dataesr/scientific-paragraphs-categorization huggingface dataset:
Correlations between cloud properties In order to characterize the relations between structure properties, we fit for linear relationships between the logarithms of properties, i. e. power laws. We employ the kmpfit module of the Python package Kapteyn  (Terlouw & Vogelaar 2015)  which provides non-linear least-squares fitting of user-specified functions. In order to take into account the estimated errors in both variables, we first obtain an initial estimate of the slope and intercept using an unweighted least-squares fit (code in scipy. stats. linregress) and then optimize these parameters in kmpfit after weighting each sample by the inverse of its effective variance. The effective variance method assumes that an error δx i in x i changes the value of y i by an amount f (x i )δx i , which can be added in quadrature to the error δy i in y i.
The latter property may be related to the fact that structures are essentially identified as isocontour levels in the cube. Small isocontours can enclose both high and low brightness regions, while intermediate-sized isocontours enclose relatively bright regions that span multiple contour levels, and the largest isocontours enclose the bulk of the emission in the cube, averaging over moderate and high-brightness regions. We note that the limited field-of-view of the ALMA mosaics confines our observations to regions of bright CO emission-the relatively high surface densities on large scales are thus partly a result of targeting the observations toward the CO peaks of low-resolution singledish maps. When it comes to other properties, however, our comparisons with the wide-field MAGMA data indicate overall consistency between the ALMA-observed regions and the larger CO cloud.
The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. AH acknowledges support from the Centre National d'Etudes Spatiales (CNES). ER is supported by a Discovery Grant from NSERC of Canada. KT was supported by NAOJ ALMA Scientific Research Grant Number 2016-03B. The work of M. S. was supported by an appointment to the NASA Postdoctoral Program at the Goddard Space Flight Center, administered by Universities Space Research Association under contract with NASA. Part of this research was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
This research made use of astrodendro, a Python package to compute dendrograms of astronomical data, SCIMES, a Python package to find relevant structures in dendrograms of molecular gas emission using the spectral clustering approach, and Astropy, a community-developed core Python package for astronomy. Facility: Mopra, ALMA, Planck, Herschel, Spitzer Software:  CASA (McMullin et al. 2007) , DUSTEM  (Compiègne et al. 2011) , astrodendro (http://www. dendrograms. org), Kapteyn  (Terlouw & Vogelaar 2015) , Scipy  (Jones et al. 2001) , SCIMES  (Colombo et al. 2015) , Astropy  (Astropy Collaboration et al. 2013).
Technical Framework The RWS approach has been fully implemented in one of the two analysis chains in operation in H. E. S. S. , named parisanalysis. All information that is relevant for the simulation of a specific run is stored in a MySQL database (DB) and read out on demand. Hence no read-in of run-specific files is necessary. The shower simulations are carried out with the KASCADE software  [10] , which has been improved and rewritten in C++ to allow more flexible usage and easy integration in the simulation chain (see  [4] ). It has been further enhanced for the present work to be able to simulate the evolution of the tracking position of the telescopes within a run. The KASCADE output is directly passed to the internal IACT simulation software. Opposed to the classical simulation approach, where generally the shower and IACT simulation are decoupled, no direct output of the shower simulation is saved in case of the implemented RWS approach, leading to a drastic reduction of needed disk space at the expense of increased computation time.
Simulation Parameter Settings Observations of IACT arrays generally cover a large range of observation conditions, implying substantial variations of the required simulation parameter space. For an efficient simulation with yet sufficient parameter coverage for each run, an automatized parameter calculation approach is indispensable. The relevant inputs for the calculation presented here are: • Array configuration, • Zenith angle range [θ min , θ max ] covered within the run, • Relative optical efficiencies ε of the participating IACTs, • Desired statistics level, defined by the differential flux at E = 1 TeV, • Spectral index Γ of the simulation, • Number of computing jobs (slices) per run. The first three are run-dependent and automatically read out from the database, whereas the three latter are set by the user.
Conclusions Here we introduced a novel simulation approach for IACT arrays which accounts for the runspecific observation and detector settings. The method has been fully developed and implemented into one of the simulation and analysis frameworks of H. E. S. S. As the concept leads to a more realistic simulation, a reduction of systematic errors is expected. The impact of this expected improvement on the PSF extraction has already been shown in Section 2. 4. 2. In this regard, morphology measurements with IACTs have been brought to a whole new level. Besides reduced systematic uncertainties, the RWS approach is furthermore computationally more efficient for the simulation of entire IACT data sets with various instrument settings and observation conditions. This will become more and more important for future, increasingly complex IACT arrays, such as the Cherenkov Telescope Array.
Characterization of four novel mib2 alleles Fortuitously, four new distinct mib2 alleles were obtained from a recent genetic screen for mutations that affect muscle development (for details see Materials and methods). Based upon complementation data and initial reporter gene-based phenotypic analysis, mib2 S0768 and Tropomyosin 1 (Tm1) and analyzed by fluorescent microscopy. (B) Heterozygous mib2 embryo with one copy of the driver rP298-Gal4 shows a stereotypic muscle pattern. (C) Homozygous mib2 mutant embryo shows extensive muscle loss (asterisks) and detachment (arrows). Homozygous mib2 mutant embryos are rescued with UAS-mib2 FL (D; encoding full-length Mib2), UAS-mib2 ΔHERC2A+ZZ (E; Mib2 without the HERC2A and ZZ domains), UAS-mib2 ΔZZ (F; Mib2 without the ZZ domain) or UAS-mib2 ΔHERC2B (G; Mib2 without the HERC2B domain).
Acknowledgments We thank  Lynn Cooley ,  Renate Renkawitz-Pohl ,  Sree Devi Menon ,  William Chia ,  Frank Schnorrer ,  Marta Carrasco-Rando  and  Mar Ruiz-Go  ´mez, the  Bloomington Drosophila Stock Center, Developmental Studies Hybridoma Bank at the University of Iowa  (DSHB, under the auspices of NICHD), and  Berkeley Drosophila Genome Project  for sharing various antibodies, fly stocks, and cDNA clones. We also thank  Nader Ezzeddine ,  Bernhard Ha  ¨ner and  Dominik Hollfelder  for invaluable technical assistance.
Direct photons at high p T in pp collisions: Isolation technique The analysis of direct photons at high p T has been performed on pp collisions at √ s = 7 TeV, using data collected in 2011. In this analysis, the photons are measured in the ALICE Electromagnetic Calorimeter (EMCal). The EMCal  [12]  is a lead-scintillator sampling calorimeter consisting of cells of Δη × Δϕ = 0. 0143 × 0. 0143 size with a coverage of 1. 4 < ϕ < π, |Δη| ≤ 0. 7. In order to collect a high-p T sample we took data with the L0-EMCal trigger based on the analog charge sum of 2× 2 cells, in correlation with the Minimum Bias (MB) trigger signal in the two V0 detectors placed at large rapidities (2. 8 < η <5. 1,-3. 7 < η < -1. 7). The clusters in EMCal are reconstructed by aggregating cells with energy deposit E cell ≥ 100 MeV to a leading cell with at least E seed ≥ 300 MeV.
Aside from its special place as a companion animal, the dog or Canis lupus familiaris is also genetically exceptional. While population bottlenecks, the popular sire effect and stringent breeding programs have led to an extreme phenotypic diversity, they resulted at the same time in a high number of genetic disorders  1, 2. Several disorders are breed-specific, others are more widely dispersed through the population. A large number of these disorders share clinical and laboratory abnormalities with human diseases and over 50% of the genetic diseases in the dog are considered to be potential models for human diseases according to the Online Mendelian Inheritance in Animals database  3. To identify the causal mutations responsible for these traits, two different approaches are often used. In genome-wide association studies (GWAS), it is generally not the causal mutation that is directly genotyped.
By comparing the power difference between 16 samples and lower sample sizes within each technique separately, the effect of sample size can be assessed. By comparing the power for two different sample sizes between the two techniques, corresponding to a GWAS:WES sample ratio of interest, the indirect effect of price on power can be estimated. Firstly, when the effect of sample size is assessed for EG-GWAS, the median power remained relatively constant for 14 and 12 samples relative to the power with 16 samples (Fig. 4 (f)). After that, it gradually decreased up until it became zero for 6 samples (3 cases vs 3 controls). The same result was obtained for GWAS using cHD data (Fig. 4(f) ). The power varied more (i. e. increased distance between upper and lower whiskers of boxplots (Fig. 4(f) ) when the sample size decreased from 14 to 12 samples, indicating that the power is influenced by which samples were retained.
Hair length is associated with an autosomal recessively inherited G > T variant in the FGF5 gene  15. For each phenotype, two analyses were performed. Firstly, using the entire dataset of 28 dogs, the p-value and the distance between the closest significant SNP were compared for EG-GWAS and cHD. Next, the sample size was stepwise reduced to find the minimal sample size necessary to identify a significant association. A detailed overview of the results and Manhattan plots are provided in Table  1  and Suppl. Fig. S4 , respectively Significant associations nearby and at the respective causal indel and variant were detected for both phenotypes. For hair length, EG-GWAS directly identifies the causative mutation (Bonferroni-adjusted p-value = 1. 87e-06)  15. The variant was also ranked number 1 in the list of most significant SNPs.
These unexplained differences between the different signal categories (exonic, non-exonic, lincRNA) might be (partially) related to underlying differences in gene count, GC content and several other sequence features that have been shown to be associated with r²  36. This study is unique and highly informative by its design as experimental data from both WES and GWAS SNP chip genotyping was available for 28 dog samples from 3 different breeds. As a consequence, GWAS and WES could each time be directly compared. Our results revealed that generally, WES outperforms GWAS inside target regions, while the opposite happens for non-target regions for monogenic traits. As complex disease loci often fall outside these protein-coding regions  14, 37  , cHD is then the method of choice for complex trait mapping.
Methods Samples. Blood and tissue biopsy samples from dogs were collected by a network of veterinarians through the French Cani-DNA biobank (http://dog-genetics. genouest. org), which is part of the CRB-Anim infrastructure [ANR-11-INBS-0003]. The work with dog samples was approved by the CNRS ethical board, France (35-238-13)  for UMR6290 and dog owners consented to the use of data for research purposes anonymously. All methods were carried out in accordance with relevant guidelines and regulations. A paired study design was adopted in which the same 28 samples (from three breeds of which 16 were Poodles, 6 Labrador and 6 Golden retrievers, respectively) underwent both WES and GWAS genotyping.
1 reference genome 45 using BWA 0. 5. 9. Duplicate reads were marked with Picard (http:// broadinstitute. github. io/picard/), and the data were sorted. Using the GATK 3. 5-0, local realignments were generated and base quality scores were recalibrated per GATK Best Practices  46. Variant calling. GATK 3. 5-0 was used to realign the reads around indels and HaplotypeCaller was used to call SNPs on the resulting BAM files. The resulting VCF files were converted to PLINK (ped and map) format file using vcftools_0. 1. 13 and bi-allelic SNPs were considered (indels were not retained). In total, 83,869 unique SNPs were further used for Poodles, 66,809 for Labrador retrievers and 66,000 for Golden retrievers.
LD analysis. Both the LD and power analyses were run from within R (version 3. 3. 1), using R-studio (version 0. 99. 902). The LD analysis was performed with Haploview 4. 2  47. The following filters were used: MAF of 0. 05, Hardy-weinberg p = 0, exclude markers with >0. 5 missing genotypes and less than 0. 5 nonzero genotypes. The missing genotypes cut-off reflects the 5 th percentile of the % genotyped distribution of the 16 Poodles to account for the higher performance variability associated with sequencing relative to GWAS assays. This cut-off accurately removed outliers as all other tagSNPs had a success rate of >90%. An in-depth discussion on the rationale of the setting applied for the Hardy-Weinberg equilibrium test is provided in Suppl. Data S3  [48] [49] [50] [51] [52]. The r² values between subsequent markers were retained and used in the downstream analysis to investigate the potential association with distance and chromosomal position.
To retain a sufficient number of SNPs, a wider range of cases was allowed (i. e 4 to 12 cases), which resulted in a total of 61 unique SNPs that were used for the simulation of phenotypes. Identification of genetic variants responsible for traits shared among dog breeds. Two phenotypes were mapped as a proof of concept: furnishing and hair length  16. The chi-squared allelic association analysis was performed with PLINK (version 1. 90b. 45) and Bonferroni corrected p-values were used  54. For the first analysis, the entire dataset (n = 28) was used. As prior knowledge exists on the location of the causal variant, the closest significant SNP was selected and both the p-value and the distance to the causal variant were retained. The aim of the second analysis was to discover the minimum sample size.
Acknowledgements We would like to thank the following funding agencies: the  Research Foundation-Flanders  for providing a travel grant ( 12G9217N   SW ) and a general  Postdoctoral Fellow grant  to the first author ( 12G9217N ), the  French National Research Agency  in the frame of the " Investing for the Future" program  for  Cani-DNA  [ ANR-11-INBS-0003 ], the  CNRS  ( Centre National de la Recherche Scientifique ) and the  Brittany Region (France)  (PhD funding for CLB). Funding for sequencing was granted by the  National Institutes of Health  ( U54 HG003067-08 , awarded to the  Broad Institute)  and funding for genotyping was granted by the  European Commission  ( FP7-LUPA ,  GA-201370 ). We thank the  Broad Institute Genomics platform  for sequencing. We thank  Amaury Vaysse  for helpful comments on the manuscript and we thank all members of the canine genetics team in Rennes for their support.
PURPOSE. Primary open-angle glaucoma (POAG) and primary congenital glaucoma (PCG) with Mendelian inheritance are caused by mutations in at least nine genes. Utilizing whole-exome sequencing, we examined the disease burden accounted for by these known Mendelian glaucoma genes in a cohort of individuals with advanced early-onset POAG. METHODS. The cases exhibited advanced POAG with young age of diagnosis. Cases and examined local controls were subjected to whole-exome sequencing. Nine hundred ninetythree previously sequenced exomes of Australian controls were called jointly with our dataset. Qualifying variants were selected based on predicted pathogenicity and rarity in public domain gene variant databases. Case-control mutational burdens were calculated for glaucoma-linked genes. RESULTS. Two hundred eighteen unrelated POAG participants and 103 nonglaucomatous controls were included in addition to 993 unexamined controls.
Fifty-eight participants (26. 6%) harbored rare potentially pathogenic variants in known glaucoma genes. Enrichment of qualifying variants toward glaucoma was present in all genes except WDR36, in which controls harbored more variants, and TBK1, in which no qualifying variants were detected in cases or controls. After multiple testing correction, only MYOC showed statistically significant enrichment of qualifying variants (odds ratio [OR] ¼ 16. 62, P ¼ 6. 31310 À16 ). CONCLUSIONS. Rare, potentially disease-causing variants in Mendelian POAG genes that showed enrichment in our dataset were found in 22. 9% of advanced early-onset POAG cases. MYOC variants represented the largest monogenic cause in POAG. The association between WDR36 and POAG was not supported, and the majority of POAG cases did not harbor a potentially disease-causing variant in the remaining Mendelian genes.
Similarly, POAG can be subclassified by age of onset, from less than 4 years for primary congenital glaucoma (PCG), between 4 and around 40 years for early-onset or juvenile-onset POAG (JOAG), to over 40 years for typical POAG. An autosomal dominant mode of inheritance is present in a proportion of individuals with POAG. Estimates place the percentage of Mendelian inherited POAG explained by known causative genes at around 5%. 6  The Online Mendelian Inheritance in Man (OMIM) database (www. omim. org; accessed October 17, 2016; in the public domain) documents seven genes (MYOC, *601652; OPTN, *602432; CYP1B1, *601771; WDR36, *609669; ASB10, *615054; NTF4, *162662; and TBK1, *604834) with potentially disease-causing variants in POAG and two (CYP1B1, *601771 and LTBP2, *602091) in PCG. Additionally, the GLC1C locus has since been linked to the gene IL20RB (interleukin 20 receptor subunit beta) via Sanger sequencing.
Whole-Exome Sequencing DNA was extracted from peripheral blood samples using the QIAamp DNA blood kit (Qiagen, Hilden, Germany) following the manufacturer's protocol. Samples were prepared with Variant calling of SNV and small indels was performed with the UnifiedGenotyper module in GATK, and variant quality scores were recalibrated according to the GATK Best Practices Guidelines. 50, 51 Variant annotation was performed using ANNOVAR  52  software using refGene, SIFT  53  (Sorting Intolerant From Tolerant), PolyPhen2 HVAR  54    ). All other genes except TBK1 and WDR36 exhibited nominal enrichment in POAG; however, the difference was not statistically significant (Table ). No TBK1 SNV or indels were detected in the POAG cohort, and only one SNV was detected in 1096 controls. Primary congenital glaucoma caused by mutations in both CYP1B1 and LTBP2 genes has an autosomal recessive pattern of disease inheritance, although both heterozygous and compound heterozygous variants have previously been reported in POAG cohorts.
59 A more recent Pakistani study of 238 cases found a significant association of ASB10 rare nonsynonymous variants in POAG with 9. 7% prevalence in cases compared to 1. 3% in controls. 60 owever, the variants did not segregate in the familial cases, and one variant identified in cases but not in the controls (p. (Arg453Cys)) has an allelic frequency of 2. 2% in the ExAC population database, emphasizing the importance of large control cohorts to avoid selection bias. The ASB10 variant p. (Arg304Cys) found in 0. 23% of our cases was previously reported in 0. 4% of cases in the Pakistani study. 60 The other three variants found in our study have not been previously reported. Our variant rates in cases and controls were lower than in both previous studies due to the additional qualifying condition of predicted pathogenicity via software prediction prior to analysis.
This scenario is likely to be rare, as the frequency of cases potentially explained by genes other than MYOC in this advanced POAG cohort is 9. 2%, which equates to an underestimation of non-MYOC variants by two to three participants (9. 6% of 29, assuming no excessive enrichment of these variants in the MYOC cohort). Moreover, JOAG participants in ANZRAG with MYOC disease-causing variants have been previously sequenced at the CYP1B1 locus, and none carried disease-causing variants in the latter gene. 27 Other limitations of this study relate to WES. The ANZRAG cases and controls were captured with a different enrichment protocol from that for the AOGC controls. Although sequencing was performed on the same platform, batch effects on top of capture differences could confound our data. We attempted to minimize this systematic error by employing joint-calling of both datasets using the same bioinformatic pipeline.
Acknowledgments Supported by the  National Health and Medical Research Council (NHMRC) Centres of Research Excellence scheme  ( APP1023911 ) and project grant ( APP1107098 ). SM is supported by an  Australian Research Council   Future Fellowship. JEC is supported by an  NHMRC Practitioner Fellowship  ( APP1065433 ). Disclosure:  T. Zhou , None;  E. Souzeau , None;  O. M. Siggs , None;  J. Landers , None;  R. Mills , None;  I. Goldberg , None; P. R. Healey , None;  S. Graham , None;  A. W. Hewitt , None;  D. A. Mackey , None;  A. Galanopoulos , None;  R. J. Casson , None;  J. B. Ruddle , None;  J. Ellis , None;  P. Leo , None;  M. A. Brown , None;  S. MacGregor , None;  S. Sharma , None;  K. P. Burdon , None;  J. E. Craig , None.
Duplicate reads were marked with Picard MarkDuplicates (version 1. 124; http://broadinstitute. github. io/picard; in the public domain). Local indel realignment and base quality recalibration were performed with GATK 49 (version 3. 2-2). Contribution of Known POAG Genes in Early-Onset Disease IOVS j March 2017 j Vol. 58 j No. 3 j 1538 Downloaded From: http://iovs. arvojournals. org/pdfaccess. ashx?url=/data/journals/iovs/936100/ on 07/31/2017.
Variants in nine Mendelian POAG genes, ASB10, CYP1B1, IL20RB, LTBP2, MYOC, NTF4, OPTN, TBK1, and WDR36, were selected. Of these genes, LTBP2 has been linked to PCG, CYP1B1 to both PCG and POAG, and the rest to POAG only. Variant loads per gene were calculated for the glaucoma case and control cohorts by summing the minor allele counts of all qualifying variants in the same gene and dividing by the average number of captured alleles for those variants to adjust for capture rate. Odds ratios were generated by comparing the glaucoma variant load with the control using Fisher's exact test to calculate P values. (Polymorphism Phenotyping v2), NHLBI GO Exome Sequencing Project (ESP) (http:// evs. gs. washington. edu/EVS/; in the public domain), 1000 Genomes Project (http://www. 1000genomes. org/; in the public domain), and Exome Aggregation Consortium (ExAC) databases (exac.
Results Evolutionary Energy reproduces ensembles of sequences with natural frequencies and repeat protein characteristics. We construct an alignment of pairs of repeats for each family: ANK (PFAM id PF00023, and final alignment of 20513 sequences of L=66 residues each), TPR (PFAM id PF00515, and final alignment of 10020 sequences of L=68 residues each) and LRR (PFAM id PF13516, and final alignment of 18839 sequences of L=48 residues each). See Methods for further details of construction. We measure J ij parameters and L 2 + 1 λ Id. For example, for pairs of ANK repeats this means 1386 h i , 959805 J ij and 34 λ Id. To reduce the number of free parameters to fit we use a L 1 -regularization which fixes to zero those parameters which do not contribute significantly to fit the frequencies. This regularization allows us to set to exactly f i (a i ), f ij (a i ,.