dataesr/scientific-paragraphs-categorization huggingface dataset:
Strikingly, most of the 78 ISG and related genes that were induced by the virus in B57 cells were not up-regulated or not even detected in A22 (i. e. , 64 genes, see map of the IFN pathway in Fig. 3 ), including canonical markers of the type I IFN response such as viperin/rsad2, isg15, ifi44, dhx58, ifit5 and irf1. In addition to these ISG homologs, fish specific ISG like gig  [30] , were up-regulated in B57 but not in A22 cells. Fourteen genes were induced in both B57 and A22 (but generally more induced in B57) and only 7 genes were induced in A22 only. Interestingly, the crfb1 gene encoding the type I IFN receptor was up-regulated in B57 only (Fig. 3 ; Additional file 3). Regarding repressed genes, those with homologs in the Schoggins's dataset that were downregulated in B57 were not repressed in A22, underscoring the differences of the two responses (Fig.
3 ). As an alternative approach to define potential ISG, we also identified, among the list of genes modulated either in B57 or in A22 by the inactivated VHSV, genes with a human homolog in the Interferome database (section type I IFN)  [31]  (Additional file 4). This analysis identified 128 genes induced more than five times in B57 cells, for only 37 in A22, confirming the trend seen for the Schoggins's ISG dataset. Hence, a strong and typical type I IFN response was induced in B57 cells derived from fish resistant to VHSV infection, while cells from the susceptible genetic background A22 up-regulated only a few genes belonging to the IFN related pathway. However, A22 cells did show some transcriptional response implicating genes of the type I IFN pathway. Thus, a few typical markers of this pathway were responding after incubation with the inactivated virus in both A22 and B57, including irf7, one cd9 and one ddit4, two tnfsf10, and several map3k5 and fintrim genes.
Waterborne infection, genotyping and QTL detection Waterborne challenges using BCB57-A22 juveniles and the VHSV strain 07-71 (serotype 1) were previously described  [10]. Two aquaria of 119 and 120 fish were infected. Dead fish were removed twice a day, surviving fish were sacrificed at the end of the challenge (day 39 post-infection), and DNA from every individual was extracted as described  [13]. Fish were then genotyped for 8 markers (OMM1053, OMM5005, Omy1009INRA, S0172/ 483H06, Omy1392INRA, InfGr2int4, and OMM1599) localized in the area of the VHSV major QTL on Omy3 (RT31 linkage group) as described  [13]. QTL detection was performed using QTLMap software with a Cox model-based method as described  [13, 41, 42]. The 95% confidence interval of the QTL was obtained using the Hendge Li (HL) method  [43].
Mapping reads and gene expression counts The read quality was checked with FastQC in the ng6 environment  [44]. Reads were then spliced-aligned to the 46,535 genes from the trout reference genome  [45]  using TopHat v2. 0. 5 software  [46]  with the following parameters -r 10 -max-intron-length set to 25,000. The average number of read per sample (R1 + R2) was 90. 5 million ±20. 3, of which 65. 9% ± 0. 7 were mapped unambiguously. Secondary mapping (ie, mapping on two or more sites) represented 6. 1% ± 0. 6 of the total number of reads. All the resulting bam files were merged to produce a unique reference alignment on which the discovery of new genes and transcripts was performed using Cufflinks 2. 0. 0  [47]  with the parameter -l set to 25,000. The resulting GTF file was used as a unique reference for the quantification step of the 12 available samples.
Identification of differentially expressed genes Differentially expressed genes between cells treated with the inactivated virus and controls were identified for each cell line. These DEG lists were then compared between A22 and B57. In parallel we also determined DEG between the control A22 and B57, to compare transcriptome differences at steady state, and the DEG between stimulated A22 and stimulated B57 to confirm that the result was similar the analysis of the stimulation within each cell line (data not shown). DEG were identified using DESeq2 1. 16 (BioConductor)  [48]  and R: 3-4-2  [49]. Briefly, raw counts of genes were subjected to a minimal pre-filtering step by removing rows for which the count sum in 12 samples is less than 1. Raw counts were normalized for library size and normalized data were fitted thanks to a negative binomial general linear model.
Functional annotation analysis Rainbow trout protein models  [45]  were compared to reference zebrafish and human proteomes using blastp (BLAST®,  [51] ), and the best blast hit annotations were collected. For analysis of multigenic families comprising members induced by the virus, the annotation table was manually curated (for example in Fig. 2 ). To perform functional annotation analysis of DEG, we used DAVID 6. 8  [52, 53]  and the zebrafish pathway database in the Kyoto Encyclopedia of Genes and Genomes (KEGG) using DAVID basic parameters (count: minimum 2; pvalue < 0. 1). Zebrafish homologs of rainbow trout DEG were used. We are aware that doing so, we lost potentially interested information regarding the expression levels of multiple trout paralogs. Modulated pathways are presented in Table  1  (induced & repressed genes after virus inoculation together), Table  2  (induced genes after virus inoculation only), and Table  3  (induced genes in B57 or in A22 at the basal level).
Acknowledgements We thank our colleague  C. Torhy  ( INRA ) for excellent technical support, and  Drs C. Langevin  and  A. Benmansour  ( INRA ) for helpful discussions. Rainbow trout isogenic lines used as broodstock to produce the QTL family are maintained at  INRA experimental facilities PEIMA (Pisciculture Expérimentale des Monts d'Arrée, Sizun, France). The VHSV in vivo challenges were performed at the  INRA experimental platform Infectiologie Expérimentale des Rongeurs et des Poissons (IERP) -Domaine de Vilvert, Jouy-en-Josas, France.
Funding This work was supported by the  Animal Health and Animal Genetics divisions of INRA  ( ACTIV  project) and by the  INRA Action incitative "Bioressources"  (grant AntiVIC-RS 2010). The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  (FP7, 2007-2013),  Research Infrastructures action , under the grant agreement No. FP7-228394  (NADIR). E. R. VERRIER was a recipient of a PhD fellowship co-funded by  Animal Genetics and Animal Health Divisions of INRA.
5 km grid spacing coupled to the Fossil Fuel Data Assimilation System (FFDAS) v2 inventories for anthropogenic CO 2 emissions and the European Centre for Medium-Range Weather Forecasts (ECMWF) land carbon model C-TESSEL for biogenic fluxes. A run using FFDAS for the Southern Ontario region was compared to a run in which its emissions were replaced by the SOCE inventory. Simulated CO 2 mixing ratios were compared against in situ measurements made at four sites in Southern Ontario -Downsview, Hanlan's Point, Egbert and Turkey Point -in 3 winter months, January-March 2016. Model simulations had better agreement with measurements when using the SOCE inventory emissions versus other inventories, quantified using a vari-ety of statistics such as correlation coefficient, root-meansquare error, and mean bias.
The Greater Toronto Area (GTA) in southeastern Canada, for example, has committed to the "Change is in the Air" initiative and is a part of the C40 Cities Climate Leadership Group, both of which call to reduce CO 2 emissions 30 % below 1990 levels by 2020 (C40 Cities, 2016; Framework for Public  Review and Engagement, 2007). However, in order to effectively guide anthropogenic CO 2 mitigation strategies, reliable inventories are needed, particularly at high spatial and temporal resolution, to gain a better understanding of the carbon cycle  (Gurney et al. , 2009; Patarasuk et al. , 2016). To our knowledge, the only spatially disaggregated CO 2 inventories available for use in the GTA are the EDGAR v. 4. 2 (Emission Database for Global Atmospheric Research) CO 2 inventory (available at annual, 0.
From a scientific perspective, gaining information about anthropogenic CO 2 emissions from urban areas has been primarily motivated by atmospheric CO 2 inversions, which are used to better understand the global carbon cycle  (Gurney et al. , 2009; Patarasuk et al. , 2016). Regardless of the motivation, quantification of CO 2 sourcesink processes currently uses two techniques: the bottomup approach and the top-down approach. In the bottomup approach, local-scale activity level information is combined with appropriate emission factors to infer emission rates. This method has been used widely to develop many inventories  (EDGAR, 2010; FFDAS, 2010; Gurney et al. , 2009)  but is limited by the accuracy of the input parameters. Conversely, in the top-down approach, inverse modelling is used to exploit the variability in atmospheric mixing ratios of CO 2 to identify the source-sink distributions and magnitudes; this method is limited by insufficient mixing ratio data and uncertainties in simulating atmospheric transport  (Pillai et al.
Geographic domain The geographic focus of this study was the GTA in Southern Ontario, Canada. The GTA is the largest urban area in Canada; it comprises five municipalities, Toronto, Halton, Durham, Peel, and York, which together have a population exceeding 6 million  (Statistics Canada, 2012b). Although the GTA comprises only 0. 07 % of Canadian land area, it represents over 17 % of the total population as a result of rapid urbanization over the past few decades  (Statistics Canada, 2012b). Therefore, high-resolution characterization of CO 2 emissions can help integrate climate policy with urban planning. This region is home to a network of measurement sites providing long-term, publicly available datasets of atmospheric CO 2 mixing ratio measurements (Sect. 2. 2; Environment Canada, 2011) which can be used to evaluate model outputs and inventory estimates.
Point emissions Point emissions are stationary sources in which emissions exit through a stack or identified exhaust. In the APEI CO inventory, the major emission sources in the point sector include public electricity and heat production (1A1a), stationary combustion in manufacturing industries and construction (1A2f), chemical industry (2B5a), pulp and paper (2D1), iron and steel production (2C1), and other metal production (2C5). Unlike the area sector, we found that applying a single CO 2 : CO ratio to every facility did not produce realistic CO 2 emissions due to the negligible emissions of CO and therefore highly variable CO 2 : CO ratios (because of a small denominator). Therefore, we used ECCC Facility Reported Data  (Environment Canada, 2015)  to identify the geocoded location and annual average CO 2 : CO for 48 individual facilities in Ontario (Table  S1  in the Supplement) and applied the specific CO 2 : CO ratios to the grid cells where the facilities were located.
On-road mobile emissions On-road emissions include the emissions from any on-road vehicles (quantified by the Statistics Canada Canadian Vehicle Survey) (Environment Canada, 2013). In the APEI CO inventory, the major emission sources in the on-road sector includes gasoline-and diesel-powered light-and heavy-duty vehicles (1A3b). The NIR estimates an Ontario total from these (and other minor on-road sources) of 4. 4 × 10 4 kt CO 2 and 1. 5 × 10 3 kt CO, producing a CO 2 : CO ratio of 29 kt CO 2 /kt CO. This ratio was applied to every on-road grid cell belonging to Ontario in the domain to convert sector CO emissions to CO 2. Temporal allocation of emissions in the on-road sector is estimated using data collected in the FEVER (Fast Evolution of Vehicle Emissions from Roadways) campaign in 2010  (Gordon et al.
Residential and commercial emissions Residential and commercial CO 2 emissions reflect on-site combustion of natural gas for electricity and heating, a source that we found was not included in the APEI CO inventory because of the high efficiency of the furnaces and resulting low CO emissions. To include the CO 2 emissions from these on-site furnaces, we used the Statistics Canada 2012 Report on Energy Supply and Demand to quantify the amount of natural gas consumed by residential and commercial buildings in Ontario, 7. 9 × 10 3 gigalitres (GL) and 4. 9 × 10 3 GL respectively  (Statistics Canada, 2012a). The Canadian NIR estimated 1879 g CO 2 m -3 natural gas as the CO 2 emission factor specific to the province of Ontario, based on data from a chemical analysis of representative natural-gas samples and an assumed fuel combustion efficiency of 99.
The poor performance of our model system when using the EDGAR v4. 2 inventory to simulate CO 2 mixing ratios was also found by a study quantifying on-road CO 2 emissions in Massachusetts, USA  (Gately et al. , 2013). In this study, EDGAR emission estimates were found to be significantly larger than any other inventory by as much as 9. 3 million tons, or > 33 %. The difference in estimates between the EDGAR v4. 2 and the SOCE inventories is likely explained by their underlying differences in methodology. Being a global product and not specifically designed for mesoscale applications, the EDGAR v4. 2 inventory estimates CO 2 emissions based on country-specific activity data and emission factors, but the spatial proxies used to disaggregate the data are not always optimal. A study performed by  McDonald et al.
Additionally, Tompsett and colleagues linked a higher level of education with seeing homelessness as the result of personal flaws  [23]. Only one study conducted in Europe (Germany, Italy, the United Kingdom and Belgium) and in the USA has focused on the opinion of the general population  [24]. In this study, small samples were obtained (no more than 323 for Europe and 435 for the USA) and data collection was spread over 5 years. Nonetheless, differences in attitudes about homelessness across countries were found; for example, Italians were more likely to consider economic factors as the primary cause of homelessness when compared to the other countries. Additionally, differences were found in the lifetime prevalence of literal homelessness within the general population, with the highest rates in the UK and in the USA.
As an introduction to the interview, respondents were informed of the purpose of the study, the intended use of the data and assured of anonymity. Interviews were carried out in the targeted countries from March 2017 to December 2017. These interviews included three modules: the first was on the knowledge, attitudes, and practices (KAP) about homelessness; the second covered the contingent valuation (CV) method; and the third was on social and demographic characteristics of the respondent.
Questionnaire Following a literature review between December 2016, and February 2017 on three electronic databases (MEDLINE, Science Direct, and COCHRANE), we identified 3 main documents on KAP about homelessness  [22, 23, 30] , with the latter two based on the same instrument. Therefore, the survey questionnaire was designed using these two survey instruments, in addition to ad hoc questions (Table  1 ). Drafts of the questionnaire were discussed within the HOME_EU Consortium study group (see acknowledgment), and the final English version received the approval of all partners. The questionnaire then went through a four-step translation process that included two translations into the targeted native language leading to one consensual version after discussion about discrepancies and input on cultural adaptation provided by each partner within the Consortium.
Sociodemographic characteristics As social and demographic data have been shown to influence KAP  [23, 32]  as well as preferences  [33] , data on socio-demographic characteristics of the respondents were collected, including gender, age, educational level, professional status, annual household income, marital status, number of dependent children, municipality, and the number of adults in the household. In addition, the number of operational landlines and mobile phones within a household was collected to adjust for selectivity due to telephone/ mobile ownership.
Data analysis Descriptive analysis Sociodemographic data, prevalence estimates of homelessness, KAP answers, and WTP estimates will be presented descriptively as the means, medians, or proportions and will be stratified by gender and country. Parametric and non-parametric statistics will be used to compare the knowledge, attitudes, practices, and maximum WTP differences between countries, respondent profiles and other relevant subgroups. Data collected in the questionnaire will be used to create several KAP indicators such as: "Good knowledge", "Poor knowledge", "Attitude of contest", "Attitude of no contest", "Exposed practice", and "unexposed practice". Evidence suggests that attitudes about homelessness are influenced by one's experience and perceptions regarding social justice, social choice and unfairness  [45].
The logit component will be used to model the predictors of protest behaviors by modeling the factors associated with zero valuesboth genuine and protest zeros. The predictors of the valuation of the HF model will be assessed through the negative binomial part of the model. The relevance of using a ZINB regression will be confirmed by calculating the Vuong test  [55]. Descriptive analyses and econometric analyses will be performed using the computer software SPSS 12 for Windows (SPSS Inc. , IBM, NY, USA), as well as RStudio version 3. 2. 1 software (RStudio, Inc. , Massachusetts, USA).
HF has been experimented with in North America and in Europe and has provided evidence of housing stability and of healthcare cost reduction  [21]  for long-term homeless people with severe mental illness. This telephone survey took place in countries where established HF programs have been presented as showing the most fidelity to the original Pathways to Housing First program (i. e. , France, Ireland, Portugal and the Netherlands)  [39]. Additionally, included in this study are Italy and Spain, where HF programs are fairly recent  [56] [57] [58] ; Sweden, where HF programs, though long-standing, present the most variations to the PHF approach  [59] ; and Poland, where no Housing First initiatives exist to date. In this study, we collected data via telephone both for the KAP survey and the CV method.
Funding This work is supported by the  European Commission  through a grant, as part of  H2020  research project  HOME_EU: Reversing Homelessness in Europe   H2020-SC6-REVINEQUAL-2016/ GA726997. The grant agreement between the  European Commission  and each research unit involved allows complete liberty and autonomy of researchers in the design of the study; the  European Commission  will take no part in data collection procedures, analyses, interpretation of the data, or decision to submit results.
However, they did not investigate the potential causes of this interannual variability. The present analysis attempts to reconsider the impact of NIG circulation on phytoplankton phenology focusing on the satellite chlorophyll a concentration (hereafter [Chl a]) phenology and analyzing the 1998-2012 period. In Sect. 2, all the datasets used and the related methods are presented. Subsequently, the temporal variability of the NIG circulation is analyzed and then the uplift (depression) of isopycnals and the nitracline during the cyclonic (anticy- clonic) regime is discussed. From the analysis of phenological metrics, differences are sought between cyclonic and anticyclonic regimes in [Chl a] phenology. Phenological metrics are used here because they provide a synthetic method to highlight specific features of the [Chl a] annual time series.
Satellite and modeling data Daily [Chl a] products with a 4 km resolution delivered by the Ocean Colour Climate Change Initiative (OC_CCI) project version 1. 0 (http://www. esa-oceancolour-cci. org/, last access: January 2016) were used. The OC_CCI dataset, which covers the period September 1997 to July 2012, was created by band-shifting and bias-correcting MERIS and MODIS data to match SeaWiFS data and by merging the datasets with a simple average  (Storm et al. , 2013). Daily data were merged to obtain 8-day resolution maps. During the period from 1997 to March 2002 (only the SeaWiFS sensor available), the 8-day average map time series contains only 80 % good data, but from April 2002 more than 96 % good data were available. To fill the gaps, a linear interpolation was applied to each time series.
Then, the time series were smoothed with a three-point median filter. OC_CCI [Chl a] products are processed with the algorithm OC4v6, which has been fitted on a global dataset  (O'Reilly et al. , 2000). It has been demonstrated that this algorithm overestimates by a factor close to 2 [Chl a] in oligotrophic conditions ([Chl a] < 0. 4 mg m -3 ) and alternative algorithms have been developed for the Mediterranean Sea  (Bricaud et al. , 2002; Volpe et al. , 2007). However, as for OC4, Mediterranean algorithms are based on the blue-green reflectance band ratio model: [Chl a] increases with a decrease in the blue-green band ratio. Hence, similar patterns in [Chl a] variations are obtained with each class of algorithms. As the present study focuses only on the intercomparison of [Chl a] patterns in the Ionian Sea, the utilization of the global algorithm is valid.
In practice, the ADT difference between rectangles S2 (18. 75-19. 25 • E and 39. 5-40 • N; see Fig. 1b ) and S1 (18. 5-19 • E and 37. 5-38 • W; see Fig. 1b ) was computed. Then, the daily time series was smoothed with a 30-day moving average filter (dotted line Fig. 2a ). As the anticyclonic (cyclonic) circulation regime is characterized by a doming (depression) of sea level, it is characterized by a positive (negative) ADT difference value. Net air-sea heat fluxes of longwave radiation and shortwave radiation, sensible heat flux, latent heat flux and precipitation were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis  (Dee et al. , 2011). Data were available at 6 h intervals and downloaded at 0. 5 • resolution. Then, daily averages were computed and the net heat flux was calculated as the sum of the four air-sea flux components.
The surface buoyancy flux per unit area was computed according to  Anderson et al. (1996) , with coefficients proposed by  Gačić et al. (2002)  for the Mediterranean Sea. Total winter buoyancy losses were then calculated as the time integral for the period from 21 December to 20 March. Daily temperature and salinity profiles for the product MEDSEA_REANALYSIS_PHY_006_004 produced by the Mediterranean Forecasting System and delivered by the CMEMS (http://marine. copernicus. eu, last access: June 2016) were used to derive the depth of the 28. 9 kg m -3 isopycnal and MLD for the time range 1993-2012. The MEDSEA_REANALYSIS_PHY_006_004 product results from a hydrodynamic model supplied by the Nucleus for European Modelling of the Ocean (NEMO), with a variational data assimilation scheme for temperature and salinity vertical profiles and satellite sea level anomaly along-track data.
In situ data A total of 71 243 temperature and salinity profiles (52 % from Argo floats) were collected from the Coriolis web portal (http://www. coriolis. eu. org/, last access: October 2015) during the period 1990 from 2012 in the region between 36-40 • N and 16-21 • E. Only the best quality (Coriolis sensu) data were retained. Then, potential density profiles were calculated and the depth of the 28. 9 kg m -3 isopycnal was determined. Nitrate concentration, temperature and salinity profiles from oceanographic cruises in the area of interest (36-40 • N, 17-22 • E) were obtained: POEM-BC-O91 (October 1991, 55 profiles), POEM-BC-A62 (April-May 1992, 40 profiles) and EMTEC (April-May 1999, 20 profiles). From nitrate concentration profiles, the nitracline depth was estimated as the depth of the 1 µM isoline  (Cermeño et al.