dataesr/scientific-paragraphs-categorization huggingface dataset:
Membrane transport proteins Membrane transport proteins were identified by comparing the protein sequences of the domatia-associated lineages against the Transporter Classification Database (TCDB). We identified 3622 proteins of the transporters superfamily in all 4 strains. Possessing 926 transporters, CBS 134916 contains the highest number of this protein family, followed by CBS 132003, CBS 134920, and CBS 135597, containing 909, 900, and 887 transporters, respectively. Striking contractions in the transporter content among black yeasts were observed in the gene families nicotinate permease (2. A. 1. 14. 11), trichothecene efflux pump (2. A. 1. 3. 47), MFS transport (2. A. 1. 2. 33), peroxisomal isopenicillin N importer (2. A. 1. 2. 86), and multidrug resistance transporter (2. A. 1. 3. 73) (Fig.
Asexual and sexual reproduction The sexual identity of the sequenced strains was investigated by homology with the mating-type locus (MAT) structure previously described in Chaetothyriales. Strains proved to be heterothallic, since either MAT1-1 or MAT1-2 genes were found in a single scaffold, in addition to the flanking genes SLA2-APN2-COX13. CBS 134916 and CBS 134920 had MAT1-1 genes, while CBS 135597 and CBS 132003 harbored MAT1-2 (Fig. 2 ). In CBS 134920 and CBS 132003 the SLA2 gene was located within the MAT locus, while in CBS 134916 and CBS 135597 this gene was not clustered but was found in a separate scaffold. Regarding protein family domains associated with the parasexual cycle, the heterokaryon incompatibility protein (HET-PF06985) ranged from 10 domains in the fungus CBS 134920 to 31 domains in CBS 134916 (Table  S4 ).
The composition of CAZymes found in the black yeasts associated to ant domatia suggests that these organisms are unlikely to be efficient polysaccharide decomposers; this contrasts with the classic view of mutualistic fungus-ant relationship, where the fungal partner degrades plant material incorporated by the ants  (De Fine Licht et al. 2010). Upregulation of membrane transporters and increased gene dosage of this family of proteins are well-known mechanisms to acquire resistance against a variety of stressors  (Sanglard et al. 2009; Morschhauser 2010; Yew et al. 2016 ). In the genomes of fungi colonizing ant domatia, the number of membrane proteins is reduced compared to other Chaetothyriales  (Blasi et al. 2017; Teixeira et al. 2017 ). This finding is consistent with the contraction of the major facilitator, sugar transporter-like domain IPR005828 represented in Fig.
The EPIMAD Registry covers a large area of Northern France (six million inhabitants) and collects all incident CD cases  [6, 7]. Data from multiplex families defined by three or more than three first-degree relatives with CD have been recorded by EPIMAD. We previously reported that 22 multiplex families and genotyping evidenced that most cases from these multiplex families were related to high frequency of NOD2 R702W, G908R and L1007fs variations  [8]. However, rare CD multiplex families did not display high frequency of R702W, G908R and L1007fs variations. Thus, in these families, a high prevalence of affected cases may rely on other major genetic susceptibility variants that remain to be determined. In order to identify new genetic variations with a major effect in CD, a Whole Exome Sequencing (WES) protocol has been initiated in a family with four CD cases among two generations.
The NOD2 gene was Sanger sequenced in all available subjects to ascertain the presence or the absence of c. 3030A>C;p. (N1010K) and of p. L1007fs (rs2066847). Sanger sequencing allowed the authors to exclude any other NOD2 variation. Like p. L1007fs, the new p. N1010K NOD2 variation was located in the Leucine-Rich Repeat (LRR) domain of the NOD2 protein, which was already implicated in CD (Figure  2A )  [9]. The heterozygous missense c. 3030A>C;p. (N1010K) was absent from the public databases GnomAD, ExACand Kaviar. Coverage metrics from WES samples in public databases were considered as correct: 99. 79% of control samples presented a coverage >20% for this region. This new NOD2 variation appeared as a very rare genetic event. The deleterious effect for N1010K variation was strongly suggested by in silico predictions (Table  1 ).
The well-conserved asparagine residue involved in the N1010K variation is a polar amino acid such as lysine. However, the proximity of a conserved threonine amino acid T1012 within the NOD2 protein sequence could form a [Asn-X-Ser/Thr]motif that could be considered as a potential target for N-glycosylation. This could lead to a deleterious substitution  [14]. Moreover, the N1010K substitution is considered to have a significant effect according to the Grantham score based on the physico-chemical difference between asparagine and lysine. Regarding the highly-conserved amino acid, the location within the functional domain of NOD2 and the absence of N1010K variation in other databases, the N1010K variation is predicted to be deleterious with CADD Phred and SIFT in silico tools, and probably deleterious with PolyPhen2.
Therefore, N1010K could disrupt or attenuate the association between the HD2 and LRR domain and could act as a loss-of-function variation. This assumption is reinforced by the significant conservation of residue N1010 and the absence of known variation in public databases. Two out of the four patients were more severely affected than the other family members with CD according to the Montreal Classification with an earlier diagnosis  [6]. Interestingly, both patients, III:2 and III:3, presented the recurrent L1007fs variation in addition to the N1010K variation in the compound heterozygous state. The L1007fs variation results in a frameshift mutation that generates a truncated NOD2 protein, which fails to co-localize to plasma membrane  [9, 16]. This variation resulting in a truncated protein is a major genetic risk factor of CD  [2, 9].
Regarding the absence of the N1010K in all screened databases (ExAC, GnomAD, Kaviar and 1000 Genomes), it is unlikely that the N1010K rare variant should display in control subjects an MAF compatible with a successful case control study. Anyway, one could hypothesize that extensive genotyping in large groups of Crohn's disease patients could reveal this variant in some patients. Future research should focus on discovering new genes that have implications in predicting either a better or poor overall prognosis. Functional studies should be performed to ascertain the deleterious affect of the N1010K variation.
Subjects One of the 22 CD multiplex families (a family with 3 or more affected first degree relatives) from the population-based EPIMAD Registry was recruited. The L1007fs (rs2066847) mutation was present in the family, but transmitted through a non-affected husband (II:1). However, none of the R702W, G908R and L1007fs were present in affected members of Generation II (Figure  1A ). In a previous study, subjects (I:2, II:2, II:6, II:1, III:2, III:3) were genotyped for the R702W, G908R and L1007fs variations of the NOD2 gene  [8]. The authors obtained an approval with a waiver of informed consent for all subjects. This work was approved by the Comité de Protection des Personnes Nord Ouest IV (the Institutional Review Board for University Hospital of Lille). The Comité de Protection des Personnes Nord Ouest IV is registered by the Office for Human Research Protections Database (IORG0009553).
Exome Sequencing Analysis and Computer Analyses WES was carried out in 7 persons from the F49M family: 4 CD affected and 3 intra-familial control subjects. For this purpose, we used a NimbleGenSeqCap E2 exome v3 capture in combination with Illumina next-generation sequencing (on a HiSeq 4000 system using paired-end reads), following the manufacturers' protocols. Sequence reads were mapped to the human reference genome (GRCh37/hg19) using BWA v0. 7. 13 software. The target was covered with a mean depth of 127. 7 reads in the 7 samples. Variant detection was performed with GATK v3. 3 software, and candidate variants were filtered out to fit a minimum depth of 8. Variants were then annotated with the Ensembl database v75 using their Perl API, and only the non-synonymous coding, stop gain or loss, frameshift, splice site and miRNA variants were kept for further analysis.
In Silico Predictions and Annotations Prediction of in silico pathogenicity for missenses was performed using the CADD software (Version 1. 3) with calculation of the CADD Phred score  [21]. A variant was predicted to be pathogenic if the CADD Phred score was above 20. Predictive tools commonly used for variant annotation including SIFT  [22]  and PolyPhen2  [23]  were also used.
Acknowledgments: We would like to thank  Nigel Quayle  for kindly correcting the manuscript. This work is issued from the Epimad registry, EPIMAD is supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  Agence Santé Publique France  and also received logistic support from the  European Charity Fundation DigestScience (Lille, France). We would also like to thank the patients and their family, who have kindly contributed to this study.
Sample The analyses described here are secondary analysis of data initially collected in the PROMIS Wave 1 cohort  [16, 17]. Recruitment took place at four PROMIS sites (University of Pittsburgh, University of North Carolina-Chapel Hill, Stanford University, and Duke University) and online via YouGovPolimetrix. com-a non-partisan online polling firm administering surveys for market research and political polling. As PROMIS item banks are targeted for use in clinical research (including clinical trials, observational research, and epidemiological studies), authors reported assembling a representative sample with a diverse severity of emotional distress.
PROMIS depression item Bank The development of the PROMIS depression item bank has been described in detail elsewhere  [17]. Items are rated on a 5-point frequency scale (never, rarely, sometimes, often, always) within a seven-day recall period with a model-based scoring system with higher values reflecting greater depression  [17]. The 28-item bank comprised a single factor combining items from cognitive (n = 17), affective (n = 9), behavioral (n = 1) and suicidal (n = 1) conceptual domains. The current analyses use data from the extended 56-item PROMIS depression item bank. Five items were eliminated for intellectual property issues, leaving 51 items in the pool available for analysis. As an item bank relates to items that have been calibrated, the 51-item set will be referred to as an item pool.
Analysis procedure Data were analyzed in using RUMM2030  [11] , one of several available software programs that provides graphical and statistical item analyses using Rasch unidimensional models for measurement. In line with the methods described above, the initial RMT analysis of the 28-item bank identified limited measurement and precision on the floor of the scale. We therefore hypothesized that adding new items could improve measurement performance by expanding the range of depression measured by the PRO-MIS items. For this, the extended item pool of 51 items* (i. e. , original 28 + 23 items) was analyzed with RMT and performance compared to original 28-item bank. For brevity, results are presented in two stages: in stage 1, the extent to which the 51-items satisfy the RMT criteria, and in stage 2, the relative measurement performance of the 51-item and the 28-item pools.
Sample In the sample, described elsewhere  [16, 17] , 8% of participants were of a clinical population and 92% of a general population. Secondary analysis was performed on a subsample (n = 825) of patients ranging in age 18-88 years (mean = 50. 91, SD = 18. 92). Of these 51% were female, and 79% white, 10% Hispanic. The sample size reported between the two different stages of analyses (both on scale and on item level) varies slightly, as RMT analyses estimates are calculated on the basis of available data and excluding extreme scores located at the floor and ceiling. Stage 1 RMT analysis: examination of the PROMIS 51-item pool measurement performance Scale-to-sample targeting Person locations ranged approximately between -6 to 4 logits relative to the item threshold locations, ranging approximately between -3 to 3 logits, and covering 60% of the depression measured in the sample (Table  1 ).
Conclusions In this study, RMT analysis supported the statistical scale performance of the PROMIS depression scales, but also identified targeting and sample measurement limitations. In practice, diagnosed anomalies may be resolved by attempting to explore the data and interpret the disparity between expected and observed scores of an evaluated scale further  [3]. Within the RMT paradigm, rating scales and constructs are modified and, if necessary, more data are collected. In this respect, the application of RMT to the PROMIS depression item bank suggests that measurement could benefit by further examination of the construct under measurement and specifically the concept of interest and context of use.
Acknowledgements The authors would like to thank the  Patient-Reported Outcomes Measurement Information System (PROMIS®) group  for making available the data on the PROMIS Depression Item Bank that was used for illustrative purposes in this paper. The authors received writing and editorial support under the guidance of the authors from  Dr. Chad Green  (Clinical Outcomes Solutions). We would also like to thank  Amlan RayChaudhury, PhD  for the editorial support in the submission process.
Data We start by describing more extensively the variables needed to capture the main concepts needed to test the predictions of our framework regarding conflict and capital cities. (All other variables will be introduced as they are used, and described in the Online Data Appendix, which also contains descriptive statistics for all variables. ) Testing our key predictions regarding the likelihood and consequences of conflict as a function of distance to the capital city requires geolocated information on the incidence of conflict. For that we use the PRIO-GRID dataset (Tollefsen, Strand and Buhaug 2012) (Advanced Conflict Data Catalogue (ACDC) project). This dataset makes available a number of different variables measured at the level of 0. 5 x 0. 5 decimal degree cells covering all terrestrial areas of the world.
Each cell is, on any given year, attributed to one single (independent) country -for cells that straddle country borders, the attribution is to whichever independent country happens to contain the largest share of the cell's territory. The dataset contains a measure of distance (in kilometers) from the cell centroid to the country's capital, but the designation of capital cities did not generally track the instances of capital city moveswe added those manually (as described in Table  2 ). However, the dataset does cover changes in capital cities due to the breakup and emergence of new countries. We will use those sources of variation as an integral part of our identification strategy, as we discuss below. 22 The dataset also records geolocated information on conflict. We use as our first main variable of interest the dummy CivConf , coded for the years between 1989 and 2008 (Hallberg 2012), which indicates whether a cell lies within an area afflicted by intrastate conflict in a particular year.
Specifically, the data classifies conflict types into: conflicts between a state and a non-state group outside its own territory ("colonial wars," coded as "1"), betwee two or more states (interstate conflicts, "2"), conflicts between a state and one or more non-state actors inside its own territory (intrastate conflicts, "3"), and intrastate conflicts with intervention from other states (internationalized intrastate conflicts, "4"). Our variable captures conflicts classified as "3" or "4," since intrastate conflict is the kind of event our framework is concerned with. The conflict-afflicted area is defined, for each year in a given conflict, as the circular polygon that encompasses all of the recorded battles. All cells that intersect this polygon will be recorded as CivConf = 1. This entails two kinds of measurement error for this variable: "false negatives" and "false positives".
For instance, suppose a cell happens to be on the border between Ghana and Cote d'Ivoire, with 40% of its territory on the former and 60% on the latter. The dataset would attribute that cell to Ghana between 1957 (when that country became independent) and 1960 (when Cote d'Ivoire did), because Cote d'Ivoire was then coded as missing. From 1960 onwards, it would attribute the cell to Cote d'Ivoire. In that case, we attributed the cell to Cote d'Ivoire for all years. capital. 23 This would bias us in favor of finding in favor of our Prediction 1. On the other hand, as long as the density of battles is also related to the density of population, the likelihood of false positives also increases with distance. This would bias results against that same prediction. There is reason to believe a priori -though by definition it is impossible to test -that false positives are actually going to be more numerous than false negatives.
The circles correspond to the conflict polygons, which yield four kinds of cells: "true positives" (blue, crossed), "false positives" (red, upward lines), "true negatives" (white, unfilled), and "false negatives" (green, downward dashes). We also want to exploit distinctions between different types of conflicts, in order to build placebo specifications, as argued in the previous section, and shed further light on the nature of the connections between distance and conflict. The first placebo is to use interstate conflict (type "2"). For other distinctions, we match the Prio-GRID data to the original conflict dataset from which it derives -namely, the UCDP/PRIO Armed Conflict Dataset (Gledditsch et al 2002, Themnér and Wallensteen 2014) -which classifies the incompatibility motivating each conflict.
We will consider a second key conflict variable from the PRIO-GRID dataset, namely Onset (Gleditsch   et al 2002, Strand 2006). This variable indicates for every cell-year pair whether a conflict started in that cell-year. It has the advantage of affording a longer time period, as it is coded for the years between 1946 and 2004, which will let us exploit the time variation more thoroughly. On the other hand, it refers to occurrences that are obviously a lot less frequent, which makes the data relatively sparse.
X ic is a vector of control variables (also from the PRIO-GRID dataset), which help us deal with a number of factors that may correlate with the likelihood of conflict: income per capita, population, and infant mortality as measures of socio-economic conditions, travel time to the nearest major city as a measure of broad isolation as well as urbanization, and a number of geographic characteristics (share of mountainous terrain and forest coverage, latitude, average temperature and precipitation). 24 µ c are  24  All of these variables are also averaged over the relevant period. country fixed effects, so that we focus on the variation within countries, and ε ic is the error term. We cluster standard errors at the country level, to allow for correlated shocks within countries. 4 ) to (  6 ) use the indicator of conflict onsets (the start of a new conflict) in each cell, averaged from 1946 to 2008 where conflict onset data are available.
All columns include the averages over the corresponding period of the following variables for each cell: log Gross Cell Products per capita (night luminosity-enhanced measures, available in 1990, 1995, 2000, 2005), log population (available in 1990, 1995, 2000, 2005), temperature, precipitation, cell size. In addition, all columns control for: infant mortality rates, proportion of mountain area (all measured in 2000), log travel time to the nearest urban area, and cell latitude. Country fixed effects are included. *** p<0. 01, ** p<0. 05, * p<0. 1. The results are in Table  3. Column (1) shows the correlation between average CivConf and the measure of distance to the capital city, for the full sample. We see no evidence of a link. However, Column (2) shows that countries with an average Polity score below (or equal to) zero -a threshold meant to encompass "autocracies" and "closed anocracies," as defined by the Polity IV dataset -display a strong negative correlation: conflict is more likely in areas that are closer to the capital.
Conflict Becomes More Likely When the Capital Is Moved Closer A different look at the link between distance to the capital and conflict comes from exploiting the variation over time in the conflict data. Generally speaking, distance to the capital is constant for a given grid cell, but there are two important kinds of exception to this rule, which afford us some variation over time. First, when the same grid cell becomes part of a different country, the relevant capital city changes as a result. Second, the same is true when a given country changes its capital. To the extent that these events are uncorrelated with time-variant grid-cell characteristics, we can consider the effects of those quasi-random "treatments" of changing distance to the capital. To better understand the nature of the variation, Table  5  lists the countries containing grid cells where distance to the capital is not a constant value, over the years, in our dataset.
Conflict Is More Dangerous Closer to the Capital We then turn to Prediction 2, namely that conflict that happens closer to the capital is more likely to dislodge the incumbent regime. For that we adapt our panel strategy, as in (  6 ), using the following specification, connecting the country-level regime change outcome and the grid-cell-level data on conflict and distance: 26 RegimeChange ct = β 0 +β 1 * Y ict ×DistCapital ict +β 2i * Y ict +β 3 * DistCapital ict +W ict B+ν i +ω t +ε ict. ( 9 ) RegimeChange ct is an indicator of whether there is a change in regime in country c, as coded in the Polity IV dataset, in the five-year interval after year t. 27  Prediction 2 entails β 1 < 0: as a grid cell becomes more distant from the capital, the connection between conflict in that cell and subsequent regime change gets weaker.
30 en it comes to measuring how isolated a capital city is, we use the axiomatically grounded family of measures of spatial concentration (or equivalently, isolation) around a point of interest proposed in Campante and Do (2010). Specifically, they show that a very simple and easily interpretable measure of isolation has a number of desirable properties (and uniquely so): the average log distance to the capital city -which for shorthand we will describe as AvgLogDistance. 31  compute the measure using the database Gridded Population of the World (GPW), Version 3 from the Socio-Economic Data Center (SEDC) at Columbia University. This dataset, published in 2005, contains the information for the years 1990, 1995 and 2000, and is arguably the most detailed world population map available. Over the course of more than 10 years, these data are gathered from national censuses and transformed into a global grid of 2.
This includes what the Polity dataset classifies as "open anocracies" (Polity score between zero and 5), as well as a few less established "democracies". We see a positive coefficient (p-value: 0. 122), only for the subsample of autocracies (Column (3)). Note also that the estimated coefficients are considerably smaller and less precisely estimated, consistent with substantial measurement error being introduced by the coarseness of the measure. In addition, Columns (  7 )-(  8 ) repeat the same exercise with the Freedom House measure of governance -reverting back to using our standard zero threshold for autocracies, and AvgLogDistance as our key independent variable. The results are very much consistent, which is unsurprising given that the measures of governance are very highly correlated (in excess of 0.