dataesr/scientific-paragraphs-categorization huggingface dataset:
| Generation of isolate-and stagespecific transcriptomes based on confocal microscopy analyses Given the morphological and temporal differences in infection development, we next asked how gene expression profiles differ between To account for the temporal variation between Zt05, Zt09, and Zt10, we combined confocal laser scanning microscopy and RNAseq and conducted microscopy analyses of tissue from the wheat leaves used for RNA extraction and transcriptome sequencing. Infected leaves were collected at up to nine time points per isolate, and samples for transcriptome sequencing were selected based on their morphological infection stage (Supporting Information Figure  S2 ; Table  1 , Supporting Information Table  S2 ). Thereby, we generated stage-specific RNA-seq datasets corresponding to the four core infection stages, allowing us to compare the isolate-specific expression profiles at the same stage of infection development.
We obtained 89. 2 to 147. 5 million single-end, strand-specific reads per replicate (total >2. 7 billion reads) that were quality trimmed and filtered. Between 4. 54% (early infection) and 76. 4% (late infection) of the reads could be mapped to the genome of the respective isolate, reflecting the infection stage-specific amount of fungal biomass (Table  1 , Supporting Information Table  S3 ; Text S1). Across all isolates, transcriptomes of stages A and B, representing biotrophic growth, cluster together and are clearly different from transcriptomes of stages C and D that likewise cluster and represent necrotrophic growth of Z. tritici (Supporting Information Figures  S9  and S10 ). Exploring the transcriptome datasets based on gene read counts shows the greatest variation of biological replicates for Zt10 at stage C (Supporting Information Figure  S11 ), possibly reflecting variability in the infection development of the two biological replicates.
DATA ACCE SS I B I LIT Y All generated RNA-seq datasets have been deposited at the NCBI Gene Expression Omnibus and are accessible with the accession number GSE106136. De novo genome assemblies of isolates Zt05 and Zt10 are available under accession numbers PEBP00000000 and PEBO00000000. The genome sequence of the reference isolate IPO323 used for transcriptome analysis of Zt09 is available at: http://genome. jgi. doe. gov/Mycgr3/Mycgr3. home. html.
Overview of RNA-seq datasets including time point of sampling, number of sequenced reads post filtering, number of mapped reads, percentage of mapped reads, and numbers of transcribed genes. a RPKM values were calculated using Cuffdiff2 and are normalized over all infection stages within the respective isolate (normalization method: geometric, dispersion method: per-condition). b 11,138 genes of IPO323 (94. 08%) found by nucleotide blast for Zt05. c 11,754 of the 11,839 genes predicted and annotated for IPO323.
TA B L E 3 Zymoseptoria tritici core necrotrophic effector candidate genes Criteria Significantly up-regulated at stage C Low/no expression during stage A and B Expression profiles Putative functions Facilitate transition from biotrophy to necrotrophy Induction of necrosis Candidate genes 35 (Supporting Information Figure S14) Candidates encoding 24 (Supporting Information Table S16) hypothetical proteins Candidates with predicted Gene Functional prediction PFAM domains GO terms function associated Zt09_chr_3_00584 PCWDE, similar to alpha-1, Glycosyl PF00128; PF00534; GO:0003824; transferases group 1 PF08323 GO:0043169; GO:0005975; GO:0009058 Zt09_chr_9_00308 PCWDE, similar to carbohydrate-binding - GO:0008150; module family 63 protein, expansin-like GO:0003674; GO:0005575 Zt09_chr_3_01063 PCWDE, similar to pectate lyase PF00544 GO:0008150; GO:0003674; GO:0005575 Zt09_chr_12_00112 cutinase-like protein PF01083 GO:0050525; GO:0016787; GO:0005576; GO:0008152 Zt09_chr_2_00663 similar to Chain A, cutinase-like protein PF01083 GO:0050525; GO:0016787; GO:0005576; GO:0008152 Zt09_chr_2_01151 PCWDE, similar to acetyl xylan esterase, PF01083 GO:0016787; GO:0008152 Zt09_chr_6_00446 PCWDE,similar to acetyl xylan esterase PF01083 GO:0016787; GO:0008152 Zt09_chr_10_00107 PCWDE, similar to glycoside hydrolase PF01670 GO:0008810; family 12 protein GO:0004553; GO:0000272 Zt09_chr_2_01205 PCWDE, similar to putative extracellular PF03330 GO:0008150; cellulase CelA/allergen Asp F7-like GO:0003674; protein GO:0005575 Zt09_chr_6_00626 similar to metalloprotease PF05572 GO:0008237 Zt09_chr_9_00038 similar to hydrophobin PF06766 GO:0005576.
Recent passive remote sensing satellites include, but not limited to, the two dedicated SM missions: the National Aeronautics and Space Administration (NASA) SMAP (Soil Moisture Active Passive) satellite and the Soil Moisture and Ocean Salinity (SMOS) satellite. Active remote sensing sensors include, but not limited to, the ESA Advanced Scatterometer (ASCAT) sensor. Furthermore, based on these (and others) passive and active datasets, a long term (~35 years of data) SM product was developed by the European Space Agency (ESA); the so-called climate change initiative (CCI) SM product. After the launch of several space-borne microwave satellites such as the SMOS satellite, calibration/validation studies using ground-based measurements are useful and important to assess the quality of the space-borne observations.
Remotely-Sensed Datasets An overview of the remotely-sensed SM retrievals used in this study is presented in Table  4. Three SM products were used: SMOS-IC, ASCAT, and CCI. We considered other products such as the NASA SMAP but these retrievals were not recommended over the study region due to coastal proximity. Although SMOS operates in a protected band (1. 40-1. 42 GHz), SMOS TB observations are negatively impacted by artificial sources of RFI signals coming from either unauthorized or unwanted man-made emissions (e. g. , FM radio signals, telecommunications signals, electric circuits, etc. )  [20, 21]. RFI signals can be weak (very difficult to detect) or strong (easily detected with unreasonable high values of TB)  [20].
5 km spatial sampling was downloaded from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) and used in this study. Soil moisture values with confidence flag other than "0" were excluded  [30]. The ASCAT dataset is provided in both degree of saturation (%) and volumetric SM units (using porosity values from the Harmonized World Soil Database; expressed in m 3 /m 3 ). In this study, we used the ASCAT data in volumetric SM units to match with SMOS and CCI SM products.
ESA CCI Soil Moisture Several global passive (e. g. , SMMR, AMSR-E, SMOS, etc. ) and active (e. g. , ERS and ASCAT) SM products are blended to produce the first long-term i. e. , 11/1978-12/2016 ESA CCI SM product  [31]. This active and passive SM combination is based on a weighted average method. The weights are proportional to the Signal to Noise Ratio (SNR) that is estimated using triple collocation analysis of each product  [32]. The Global Land Data Assimilation System (GLDAS), provided by Noah Land Surface Model  [33] , is used to scale the various datasets into a common model SM climatology using a Cumulative Density Function (CDF) matching approach. The CCI SM product can be freely downloaded from https://www. esa-soilmoisture-cci. org/, which is delivered as daily volumetric (m 3 /m 3 ) SM product in a NETCDF format with a spatial resolution of 0.
Methodology The CCI, ASCAT, SMOS-IC SM products were evaluated against the French AQUI in situ observations. We considered all available SM retrievals from each product within the 2010-2017 period and also considered only overlapping dates where we have observations from all the three products (only if data availability is higher than one month). For each station, we extracted the corresponding SM time series from the original grids of each product (e. g. , 25 km from SMOS-IC, etc. ) based on its latitude and longitude (see Supplementary Figure  S1  for the AQUI in situ stations distributed in the "contributing area" for a single grid point for SMOS, ASCAT, and CCI). Finally, the metrics between the three remotely-sensed SM products and the AQUI in situ observations were calculated separately for each station after excluding pixels with frozen conditions (using flags provided with each product).
The total deflection of the DM particle is then expected to be ∆θ ∼ δθ/ √ N scat O(10 -5 ). We can thus approximate the trajectories of DM particles as straight lines, allowing us to use the standard nuclear stopping formalism and avoiding the need for complicated Monte Carlo calculations. With this formalism in hand, we derive the final velocity distribution at the detectors in Sec. IV, followed by the expected signal rate and resulting constraints in Sec. V. The final constraints are shown in Fig. 5 , for DM masses in the range 1 GeV to 10 15 GeV, with the caveat that at low masses these results need to be supplemented with dedicated Monte Carlo simulations. All code for performing the calculations in this paper, along with all results and plots, is made freely available online, along with the numerical code verne, at https://github.
• Earth -particles then propagate in a straight line from the surface of the Earth to the detector. We include 8 different Earth elements -O, Si, Mg, Fe, Ca, Na, S, Al -using density profiles tabulated in Ref. [58]  (based on data from Refs. [59, 60] ). • Shielding -finally, the particles propagate through any shielding which surrounds the detector. In the case of the atmosphere and the Earth, the path length depends on the angle of incidence, while for the shielding we assume a constant path length, regardless of incoming direction. Explicit expressions for the path length through the atmosphere and Earth are given in Appendix A. For the CDMS-I detector as SUF, we assume that the detector is located 10. 6m underground and is surrounded by 16cm of lead shielding 3. For the CRESST 2017 surface run at MPI, the only substantial shielding comes from the walls of the surface building itself, consisting of 30cm of concrete.
At larger masses than this, the limit from CDMS-I disappears, as we would not expect any substantial number of DM particles to impinge on the detector over the course of the 1 year exposure. In the case of the CRESST 2017 surface run, we truncate the limit at a mass of O(10 8 ) GeV. For a 5 mm-scale crystal exposed over 2. 27 hours, the number of 10 8 GeV DM particles crossing the detector is O(200) and above this mass, the flux of DM particles is too small to explain the O(500) events in the CRESST 2017 surface run dataset. 6  We also plot the limit obtained using our procedure in the mass range 1 GeV to 10 5 GeV. For DM particles heavier than around 50 GeV (the mass of an iron nucleus), the assumptions we have made here should not be too strongly violated. In particular, as long as m χ > m i the deflection of DM particles will be predominantly in the forward direction and so, with a large number of scatters, the typical deflection should not be too large.
ACKNOWLEDGMENTS The author thanks Laura Baudis, Jonathan  Davis   tration of the coordinate system used to calculate the impact of DM scattering in the Earth. The incoming direction of a single DM particle towards the detector is denoted by v, while the direction of the mean DM flux is denoted vχ = -v lab (t). In the associated text, we give expressions for the DM path length to the detector and the velocity distribution in this coordinate system. With this, we can calculate the final speed v f from the initial speed v i (at the top of the atmosphere), along a given trajectory, using v f = v i + 0 dv dD (v, r) dD. (A3) We emphasise that dv/dD (given explicitly in Eq. (  11 )) depends on both r and v and so Eq. (A3) must be solved numerically using an ODE solver. The DM velocity distribution is given in vectorial form in Eq.
ACKNOWLEDGMENTS We thank  P. -A. Defossez ,  C. Francastel ,  V. Mezger , and  J. Weitzman  for critical reading of the manuscript;  E. Dunleavy ,  A. Hamiche , and  S. Jackson  for sharing reagents; and the  ImagoSeine core facility (Institut Jacques Monod)  for assistance with confocal microscopy. This work was supported by the  European Research Council  (starting grant  ERC-2013-StG-336427  ''EpIn''), the  French National Research Agency  ( ANR-12-JSV6-0002-01 ), the '' Who am I''? laboratory of excellence  ( ANR-11-LABX-0071 ),  EDF Radiobiology program  ( RB 2014-01 ), the  Fondation ARC  ( SFI20121205449 ), and  France-BioImaging  ( ANR-10-INSB-04 ). S. P. is an EMBO Young Investigator. S. A. was the recipient of PhD fellowships from University Paris 6 and  La Ligue  contre le Cancer.
, 2017). However, two of the four new genes we report (PRKRA and NLRP8) have clear functions in innate immunity, and HDAC7 plays a central role in the development of T cells in the thymus. Roles for both innate immune function and thymic development in MS pathogenesis are also supported by pathway analyses of our most recent GWAS data (International Multiple Sclerosis Genetics Consortium et al. , 2017), an independent observation due to the lack of linkage disequilibrium (LD) between the variants in this study and those in our GWAS and the non-overlapping sample collections. Our data thus expand the scope of immune function relevant to MS pathogenesis. The mechanisms whereby our newly discovered variants alter MS risk will require detailed experimental dissection: even when we can directly implicate specific genes and variants, these can have diverse consequences across multiple cell types.
CONTACT FOR REAGENT AND RESOURCE SHARING d EXPERIMENTAL MODEL AND SUBJECT DETAILS d METHOD DETAILS d QUANTIFICATION AND STATISTICAL ANALYSIS B Exome chip coverage of ExAC variants B Univariate association analysis B Heritability estimation d DATA AND SOFTWARE AVAILABILITY SUPPLEMENTAL INFORMATION Supplemental Information includes three figures and five tables and can be found with this article online at https://doi. org/10. 1016/j. cell. 2018. 09. 049. R. L. , and P. V. D. are Clinical Investigators of the Research Foundation Flanders (FWO-Vlaanderen). A. G. and B. D. are supported by the Research Fund KU Leuven (C24/16/045) and the Research Foundation Flanders (FWO-Vlaanderen) (G. 0734. 15). The SiGN study was funded by a cooperative agreement grant from the US National Institute of Neurological Disorders and Stroke, National Institutes of Health (U01-NS069208).
Germany We would like to thank Verena Grummel and Nadine Miksch for technical support. We thank Sabine Fleischer for continuous and extensive support with biobanking logistics. This work was supported by the German Ministry for Education and Research (BMBF, ''German Competence Network Multiple Sclerosis'' [KKNMS]) [01GI0916, 01GI0917] and the Munich Cluster for Systems Neurology (SyNergy). The KORA study was initiated and financed by the Helmholtz Zentrum Mu ¨nchen-German Research Center for Environmental Health, which is funded by the BMBF and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universita ¨t, as part of LMUinnovativ. The popgen 2. 0 network is supported by a grant from the German Ministry for Education and Research (01EY1103).
This study makes use of data generated as part of the  Wellcome Trust   Case Control Consortium 2 project  ( 085475/B/08/Z  and  085475/Z/08/Z ), including data from the British 1958 Birth Cohort DNA collection (funded by the  Medical Research Council  grant  G0000934  and the  Wellcome Trust  grant  068545/Z/ 02 ) and the  UK National Blood Service controls  (funded by the  Wellcome Trust ). The study was supported by the  Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre, UK Medical Research Council  ( G1100125 ) and the  UK MS Society  ( 861/07 ). We thank the  NIHR  and  NHS Blood and Transplant. TwinsUK is funded by the  Wellcome Trust, Medical Research Council ,  European Union , and the  NIHR-funded BioResource, Clinical Research Facility, and Biomedical Research Centre based at Guy 's and  St.
, 2006). We performed the projection and outlier detection and removal steps a total ten times to gradually remove more subtle population outliers. We compiled cases and controls into strata for analysis as shown in Table  S1. In total, we removed 17,938/76,140 (24%) samples either due to low data quality or as population outliers, leaving a final dataset of 27,891 cases and 30,298 controls in 13 strata (Figure  S1  and Table  S1 ). Separately, we included summary statistics from 4,476 MS cases and 5,714 controls from Germany, genotyped on the exome chip as previously described  (Dankowski et al. , 2015) , giving us a total of 32,367 MS cases and 36,012 controls for analysis.
Frauke Zipp has recently received research grants and/or consultation funds from Genzyme, Merck Serono, Roche, Novartis, Sanofi-Aventis, Celgene, ONO and Octapharma. Benedicte Dubois and An Goris have received travel/consulting fees and/or research funding from Novartis, Merck-Serono and Roche. B. D. has received consulting fees and/or funding from Biogen Idec, Sanofi-Aventis and Teva ACKNOWLEDGMENTS Australia Sample collection and genotyping was supported by NHMRC project grant APP605511. Belgium B. D. , The Heinz Nixdorf Recall Study was supported by the Heinz Nixdorf Foundation Germany, the BMBF, and the Deutsche Forschungsgemeinschaft (DFG; ER 155/6-1, ER 155/6-2). Italy Sample collection and genotyping was supported by the Italian Foundation for Multiple Sclerosis (FISM grants, special project ''Immunochip'' 2011/R/1, 2015/R/10) and Fondazione Cariplo (grant 2010-0728).
Stressful life events (SLEs) and neuroticism are risk factors for Background: major depressive disorder (MDD). However, SLEs and neuroticism are heritable and genetic risk for SLEs is associated with risk for MDD. We sought to investigate the genetic and environmental contributions to SLEs in a family-based sample, and quantify genetic overlap with MDD and neuroticism. A subset of Generation Scotland: the Scottish Family Health Study Methods: (GS), consisting of 9618 individuals with information on MDD, past 6 month SLEs, neuroticism and genome-wide genotype data was used in the present study. We estimated the heritability of SLEs using GCTA software. The environmental contribution to SLEs was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression (LDSC) we analysed the genetic overlap between MDD, neuroticism 1 2 1 2 2 3 2 2 1 1 4 2 4,5 3,6 3,7 3,8 9 2,3,5 5,10 1,.
Sample description The individuals used in this study were a subset of Generation Scotland: the Scottish Family Health Study (GS), which has been described in detail elsewhere  [25] [26] [27] 29. Bri fly, GS comprises 23,690 individuals aged 18 years and over, recruited via general practitioners' throughout Scotland. In 2014, re-contact of GS participants began as part of a data collection initiative designed to re-assess the mental health of participants. In total, 21,526 GS participants were re-contacted by post and asked to return a questionnaire by post or via a URL link to complete online. In total, 9,618 participants volunteered as part of the mental health follow-up (45% response rate), and these are the participants used in this study. A full description of the re-contact procedure and data collected is provided elsewhere  28.
Neuroticism was assessed during the initial contact of the full GS cohort using the Eysenck personality questionnaire  32. Current psychological distress was assessed using the General Health Questionnaire-28 and additional detail is reported in the cohort paper  28. Genome-wide genotype data generated using the Illumina Human OmniExpressExome-8-v1. 0 array and was available for 8734 of the 9618 individuals from the GS subset. Genotyping is described in greater detail elsewhere  25. Population outliers were identified and removed from the sample. Quality control of genotypes involved removing SNPs with a call rate < 98%, a missing rate per individual ≥2%, a minor allele frequency (MAF) <1% and Hardy-Weinberg equilibrium (HWE) p ≤ 1×10 -6. In total, 561,125 autosomal SNPs remained and were used in subsequent analyses.
This process was repeated until all the remaining components were significant in either the LRT or Wald test. This method is described in more detail by Xia et al  24. There were 659 couple pairs, 1928 full sibling pairs and 4523 nuclear families (containing at least 2 individuals) in the present sample. The number of non-zero elements of the KFSC matrices for whom genotypic and all phenotypic information are available in the present sample are shown in Supplementary Table  3. The G matrix does not contain any non-zero elements. Polygenic risk score (PRS) analyses Polygenic risk scores (PRS) were created in PRSice v1. 25 software using the raw genotype data from a target sample (GS) and summary statistics from an independent discovery sample  36. This method calculates the sum of associated alleles an individual in the target sample carries across the genome, weighted by their effect size in an independent discovery GWAS.
Eighteen association tests were carried out between the MDD-PRS/N-PRS and traits of interest, which gave the Bonferroni corrected p-value of 0. 0028 as the threshold for statistical significance (tests presented in Table  3  and Table 4 ). All variables were log transformed towards normality where necessary. Continuous variables were scaled to have a mean of 0 and a standard deviation of 1, such that the reported regression coefficients (betas) are standardized. Mixed linear models implemented in the ASReml-R v3. 0 software package were used to test the association between MDD-PRS and traits of interest. When associations between binary traits and PRS are reported Taylor series transformation was used to convert beta and standard error values from the linear scale to the liability scale. Age, sex and four MDS components were fitted as fixed effect covariates.
Sex, age and 20 MDS components were fitted as fixed effect covariates. Genotypes were imputed using the Haplotype Reference Consortium (HRC) reference panel. Individuals with missingness ≥3% were excluded along with SNPs with a call rate ≤98%, HWE p-value ≤ 1 × 10 -6 and a MAF ≤ 1%. Genotype SNP data were phased using SHAPEIT2 and imputation performed using PBWT software  40. Post-imputation SNPs with more than two alleles, monomorphic SNPs and SNPs with an INFO score < 0. 8 were removed. QQ plots for the GWAS of independent and dependent life events are shown in Supplementary Figure  2  and Supplementary Figure  3.
Determining the familial environmental effects on SLEs is challenging when families will endorse the same events solely because they have occurred within the same social network, such as 'did a close relative of yours die?'. This is also true for couples where major financial crises will be reported by both spouses due to shared assets. We attempted to control for this by creating a personal SLE category and also excluding events that could be inferred by spouses, however people may still endorse an event that happens to a spouse or family member as their own as they find it to be stressful to themselves. It is not possible to ascertain with the data available from this cohort, whether events endorsed by members of a couple reflect the same event, or whether each individual experiences an independent event.