dataesr/scientific-paragraphs-categorization huggingface dataset:
Truffles From Two Distant Orchards Have Similar Microbiomes We first compared the microbiome composition of truffles collected from the French and Swiss orchards. An average of 66 ± 6 (SE) OTUs were detected in the French samples, while 23 ± 3 were recorded in average in the Swiss ones. This important difference could be a bias due to the use of MiSeq Illumina sequencing (French orchard) and 454 pyrosequencing (Swiss orchard). Indeed, Illumina sequencing allows for larger numbers of reads per sample and may provide a better access to rare OTUs. This hypothesis was confirmed on another set of data obtained from T. melanosporum fruiting bodies analyzed both by 454 and MiSeq Illumina sequencing  (Deveau et al. , unpublished data). The two samples that were analyzed by both methods strongly differed in richness (21 vs.
2%; P < 0. 01) and Firmicutes (FR: 2. 7 ± 1. 7%, CH: 0. 1 ± 0. 02%; P > 0. 05) were less frequent, with Actinobacteria being the only phylum with a significantly different abundance between the two orchards. OTUs of d-Proteobacteria, Acidobacteria, and Verrucomicrobia were found at very low levels in some samples of the two sites. Strong similarities between the two orchard's samples were also observed at the genus level, since the most represented OTUs belonged to the same genera: Bradyrhizobium (FR: 65. 1 ± 8. 8%, CH: 58. 6 ± 6. 9%), Pseudomonas (FR: 8. 1 ± 3. 4%, CH: 3. 4 ± 1. 4%), Pedobacter (FR: 4. 3 ± 3. 3%, CH: 13. 8 ± 4. 9%), Polaromonas (FR: 5. 4 ± 5. 0%, CH: 9. 2 ± 4. 4%), and Flavobacterium (FR: 2. 5 ± 1. 2%, CH: 0. 8 ± 0. 7%). Twenty-three additional genera were shared between the two datasets.
15 The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen dimer (O4) and ozone (O3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region and NO2 in an additional (smaller) wavelength range in the visible. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in an unprecedented set of measurements made 20 under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products.
The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the reference, and the RMS error provides an estimate of the measurement noise or 25 dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the measurement performance of all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques.
This effort resulted in the first successful large-scale intercomparison of both MAX-DOAS and zenith-sky ground-based remote sensors of NO2 and O4 slant columns  (Roscoe et al. , 25 2010). Datasets of NO2, aerosols and other air pollution components observed during CINDI were documented in a number of peerreviewed articles  (Piters et al. , 2012; Roscoe et al. , 2010; Pinardi et al. , 2013; Zieger et al. , 2011; Irie et al. , 2011 and Frieß et al. , 2016) , providing an assessment of the performance of ground-based remote sensing instruments for the observation of NO2, HCHO and aerosol. Recommendations were issued regarding the operation and calibration of the instruments, the retrieval settings, and the observation strategies for use in ground-based networks for air quality monitoring and satellite data validation.
Instead, validation requires a technique that can deliver columnintegrated and vertically resolved information on the key tropospheric species measured by satellite instruments such as NO2, HCHO and SO2 with a horizontal representativeness compatible with the resolution of space measurements (e. g. 3. 5x7 km 2 for S-5p). 5 Hence, the specific goals of CINDI-2 were to support the creation of high-quality ground-based data sets as needed for long-term measurements, trend analysis and satellite data validation. To achieve this, it is essential to characterise and better understand the differences between a large number of MAX-DOAS and zenith-sky DOAS instruments and analysis methods, and to contribute to a harmonisation of the measurement settings and retrieval methods. The design of the CINDI-2 campaign and the development of 10 the measurement protocol, adhered to specifically during the official intercomparison phase, was based on the experience gained during the first CINDI campaign in 2009 as well as more recent projects and campaigns such as the MAD-CAT campaign in Mainz, Germany, in 2013 (e.
Investigation of instrument performance With MAX-DOAS-type instruments having gained popularity in recent years and their usage becoming more widespread, the need for a reliable and clearly documented assessment process is becoming more pressing. A semi-blind intercomparison campaign such as CINDI-2 provides the ideal conditions to obtain a data set for such a process and the opportunity to involve as many MAX-DOAS 25 instruments as possible. Three criteria based on the regression analysis discussed in Section 3. 7 (slope, intercept and RMS) have been selected to assess the performance of each of the participating instruments with regard to the eight MAX-DOAS and four zenith-sky products. For each of these parameters, specific limits have been set as listed in Table  4. These were semi-empirically derived from a visual inspection 30 of the distribution of the slope, intercept and RMS values for each of the eight CINDI-2 MAX-DOAS and four zenith-sky data products.
7 and displayed in Figure  10  and 15 but with all individual elevation angles added up resulting in one single value for each parameter, instrument and data product. This means that only 3 values are displayed for each instrument. The green shaded areas denote the limits defined in Figure  18  with all parameters falling within the limits being displayed as blue dots while values in red are not meeting the respective criterion. Note that not all of the 36 instruments measure NO2vis. For the slope of the NO2vis regression analysis shown in the top panel, two 5 instruments (uto-36 and amoiap-2) fall outside the limit. One other instrument (iupb-37, the imaging instrument) is not meeting the criterion set for the intercept (see middle panel) and one (nasa-31) for the RMS (bottom panel). One of such summary plots has been produced for each of the eight MAX-DOAS and four zenith-sky data products which can be individually viewed in Sections 1 and 2 of the Supplement.
This assessment matrix shows the outcome for all 36 instruments, eight data products for MAX-DOAS and four data products for zenith-sky mode. Any box coloured with green denotes that all three 20 assessment criteria for that instrument and data product have been fulfilled. Boxes marked with yellow and orange denote that one or two criteria, respectively, have not been met, while red means that all three criteria have not been met and pink indicates that this data set has at least one extreme outlier. Additionally, both the reported dSCD regression RMS and the DOAS fit RMS are used to sort the data products accordingly, with the smallest median RMS being assigned the lowest number in each case.
The last six instruments listed under the custom-built category, however, struggle to either meet two criteria or to meet all criteria for one of the measured data products. 35  Additionally, HCHO or O3uv data sets measured by three of the instruments (aiofm-1, csic-10 and bsu-5) contain extreme outliers. In the case of aiofm-1, the extreme outliers in the O3uv data set can be at least partly attributed to an issue with the DOAS settings, most probably related to the ozone cross-sections used during the data processing. The seven Pandora and five EnviMes instruments show overall a more consistent picture. Four of the Pandoras are meeting all 40 categories and two of the other Pandoras satisfy all but one of the criteria for one or two of the data products. Nasa-31, however, experienced problems during operation and had some dirt inside the head sensor which was moving around and blocking part of the instrument FOV as well as having a loose tracker shaft.
This caused significantly reduced signal-to-noise and an increased pointing uncertainty (see the large error bar for this instrument in Figure  6 ) that had negative consequences for all data products analyzed in the campaign. These problems were detected during the campaign and an attempt was made to fix them. In spite of these issues, most criteria were still met. It should be noted though that the behaviour displayed by nasa-31 did not fully represent the observational capabilities of a Pandora, as clearly evidenced by results from other instruments of the same type. The EnviMes 5 instruments performed overall well when measuring NO2 but struggled more to fulfil all criteria for the HCHO and O3uv data sets apart from niwa-29 which satisfied all criteria for HCHO and O3uv while not satisfying one of the criteria for both of the O4 data sets and one of the NO2 data sets.
Most of the six miniDOAS instruments measured NO2 satisfactorily in all three wavelengths ranges and only not satisfy one criterion in the O4 data sets but experienced discernible difficulties when measuring HCHO and O3 which includes all criteria failed and extreme outliers. 10 The zenith-sky twilight data set (rightmost four columns in Figure  22 ) show a consistent performance for all custom-built, Pandora, EnviMes and SAOZ type instruments and all four data products (apart from nasa-31, see discussion above) with in most cases (90%) all criteria satisfied and in just eight cases one criterion not satisfied. The performance of the miniDOAS instruments is for the zenith-sky data more variable with one instrument (cma-8) not satisfying any of the criteria for O3vis and another (nust-33) failing 15 two out of three criteria for the NO2uv product.
On the opposite, the data products measured by aiofm-1 have an excellent fit RMS rating corresponding to a very low measurement noise, while none of the data products satisfies all criteria and O3uv has at least one 25 extreme outlier. This apparent inconsistency reflects the nature of the performance assessment methodology, which puts larger emphasis on the assessment of systematic biases on measured dSCDs than on the noise. We have also seen that the comparison noise in regression analyses is for some of the products (NO2, O4) dominated by atmospheric/observation geometry effects rather than by actual instrumental noise.
aeronomie. be/). 35 The semi-blind CINDI-2 intercomparison exercise, presented here, concludes with the comparison and assessment of the retrieved dSCDs of a limited number of mature data products (NO2, O4, O3 and HCHO). However, additional species (e. g. HONO, glyoxal, BrO, H2O) were also measured during the campaign, some of them being the subject of ongoing studies to be published separately. In particular, the tropospheric ozone column retrieval has been investigated in depth  (Wang et al. , 2018)  and a publication on HONO 40 retrievals is under way  (Wang et al. , to be submitted to AMT, 2019) as a follow-up of the first HONO intercomparison during the MAD-CAT campaign  (Wang et al. , 2017c). In addition to dSCD measurements, the subsequent steps in MAX-DOAS retrievals, i. e. their conversion into vertical column and profile information is also further investigated in a CINDI-2 profiling working group and as part of the ESA FRM4DOAS project  (Frieß et al.
We gratefully acknowledge the KNMI staff at Cabauw for their excellent technical and infrastructure support during the campaign. Funding for this study was provided by ESA through the CINDI-2 (ESA Contract No. 4000118533/16/I-Sbo) and FRM4DOAS (ESA Contract No. 4000118181/16/I-EF) projects and partly within the EU 7 th Framework Programme QA4ECV 30 project (Grant Agreement no. 607405). The BOKU MAX-DOAS instrument was funded and the participation of S. F. Schreier was supported by the Austrian Science Fund (FWF): I 2296-N29. The participation of the University of Toronto team was supported by the Canadian Space Agency (through the AVATARS project) and the Natural Sciences and Engineering Research Council (through the PAHA project). The instrument was funded by the Canada Foundation for Innovation and is usually operated at the Polar Environment Atmospheric Research Laboratory (PEARL) by the Canadian Network for the Detection of Atmospheric Change 35 (CANDAC).
org/10. 5194/amt-2019-157 Preprint. Discussion started: 27 May 2019 c Author(s) 2019. CC BY 4. 0 License. US National Science Foundation (award AGS-1620530). H. Finkenzeller is recipient of a NASA graduate fellowship. M. Vrekoussis acknowledges support from the University of Bremen and the DFG Research Center/Cluster of Excellence "The Ocean in the Earth System-MARUM. Financial support through the University of Bremen Institutional Strategy in the framework of the DFG Excellence Initiative is gratefully acknowledged for A. Schönhardt. Table 2: Overview of the main characteristics of the instruments taking part in the semi-blind intercomparison campaign. The table lists the type, specific ID and model name for each participating instrument (columns 1-3). In columns 4-5, it also specifies whether instruments could take azimuthal scans (ASc) and/or be operated in direct-sun mode (DS).
Acknowledgements. 5 6 5 We gratefully acknowledge the KNMI staff at Cabauw for their excellent technical and infrastructure support during the campaign. Funding for this study was provided by ESA through the CINDI-2 (ESA Contract No. 4000118533/16/I-Sbo) and FRM4DOAS (ESA Contract No. 4000118181/16/I-EF) projects and partly within the EU 7 th Framework Programme QA4ECV 30 project (Grant Agreement no. 607405). The BOKU MAX-DOAS instrument was funded and the participation of S. F. Schreier was supported by the Austrian Science Fund (FWF): I 2296-N29. The participation of the University of Toronto team was supported by the Canadian Space Agency (through the AVATARS project) and the Natural Sciences and Engineering Research Council (through the PAHA project). The instrument was funded by the Canada Foundation for Innovation and is usually operated at the Polar Environment Atmospheric Research Laboratory (PEARL) by the Canadian Network for the Detection of Atmospheric Change 35 (CANDAC).
Mean screen luminance was controlled (9 cd/m 2 ). Eye position was monitored using an EyeLink 1000 infrared eye-tracking system (SR Research). Experiments were controlled by EventIDE software (OkazoLab). The monkey started the trial by fixating a central green square subtending 0. 2 degrees of visual angle for 500 ms within a tolerance window of 1 to 1. 4 DVA. A visual stimulus of the peripheral location was then presented for 0. 5 or 2 s. We used drifting sinusoidal gratings as visual stimuli with a fixed temporal frequency (three cycles per second) and a fixed spatial frequency (one cycle per degree). The animals were rewarded by a small drop of liquid (water) at the end of each correct fixation trial. We imposed an intertrial interval of 3 s. We also used control trials with the same temporal organization but without any peripheral visual stimulus (SI Appendix, Fig.
To sample cerebral blood volume variations, we repeated this sequence 200 times at a 500-Hz frame rate, corresponding to a 400-ms acquisition time. In order to remove tissue-motion artifacts from the dataset, we applied a recently developed clutter-filter technique on rats based on singular-value decomposition  (33)  without any modifications for application to the awake monkey. Finally, one ultrasensitive Doppler image was formed for each ultrafast data block with a sampling rate of 1 Hz by averaging the 200 compounded and filtered ultrasonic images. Therefore, we extracted CBVs from ultrasensitive Doppler images with a sampling rate of one image per second. During a behavioral session of 60 min, we obtained 3,600 images (98 × 128 pixels). We could image 14 mm along the cortical surface and 10 mm in depth.
Our spatial resolution was 0. 11 × 0. 1 mm and the width was 0. 4 mm. When working on retinotopic maps, we usually only performed all fUS imaging acquisitions required for a complete map during the behavioral session. For ocular dominance maps, we performed all required acquisitions on both eyes during the session. Data Processing. We then analyzed all of the fUS image acquisitions using Matlab (version R2017b; MathWorks). CBV signals were 10 times extrapolated (cubic spline interpolation; temporal resolution, 0. 1 s) and 3D smoothed (convolution kernel,  [3 3 3] ; box filter, SD 0. 65). A log file created by EventIDE during each behavioral session was used to extract CBV signal with the beginning of each correct trial. This signal was then normalized with the baseline (the average of the signal 5 s before the onset of the visual stimulus).
This work was supported by the European Research Council (ERC) Synergy Grant Scheme (holistic evaluation of light and multiwave Blaize et al. PNAS Latest Articles | 9 of 11 applications to high resolution imaging in ophthalmic translational research revisiting the helmholtzian synergies, ERC Grant Agreement 610110) and by French state funds managed by the Agence Nationale de la Recherche (ANR) within Programme Investissements d'Avenir, Laboratoire d'Excellence (LABEX) LIFESENSES (ANR-10-LABX-0065) and Institut Hospitalo-Universitaire FOReSIGHT (ANR-18-IAHU-0001).
The traits measured in each cohort vary according to original study purpose, but together cover a comprehensive range of human phenotypes (Fig. 1 , Supplementary Data 7). The five most frequently contributed traits (height, weight, body mass index, systolic and diastolic blood pressure) are measured in >1,000,000 individuals; a further 16 traits are measured >500,000 times. We find that F ROH is significantly associated with apparently deleterious changes in 32 out of 100 traits analysed. Increased F ROH is associated with reduced reproductive success (decreased number and likelihood of having children, older age at first sex and first birth, decreased number of sexual partners), as well as reduced risk-taking behaviour (alcohol intake, ever-smoked, selfreported risk taking) and increased disease risk (self-reported overall health and risk factors including grip strength and heart rate).
Traits affected by F ROH. To estimate the effect of inbreeding on each of the 100 phenotypes studied, trait values were regressed on F ROH within each cohort, taking account of covariates including age, sex, principal components of ancestry and, in family studies, a genomic relationship matrix (GRM) (Supplementary Data 3). Cross-cohort effect size estimates were then obtained by fixedeffect, inverse variance-weighted meta-analysis of the withincohort estimates (Supplementary Data 10). Twenty-seven out of 79 quantitative traits and 5 out of 21 binary traits reach experiment-wise significance (0. 05/100 or p < 0. 0005; Fig. 3a, b ). Among these are replications of the previously reported effects on reduction in height  13  , forced expiratory lung volume in one second, cognition and education attained  6.
d F ROH is a better predictor of rare variant homozygosity than F GRM. The excess homozygosities of SNPs, extracted from UK Biobank imputed genotypes, were calculated at seven discrete minor allele frequencies (F MAF ), and regressed on two estimators of inbreeding in a bivariate statistical model (see Supplementary Note 5). The homozygosity of common SNPs is better predicted by F GRM , but rare variant homozygosity is better predicted by F ROH. The results from real data (Fig. 4b , Supplementary Figs  15a, b    significance. A meta-analysis of the ratio b β F ROH wSibs : b β F ROH for all traits is significantly greater than zero (Fig. 5d ; average = 0. 78, 95% CI 0. 53-1. 04, p = 7 × 10 -10 ), indicating a substantial fraction of these effects is genetic in origin. However, for both adoptees and siblings, the point estimates are less than one, suggesting that non-genetic factors probably contribute a small, but significant, fraction of the observed effects.
Methods Overview. Our initial aim was to estimate the effect of F ROH on 45 quantitative traits and to assess whether any of these effects differed significantly from zero. Previous work  7, 11  has shown that inbreeding coefficients are low in most human populations, and that very large samples are required to reliably estimate the genetic effects of inbreeding  13. To maximise sample size, a collaborative consortium (ROHgen 6 ) was established, and research groups administering cohorts with SNP chip genotyping were invited to participate. To ensure that all participants performed uniform and repeatable analyses, a semi-automated software pipeline was developed and executed locally by each research group. This software pipeline required cohorts to provide only quality-controlled genotypes (in plink binary format) and standardised phenotypes (in plain-text) and used standard software (R, PLINK  12, 32  , KING  33  ) to perform the analyses described below.
Occasionally, cohorts had been genotyped on different SNP genotyping microarrays and these were also separated into sub-cohorts. There was one exception (deCODE) to the single microarray rule, where the intersection between all arrays used exceeded 150,000 SNPs. In this cohort the genotype data from all arrays was merged since the correspondence between F ROH for the individual arrays and F ROH the intersection dataset was found to be very strong (β merged;hap = 0. 98, r 2 = 0. 98; β merged;omni = 0. 97, r 2 =0. 97). Dividing studies using these criteria yielded 234 sub-cohorts. Details of phenotypes contributed by each cohort are available in Supplementary Data 4. Ethical approval. Data from 119 independent genetic epidemiology studies were included. All subjects gave written informed consent for broad-ranging health and genetic research and all studies were approved by the relevant research ethics committees or boards.
PubMed references are given for each study in Supplementary Data 2. Genotyping. All samples were genotyped on high-density (minimum 250,000 markers), genome-wide SNP microarrays supplied by Illumina or Affymetrix. Genotyping arrays with highly variable genomic coverage (such as Exome chip, Metabochip, or Immunochip) were judged unsuitable for the ROH calling algorithm and were not permitted. Imputed genotypes were also not permitted; only called genotypes in PLINK binary format were accepted. Each study applied their own GWAS quality controls before additional checks were made in the common analysis pipeline: SNPs with >3% missingness or MAF <5% were removed, as were individuals with >3% missing data. Only autosomal genotypes were used for the analyses reported here. Additional, cohort-specific, genotyping information is available in Supplementary Data 2.