dataesr/scientific-paragraphs-categorization huggingface dataset:
SNP loci were identified using the GATK HaplotypeCaller (version 3. 5-0;  McKenna et al. , 2010). We filtered the SNP loci for read depth ≥50 and then for presence in all populations using BEDtools (version 2. 26) and custom scripts written in R (version 3. 3. 3; R. Core. Team, 2017). One population, VKor08_A, did not provide sufficient data for analysis of SNP frequencies. We used SNP data to generate a parsimony-based molecular phylogeny with the branch-and-bound algorithm in PAUP* (4. a build 159;  Swofford, 2002)  and estimated internal node support with the bootstrap algorithm in PAUP*. Because there was no bootstrap support for internal nodes, but strong genetic differentiation among populations, we tested the relationship between host use distance and genetic distance, as measured by F ST.
Because A. certus individuals were pooled within populations to make the libraries for sequencing, we used read depths to estimate allele frequencies for SNP loci. We filtered the data for SNP loci that were present in all populations and had read depth ≥50, and we used the numbers of individuals in each pool in calculating F ST between populations with the calcPopDiff function in the polysat R package (version 1. 7-2;  Clark, 2017). Using Mantel's permutation test, we compared the genetic and parasitism distance matrices (10,000 permutations with the mantel. randtest function in the ade4 R package). To explore the relationship between genetic differentiation and differences in parasitism, we determined whether the position of reduced-representation loci in the genome was genic (defined as occurring either within a gene or ≤10 kb upstream or downstream of a gene) or nongenic and then categorized genic loci based on SNP content (defined as "with SNP" or "without SNP").
We found 432 genes near loci with SNPs, and 328 genes near loci without SNPs. Among the with-SNP genes, 405 had BLASTP hits in the RefSeq database, and 302 had gene ontology (GO) annotations. Among the without-SNP genes, 306 had BLASTP hits in the RefSeq database, and 213 had GO annotations. Several molecular functions and biological processes were present only among the with-SNP (e. g. , mRNA splicing via spliceosome) or without-SNP genes (e. g. , G protein-coupled receptor signaling pathway) (Figure  6 ), but none of these differences were significant in enrichment tests when corrected for multiple comparisons (Fisher's exact test, False Discovery Rate = 0. 05).
It might be driven by other adaptations, drift, or mutation. However, that we found any relationship between genetic differences and differences in host use is surprising, given our limited sampling of the A. certus genome. Our results imply that adaptation to different aphid species is quite local, that is at the level of counties rather than countries, because populations separated by 35-103 km showed differences in parasitism of aphid species. Variation in aphid abundances may reflect the spatial scale of crop and noncrop patchiness in the regions where these populations were collected. Unfortunately, we lack historical data for variation in abundances of these aphids. Nonetheless, several other lines of evidence suggest that adaptation to locally abundant aphids is likely for these parasitoid populations.
Aims. We use four observational data sets, mainly from the Rosetta mission, to constrain the activity pattern of the nucleus of comet 67P/Churyumov-Gerasimenko. Methods. We develop a numerical model that computes the production rate and non-gravitational acceleration of the nucleus of comet 67P as a function of time, taking into account its complex shape with a shape model reconstructed from OSIRIS imagery. We use this model to fit three observational data sets: the trajectory data from flight dynamics; the rotation state, as reconstructed from OSIRIS imagery; and the water production measurements from ROSINA, of 67P. The two key parameters of our model, adjusted to fit the three data sets all together, are the activity pattern and the momentum transfer efficiency (i. e. , the so-called "η parameter" of the non-gravitational forces).
Water production rate The total water production rate is an important constraint for any model of cometary activity, and a significant effort has been made to measure it for 67P. As summarised by  Hansen et al. (2016) , a number of different instruments have all been used and comparing and synthesising their results in non-trivial. Here, we use the ROSINA (Rosetta Orbiter Spectrometer for Ion and Neutral Analysis) measurements, empirically corrected for spacecraft position, as our observed data points, O Q. Hansen et al. (2016)  describe how estimating the uncertainty in these data, σ Q , is difficult so we use the bounds on the power-law, fitted with heliocentric distance to the inbound ROSINA data, as given in their Table  2. We point out that ROSINA data are inferred from local measurements in the coma of 67P.
For comparison with the observed water production rate and torque, we can simply evaluate our model at the time of each measurement, producing C Q and C τ , and directly compare. For the trajectory, however, a full N-body integration must be performed and the resulting position compared at each time (C R ). To do this we use the open-source REBOU N D code 1 (Rein & Liu 2012), complete with full general relativistic corrections  (Newhall et al. 1983 ) as implemented by the REBOU N Dx extension package 2. All the major planets are included, as well as Ceres, Pallas, 1 http://rebound. readthedocs. io/en/latest/ 2 http://reboundx. readthedocs. io/en/latest/index. html and Vesta, and are initialised with their positions in the J2000 ecliptic coordinate system according to the same ephemerides used in the Rosetta trajectory reconstructions (NASA/JPL solar system solution DE405;  Standish 1998 ).
Optimisation In order to constrain the unknown parameters in our model, such as the the surface active fraction, we perform a bounded least-squares fit to the data using the dogleg optimisation routine  (Voglis & Lagaris 2018)  provided in the scientific Python package. The optimisation proceeds, attempting to minimise the standard objective function Obj = N j=0 O j -C j σ j 2 , ( 9 ) with observed minus computed residuals, O -C, and observation uncertainties, σ, at each time-step, j, up to the total N. The root mean squared residuals are then RM S = Obj/N. In our case we have three separate datasets to fit to (R, Q and τ ), a multi-objective optimisation problem, and we therefore use a linearly-scaled combination of the three to generate a combined objective function. The term inside the brackets in Eq.
(  9 ) then becomes the sum of the three terms λ R O Rj -C Rj σ Rj , for 0 < j ≤ N R , λ Q O Qj -C Qj σ Qj , for N R < j ≤ N R + N Q , λ τ O τ j -C τ j σ τ j , for N R + N Q < j ≤ N, (10) respectively, where N = N R + N Q + N τ runs over the combined number of points in all three datasets, and the λ scaling coefficients are variables, which themselves must be optimised in order to give the desired weighting to each dataset. We set λ R = 1 and scale the other lambdas relative to it, by bootstrapping from the residuals of a preliminary run, to give roughly equal weighting to all three datasets. Due to the very small relative errors for range (σ R /R ∼ ±1 km /1 AU), this results in large values for the other lambdas (λ Q ∼ λ τ ∼ 50) to give equal weighting. For each optimisation, we fix the coupling factor, η, at a constant value and parametrise the model in terms of effective active fraction, x, across the surface.
More work will be needed to better understand the activity of 67P using data collected during the Rosetta mission. The solution found in this article through the process described in section 4 is non-unique on the one hand, and could probably be improved on the other hand. Improvements may come from a better understanding of the ranging data, resulting in the extraction of clean and accurate non-gravitational acceleration of the comet as a function of time around perihelion. The change in the direction of the spin axis or angular velocity could also provide a valuable additional data set that could help to constrain the activity pattern and reduce the number of solutions. In their interpretation of the temporal evolution of the rotational parameters,  Kramer et al. (2019)  did not introduce a temporal variation of the activity around perihelion but considered instead a spatially heterogeneous surface with 36 "patches" having different water-ice coverage.
Acknowledgements. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 686709. This work was supported by the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  16. 0008-2. The opinions expressed and arguments employed herein do not necessarily reflect the official view of the  Swiss Government. We thank the authors of several python package libraries which were used extensively here; these include NumPy, SciPy, SpiceyPy, REBOUND and REBOUNDx. We are grateful to  L. Maquet  for fruitful discussions in the course of this work. We also thank the reviewer,  Dennis Bodewits , for his comprehensive and useful review.
For optimal estimation of the concentration of the odor stimulus, calibration curves were performed using solid phase microextraction (SPME) combined with gas chromatographymass spectrometry (GC-MS) [Perkin Elmer Clarus 600 operated with Perkin Elmer TurboMass (  2008 ) software] to identify the compounds and obtain direct measurement of liquid concentrations within the headspace from the stock dilution 1:1,000 amyl acetate: mineral oil (0. 01 ppm) and for each 10-Fold dilution step (1:1,000; 1:10,000; 1:100,000; 1:1,000,000; 1:10,000,000; 1:100,000,000; 1:1,000,000,000). Three concentrations of amyl acetate were presented daily for each dog in a training session. Additionally, blank runs (i. e. , sessions with the eight positions arms containing controls) were randomly included throughout the sessions.
Data collection A semi-structured interview guide was developed specifically for this study, including open-ended questions on causes/experiences of stress and use of coping strategies (see supplementary materials, Additional File 2). Additional questions explored preferences for counselling and self-help interventions, findings for which are reported elsewhere  [24]. Two researchers (usually RP and MS; both females and holding postgraduate degrees in public health) co-facilitated each FGD over 45-60 min. One researcher moderated the discussion, while the second researcher maintained notes and asked clarifying questions. Other interviewers (see Acknowledgments) included both males and females. FGDs were conducted in Hindi  (12) , English  (9)  and Konkani  (1). All but two FGDs were audio-recorded, as administrators at the private-sector school denied permission for audio-recording.
All audio-recordings were transcribed verbatim. The sole Konkani FGD was further translated into English, as none of the coders were Konkani speakers. We analyzed detailed notes from the two FGDs which were not audio-recorded. Data saturation was discussed within the team on an ongoing basis. Interim FGD summaries were continuously monitored for emergent themes by the lead researcher (RP) in consultation with co-authors. FGDs were concluded when saturation was reached within each subsample (boys/ girls, older/younger adolescents across the two sites). Overall, 22 FGDs were conducted: 19 in schools and three at local community sites.
Analysis Thematic analysis was undertaken using a framework approach  [20, 21]. Transcripts were coded using Nvivo 11 software. Development of the analytical framework began with a set of deductive codes derived from the research questions and background literature. The framework was refined to include codes emergent from the data. Initial codes were assigned to discrete responses comprising phrases, sentences or paragraphs communicating a relevant idea. These were ordered into categories conveying inter-related ideas. The transcripts were distributed among three authors (RP, MS, MK) for coding. RP and MS organised the data in a matrix containing codes and categories in columns, and FGDs in rows. Themes were generated by comparing and contrasting data within and across the FGDs according to age, gender and site attributes.
O besity correlates with hematologic malignancies including leukemias, but risk of specific leukemia subtypes like acute promyelocytic leukemia and underlying molecular mechanisms are poorly understood. We explored multiple datasets for correlation between leukemia, body mass index (BMI) and molecular features. In a populationbased study (n=5. 2 million), we correlated BMI with promyelocytic leukemia, and other acute myeloid, lymphoid or other leukemias. In crosssectional studies, we tested BMI deviation in promyelocytic leukemia trial cohorts from that expected based on national surveys. We explored The Cancer Genome Atlas for transcriptional signatures and mutations enriched in promyelocytic leukemia and/or obesity, and confirmed a correlation between body mass and FLT3 mutations in promyelocytic leukemia cohorts by logistic regression.
UK population-based study: data collection and statistical methods Full details of the methods for the UK population study were described previously. 6 The study was approved by the London School of Hygiene and Tropical Medicine Ethics Committee. To identify outcomes of specific leukemia sub-types, Clinical Practice Research Datalink (CPRD) clinical records were searched for codes relating to specific leukemia subgroups. We controlled for multiple co-variates at time of the BMI record(s): age, smoking status, alcohol use, previous diabetes diagnosis, index of multiple deprivation, calendar period, and stratified by gender. We excluded people with missing smoking [49,206 of 5. 24 million (0. 9%)] and alcohol [394,196 of 5. 24 million (7. 5%)] status. Confidence intervals (CI) in Figure  1  are presented at the 99% level; all other CIs are presented at the 95% level.
Cross-sectional studies: data collection and statistical methods Acute promyelocytic leukemia cases from Spain were extracted from the PETHEMA database to include 414 cases diagnosed between 1998 and 2012. APL cases from Italy, where 134 adult patients were treated under the AIDA protocol, were included in the previously described cohort. 10 APL cases from the USA includ-ed the entire cohort of the published AML The Cancer Genome Atlas (TCGA) project 11 (n=20) plus 22 additional APL cases, unselected for any clinical variable, diagnosed at Washington University, St. Louis, MO, USA (Expanded TCGA cohort). For all case cohorts, BMI was measured at the time of diagnosis. 2] [13] [14]  Data sources for expected BMI in the local population are described in the Online Supplementary Appendix. We compared the distribution of BMI observed in the three APL case cohorts to the distribution of BMI expected in the general population of the same countries.
Specifically, to calculate the expected distribution of BMI in Italy, we used data from the Italian National Institute of Statistics,  14  and we selected the area of Lazio, where the APL cases were diagnosed, in the years 2000-2010. For Spain, we used data from the Eurostat,  15  and we selected the general population of Spain in the year 2008, the only year available. For both Italy and Spain, the expected BMI distribution was calculated using the available age-and gender-specific BMI distribution of the general population classified into three categories (<25, 25-29. 9, ≥30). For the USA, we used the 2009-2010 data from the American National Health and Nutrition Examination Survey. 16 The expected BMI distribution was calculated using the available race-, age-, and gender-specific BMI distribution of the general population classified into four categories (<25, 25-29.
Expression data analysis Expression data (RPKM matrix) were down-loaded from the AML TCGA data portal. Cases with available RNAseq, BMI and French-American-British (FAB) classification data (177 of 200) were used in the present study. Cases were classified by FAB in "APL" (FAB="M3") and "non-APL" (FAB ≠ "M3"), and by BMI in "obese" (BMI ≥ 30) and "non-obese" (BMI < 30). Genes with < 0. 2 reads per kilo base per million mapped reads (RPKM) in at least 75% of patients were removed. 11 The Quantitative Set Analysis for Gene Expression method, as implemented in the quSAGE package  15  in the R programming language (v3. 2. 3), was used to conduct supervised gene set enrichment analysis. For each expressed gene, the quSAGE algorithm calculates a probability density function (PDF) of differential expression between two groups of samples.
For each gene set, it then calculates "activity", i. e. the mean difference in log-expression of individual genes included in a gene set. Gene sets with False Discovery Rate (FDR) < 0. 05 were considered significant. We focused on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and chemical and genetic perturbations (CGP) gene set collections, down-loaded from MSigDB (http://software. broadinstitute. org/gsea/msigdb/). The CGP collection was used to confirm enrichment of previously identified APL-specific gene signatures  16  (Online Supplementary Table  S1 ). We focused on the KEGG collection, as it is enriched for metabolism-associated gene annotations. 16 The script to generate the present results is available on request.
Mutational data analysis For the analysis of the TCGA data, mutational data were retrieved from the TCGA AML paper,  11  and AML driver genes (restricted to those with at least 2 mutations in the dataset) were down-loaded from IntOgen. 17 For each gene, different mutations were conflated so that gene status in each patient was either "mutated" or "wild-type". For each gene, we then calculated the number of mutated or wild-type patients in the obese or nonobese groups, and calculated odds ratios (OR), 95% confidence intervals (CI), and P-values by Fisher's test with Benjamini-Hochberg correction. Only genes with >1 mutation in the dataset were considered, using the fdsm package in R. For the analysis of the retrospective cohort, FLT3 Internal Tandem Duplication (ITD) mutational data were provided by the referring centers.
Gender (female 53. 0%, 55. 2%, 50%, respectively) and age (median of 45, 45, 47 years, respectively) were similarly represented. Information on ethnicity was unavailable for the Spanish and Italian cohorts, whereas white, black and hispanic ethnicities were represented in the US cohort. To generate control groups for comparison, we obtained anthropometric data from epidemiological surveys of the general population in the different countries. As the prevalence of obesity has increased dramatically in most coun-     3 ; see also Methods section). In all three cohorts, there was strong evidence that the observed BMI distribution for cases across World Health Organization (WHO) BMI classes was different from that expected under the null hypothesis of no association (Italy: P<0. 001; Spain: P=0.
The possibility of matching transcriptional and mutational profiles from TCGA to patient clinical and BMI data provided an opportunity to generate hypotheses grounded on actual data. However, extracting biological significance from large molecular datasets remains challenging. Shifting the analytical focus from single genes to gene sets or pathways may allow signals to be captured even when the changes affecting individual genes are minimal, provided they are coherent. The gene setbased method we used here for transcriptional analysis does not assume equal variances, resulting in improved sensitivity and specificity over similar competing methods. 15 Our main finding is the upregulation of several genes involved in the metabolism of pro-inflammatory ω-6 polyunsaturated fatty acids (PUFA, linoleic and arachidonic) in APL.
The proximity of the dwarf irregular galaxy IC 10 (∼715 kpc;  Sakai et al. 1999; Sanna et al. 2009; Kim et al. 2009)  makes it an opportune object to investigate the relation between the local properties of the different gas phases and the larger scales characteristics in a low metallicity environment and to understand which combinations of emission lines provide useful diagnostics as a function of the spatial scale considered. Moreover, IC 10 has been mapped in all of the tracers available with Spitzer and Herschel, providing larger data cubes than currently available in the optical. Combinations of ionic and atomic tracers should provide the constraints necessary to build a self-consistent model of the ISM and thus assess the physical properties (e. g. , density, filling factor) of the different ISM phases.
In the present paper we perform a study of the ionised gas: the dense H ii regions as well as the diffuse ionised gas of IC 10. The best solutions in this study will then set a reference model of the ionised gas distribution and characteristics that will be used to further model the associated neutral and molecular gas components, in a subsequent paper. This paper is organized as follows. First, we present an overview of IC10 (Section 2) and the dataset used in this study (Section 3). Next, in Sections 4 we describe our modeling strat- egy. Modeling the MIR and FIR line emission we determine the physical properties of the ionised gas in the H ii regions as well as at larger scales (Section 5). Finally, we discuss the results obtained (Section 6) and we summarize our conclusions in Section 7.
Data IC 10 has been observed with the Herschel/PACS (Photodetector Array Camera and Spectrometer;  Poglitsch et al. 2010 ) instrument, as part of the DGS sample, and it has also been observed in the MIR with the IRS (Infrared Spectrograph;  Houck et al. 2004 ) onboard Spitzer, providing access to a wide range of gas tracers. In this section, we present the data used in our study.
In Figure  5  we show the line maps prior to convolution and resampling (points 3 and 4), and Table  2  reports the general properties of those lines. The individual high-resolution pointings (Appendix, Fig. A. 1) were retrieved from the CASSIS spectral database  (Lebouteiller et al. 2011; 2015). For some pointings, the emission is extended, and, as a consequence, the SH and LH spectra do not align because both modules have different slit sizes (4. 7 × 11. 3 for SH, 11. 1 × 22. 3 for LH). For such cases, we used the wavelength-dependent extended source flux calibration available in CASSIS, and applied a scaling factor to match SH  to LH in the overlapping wavelength range of 19. 0 -19. 5 µm. Some pointings are dominated by a point source, and in such cases the SH and LH spectra, after using a point-source flux calibration (a factor between 1.
The PACS array consists of 5 × 5 spatial pixels covering a total field of view 47 × 47. The velocity resolution is ∼ 90 km s -1 at 60 µm, ∼ 125 km s -1 at 90 µm and ∼ 295 km s -1 at 120 µm (PACS Observer's Manual 2013). The observations have been done in unchopped mode, in which an offset position is observed before and after the IC 10 observation. The data cubes have been reduced with Herschel Interactive Processing Environment (HIPE, Ott 2010) v12. 0. 0 and then processed with PAC-Sman  (Lebouteiller et al. 2012)  for the line fit and map construction. To estimate the uncertainty on the fit parameters, a Monte-Carlo approach was used. Details of the observations, the reduction of the PACS data and uncertainties can be found in  Cormier et al. (2015). Figure  3  shows the lines for the Main zone (M; I ν /I inc ν = 1-e -τ τ.