dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome sequencing As of 25 May 2021, 623 HA sequences (20. 6% of influenza-positive) were generated (GISAID accession numbers available in Supplementary Data 5), corresponding to 343 (55. 1%) A(H1N1)pdm09, 93 (14. 9%) A (H3N2), 187 (30. 0%) B/Victoria-like, and 1 (0. 1%) B/Yamagata-like viruses, all originating from the Northern hemisphere.
Carbon Data: The InVEST model requires biophysical data including carbon storage and accumulation rates, soil and biomass disturbance rates, and carbon decay rates. The Carbon storage (Figure  4 ; Table  1 ) and accumulation rates (Table  2 ) of each LULC type are de ned by the Intergovernmental Panel on Climate Change's (IPCC) database and local values from recent blue carbon studies (Table  1 ; Table  2 ). The speci c data from these studies are used because they are comparable to our study area based on the same habitat types. The biomass and soil disturbance rates are the percentage of carbon lost as a result of LULC changes. These percentages are speci c to vegetation types and are classi ed into three categories of impact: high, medium and low (Table  3 ). In our case, we used the percentages of disturbance provided by the Natural Capital Project  (Sharp et al.
This paper examines the relationship between locus of control (LOC) and the demand for supplementary health insurance (SUPP). Drawing on longitudinal data from Germany, we document robust evidence that individuals internal LOC increases the take up of supplementary private health insurance (SUPP). We find that the effect of one standard deviation increase in the measure of internal LOC on the probability of SUPP purchase is equivalent to a 14 percent increase in household income. Second, we find that the positive association between self-reported health and SUPP becomes small and insignificant when we control for LOC These results suggests that LOC might be an unobserved individual trait that can partly explain previously documented evidence of advantageous selection into SUPP. Third, we find comparable results using data from Australia, which enhances the external validity of our results.
, 2008 , Buchmueller et al. , 2013 ; Schmitz, 2011 ). 5  We test our empirical claims using longitudinal survey data from Germany. In Germany, statutory public health insurance (SHI) is typically paid through both employee and employer contributions (even when unemployed or retired) as well as their dependents. 6 Individuals benefiting from the SHI can also purchase additional supplementary insurance (SUPP). SUPP extends health care coverage beyond that of SHI, and its premium is mainly adjusted based on age and, when observable, chronic conditions. Finally, those individuals above a certain income threshold can choose to have substitutive insurance (SUBST) rather than SHI and SUPP as described below. One potential reason for the influence of LOC on SUPP in Germany is the presence of insurance underwriting.
Unlike previous research, this study adds to the literature on behavioral household finance , and more specifically it suggests that individuals have ex-ante preferences for the financing health care. Third, this is the first paper that exploits the panel dimension of the LOC data allowing to control for unobserved time-invariant heterogeneity to estimate the role of LOC on economic outcomes. Previous evidence assumed LOC to be a fix trait, but we show that it does indeed vary over the life course of an individual. Fourth, we examine whether LOC plays a role in explaining previous evidence of positive health selection into SUPP, however our study refer to the uptake of supplementary private insurance by publicly insured individuals. 9 Finally, to enhance the external validity and economic significance of our estimates, we report evidence from two large countries with substantial SUPP markets that complement the coverage of a mainstream insurer: Germany and Australia.
This is an important contribution to the paper. Finally, we show that LOC is a confounding variable that partly explains the observed positive association between health and the uptake of SUPP, suggestive of advantageous selection into SUPP. Section 2 describes the German institutional , including an institutional background on the market for SUPP and the role of LOC. Section 3 describes the data and our empirical strategy. Section 4 displays the main results, and Section 5 analyses the role of LOC in the positive health selection into purchasing SUPP, finally Section 6 shows a comparable analysis using Australian data to strengthen the validity of our finding. Section 7 contains our concluding remarks.
Data In the main part of the analysis, we use data from the German Socio-Economic Panel ( SOEP, 2019 ). The SOEP is a longitudinal household survey that began in West Germany in 1984 and in East Germany in 1990. It collects information on a wide range of factors including the purchase of supplementary health insurance, alongside a rich set of individual records including LOC as well as related concepts such as willingness to take risks and other personality traits. We use all waves of SOEP from 1999 to 2016, except the 2009 and 2015 waves because it did not collect information on SUPP for those years. We exploit the data only from 1999 because it was the first time that LOC was measured. Our sample is restricted to individuals who are between 25 and 90 years old. 13 After dropping observations with missing values for the variables , our final sample includes 24,274 individuals, constituting an unbalanced panel including 231,784 observations.
41 Because wealth is not measured in the years that coverage type is available for Australia (2009 and 2013), we attach individual specific average wealth from the years 20 02, 20 06 and 2010 to this smaller sample. Table  C6  in the appendix shows that our results for Australia are robust to attaching wealth from the previously available year to the data (year 2006 wealth to the year 2009 data and wealth from the year 2010 to the year 2013 data). costs of health care use). We document robust evidence that individuals' LOC predicts the purchase of SUPP in Germany, where statutory social insurance is available to the entire population, and in Australia, where there is a similar institutional setting, but where SUPP is community rated. We also find that the inclusion of LOC in our specification for SUPP choice partly explains previous evidence of positive (health) selection into insurance.
That is, the inclusion of LOC renders the negative association between poor self-reported health and SUPP uptake insignificant. Finally, we document consistent evidence for Australia, a country which presents a similar institutional setting and where longitudinal data for both LOC and SUPP is available. The positive association between LOC and SUPP is not driven by individuals with an internal LOC having increased health care use. Instead, our results suggest that individuals with a more internal LOC are more likely to undertake preventive measures  ( Cobb-Clark et al, 2014 ) , and therefore they are more likely to be healthy and less likely to face health shocks. Thus, we find that individuals' LOC can partly explain advantageous selection into SUPP  ( Cutler et al, 2008 ). Our main results carry important implications for policy.
Methods This study was part of a larger international study COMET-G, conducted in 40 countries including Greece and France. In France, the study took place during the lockdown period from 11 April 2020 to 1 May 2020. All data were collected online and anonymously. Participants were informed about this study through social networks and news sites. The study design was cross-sectional, the questionnaire was carried out once with estimation during the lockdown of alcohol and tobacco use as well as anxiety, depression, suicidal ideation and other addictive behaviors, in comparison with before lockdown. All participants gave their informed consent before completing the questionnaire. The study was approved by the Ethics Committee of the Faculty of Medicine of Thessaloniki, Greece. In France, the study was carried out in agreement with the Ethics Committees of the Nord-Essonne public hospital.
The protocol and questionnaire used in this study are shown in Supplementary Table  1. Demographic data and data pertaining to general health, previous psychiatric history, current symptoms of anxiety (using the STAI-Y1 state, 16), depression (using the CES-D, 17) and suicidal ideation (using the RASS, 18) were collected; as well as changes in sleep, tobacco and alcohol use, sexual behavior, family relationships, financial situation, eating and exercising and finally, religion/spirituality. Additionally, beliefs concerning the COVID-19 outbreak, including the measures taken and conspiracy theories, were investigated. According to a previously developed method by Fountoulakis  (18, 19) , a cut-off score > 24 for the CES-D was used to identify clinical cases of major depression; a STAI-S score > 38 was used to identify clinical cases of anxiety.
1a , upper panel). We also used linear mixed models (LMMs) to detect SNPs with frequency trajectories that differed significantly between treatment and control lines (Fig. 1a , lower panel). In total, these tests uncovered 18,072 candidate SNPs with signatures of selection (out of a total of 353,188 SNPs tested). To account for genetic linkage among SNPs, we used a recently developed approach to group candidate SNPs with signatures of selection into 121 putatively independent "haplotype blocks," consisting of 4977 proximate SNPs with correlated frequency shifts  45  (Fig. 1a , shaded boxes and colored points). The 121 selected haplotype blocks were very large in size (mean = 1. 89 Mb [716 kb-6. 6 Mb]; Supplementary Data 1), covering ~44% of the 518 Mb genome assembly, potentially due to strong selection pressure and few generations of recombination to disassociate linked SNPs.
Salinity is arguably the strongest driver of aquatic biogeographic distributions  [81] [82] [83]. Therefore, Fig. 3  Experiment-selected loci also exhibit signatures of selection in the wild populations from the Baltic Sea. a Locations sampled for pooled genomic sequencing across the Baltic Sea. Sampling sites are colored according to their mean annual salinity estimated from the International Council for the Exploration of the Sea (Supplementary Data 3). The map was generated using data from https://www. naturalearthdata. com (Accessed June 2020). b SNPs underlying experiment-selected alleles harbor a strong signature of polygenic selection across the wild populations, as captured by the Q X statistic. The Q X statistic measures how quickly the experiment-selected loci evolve and how strongly SNP frequencies covary across wild populations.
Methods Population sampling, design of the laboratory evolution experiment, and sequence data collection. All copepod populations examined in this study, including that used in the laboratory evolution experiment and those surveyed in the wild are considered Eurytemora affinis proper, which is the European clade of the Eurytemora affinis species complex  [85] [86] [87]. The E. affinis copepods used in the laboratory natural selection experiment were collected from Kiel Canal in Kiel, Germany (latitude = 54°19′ 59. 88′′N, longitude = 10°9′0′′) in 2017 (~1000 copepods) and on May 30, 2018 (85 gravid females and 40 juveniles). The two collections of copepods were mixed and maintained at 15 PSU to increase population size and acclimate to laboratory conditions. Two samples of adult copepods (25 male and 25 female each) from the mixed culture were collected for pooled wholegenome sequencing (Pool-seq) to represent the starting population for the laboratory natural selection experiment and capture variance in starting SNP frequency.
Individual adult copepods (N = 50; 25 male and 25 female) were collected for sequencing from each laboratory selection line at generations six (after one generation at 0 PSU in the treatment lines) and ten (after five generations at 0 PSU in the treatment lines). Two of the control lines (BS3C and BS4C) were also sampled at generation 20 to increase the number of sampled timepoints for our LMM test for selection. Sampled copepods from each line (Supplementary Data 6) were pooled and their DNA was extracted using the DNeasy Blood and Tissue Extraction kit (Qiagen, Inc. ). Paired-end whole-genome sequencing libraries were prepared using the Nextera DNA kit (Illumina Inc. ) and sequenced on four lanes of Illumina Hi-Seq 4000 and one lane of Illumina NovaSeq 6000 at the University of Chicago Genomics Facility, generating an average of ~117 million paired-end (100 bp) reads per pool.
Additionally, wild E. affinis populations were collected from eight locations in the Baltic Sea (Fig. 3 ; Supplementary Data 3) using bongo and WP2 nets with 100 μm mesh. These sampling locations spanned a range of mean annual salinities from low (~3 PSU) to higher salinity (~19 PSU). Mean annual salinity for each site was calculated from the International Council for the Exploration of the Sea (ICES) database (https://ocean. ices. dk/Helcom/Helcom. aspx?Mode=1; accessed June 2020), using data collected from 1995-2020. From each population, individual copepods (ranging from 50 to 200 in number) were pooled and their DNA was extracted using the DNeasy Blood and Tissue Extraction kit (Qiagen, Inc. ). Pairedend whole-genome sequencing libraries were prepared using the Illumina Nextera DNA kit (Illumina, Inc.
To generate RNA-seq data for assembly of the reference transcriptome, approximately 100 individuals of all life stages were collected from the European E. affinis laboratory culture derived from Kiel, Germany. Animals were pooled and subjected to a Trizol/Qiagen RNeasy hybrid total RNA extraction protocol. An mRNA sequencing library was prepared using the Illumina TruSeq Stranded mRNA kit and sequenced on an Illumina HiSeq 4000 sequencer, generating approximately 186 million 100 bp paired-end reads. Raw RNA-seq reads were processed using BBDuk in the BBtools v38. 37 package (https://sourceforge. net/ projects/bbmap/) to filter adapter sequences, low complexity sequences, and low quality (Q < 10) bases using a sliding window (ktrim = r k = 23 mink = 11 hdist = 1 qtrim = w trimq = 10 minlen = 36 entropy = 0.
01 entropywindow = 50 entropyk = 5 tbo). To filter sequences that may have originated from microbial contamination prior to assembly, reads were mapped against a database of reference and representative bacterial, archaeal, and fungal genomes from the NCBI RefSeq using Bowtie 2 v2. 3. 5 and were removed if they mapped concordantly  97. Cleaned reads were assembled using Trinity v2. 6. 6 in strand-specific mode  98. mRNA expression estimates were made using RSEM v1. 3. 1  99  with cleaned reads mapped to the Trinity assembly with Bowtie 2  97. Only the most highly expressed Trinity "isoform" per 'gene' was retained to obtain the most highly supported transcriptome assembly. Transcript sequences were clustered to 95% similarity using CD-HIT v4. 7 100 to reduce redundancy associated with allelic variation and assembly errors.
Transdecoder v5. 5 101 was used to predict open reading frames and coding sequences. Predictions included BLASTP 102 (BLAST + v2. 7. 1) hits to annotated proteins of the E. affinis complex draft genome and HMMER v3. 2. 1 (http://hmmer. org) hits to the Pfam protein database  103. The resulting non-redundant transcriptome assembly was used to anchor the de novo assembly of Pool-seq data around coding regions to obtain a pseudo-reference genome. First, the "left" pairs of Pool-seq reads from the starting laboratory population were mapped to the coding sequences of the transcriptome with BWA-MEM v0. 7. 17  104  , retaining reads with a mapping quality >20. The corresponding "right" pairs of the mapped reads were extracted and the mapped reads were assembled using Trinity v. 2. 6. 6  98  , as Trinity was developed to assemble highly variable sequences with potentially uneven coverage.
Raw Pool-seq reads collected from the laboratory selection lines and Baltic Sea wild populations were processed with Trimmomatic v0. 39  105  to filter adapter sequences and low-quality bases using a sliding window (LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36). Quality-filtered Pool-seq reads were mapped to the pseudo-reference genome using BWA-MEM v0. 7. 17  104  retaining only reads that mapped concordantly with a mapping quality >20. Duplicate reads were removed using Picard v2. 18. 27 (http://broadinstitute. github. io/picard) and regions around indels were realigned using GATK v3. 8 106. For the laboratory lines and wild populations separately, SAMtools v1. 3. 1 was used to convert BAM files into mpileup format after removing low-quality alignments and bases (Q < 20). VarScan v2.
4. 3 107 mpileup2cns was used to call SNPs for each of the mpileup files using the following options: "-min-coverage 20 -min-avg-qual 20 -min-var-freq 0. 0001 -variants -output-vcf". The resultant VCF files were processed using the R package poolfstat v. 1. 1. 1 108 retaining bi-allelic SNPs with 4 reads required for a base call, an overall minimum MAF of 0. 01, and a minimum and maximum coverage of 10 and 200 reads, respectively. This procedure resulted in 367,846 SNPs called in the laboratory samples and 693,438 SNPs called in the wild samples. Estimating effective population size. We used the change in SNP frequencies over time to estimate the effective population sizes (N e ) for each line with the R package poolSeq v0. 3. 5 109,110 and WFABC v1. 1  111. Using the poolSeq model, we made estimates using both the "Plan I" (census-size dependent) and "Plan II" (census-size independent) methods, and, given the uncertainty in the census population size, assumed a range of reasonable census sizes for the "Plan I" calculations.
We made N e estimates in genomic windows of 1000 SNPs in size and took the median of those windows (Supplementary Data 8). Detecting signatures of selection in the laboratory natural selection experiment. Multiple approaches were used to test for signatures of natural selection imposed by decreasing salinity in our SNP frequency data. We used a version of the CMH test that uses allele frequency data from multiple timepoints, and also accounts for overdispersion due to genetic drift and Pool-seq sampling, to detect SNPs with frequencies that changed more than expected under random genetic drift across lines 112. We also performed a similar Chi-square test to detect linespecific signatures of selection using the same R package. These tests were performed considering only the eight treatment lines with three sampling time-points, using the mean SNP frequency from the two starting laboratory population samples as the generation zero frequency for each line.
The CMH and Chi-square test statistics were calculated using estimated effective population sizes for each line to calibrate p values accounting for genetic drift (Supplementary Data 8). P values were converted to q-values to correct for multiple testing using the R package qvalue 113 and SNPs with a q value < 0. 05 were considered significant. As the CMH and Chi-square tests did not take full advantage of our sampling design with control and treatment lines, SNPs were also tested for signatures of selection using LMMs in the R package lme4 114 , considering data from all control and treatment lines. The goal of this test was to detect SNPs with frequency trajectories that were significantly different between treatment and control lines. Therefore, we could detect SNPs with deterministic signatures of directional selection to decreasing salinity, while accounting for the effects of random genetic drift and selection to laboratory conditions.
LRT test statistics were compared to the Chi-square distribution with two degrees of freedom to obtain p values. P values were converted to q values to correct for multiple testing  113  and SNPs with a q value < 0. 05 were considered significant. To account for linkage among SNPs, which could inflate signatures of selection for neutral SNPs, putatively independent selected "haplotype blocks" were identified using the R package haplovalidate  45, 119  considering the eight treatment lines with three sampled timepoints. This approach identifies and delimits selected haplotype blocks by clustering candidate SNPs with correlated frequency changes. The union of candidate SNPs from all three tests for selection (CMH test, linespecific Chi-square, and LMM; N = 18,072) were used as input to haplovalidate, which was run with default settings.
Identified selected haplotype blocks were considered as selected alleles. The median frequency of SNPs characterizing each haplotype block was used to estimate allele frequencies at each timepoint in each line. To determine whether an allele had responded to selection in each line and timepoint, we simulated 10,000 iterations of neutral evolution using the R package poolSeq 109 for each allele's starting frequency using an effective population size of 1750 (the approximate average of N e estimates across lines and methods; Supplementary Data 8). Alleles were then considered to be under selection in each line and timepoint if the frequency had increased more than the 99. 9th percentile of neutral simulations at generation ten. Selection coefficients (s) were estimated by linear modeling with lme4 114 from the slope of the linear regression of logit-transformed allele frequencies against generation, averaged across lines.
1 ), we performed extensive Wright-Fisher simulations using SLiM v3. 7 120. We included both population genetic and quantitative genetic characterizations of epistasis (Table  2 ; Supplementary Fig. 1 ; Supplementary Table  1 ) to test which could best explain the extent of parallelism in our experimental data. We simulated ten replicate lines under selection with a constant population size of 1750 individuals, based on the average effective population size estimated across lines and methods (Supplementary Data 8). Mirroring the design of our E&R experiment, we recorded allele frequencies of selected loci in every population at generations zero (starting allele frequencies), six, and ten. To introduce the additional variance in allele frequencies due to population sampling and sequencing, we sampled 100 individuals from each population to be included in each "pool", and then sampled allele copies from the pool according to empirical sequencing coverage of each locus.
In addition, we included several quantitative fitness functions with epistatic effects, in which the positive fitness effects of alleles increase in the presence of additional alleles (Table  2b ; Supplementary Fig. 1 ). The quantitative trait (QT, quantitative fitness) models were chosen to replicate the models used in similar simulation studies of replicated evolution experiments  38, 50  and are also available in the simulation software MimicrEE2 121. We simulated under multiple different quantitative fitness functions for two reasons: (1) our experiment allowed individuals to survive and replicate naturally (i. e. , we did not select and transfer individuals to the next generation), and therefore the exact mode of selection is unknown, and (2) our experimental design included a moving optimum (salinity declined over generations 2-6), which was not perfectly captured by available fitness functions.
g. , Fig. 2a, c ) and the mean Jaccard index between populations was recorded for each iteration. As with the empirical data, simulated alleles were considered under selection in a given line and timepoint if their frequency change was greater than 99. 9% of neutral simulations performed using the R packages poolSeq 109. To estimate the α parameter of the positive epistasis model and determine whether the positive epistasis model could recreate the observed levels of parallelism, we performed Approximate Bayesian Computation using the R package EasyABC v1. 5 123 and the simulations as above using SLiM 3. The mean empirical Jaccard index at generations six and ten was used as target summary statistics. The Lenormand sequential algorithm 124 was used to estimate α using a uniform prior distribution [0,50].