flow is shown in Supplementary Figure  3. Functional validation. We used the online tool RNAfold (http://rna. tbi. univie. ac. at/cgi-bin/RNAfold. cgi) to predict the effects of identified SNPs on the change of lncRNA.
We also scanned the data from RNA-seq experiments available at the Encyclopedia of DNA Elements (ENCODE) portal (http://www. encodeproject. org/) to determine in which tissues RNA transcripts (including NEXN-AS1) that include the identified SNPs may be expressed  23. Statistical methods. The statistical methods have been detailed in a previous publication  8. Briefly, the association between each SNP and lung cancer risk was assessed by an additive genetic model of the minor allele, using R (v2. 6), Stata v1. 0 (Stata College, Texas, US), SAS software (version 9. 3; SAS Institute, Cary, NC, USA) and Plink (v1. 06) software. Specifically, poorly imputed SNPs defined by an information score < 0. 40 with IMPUTE2 or an r-square < 0. 30 with MaCH were excluded from the analyses. The I 2 statistic to quantify the proportion of the total variation due to the heterogeneity and the Chi-square-based Cohran's Q statistic to test for heterogeneity were calculated  37.
This G2/M accumulation was associated neither with the resistance to FasL-induced apoptosis (Fig 2 ) nor an increase in FasL-induced cell proliferation (Fig 5B ), suggesting an additional role of pY232 that is independent of FasL-induced apoptotic and proliferative functions of the protein. Data presented here were obtained from a small number of patients. Thus, generalizations about the various extents of Fas phosphorylation in different types of cancer should be made with caution. However, our data revealing that the outcome of Fas signaling is determined by its pY status of the death domain and that the Fas pY status may differ among different cancer types and may respond to anticancer treatment provide a basis for further studies in larger sets of human cancer samples, as well as an opportunity to develop a practical means to predict the outcome of Fas signaling in different pathologies that can lead to the use of Fas pY screening to aid Fas-related therapeutic design and maximize the chance of therapeutic success.
After incubation at 37°C for indicated times, the plate was rapidly transferred from the incubator to the refrigerating chamber, and 1 ml of PBS at 0°C was added to each well to stop the activation. Cells were then rapidly fixed with ice-cold 4% paraformaldehyde for 20 min on ice and counter-stained with DAPI for nuclear detection before mounting on a slide in the presence of mounting medium (Fluoromount, Sigma-Aldrich). Fluorescence images were taken using a spinning disk confocal microscope (Olympus/Andor/Yokogawa system) with a 100×oil/1. 4 numerical aperture objective lens. Images were deconvolved with Huygens software (Scientific Volume Imaging).
Funding: This work was supported by institutional funds from the  Centre National de la Recherche Scientifique (CNRS)  and the  Institut National de la Santé et de la Recherche Médicale (INSERM) , and by grants from the  Institut National du Cancer (INCa ;  PLBIO09-317 ); the  University of Nice , the  Agence Nationale de la Recherche  ( ANR-10-BLAN-1226 ;  ANR-11-LABX-0028-01 ). KL was supported by funding from the  Canceropole PACA , and LTN was supported by a scholarship from the ministry and.
In light of daunting global sustainability challenges such as climate change, biodiversity loss and food security, improving our understanding of the complex dynamics of the Earth system is crucial. However, large knowledge gaps related to the effects of land management persist, in particular those human-induced changes in terrestrial ecosystems that do not result in land cover conversions. Here we review the current state of knowledge of ten common land management activities for their biogeochemical and biophysical impacts, the level of process-understanding and data availability. Our review shows that ca. one tenth of the ice free land surface is under intense human management, half under medium and one fifth under extensive management. Based on our review, we cluster these ten management activities into three groups: (1) management activities for which datasets are available, and for which a good knowledge base exists (cropland harvest and irrigation); (2) management activities for which sufficient knowledge on biogeochemical and biophysical effects exists but robust global datasets are lacking (forest harvest, tree species selection, grazing and mowing harvest, N-fertilization); and (3) land management practices with severe data gaps concomitant with an unsatisfactory level of process understanding (crop species selection, artificial wetland drainage, tillage and fire management and crop residue management, an element of crop harvest).
, 2008 (Krausmann et al. , , 2013;; Meyfroidt & Lambin, 2011; Kastner et al. , 2012; Kissinger et al. , 2012; Ray et al. , 2012; Ray & Foley, 2013). Cropland shows the highest land-use intensity, compared to grazing land or forest, in terms of inputs to land (capital, energy, material) as well as outputs from land  (Kuemmerle et al. , 2013; Niedertscheider et al. , 2016). The spatial extent of cropland is probably the best-described land-use feature at the global scale, with many datasets existing (see Table  2 ). ). Nevertheless, major uncertainties remain related to cropland patterns in some world regions, particularly across large swaths of Central, Southern and Northern Africa, Brazil and Papau New Guinea  (Ramankutty et al. , 2008; Fritz et al. , 2011 Fritz et al. , , 2015;; Anderson et al.
Nowadays, 30% of the global wheat fields (0. 7 Mkm 2 ), 20% of the maize fields (0. 3 Mkm 2 ), and half of the global citrus, sugar cane, and cotton crops are irrigated  (Portmann et al. , 2010). Moreover, cropland irrigation accounts for approximately 70% of global freshwater consumption  (Wisser et al. , 2008). Rice cultivation requires a particularly intensive form of irrigation, involving regular flooding of fields for longer periods  (Salmon et al. , 2015). Irrigation datasets exist and are relatively robust, in particular for rice, but large similar problems of uncertainties prevail as with cropland maps (see above  ; Salmon et al. , 2015). Furthermore, Earth system effects depend on actually applied irrigation, which is much less documented than area equipped for irrigation.
03 to 0. 1 Wm -2  (Boucher et al. , 2004). 2. 9. Artificial drainage of wetlands 2. 9. 1. Extent and data availability Drainage aims at improving soil characteristics for agriculture and at facilitating the use of machinery. While historically drainage relied on channels and sewers, currently prevailing drainage systems often also use subsurface hollow-pipes or similar technologies  (FAO, 1985). Approximately 11% of global croplands, or 1. 6 Mkm², are subject to artificial drainage  (Feick et al. , 2005) , but the strongest biogeochemical and biophysical effects of drainage are expected when wetlands are drained, e. g. , peatlands, inland flood plains, coastal wetlands, or lakes. Wetlands are estimated to cover 5. 3-26. 9 Mkm 2  (Melton et al. , 2013) , of which 0. 18 Mkm 2 are probably drained (SI), but data are scarce.