dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Handling and CNV Visualization To facilitate further data processing and visualization of CNV events, we generated an HDF5 database consisting of sample metadata, CNV calls, probe information, LRR intensity and BAF values for all assays. Normalized intensity values were also generated by converting the GCcorrected, median-centered LRR measures into Z-scores within each sample and inserted into the database. Visualization of cluster plots (Figures S2A, S2B, S3C); median Z-score outlier detection (MeZOD) CNV calls  (Vacic et al. , 2011 ) (Figure  3B ); and probe-level CNV plots (Figures  S5  and S6 ) were generated from Z-scores of intensity data using in Matplotlib. Python code to create the HDF5 database and perform associated plotting functions is available on bitbucket (https://bitbucket.
The Z-scores for all probes spanning the CNV locus were then subjected to second round of normalization across all samples. A Gaussian mixture model (GMM) was fit to this distribution of Z-scores using the SciKit-learn Python package. The optimum number of clusters was automatically determined by minimization of the Bayesian Information Criterion (BIC) and corrected, when necessary, by manual adjustment. Individuals were assigned to a cluster only if the posterior probability of assignment exceeded 0. 95. Python code to perform GMM-based genotyping at specific loci is available on bitbucket (https://bitbucket. org/ucla_coppolalab/tscnv). Copy number state was inferred by examining the original LRR intensity values for samples within each cluster. We inspected for allele frequency differences between controls and cases for all clusters and found no significant difference (Fisher's exact test, Table  S2 ).
Call Filtering and Delineation of Rare CNVs Calls were removed from the dataset if they spanned less than 10 markers, were less than 30kb in length, or overlapped by more than 0. 5 of their total length with regions known to generate artifacts in SNP-based detection of CNVs. This included immunoglobulin domain regions, segmental duplications, and regions that have previously demonstrated associations specific to Epstein-Barr virus immortalized cell lines  (Shirley et al. , 2012). In addition, we removed calls that spanned telomeric (defined as 100kb from the chromosome ends), centromeric regions, and gaps in the reference genome. As described elsewhere (CNV and Schizophrenia Working Groups of the Psychiatric Genomics Consortium, 2017), we assigned all CNV calls a specific frequency count using PLINK v.
1. 07 (Purcell et al. , 2007) , with the option --cnv-freq-method2 0. 5. Here, the frequency count of an individual CNV is determined as 1 + the total number of CNVs overlap by at least 50% of its total length (in bp), irrespective of CNV type. We then filtered our callset for CNVs with MAF < 1% (a frequency of 65 or lower across 6,527 samples Conservatively, CNV regions were only considered common if present at a frequency of > 10% within any individual dataset above. Exclusion intervals and code to perform regional filtering of CNVs is available on bitbucket (https://bitbucket. org/ucla_coppolalab/tscnv).
Each CNV was scored by calculating the median of LRR intensity Z-scores (LRR-Z) for all probes within the region. To qualify CNVs based on BAF banding, we calculated the proportion of probes within the CNV region that showed evidence of a duplication event (BAF of [0. 25-04] or [0. 6-0. 8]), and denoted this measure "BAF-D. " Based on thresholds established through manual inspection of large CNVs, we flagged deletions that had a LRR-Z > -2 or BAF-D > 0. 02, and duplications with a LRR-Z < 1 or BAF-D < 0. 1. To avoid differential missingness caused by subtle differences between datasets, we did not impose any hard cutoff for CNV exclusion based on these metrics. These thresholds were applied to flag CNV calls with marginal scores for manual inspection for the exclusion of obviously misclassified events.
Through this in silico validation process we discovered multiple instances of large CNV calls likely due to individual mosaicism (Figure  S3A  and S3B), and removed these events from subsequent analysis. Furthermore, for each rare CNV call, we used distribution of summarized intensity information across all individuals. For each rare CNV region, we quantified proportion of samples whose LRR-Z metric fell outside of [-2. 3, 1. 3] and further inspected these regions manually. Putative rare CNV loci that showed substantial evidence for extensive polymorphism were subsequently scored for frequency using the GMM genotyping method described above (Figure  S3C ) and conservatively, only removed if shown to be variant in more than 10% of the samples across the entire dataset. Python code to generate in silico metrics for CNV calls from intensity data stored in an HDF5 database is available on bitbucket (https://bitbucket.
Global CNV Burden Analysis We measured global CNV burden using three separate metrics: the number of rare CNVs (CNV count), the total length of all CNVs (CNV length) and the total number of genes intersected by CNVs (CNV gene count). To examine the effect of different covariates on different metrics of global CNV burden, we first fit a linear regression model for each burden metric, using the glm function in R: Burden_metric ∼ subject_sex + ancestry_PCs + LRR_SD In the above model, ancestry_PCs included the first ten PCs derived from SNP data and, as various assay intensity quality metrics are highly correlated, LRR_SD was used as a single measure of assay quality. Across the entire dataset, only PC2 and LRR_SD were associated (p < 0. 05) with global CNV burden as measured by CNV count. To assess for a global burden difference between TS cases and controls, we fit the following general logistic regression model: 𝑇𝑇𝑇𝑇_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ∼ 𝐵𝐵𝑠𝑠𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵_𝑚𝑚𝐵𝐵𝑠𝑠𝐵𝐵𝑚𝑚𝑚𝑚 + 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝐵𝐵𝑚𝑚𝑠𝑠_𝑠𝑠𝐵𝐵𝑠𝑠 + 𝑃𝑃𝑃𝑃1 + 𝑃𝑃𝑃𝑃2 + 𝑃𝑃𝑃𝑃3 + 𝑃𝑃𝑃𝑃4 + 𝐿𝐿𝐿𝐿𝐿𝐿_𝑇𝑇𝑆𝑆 Independent variables included burden metric, subject sex, and the first 4 ancestry PCs.
9), stratified by CNV type, and compared the relative risk of such events to all large, singleton genic CNVs. In Figure  2 , global burden was analyzed separately for genic CNVs and non-genic CNVs. Since we were interested in comparing the relative contribution to TS risk by different measures of burden and across various categories, sizes, and frequency classes, the ORs presented in Figure  2  and Figure  S4  are calculated from standardized CNV burden metrics. For Figure  3  and elsewhere, we calculated ORs using the unstandardized value of actual CNV counts, as this is directly interpretable. R code to perform burden analysis is available on bitbucket (https://bitbucket. org/ucla_coppolalab/tscnv).
Quantification and Statistical Analysis All statistical analyses were performed using R v3. 4. 0 or PLINK v1. 07. R code and example commands to perform all statistical analyses are available on bitbucket (https://bitbucket. org/ucla_coppolalab/tscnv). For sensitivity assessment (see "Sensitivity Assessment"), as also indicated in the main text and/or Tables  S2  and S3 , we used a Fisher's exact test to test for a difference between cases and controls in: the frequency of nonreference genotypes at common CNV loci (Table  S2 ), the sensitivity of HMM-based CNV calls at these loci individually (Table  S3A ), and the sensitivity of HMM-based CNV across all loci (Table  S3B ). For Table  S3C , we used Welch's t test to test if the average sensitivity per individual differed between cases and controls, assuming that the variance of this measure was not necessarily equal between groups.
). Furthermore, we removed calls if they shared more than a 50% reciprocal overlap in length to common CNVs regions derived from several large, publically available SNP-array datasets, compiled by the Database of Genomic Variation and Phenotype in Humans using Ensembl Resources (DECIPHER; https://decipher. sanger. ac. uk/index):1. 845 population samples from the Deciphering Developmental Disorders study. 2. 450 population samples from the 42M genotyped study. 3. 5919 population samples from the Affy6 study.
Acknowledgments The authors thank the patients with  Tourette Syndrome  and their families, and all the volunteers who participated in this study. This study was supported by the  US NIH  Grant  U01 NS040024  to D. L. P. , C. A. M. , and J. M. S. and the TSAICG;  NIH ARRA  Grant  NS040024-09S1  and  NIH  Grants  K23 MH085057  and  K02 NS085048  to J. M. S. ;  NIH ARRA  Grant  NS040024-07S1  and  NIH  Grant  NS016648  to D. L. P. ;  NIH  Grant  MH096767  to C. A. M. ;  NINDS Informatics Center for Neurogenetics and Neurogenomics  Grant  P30 NS062691  to G. C. and N. B. F. ; grants from the  Tourette Association of America (TAA)  to P. P. , D. L. P. , C. A. M. , and J. M. S. ; and from the  German Research Society  to J. H. P. S. , M. G. , H. S. S. , R. A. K. , Y. D. , G. R. , C. L. Budman, G.
I. A. M. has participated in research funded by the  National Parkinson Foundation ,  TAA, Abbvie, Auspex, Biotie, Michael J. Fox Foundation, Neurocrine ,  Pfizer , and  Teva , but has no owner interest in any pharmaceutical company. I. A. M. has been reimbursed for speaking for the  National Parkinson Foundation  and TAA. M. S. O. serves as a consultant for the  National Parkinson Foundation  and has received research grants from  NIH ,  NPF , the  Michael J. Fox Foundation , the  Parkinson Alliance, Smallwood Foundation , the  Bachmann-Strauss Foundation , the  TAA , and the  UF Foundation. M. S. O. 's DBS research is supported by  NIH  grants  R01 NR014852  and  R01 NS096008. He has previously received honoraria, but in the past >60 months has received no support from industry. M. S. O.
html TSAICG Scripps genotypes Tourette Scripps https://www. findtsgene. org https://genomics. scripps. edu/ADVISER/Home. jsp samples (n = Genome 1000 Syndrome Research 1000 ftp://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/20130502/supporting/hd_g 2,243) Advisor Genomes Associatio Institute Genomes enotype_chip/ GemTools Project sample genotypes Perl and HapMap3 BASH scripts CNV data for data GGRI samples (n = 1,172) processing, consensus Population CNV calling, CNV regions and call filtering RefSeq hg19 Python code gene for CNV annotation genotyping, Gene pLI sensitivity scores analysis, and and in silico regions visualization, CNV exclusion n Lee et al. , Project Internatio 2010 nal This Internatio Consortiu paper nal m for HapMap Genetics Gilles de Project DECIPHE la R Tourette Database Syndrome UCSC Genome-This Genome wide paper Browser Associatio Lek et al.
, n Study 2016 Replicatio n Initiative paper This http://wpicr. wpic. pitt. edu/WPICCompgen/GemTools/GemTools. htm https://bitbucket. org/ucla_coppolalab/tscnv ftp://ftp. ncbi. nlm. nih. gov/hapmap/cnv_data NA https://decipher. sanger. ac. uk/ https://genome. ucsc. edu https://bitbucket. org/ucla_coppolalab/tscnv http://exac. broadinstitute. org/ Deposited Data https://bitbucket. org/ucla_coppolalab/tscnv TSAICG and validation This dbGaP: phs001380. v1. p1 (https://www. ncbi. nlm. nih. gov/projects/gap/cgi- GGRI CNV burden genotype data R code for Contact for Reagent and Resource Sharing paper paper bin/study. cgi?study_id=phs001380. v1. p1) This https://bitbucket. org/ucla_coppolalab/tscnv analysis Software and Algorithms Beeline v1. 0 Illumina https://www. illumina. com/techniques/microarrays/array-data-analysis-Other Cardiff Green experimental-design/beeline.
Description of the study area Data for this study were collected between September and November, 2014. The study area comprised of four districts within Zambia (Rufunsa District in Lusaka Province, Mpika and Chama Districts in Muchinga Province, and Mambwe District in Eastern Province) where sporadic cases of rHAT are reported (Fig. 1 )  [29]. These areas are predominately rural, where the economy is based on mixed crop-livestock farming and smallscale trades. Over 60% of the population is considered to live below the poverty line, of whom 40% live in absolute poverty. Farming activities revolve around a variety of different main crops such as maize, groundnuts, soya beans, cotton and tobacco, grown during the rainy season. Wetlands are significant areas for keeping cattle (herd sizes are in most cases fewer than 80 cattle), while also facilitating the survival of a low to moderate tsetse population density.
Of these, 22 patients came from Chama, 11 from Rufunsa, 28 from Mpika and three from Mambwe Districts. All cases (n = 64) were traced and all patients or their families were interviewed with at most two visits. Structured questionnaires were administered to all patients or their close relatives (care givers) to collect information on the economic and social impacts of rHAT in the communities or districts. Hospital records of patients who were interviewed were retrieved, where possible, to confirm the time period the patient was undergoing treatment. Patient data were obtained together with national statistics from the Ministry of Health. Focus group discussions were also conducted with the affected communities  [30]. The focus groups comprised seven to ten people per discussion and included health workers, people who had suffered from rHAT and their relatives or friends, so as to obtain in-depth information on concepts, perceptions and ideas of the group regarding the social consequences of the disease.
Data analysis Focus group discussion data were analysed using inductive approaches and thematic coding, carried out by two independent researchers. All structured interview data were entered and analysed using Excel (Microsoft Office Excel® 2007). Qualitative data were entered into Microsoft Word™ and analysed manually based on accepted methods of coding and memowriting  [30, 31]. All transcripts were de-identified and personal identifiers were removed to protect individual patient data. For quantitative data, descriptive statistics were generated for variables under study, and analysis of variance (ANOVA) was used to determine associations among the variables. Significance was accepted at 95% confidence level for all analyses.
Patients' monthly household gross cash income by district One household had a monthly income of $ 1111. 11, excluding this outlier would have reduced the average income for Chama to $161. 7 and for the whole dataset to $61. 2. For the whole dataset, 10 households had incomes over $100, of these 5 were over $300 and 8 were in Chama District and 2 in Rufunsa, thus none in Mambwe or Mpika b No interquartile range is presented for Mambwe as there were only 3 patients c Household cash income could not be obtained for one patient.
For instance, the highest reachable SOC stock for a given pedoclimatic condition under a given land-use could correspond to the mean of the top 10% of the measured SOC stocks for these conditions. 25 Depending on the operational question, this approach may be conducted at different scales from regional to global. For example, SOC stocks under natural vegetation have been termed as "reference SOC stocks" and have been determined for 7 IPCC soil classes and 10 IPCC climatic zones from available world databases  (IPCC, 2006; Batjes, 2011)  A data driven approach to estimate highest SOC stock values or SOC storage rates can easily be implemented in several pedoclimatic regions. However, estimating empirically highest SOC stock values has several limitations. First, the observed 20 SOC stock values for given pedoclimatic conditions (or part of the top 10% values) might be well below the maximum, if for example all pastures were degraded in the considered pedoclimatic condition or if steady-state conditions for SOC stocks were not attained for most soils.
Second, this approach allows estimating a soil storage potential value for known practices. If new storing practices are designed or implemented in the considered region, the highest reachable SOC stock value could obviously not be calculated. Another limitation is that the impact of land-use change is generally better documented than the effects of 25 management practices. Therefore, while estimating highest reachable SOC stocks or SOC storage rates may be straightforward in a context of land-use change, it would be more difficult to implement in a context of land management modification. Indeed, most soil databases, such as Lucas-Soil or ISRIC, do not systematically provide comprehensive information on management practices. However, with the active development of soil monitoring networks, regional to local estimates of highest reachable SOC stocks and SOC storage rates may progressively become available worldwide for management practices.
Such data-driven or statistical approaches have already been explored to assess or refine estimates of highest SOC stock values or default IPCC SOC storage rates. For instance, Sparling et al. (2003) have estimated desirable values of soil organic carbon in New Zealand, which were proposed to be above the lower quartile of SOC stocks in New Zealand pasture soils, for each major soil type. Reference values of SOC stocks under natural vegetation have recently been used to assess soil C storage 10 potential in Nigeria (Apka et al. , 2016). Stolbovoy & Montanarella (2008) estimated the potential of SOC gain of European soils by subtracting the observed SOC stocks per soil typological unit, with the maximum observed SOC stocks in the same typological unit and the same climate, using the European Soil Portal databases.
The XXL survey currently covers two 25 deg 2 patches with XMM observations of ∼ 10ks. We summarise the scientific results associated with the first release of the XXL data set, that occurred mid 2016. We review several arguments for increasing the survey depth to 40 ks during the next decade of XMM operations. X-ray (z < 2) cluster, (z < 4) AGN and cosmic background survey science will then benefit from an extraordinary data reservoir. This, combined with deep multi-λ observations, will lead to solid standalone cosmological constraints and provide a wealth of information on the formation and evolution of AGN, clusters and the X-ray background. In particular, it will offer a unique opportunity to pinpoint the z > 1 cluster density. It will eventually constitute a reference study and an ideal calibration field for the upcoming eROSITA and Euclid missions.
They are based on the brightest 100 clusters and 1000 AGN samples. Both Xray catalogues along with two associated VLA and ATCA radio source lists are available at the CDS. They can also be retrieved in a more extensive form, along with the XMM images and exposure maps, via the XXL databases (Table  1 ). The XXL team pays special attention to the delivery of well-validated catalogues and, beside the science publications, considers this legacy aspect a priority commitment. Table  1  The XXL databases. As of the 2016 release, the X-ray catalogues are limited to the brightest 100 clusters and 1000 AGN. Incrementally deeper releases will follow. In addition, the database in Milano provides the X-ray raw and wavelet MOS+pn mosaic images as well as all exposure maps for both XXL fields up to AO11.
15 in our analysis and to proceed with the simultaneous modelling of cosmology, selection effects and cluster evolution. We shall also benefit from the deep high-quality optical coverage of the XXL-N field by the Hyper-Suprime-Camera on the Subaru telescope (HSC Wide Survey 3  ), which will greatly improve the lensing determination of our cluster masses. A second data release at greater depth will occur in 2017 along with associated scientific articles. We foresee the final data release, including the cluster selection function, for the end of 2018.
The X-ray background The XXL survey enables for the first time the study of the diffuse X-ray background (XRB) on large scales at a high angular resolution and high sensitivity (last studies were on the ROSAT All-Sky Survey data). A first impression of the scientific potential of the X-ray data is rendered by Fig. 2. We are currently working on the characterisation of the structures remaining after source extraction. We are undertaking an auto-correlation study of the map pixels as well as correlations between the X-ray and various maps (HI, IR, FIR) and catalogues (optical and IR galaxies).
The main goal is to derive competitive standalone cosmological constraints from the clusters and AGN present in these particular two areas. Furthermore, the global merit of the project will be greatly enhanced thanks to the synergy between the many associated surveys, from UV to radio. With the new very sensitive instruments such as the HSC in the optical and NIKA2 in the S-Z domains, the scientific potential of the data set will serve a very large scientific community. In the same spirit, we advocate the opening of joint XMM-Chandra Very Large Programmes: along with hydrodynamical simulations, this will definitively enlighten the physics and evolution of the low-mass z ∼ 0. 5 and high-redshift clusters, in relation to galactic nucleus activity. XXL-II will bridge the gap between the expected eROSITA and Athena performances in terms of combined sensitivity, coverage and angular resolution.
This approach has been proven to be an effective and a pratical method of distinguinshing good quality datasets without the need to measure the aerosols distributions  [3]  , that is a rather difficult task. • Refraction. The wavelenght independency of the refractive index was assumed since n(λ)-1 changes only 5 % over the wavelenght range 300-600 nm, which is the range typically covered by photomultipliers. The influence on gamma-ray observations can therefore be considered as negligible  [1]. The vertical atmospheric profiles used within KASKADE  [4] , one of the two main simulation frameworks used within the H. E. S. S. collaboration, was derived from balloon soundings at Windhoek airport in the 1990s, averaged over a whole year. A verification of this parametrisation is the main aim of the work presented here.
Experimental GNNA120 atmospheric model Worldwide, the atmosphere is being continuously monitored by a fleet of satellites, networks of weather stations, ballon soundings, etc. Here we exploited different databases belonging to NOAA and NASA's monitoring and modeling centers, respectively. A full atmospheric profile model, named experimental GDAS -NRLMSISE for Namibian Atmosphere up to 120 km (GNNA120) model is introduced. For this new description of the atmosphere above H. E. S. S. two different sources of data have been chosen. • Low altitudes. The Global Data Assimilation System  [5]  (GDAS) is a system used by the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) to place observations into a gridded model space for the purpose of starting weather forecasts with observed data.
The GDAS grid spans the whole Earth with a grid-size is of 1 • High altitudes. The Community Coordinated Modeling Center (CCMC) provides several empirical models. The NRLMSISE-00 Model 2001  [7]  describes the neutral temperature and densities for the Earth's atmosphere up to thermospheric heights. It includes temporal and spatial variatons at various scales based on an analytical model. The profiles that could be extracted from GDAS only present data up to around 25 km. Thus, once one runs out of data from the GDAS database, the data is taken from the NRLMSISE-00 model up to 120 kilometers, in layers of 5 kilometers. It has been verified that both models agree very well in the overlapping region, thus no artefacts are induced by this combination.