dataesr/scientific-paragraphs-categorization huggingface dataset:
97 ± 0. 02 ppb yr -1 ) is close to that seen in the NDACC and surface datasets, except at Sodankylä and Ny-Ålesund. The X N 2 O trend derived from TCCON measurements (apart from Sodankylä and Ny-Ålesund) is 0. 81 ± 0. 04 (1σ ) ppb yr -1 , which is slightly smaller compared to the results from NDACC (0. 93 ± 0. 04 ppb yr -1 ) and flask sample measurements (0. 93 ± 0. 02 ppb yr -1 ). The TCCON AVK (Fig. 2 ) indicates that the TCCON retrieval in the lower and middle troposphere includes a 30 %-50 % contribution from the a priori assumption (Eq. 3). As mentioned in Sect. 2, TCCON uses a stand-alone code to create the a priori profile for each site  (Toon and Wunch, 2014). The a priori N 2 O profile has a trend of 0. 1 % yr -1 , which is much lower than the true state of the atmosphere (about 0.
Conclusions N 2 O is an important greenhouse gas and it can generate nitric oxide, which depletes ozone in the stratosphere. It is a common target gas for both TCCON and NDACC networks. However, to our knowledge, no intercomparison between both datasets is available in literature. In this study, a global view of the X N 2 O measurement differences between these two networks is presented at seven sites (Ny-Ålesund, Sodankylä, Bremen, Izaña, Réunion, Wollongong and Lauder). The mean and standard deviation of the difference between the NDACC and TCCON X N 2 O (NDACC-TCCON) are -3. 32-1. 37 ppb (-1. 1 %-0. 5 %) and 1. 69-5. 01 ppb (0. 5 %-1. 6 %), which are within the uncertainties of the two datasets. The NDACC retrieval has good sensitivity throughout the troposphere and stratosphere, and the choice of the a priori profile has limited influence (within 0.
for ocean, the Emission Database for Global Atmospheric Research EDGAR v4. 2 FT2010 (European Commission, 2013) for non-soil anthropogenic sources, and the Global Fire Emission Database GFED v4. 1s (Van Der Werf et al. , 2017) for biomass burning. These a priori inventories correspond to a global flux of 17. 9-18. 8 TgN yr -1 for 2007-2014. In the a posteriori simulation, N 2 O surface fluxes in the model have been optimized on the basis of surface measurements using a 4D-Var inversion framework as described by Wells et al. (2018). The a posteriori global flux ranges from 15. 5 to 17. 9 TgN yr -1. Stratospheric loss of N 2 O by photolysis and reaction with O( 1 D) is included in the model and leads to an atmospheric lifetime of approximately 127 years. Global GEOS-Chem output shown here is monthly averages for 2007-2014, with horizontal resolution of 4 • latitude ×5 • longitude and 47 vertical levels from the surface to 0.
Acknowledgements. Minqiang Zhou is supported by the Belgian complementary researchers program. We would like to thank  TCCON  and  NDACC  networks for making the data publicly available. The FTIR sites at Réunion are operated by the  BIRA-IASB  and locally supported by LACy /UMR8105 ,  Université de La Réunion. We would like to thank  Nicolas Kumps ,  Bart Dils and Francis Scolas (BIRA-IASB)  for their contributions to the FTIR measurement maintenance and  Edward Dlugokencky  ( NOAA ) for sharing the flask sample measurements. Development of the  GEOS-Chem  N 2 O simulation was supported by  NOAA  (grant no. NA13OAR4310086 ) and the  Minnesota Supercomputing Institute. The Lauder FTIR measurements are core funded by  NIWA from New Zealand's Ministry of Business, Innovation and Employment. Wollongong TCCON  and NDACC measurements are supported by the  Australian Research Council , grants  DP160101598 ,  DP140101552 ,  DP110103118 ,  DP0879468  and  LE0668470.
RESULTS Among the members of the French Eczema Association, 202 completed the questionnaire. Finally, 32 participants were excluded: 13 subjects were too young (< 14 years) to understand the questionnaire because it was not adapted for children, and 19 subjects reported another diagnosis (psoriasis, contact eczema, dyshidrosis). A total of 170 participants were included. Demographic data are presented in Table  I.
Annexin V/7-AAD apoptosis measurement by flow cytometry Cell apoptosis was assessed using Annexin V/7-Amino actinomycin D (7-AAD) apoptosis kit (Beckman Coulter-Immunotech, Marseille, France) according to the manufacturer's instructions. Briefly, 10 5 serum-starved BxPC3 cells were transfected with 50 nM ITCH or scramble control siRNAs, as described above. Alternatively, BxPC3 cells were pre-treated with 15 μM of ITCH chemical inhibitor CI. After 48 h, cells were washed and treated with 50 μg/ml of anti-HER3 antibody 9F7-F11, with or without 100 ng/ ml of NRG1 for 96 h. As positive control, 300 nM staurosporine (Sigma, Saint-Louis, MO) was incubated with BxPC3 cells for 6-20 h. After Annexin V/7-ADD labeling of treated cells, data were acquired on a Gallios flow cytometer and analyzed with the Kaluza software (Beckman Coulter).
HER3 and c-FLIP ubiquitination status were assessed using the anti-K48 ubiquitin antibody. Whole cell lysates (WCL) were analyzed using the appropriate antibodies. Quantification of signal intensity (SI) with ImageJ software is indicated below the images, in comparison to SI = 1. 0 ±. 0 for untreated control. Significant increase or decrease of the densitometry, compared to control, is indicated in bold. β-actin was evaluated as loading control TRAIL-induced apoptosis  [27]. We already demonstrated that USP8 controls ITCH stability during 9F7-F11-induced HER3 ubiquitination and degradation  [33]. Here, we found that USP8 was present in the c-FLIP L -ITCH complex in untreated cells, but was slightly reduced after 2 h of 9F7-F11 incubation (Fig. 3 , IP c-FLIP), as previously reported  [39]. USP8 was constitutively present also in the HER3-ITCH complex, and its expression slightly increased upon incubation with 9F7-F11 up to 1 h (Fig.
Cells were then incubated with 9F7-F11, with or without NRG1, or medium as control for 4 h. After immunoprecipitation of total protein extracts (2 mg) with the anti-c-FLIP antibody H-202, c-FLIP, ITCH and USP8 expression and c-FLIP ubiquitination were analyzed by western blotting. BxPC3 cells were transfected with siSC, siITCH (b) or siUSP8 (c) for 72 h, and then incubated with 9F7-F11 for 4 h. Expression of ITCH, c-FLIP and USP8 was assessed in total protein extracts by western blotting. Protein level was measured with the ImageJ software and indicated as signal intensity (SI), relative to untreated control (SI = 1. 0 ±. 0). Significant increase or decrease of the densitometry, compared to control, is indicated in bold. β-tubulin was evaluated as loading control USP8 interaction with c-FLIP L and promoted ITCH recruitment, allowing c-FLIP L ubiquitination (Fig.
Funding This work was supported by the program " Investissement d'Avenir " (grant agreement:  LabexMabImprove ,  ANR-10-LABX-53-01 ) and by the grant  AAP13  " Fonds Unique Interministeriel "  FUI UmAbHER3 F120402  M. CLC was supported by  INSERM  through the  FUI UmAbHER3. CS was supported by the  Conseil Régional de Bourgogne , the  European Union  and the  University of Burgundy.
Thus deregulation of DBHmediated synthesis of noradrenaline could have downstream effects on BDNF secretion. An additional factor in BDNF secretion is the intracellular sorting of BDNF to the secretory pathway, which is regulated by binding of the BDNF pro-domain to the luminal domain of Sortilin  [35]. This interaction prevents BDNF degradation, targets BDNF to the secretory pathway  [36]  and facilitates the release of mature BDNF  [37]. The aims of this first study of Stage 2 of the Epistasis Project (the results of Stage 1  [38] [39] [40] [41] [42] [43] [44]  are summarised in Table  1 ), were to further evaluate the contribution of genetic variation at the BDNF locus to AD risk in a large case-control dataset (n=8,136) and determine whether the association could be masked by genetic epistasis between BDNF variants and sex, age, APOE ε4 status and variants at loci encoding proteins that control BDNF secretion (DBH and Sortilin).
Polymorphism selection We downloaded Caucasian European (CEU) population data from the HaploView platform (release 27) using gene coordinates (gene +/-10kbp flanking regions) extracted from the UCSC MySQL server (hg18) at https://genome. ucsc. edu/. Polymorphisms with a genotype rate <50%, and that were in Hardy-Weinberg equilibrium (p<0. 001) and with a minor allele frequency (MAF) <5% were removed. The remaining polymorphisms were introduced into the tagger function in HaploView. A tagging threshold of r 2 >0. 8 and log odds (LOD) score>3. 0 was used. The "Aggressive Tagging" approach was implemented, which allows a haplotype of up to three polymorphisms to be used as a proxy. We identified 34 polymorphisms (5 in BDNF, 23 in DBH and 6 in SORT1) that have a minor allele frequency >5% in Caucasian Europeans and that cover 80% of the variation in these genes (Table  3 ).
Genotyping and imputation Genotyping for all centres except Rotterdam (below) was performed by LGC (Hoddesdon, Hertfordshire, UK), using the KASPar technology by KBioscience http://www. kbioscience. co. uk. Six to eight duplicate samples and one blank (H 2 O) sample were included on each plate. All blank samples were negative. The concordance between duplicates was 100% with the exception of one discordant sample for BDNF rs11030104 in the Bristol dataset, which was removed from analyses. Where data were available from previous in-house TaqMan genotyping, LGC genotypes were 100% concordant (BDNF rs6265; n=483, DBH rs1611115; n=396). Genotyping in the Rotterdam cohort was done on Version 3 Illumina-Infinium-II HumanHap550 SNP array (Illumina, San Diego, USA). Genotypes for the Rotterdam cohort were imputed from whole genome data using MACH software (http://www.
That being said, it is commonplace in genetics to round these values to 0/1/2 with the implied corresponding genotypes. The supplementary table shows the genotype counts and allele frequencies as well as genotyping rate for all individual centres. The genotyping rate for each polymorphism ranged from 0. 99 to 1. 00 in the total dataset. All genotypes were confirmed to be in Hardy-Weinberg equilibrium (α=0. 001) in controls from all individual centres. APOE ε4 genotypes were determined according to the genotypes at rs429358 and rs7412. Tests for pair-wise intragenic linkage disequlibrium for the 5 BDNF polymorphisms and the 6 SORT1 polymorphisms were consistent with the Haploview data (r<0. 8) with the exception of rs6265 and rs11030104 in BDNF (r 2 =0. 9) and rs2228604 and rs10745354 in SORT1 (r 2 =1.
Statistical analyses All analyses were performed in R version 3. 5. 0 [54]. The relative risk (odds ratios and 95% confidence intervals; CI) for AD was calculated for each of the 10 polymorphisms brought forward for analysis using a general linear model for dominant and recessive inheritance models controlling for age-at-extraction, sex, APOE ε4 allele and individual centre. The Akaike Information Criterion (AIC) was used to compare goodness-of-fit, with smaller values of AIC corresponding to preferable models. The statistical power for each sample included in the study to detect a significant (α=0. 05) association of a polymorphism with a minor allele frequency 0. 2 (mean of polymorphisms included in the study) with AD risk (OR=1. 2) under a dominant model and assuming a prevalence of AD of 10% in the population aged over 65 is as follows: Total dataset 97%, Northern Europe 92% (Bristol 14%, Nottingham 14%, OPTIMA 17%, Rotterdam 78%), Spain 31% (Madrid 18%, Oviedo 11%, Santander 13%).
All tests were first performed in the total dataset and subsequently stratifying by geographical region: Northern Europe=Bristol, Nottingham, OPTIMA and Rotterdam; Spain=Madrid, Oviedo and Santander. When an association (p<0. 05) was observed in the Northern European dataset, analyses were subsequently tested in the Rotterdam subset as the only collection centre with sufficient statistical power to detect an association. Interactions were assessed using the Synergy Factor (SF) described in  [55]. Using this method, the SF=actual OR (joint effect of two factors) / predicted OR (product of the OR for each factor alone). The null hypothesis states that the actual OR is equal to the predicted OR. This method assesses interactions on a multiplicative scale rather than an additive scale. Throughout the study, significance was considered at the α=0.
Genetic variation at the BDNF locus is associated with increased AD risk We tested for association of the 4 BDNF polymorphisms with altered AD risk in the total population. The model preferred (lowest AIC) for BDNF1 was the recessive inheritance model, whereas the preferred models for BDNF2-4 were the dominant models. The OR and 95% CI for the preferred models of each polymorphism are plotted in Fig1. The BDNF1 polymorphism, previously shown to regulate BDNF trafficking, was not associated (at the α=0. 05 level) with altered AD risk in the total (rs6265 MM; OR=1. 2, p=0. 3), Northern European (OR=1. 0, p=0. 8) or Spanish (OR=1. 6, p=0. 1) datasets (Fig1A). However, the OR in the total dataset was of a similar yet greater magnitude to those for BDNF2 (Fig1B; rs12288512 A allele OR=1. To determine whether interactions between BDNF polymorphisms could explain the contradictory findings for rs6265 (BDNF1) across multiple populations, we sequentially added pair-wise interactions between the 4 BDNF polymorphisms into the model.
Women showed increased susceptibility to AD risk-modifying polymorphisms at the BDNF locus We next tested whether interactions between BDNF polymorphisms and age-atsampling, sex and possession of the APOE ε4 allele could contribute to AD risk. We found no association of the BDNF1*sex interaction with AD risk (SF=0. 95, p=0. 9). However, inclusion of BDNF2*sex, BDNF3*sex or BDNF4*sex interactions improved the AIC for the model and the synergy factors (SF) for these interactions are plotted in Fig2A-C. We observed a positive synergistic interaction between BDNF2, BDNF3 and BDNF4 and sex in the total dataset; sex*BDNF2 (SF=1. That being said, these interactions may be of less importance since including them in the models did not improve the AIC. Interactions between other BDNF variants and ε4 were not associated with AD risk (p>0.
Northern European women We have previously identified an association of the DBH polymorphism, rs1611115, with increased AD risk in men aged <75  [39]  in a sample set that overlaps with that included in this study. Therefore, unsurprisingly, when testing for an association of DBH with AD risk in the total population, we found that the DBH polymorphism was associated with increased risk of AD in the total dataset (rs1611115T+ OR=1. 12, p=0. 04), albeit that the association was not apparent in either of the regional subsets.
SORT1 genetic variation is not associated with AD risk Finally we tested for an association of SORT1 polymorphisms with AD risk. We found no association in the total, Northern European or Spanish populations (p>0. 05). We tested for an association of interactions between the SORT1 polymorphisms and age-atsampling, sex and possession of the APOE ε4 allele with AD risk. We observed an antagonistic interaction between SORT1e (rs1149175 GG) and sex in the Spanish dataset (SORT1e OR=2. 2, sex OR=2. 1, SF=0. 4, p=0. 007) and SORT1e was associated with increased AD risk in Spanish men (OR=2. 27, p=0. 002). That being said, these interactions may be of less importance since including them in the models did not improve the AIC. None of the interactions with the BDNF variants contributed to AD risk in the total or regional subsets (p>0.
0008, Bonferroni cut-off = 0. 0002), these findings warrant replication in large independent cohorts. The strongest finding of this study is the interaction between BDNF variants 2-4 (rs12288512, rs113030102, rs11030119) and sex (SF=1. 3 to 1. 5) such that women carriers from Northern Europe were at greater risk of AD (OR=1. 3 to 1. 4) than men (OR=0. 9 to 1. 0), associations that were significant even after accounting for multiple testing in at least one dataset. The evidence for the BDNF2*sex interaction reported here is particularly strong, as it was observed in both the Northern European (increased risk in women) and Spanish (decreased in men) subsets as well as the total dataset. This is not the first study to identify sex differences related to the BDNF locus in AD, albeit that a previous study reported an association of BDNF1 (rs6265) with increased AD risk in women from the Han Chinese population  [56] , a variant that did not interact with sex in this study (p=0.
p=0. 1 and OR=1. 3, p=0. 1). Addition of interactions between the DBH polymorphism and age-at-sampling, sex and possession of the APOE ε4 allele did not improve the AIC for the model. Since DBH and BDNF have both been implicated in regulating noradrenergic neurotransmission, we sought to determine whether polymorphisms at the BDNF and DBH loci could interact to alter AD risk. The addition of interactions between DBH and all 4 BDNF polymorphisms improved the AIC for the model. The SF for the interactions are plotted in Fig3A-D. A synergistic BDNF1*DBH interaction contributed to AD risk in the total (SF=2. 2, p=0. 007) and Northern European (SF=2. 2, p=0. 02) and Rotterdam (SF=2. 2, p=0. 03) datasets. While the SF (SF=1. 4) was comparable in the Spanish population (p=0. 3), the interaction did not reach our cut-off for significance.
Since the BDNF polymorphisms have different effects in men and women, we next tested the BDNF*DBH interactions in male and female subgroups separately. Due to testing rs6265 under a recessive inheritance model, the low number of male MM (rs6265), T+ (rs1611115; dominant model) carriers in the Spanish dataset (n=7), meant that the BDNF*DBH1 interaction could only be tested in the Northern European dataset. In that dataset, the BDNF*DBH1 interaction contributed more to AD risk in women (SF=2. 4, p=0. 04) than men (SF=2. 0, p=0. 2). The remaining BDNF*DBH interactions did not contribute to the AD risk (p>0. 1) and it should be noted that none of the BDNF1*DBH interactions surpassed the Bonferroni cut-off (p<0. 0002) for this study.
Acknowledgements The authors are indebted to all the donors and their families for their participation in this study. We would also like to thank the  Alzheimer's Research UK  and  South West Dementia Brain Bank (SWDBB)  for providing brain tissue for DNA extraction included in this study. Stage 2 of the  Epistasis Project  was funded by the  Jamuna Trust  and the  Herbertpur Trust. The SWDBB is part of the  Brains for Dementia Research programme , jointly funded by  Alzheimer's Research UK and Alzheimer's Society  and is supported by  BRACE (Bristol Research into Alzheimer's and Care of the Elderly)  and the  Medical Research Council. This research benefitted from funding awarded to the  NIHR Great Ormond Street Hospital Biomedical Research Centre. OB, P S-J and ER were supported by grants from,  IDIVAL ,  Instituto de Salud Carlos III (Fondo de Investigación Sanitario)  and  European Funds for Regional Development (FEDER) ;  PI15/00058 ,  CP13/00091 ,  PI08/0139 ,  PI12/02288 ,  PI16/01652 ,  PI13/01008 ),  JPND  ( DEMTEST PI11/03028  and the  CIBERNED program.
The  Rotterdam Study  is funded by  Erasmus Medical Center  and  Erasmus University, Rotterdam ,  Netherlands Organization for the Health Research and Development (ZonMw) , the  Research Institute for Diseases in the Elderly  ( RIDE1  and  2 ), the  Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports , the  European Commission (DG XII) , and the Municipality genotype data for the Rotterdam Study is supported by the  Netherlands Organisation of Scientific Research NWO Investments  (nr. 175. 010. 2005. 011 , 911-03-012 ).
(OR=1. 1 , 1 p=0. 1 and OR=1. 3, p=0. 1). Addition of interactions between the DBH polymorphism and age-at-sampling, sex and possession of the APOE ε4 allele did not improve the AIC for the model. Since DBH and BDNF have both been implicated in regulating noradrenergic neurotransmission, we sought to determine whether polymorphisms at the BDNF and DBH loci could interact to alter AD risk. The addition of interactions between DBH and all 4 BDNF polymorphisms improved the AIC for the model. The SF for the interactions are plotted in Fig3A-D. A synergistic BDNF1*DBH interaction contributed to AD risk in the total (SF=2. 2, p=0. 007) and Northern European (SF=2. 2, p=0. 02) and Rotterdam (SF=2. 2, p=0. 03) datasets. While the SF (SF=1. 4) was comparable in the Spanish population (p=0. 3), the interaction did not reach our cut-off for significance.
To explore these interactions further, BDNF2-4 were tested for association with AD in women and men separately in the datasets that showed a synergistic interaction (Fig2D-F). While BDNF2 was associated with reduced AD risk in Spanish men (OR=0. 6, p=0. 03), all three polymorphisms were associated with increased risk in women from the total (BDNF2; OR=1. 3, p=0. 0006, BDNF3; OR=1. 3, p=0. 00008, BDNF4; OR=1. 2, p=0. 007, respectively) and Northern European (OR=1. 3, p=0. 0007, OR=1. 4, p=0. 00005, OR=1. 2, p=0. 001, respectively) and Rotterdam 5, p=0. 002), sex*BDNF3 (SF=1. 4, p=0. 002) and sex*BDNF4 (SF=1. 3, p=0. 02). The interactions were observed in the Northern European dataset (SF=1. 4, p=0. 02; SF=1. 4, p=0. 007; SF=1. 3, p=0. 02, respectively) and Rotterdam (SF=1. 4,p=0. 02,SF=1. 5;p=0.
01,SF=1. 4,p=0. 04,respectively) datasets and the sex*BDNF2 interaction was also observed in the Spanish population (SF=1. 94,p=0. 03). (OR=1. 4,p=0. 0001,OR=1. 4,p=0. 00003,OR=1. 4,p=0. 0002, respectively) datasets, associations that surpassed the Bonferroni cut-off for this study (p<0. 0002) in at least one of the datasets. We next tested for association of interactions between BDNF variants and the APOE ε4 allele. The BDNF3*ε4 interaction (SF=0. 47, p=0. 02) was associated with altered AD risk in the Spanish dataset and BDNF3 was nominally associated with decreased AD risk in Spanish ε4 carriers (OR=0. 58, p=0. 05) but not in non-carriers (OR=1. 2, p=0. 2).
Methodology Through a series of workshops and discussions among the authors during which we drew on our knowledge of the CPR literature (e. g. Ostrom, 1990 Ostrom, , 2005 Ostrom, , 2010a, b;, b; McCay, 2002; Heikkila and Gerlak, 2005) , augmented by non-systematic searches of Google Scholar and the Web of Science database we searched through the articles that had cited the emergence references listed in Table  1  to determine whether the conditions for emergence had been updated, adapted or evaluated. In addition we asked two common property researchers  (Michael Cox (e. g. Cox et al. , 2010)  and Michael Schoon (e. g. Schoon and Cox, 2018) ) to review the conditions we identified and point us to additional literature we may have overlooked. To evaluate the presence or absence of the conditions for emergence and robustness in CAMPFIRE for this exploratory synthesis we combined searching through the peer-reviewed articles that had cited the initial peer-reviewed papers on CAMPFIRE  (Child, 1993; Metcalfe, 1994; Child, 1995 , Madzudzu, 1995; Child, 1996a, b)  for relevant papers together with the authors knowledge of research outputs from CAMPFIRE.
Acknowledgements We would like to thank  Edwin Muchapondwa ,  Diane Skinner  and  Brian Child  for their inputs on CAMPFIRE in Zimbabwe, based on their personal experience with the initiative. We would also like to thank  Common Property Researchers Michael Cox  and  Michael Schoon  for their review of the principles of emergence that we identified. We thank the  Australian Research Council CEED  and Project on Marine Conservation  CCM RC 130004  (IniciativaCientificaMilenio) and the  Estaci on Costera de Investigaciones Marinas (ECIM) ,  Las Cruces ,  Universidad Cat olica de Chile  for funding the workshops that led to this publication. Duan Biggs is supported by an  Australian Research Council   Discovery Early Career Researcher Grant  ( DE 160101182 ). Julia van Velden  and  Abigail Brown  assisted with final proof-reading and formatting of this paper.