dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative Real-Time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) Total RNA was extracted from THP-1 macrophages using RNAeasy kit (Qiagen) and was reverse transcribed into cDNA using the SuperScript III First-Strand Synthesis System (Invitrogen) and oligo-dT, according to the manufacturer's protocols. Quantitative PCR was performed in duplicate for each studied gene using a CFX96 Real Time PCR System (Biorad). KAPA SYBR Fast Universal Ready mix Kit containing SYBR Green dye was used for amplification following the manufacturer's instructions (Kapa Biosystems). Quantification was achieved using the CFX manager software 2. 1 (Biorad). Gene expression was expressed as relative copy number (RCN). RCN was calculated using with the following equation: RCN = 2 -Ct × 100 where Ct corresponds to average Ct values of each studied gene subtracted from average Ct of GADPH (housekeeping gene).
RCN represents gene expression as number of copies relative to the 100 copies of housekeeping gene. Oligonucleotide primers were designed in PRIMER3 PLUS and analyzed in OligoAnalyser online softwares. The following set of validated primers, synthesized and purchased from Sigma, were used: Atg5: GCAAGCCAGACAGGAAAAAG (F), GAC CTTCAGTGGTCCGGTAA (R); Atg14: TCCATTTTCCCATCC AGTTC (F), AAGTCAGTCTCCACCACCAA (R); Beclin-1: TGAGGGATGGAAGGGTCTAA (F), TGGGCTGTGGTAAGT AATGG (R); LC3B: AACGGGCTGTGTGAGAAAAC (F), AGT GAGGACTTTGGGTGTGG (R); STX17: CCAGCCAAACTGA CAAGAAA (F), ACACCCCAGCAAACAACAA (R); hVps34: ATGGAAGCCGATGGATGTAG (F), CCTCACAGTTGGGTT GGTG (R); ULK1: CACACGCCACATAACAGACA (F), CCC CACAAGGTGAGAATAAAG (R); UVRAG: GAGTTGGGG TGTCTGGTAGG (F), AATCTGAATGCGGGAATGAC (R). All data were normalized to GAPDH: CCATGTTCGTCATGGGTG TG (F); GGTGCTAAGCAGTTGGTGGTG (R).
ACKNOWLEDGMENTS We would like to thank  Mamadou Daffé  and  Jérôme Nigou  for reagents and helpful discussion as well as  Stevie Jamet  for his assistance with RT-PCR experiments. We wish to acknowledge  TRI-Genotoul Imaging facility (Toulouse, France). The work in the authors' laboratory has been supported by  EU   FP7 Marie Curie Career Integration Grant   293416 ,  University of Toulouse III , the organization " Vaincre la mucoviscidose " and Centre.
Background: RAD-seq is a powerful tool, increasingly used in population genomics. However, earlier studies have raised red flags regarding possible biases associated with this technique. In particular, polymorphism on restriction sites results in preferential sampling of closely related haplotypes, so that RAD data tends to underestimate genetic diversity. Results: Here we (1) clarify the theoretical basis of this bias, highlighting the potential confounding effects of population structure and selection, (2) confront predictions to real data from in silico digestion of full genomes and (3) provide a proof of concept toward an ABC-based correction of the RAD-seq bias. Under a neutral and panmictic model, we confirm the previously established relationship between the true polymorphism and its RAD-based estimation, showing a more pronounced bias when polymorphism is high.
Using more elaborate models, we show that selection, resulting in heterogeneous levels of polymorphism along the genome, exacerbates the bias and leads to a more pronounced underestimation. On the contrary, spatial genetic structure tends to reduce the bias. We confront the neutral and panmictic model to "ideal" empirical data (in silico RAD-sequencing) using full genomes from natural populations of the fruit fly Drosophila melanogaster and the fungus Shizophyllum commune, harbouring respectively moderate and high genetic diversity. In D. melanogaster, predictions fit the model, but the small difference between the true and RAD polymorphism makes this comparison insensitive to deviations from the model. In the highly polymorphic fungus, the model captures a large part of the bias but makes inaccurate predictions.
Nevertheless, this Allele Drop Out (ADO) leads to underestimate the polymorphism, because of the linkage disequilibrium between the restriction sites and SNPs within the RAD sequences. In more simple terms, individuals or haplotypes that are more closely related than the population average tend to share the same state at the restriction site (presence or absence), and are thus over-represented in RAD-seq datasets. Here we focus on this latter bias, hereafter simply referred to as "the RAD-seq bias". The impact of ADO has been investigated in several earlier studies  [10, 11] , where difference in coverage between loci was proposed as a solution to detect null alleles. Here we do not consider this option, which requires a high sequencing depth that is not always achieved. We rather aim at a better understanding of this bias, through the confrontation of simulated and empirical data.
Simulations and genetic diversity measures To measure the theoretical impact of the RAD-seq bias, we simulated sequence data and retrieved RAD tags in silico. Each simulation consisted in the generation of coalescents for 1000 genetically unlinked loci, with complete linkage within loci, in four haploid lineages, using the ms programme  [14]. Seq-gen was then used to produce sequences of 10 kb for each locus  [15]. To generate RADseq data, we randomly merged by pairs the four haploid genomes to form two diploids, and searched ten randomly defined restriction sites of 8 bp (searching for more than one motif increases the number of RAD loci without increasing the alignment size and computing time). This yielded an average of 1500 RAD loci of 100 bp per genome. In the first model, we assumed the population was diploid, unstructured, and θ, the population mutation rate (4*Ne*μ) was homogeneous along the genome.
ABC for the estimation of nucleotidic diversity from RAD data We used Approximate Bayesian Computations (ABC) to correct RAD-seq estimates of genetic polymorphism. In these simulations, as in our first model, we assumed the population was diploid, unstructured, and θ was homogeneous along the genome. We considered the following summary statistics: (1) π RADobs , the observed nucleotidic diversity in RAD-seq data (average distance between individuals) and (  2 ) the proportion of loci in each individual shared with the other one. Calculation of the posterior distribution of θ for each observed dataset was performed with functions from the R package abc  [16]. We used a tolerance rate of 0. 05 and local linear regressions to adjust the accepted simulations to the observed data, and tested our approach by cross-validation.
Results The RAD-seq bias under a neutral and panmictic model In order to validate our approach, we first aimed at retrieving the previously established effects of ADO on the RAD-seq polymorphism in unstructured populations under a neutral model. To this end, RAD-seq data was obtained from simulated genomes. In a panmictic population evolving neutrally, the population mutation rate (θ = 4*Ne*μ) is expected to equal the nucleotidic diversity π, the average distance between haplotypes sampled randomly within the population. In the present analysis, π is measured using pairs of diploid specimens. As expected, we observed in our simulations that π true (the mean genetic distance between individuals at all RAD loci, regardless of the actual state of the restriction site) is an unbiased estimate of θ.
In addition, we find that heterogeneity in θ amplifies the bias (Fig. 2 ). We propose the following interpretation for this result: in regions with higher polymorphism, the chances of gaining or losing a RAD locus due to mutations at the restriction site are higher, and thus the density of RAD markers shared across individuals in these regions is lower. In other words, genomic regions with a higher degree of polymorphism tend to be under-represented in RAD-seq data. The impact of deviations from panmixia on the RADseq bias was investigated using a third model. Here we had the a priori intuition that population structure should reduce the intensity of the RAD-seq bias. Indeed, the bias in the sampling of coalescents in RAD-seq data arises from the fact that pairs of haplotypes corresponding to shorter coalescents are more frequently associated with restriction sites having the same state (present or absent), and are thus overrepresented in RAD data.
Comparing empirical data with simulations We used in silico digestions of full genomes (with phased haplotypes) from natural populations to assess the concordance between simulated and real data. Most species for which such data is available harbour a low to moderate genome-wide diversity (below 2 %), under which circumstances the RAD-seq bias is expected to be negligible. This is for example the case in the fruit fly D. melanogaster. In this species, π true ranged from 0. 61 % to 0. 73 % in the four populations studied. For such values, simulations under the neutral panmictic model predict that π RAD should only be 5 % lower than π true (Fig. 1b ). The observed π RAD values fit this prediction, ranging from 0. 59 % to 0. 70 % in the four populations. However, with so small differences between π RAD and π true , this case study provides only limited power to assess the pertinence of the model.
We thus looked for full genome data from natural populations of more polymorphic species, which would provide more informative comparisons between simulated and real data. To our knowledge, the appropriate data exist only for the fungus Schizophyllum commune (NB: transcriptome data exist for several other species harbouring high polymorphism  [17] , but these sequences are not appropriate to mimic a RADseq experiment because i) these datasets do not provide phased haplotypes, and ii) the presence of introns in gene leads to reduce the genetic linkage between sites within mRNAs, which mitigates the RAD-seq bias). Sequences available from this species originate from two populations, from North America and Russia, each characterised by very high polymorphism (π TRUE = 9. 7 % and 7. 4 %, respectively).
Partial corrections of the RAD-seq bias through ABC under a neutral panmictic model We explored the possibility of using simulations from the neutral panmictic model to correct, at least partially, the RAD-seq estimates of polymorphism, through Approximate Bayesian Computation. The principle of this approach is to use the simulated relation between the true polymorphism and some summary statistics (e. g. proportion of shared RAD loci between specimens, proportion of homozygous loci) to infer corrected polymorphism values from the values of these statistics in empirical data. We developed such an ABC approach and first performed a cross-validation test, that is, tested our ability to correctly infer the input parameter values of simulations using the simulated data itself (here the simulated data is treated as an observation and is thus called "pseudo-observed").
Pseudo-observed data was generated with different θ values, and for each simulated data set, the observed RAD genetic distance was calculated, as well as the proportion of RAD loci shared between the two specimens. Using these two summary statistics, we were able to precisely infer the input parameter values (Fig. 5 ). Noteworthy, this cross validation is only a quality control, showing that the ABC approach can be used to retrieve the true polymorphism from the observed RAD data if the data follow the exact same model as the simulations. We then tested this approach on real data, using in silico RAD-seq from D. melanogaster and S. commune (Fig. 5 ). The ABC-corrected RAD polymorphism value was close to the true polymorphism in D. melanogaster, where the RAD-seq bias was very low anyway. In S.
Discussion Building on previous studies  [8, 9] , we further explored here the impact of allele drop-out on the estimation of genetic diversity from RAD data. We first confirmed earlier findings based on simulations in a neutral and panmictic model: RAD-based estimates of diversity are lower than the true polymorphism, and this bias becomes more pronounced as the true polymorphism increases. Using more elaborate models, we further showed that deviations from the neutral and panmictic model can have complex and contradictory outcomes. Assigning different degrees of polymorphism to different regions of the genome, which mimics the effects of natural selection on genomic variation, tends to exacerbate the RAD-seq bias. This probably results from an excessive contribution to the data of the least polymorphic genomic regions, subject to the most intense purifying selection.
We also simulated sampling of specimens from more or less isolated subpopulations, and thus showed that population structure should mitigate the bias. In other words, RAD-seq data tends to under-estimate divergence within but not between populations. We used "ideal" empirical data, that is, RAD-seq data obtained from in silico digestion of full genomes from natural populations, to assess potential deviations from the neutral and panmictic model. Data from the fruit fly D. melanogaster confirmed that the bias is of negligible importance when the polymorphism is low, offering little power to assess the validity of the model. On the contrary, in the fungi S. commune, where the true polymorphism approaches 10 %, the bias is severe, producing a 50 % underestimation of the diversity. The neutral and panmictic model captures most of this effect, but the observed RAD-based values nevertheless fall out of the model predictions.
The fact that simulations can capture the RAD-seq bias, at least in part, opens the possibility of correcting estimations through an ABC approach. We developed such an approach based on simulations from the neutral panmictic model, where the number of parameters to be estimated is low enough. The results are encouraging: the corrected RAD-polymorphism values are much closer to the real polymorphism than the raw values. However, in accordance with the above-discussed deviations from the model, the corrections are inaccurate. It is clear that robust estimations of diversity measures from RAD-seq data would require more elaborate ABC models, including the potential effects of population structure and selection, or other, yet unidentified, relevant parameters. However, our simulations suggest that a given observed RAD polymorphism might be indicative of a certain θ value if the population is panmictic, a smaller θ if individuals were sampled from slightly divergent populations, or a larger θ if selection produced strong heterogeneity in θ along the genome.
Conclusion Our analysis confirmed the tendency of RAD data to underestimate polymorphism. Regardless of the model used, simulations indicate this bias is of minor importance when the polymorphism is below 2 %, which is the case in most species, at least in animals  [17]. In silico RAD experiments on full genome data from natural populations confirm this prediction, which would undoubtedly be reinforced by more realistic RAD datasets, where all sorts of additional biases, from technical issues at the bench to downstream bioinformatics, introduce more important sources of uncertainty  [3] [4] [5] 18]. Nevertheless, when the polymorphism is large the RAD-seq bias becomes of significant concern, and needs to be kept in mind. While ABC-corrections based on a neutral and panmictic model can partially solve the problem, deviations from this model introduce some uncertainty in these corrections.
Promoted products Each food product on offer in the stores was matched to a corresponding generic food in the French food composition database associated with the INCA 2 national food consumption survey  [57]. We then identified the foods to be promoted during the in-store marketing intervention based on three criteria. Hence, the promoted products had to: 1. Be of "good nutritional quality", defined as having a SAIN/LIM ratio  [51, 58]  above the median for the 1,304 foods included in the INCA 2 French food composition survey; 2. Be "inexpensive", defined as having a selling price below the first tertile of the mean national price of foods from the same food category in the French database, as previously described  [59] ; 3. Be a "source of" at least two nutrients, according to European regulations on nutritional claims  [60].
Purchasing data Detailed monthly purchasing data for membercustomers (n = 11,281) were collected from the two intervention stores and the two control stores during the intervention period (January to June 2014) and in the year preceding the intervention (January to June 2013). Data from 2013 and 2014 were collected during the same months, avoiding any potential seasonal effect. Only customers who had made 100 % of their loyalty card recorded purchases in the same store (among the four stores) and who purchased at least one food product during each of the two periods (2013 and 2014) were selected, resulting in a sample size of 6,625 customers.
Primary outcome measures Purchasing data The food products were classified according to six food categories: fruits and vegetables (such as fresh or processed vegetables, fresh or processed fruits, fruits juices, nuts, and soups), starches (such as breakfast cereals, legumes, breads, potatoes, pasta, and rice), meat/fish/ eggs (including deli products), mixed dishes and sandwiches, dairy products (such as cheese, milk, and fresh dairy products) and other. The "other" food category included foods for which very few products were promoted, mainly due to having a SAIN/LIM ratio lower than the median; these included fatty/salty/sugary products, fat products, seasonings, water, and other beverages. Fresh fruits and vegetables that benefited from one-time, limited discounts were not promoted. For each member-customer in control stores and in intervention stores, we calculated the contribution of promoted products to the total spending on food and to the spending by food category for the intervention period (2014) and the baseline period (2013).
In-depth surveys A total of 144 in-depth surveys were conducted from May 20 to June 26 in the two intervention stores. We used loyalty card numbers, which were collected during the survey, to match the customers' survey responses to their purchasing data. Twenty-eight customers could not be matched with their purchasing data and were excluded from the sample, leaving a final sample size of 116 customers. During the survey, customers were asked if they had seen the intervention materials as they were shopping at the store (awareness of the program) and if they considered such a program to be useful (perceived usefulness of the program). They also took a quiz assessing their knowledge of nutrition and a quiz assessing their understanding of the labeling system. In addition, we showed each customer eight promoted products and asked if these products were easy to find in the store (placement-related attractiveness), if they considered these products to be appetizing (taste-related attractiveness), and if they would be able to prepare a meal with these products (preparation-related attractiveness).
We summarized the answers to the quizzes and the questions on attractiveness using a scoring system, with a score on 100 for each of the following outcomes: knowledge of nutrition, understanding of the labeling system, placementrelated attractiveness, taste-related attractiveness, and preparation-related attractiveness. For each customer, we used the matched purchasing data from their loyalty cards to calculate the contribution of promoted products to total food spending during the intervention, as well as shopping profile variables (frequency of shopping at the store, average spending in the store per month, average spending in the store per visit). We also collected sociodemographic information.
Strengths and limitations This study is not without limitations. First, only four grocery stores were included in the study. In fact, the social marketing approach required regular attendance of the research team in the stores to implement the intervention by installing, following up and adapting the intervention materials, ie shelf labels, posters, leaflets, prime placement, taste-testing booth. To maintain the desired level of quality for this intervention, we could not extend it to more than two intervention stores and two control stores. Second, we could not compare the stores based on quantitative data. Indeed, we did not have access to any quantitative data from the stores (size, number of employees, whole store sales, number of visits per day) as the retail chain managers considered them as critical.
Fourth, we selected only member-customers for purchasing data analysis and in-depth surveys. Using data from loyalty card was the only way for us to get purchasing data at the individual level and to track purchases over time. Therefore, this resulted in sample sizes (6,625 customers for the purchasing data analysis and 144 customers for the in-depth survey) and findings may not be applicable to non member-customers. Despite these limitations, strengths should also be noted. First, we have assessed the impact of the MAN-GER TOP on purchases at the customer level rather than on sales at the store level. This seemed to us more relevant since the objective of the intervention was to improve customers' purchasing behaviors rather than increasing stores sales of promoted products. Second, using data from the loyalty cards allowed us to track customers' purchases from one year to another.
Procedures and outcome measures Baseline data collected at inclusion included patient characteristics, medical history, care setting, type of AF, date and method of diagnosis, symptoms, and anticoagulant (AC) treatment (VKAs, factor Xa inhibitors [FXas], and direct thrombin inhibitors [DTIs], as well as AP treatment). Ethnicity was classified by the investigator in agreement with the patient. 5 ata on all components of the CHA 2 DS 2 -VASc and HAS-BLED risk stratification schemes were collected to assess the risks of stroke and bleeding retrospectively. Vascular disease was defined as peripheral artery disease and/or coronary artery disease (CAD) with a history of acute coronary syndromes (ACS). Hypertension was defined as a documented history of hypertension or blood pressure. 140/90 mmHg at rest.
Collection of follow-up data occurred at 4-month intervals up to 24 months. Outcome measures included clinical events, therapy persistence, and healthcare utilization. 5, 6 The incidences of stroke/SE, pulmonary embolism, ACS, hospitalization, death (cardiovascular and non-cardiovascular), heart failure (occurrence or worsening), and bleeding (severity and location) were recorded. Submitted data were examined for completeness and accuracy by the coordinating centre (Thrombosis Research Institute [TRI], London, UK), and data queries were sent to study sites.
Data collection/quality control/auditing GARFIELD-AF data are captured using an electronic case report form (eCRF) designed by Dendrite Clinical Systems Ltd (Henley-on-Thames, UK). Oversight of operations and data management are managed by the sponsor and coordinating centre (TRI), with support from Quintiles (Durham, NC, USA), The University of Birmingham Department of Primary Care Clinical Sciences (Birmingham, UK), Thrombosis Research Group-Brigham and Women's Hospital (Boston, MA, USA), and AIX-IAL (Paris, France). The GARFIELD-AF protocol requires that 20% of all eCRFs are monitored against source documentation, that there is an electronic audit trail for all data modifications, and that critical variables are subjected to additional audit. 5 Data for the analysis in this report were extracted from the study database on 3 August 2015.