dataesr/scientific-paragraphs-categorization huggingface dataset:
2  shows an example of a pole figure calculation by application of Eq. (  3 ). Calculation details can be found in  [44]. In ANAELU, IPFs are represented by Gaussian distributions, Eq. (  4 ). R 0 is a scale factor, which is adjusted by normalization conditions. ˝ characterizes the orientation distribution width. R y 0 (h) = R 0 e -[ (h)/˝] 2 (4) The current version of ANAELU approximates a 2D-Rietveld refinement program. The program models the intensities distribution along the Debye rings, it represents the diffractogram background by using a smooth surface, compares the calculated and experimental patterns quantitatively, calculates descriptors of goodness of fit and allows variations of textural parameters in the search of an optimal fit. Crystallographic calculations are performed by using the CrysFML Fortran library  [45].
The computer-user interface is programmed using the wrapper wxPython  [46]  and the Fortran-Python mixed programming is achieved through the F2PY binding module  [47]. The automatic fitting of additional microstructural parameters in under development. Figs. 3  and 4  illustrate a real-world application of ANAELU to functional materials. The investigated sample is a film of ZnO nanorods that have grown with a tendency for their crystallographic axes c to be oriented perpendicular to the glass substrate. Fig. 3  displays a cross sectional SEM micrograph of the considered sample  [48]. Figure  S1  of the Appendix/Supplementary Information describes the grazing incidence 2D-XRD experimental setup at the Stanford synchrotron beamline 11-3. Field (a) of Fig. 4  shows a representative sector of the two-dimensional measured diffractogram.
The usual way to present the elasto-piezo-dielectric tensors is by using the compact matrix notation  [50, 51]. In this one, piezoelectricity is represented by a 3 × 6 matrix and elasticity with 6 × 6 matrices. The methodology for measuring the mentioned properties is described in  [52] [53] [54]. The primary reference for material properties databases is the Landolt-Bornstein's classic work  [55, 56]. The Materials project  [57, 58] , from California's University at Berkeley, offers numerous theoretically calculated properties. The database Material Properties Open Database (MPOD)  [24]  facilitates free access to a relatively wide collection of single crystal's tensor properties, experimentally determined. Eqs. (  8 )-(  10 ) and Figs. 5 6 7 show a selection of ZnO single crystal elasto-piezo-dielectric matrices and their surface representations.
Conclusion The occurrence of axial symmetry textures is common in piezoelectric ceramics and is one of the determining factors in the effective properties of these materials. In the present work a computer-aided methodology is proposed to estimate the contribution of texture to the physical properties of piezoceramics, given the elasto-piezo-dielectric matrices of the single crystal and the two-dimensional X-ray diffraction pattern of the considered sample. The ANAELU program, using a Rietveld-type methodology, is capable of characterizing the inverse pole figure of the symmetry axis. MPOD database provides the user with experimentally obtained physical properties data from single crystals. The GISELLE software package approximately predicts the polycrystalline properties under the Voigt, Reuss and Hill averages, adapted to the elasto-piezo-dielectric case.
Acknowledgments The sponsorship given by  Consejo Nacional de Ciencia y Tecnología  ( CONACYT, México ), Projects  257912  and  270738 , is appreciated. Support from the Project  MAT2017-86168-R  " Piezocerámicas ecológicas para la generación de ultrasonidos " ( CSIC, Spain) , is acknowledged. This research has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 689868 ,  SOLSA  project.
Dinophytes are widely distributed in marine-and fresh-waters, but have yet to be conclusively documented in terrestrial environments. Here, we evaluated the presence of these protists from an environmental DNA metabarcoding dataset of Neotropical rainforest soils. Using a phylogenetic placement approach with a reference alignment and tree, we showed that the numerous sequencing reads that were phylogenetically placed as dinophytes did not correlate with taxonomic assignment, environmental preference, nutritional mode, or dormancy. All the dinophytes in the soils are rather windblown dispersal units of aquatic species and are not biologically active residents of terrestrial environments.
Environmental sampling and data Sampling and sequencing of tropical soils originally took place in lowland rainforest in Costa Rica,  Panama, and Ecuador (Mah e et al. 2017). While the methodologies and bioinformatics can be found in detail in Mah e et al. (  2017 ), here in brief: the extracted soil DNAs were amplified for the hypervariable V4 region of the SSU-rRNA locus using general eukaryotic primers  (Stoeck et al. 2010) ; this short region has relatively strong phylogenetic signal, although it is not as strong as the full-length SSU-rRNA  (Dunthorn et al. 2014; Gottschling et al. 2020). Illumina sequencing reads were clustered into OTUs using swarm v2  (Mah e et al. 2015)  and taxonomically assigned to the Protist Ribosomal Reference database v203  (Guillou et al. 2013 ) using VSEARCH v1.
Reference tree From GenBank  (Sayers et al. 2020) , 228 ingroup dinophytes, plus 10 outgroups, were downloaded, then aligned with MAFFT v6. 624b  (Katoh and Standley 2013)  using the -auto option (File S2). Based on previous analyses (Chac on and Gottschling 2020;  Gottschling et al. 2012 Gottschling et al. , 2020;; Zerdoner Calasan et al. 2019) , the full sequences of each species were used without excluding ambiguously aligned positions sites. Phylogenetic inferences of the reference alignment were carried out by using Maximum Likelihood (ML), as described in detail by  Gottschling et al. (2012 Gottschling et al. ( , 2020)) , as implemented RAxML v8. 2. 10 (Stamatakis 2014) with the GTR + G substitution model. To determine the best-fitted ML tree, we executed 10-tree searches from distinct random stepwise addition sequence Maximum Parsimony starting trees and performed 1,000 nonparametric bootstrap replicates.
Phylogenetic placement of environmental OTUs The OTU representative sequences obtained from Swarm were aligned against the reference alignment using PaPaRa v2. 0  (Berger and Stamatakis 2011 ) and phylogenetically placed onto the ML reference tree using the Evolutionary Placement Algorithm  (Berger et al. 2011)  as implemented in RAxML. Next, all OTUs that were placed with at least 95% probability (combined likelihood weight ratios) in the dinophyte clade were extracted and visualized, using Gappa v0. 1. 0 (Czech et al. 2020). For details on the extraction, see  Czech et al. (2018) ; details of the workflow are published in a GitHub code repository (https://github. com/lczech/dinoflagellate-paper).
For coalescence prevention, we use an implementation  [6]  of the multimarker volume-of-fluid method  [7, 8]  that removes its dependence on the number of bubbles and makes it applicable to systems with thousands of noncoalescing bubbles. The source code of our solver Aphros, documentation, and examples of simulations are available on GitHub  [9]  https://github. com/cselab/aphros. The simplest setup is the forced collision of two bubbles shown in Fig. 1. The bubbles, initially separated, move towards each other under the force applied to the outer sides. Coalescence, if allowed, starts with a neck propagating along the surface of the newly formed bubble. Our simulations  [10, 11]  describe this process in close agreement with experimental data  [12]. Without coalescence, the bubbles deform and eventually repulse.
Finally, the image captured by the imaging system (Gel Doc XR; Bio-Rad, Hercules, CA, USA) was analyzed using the QUANTITY ONE software (Bio-Rad). Nine samples that were representative of the PCR-RFLP pattern were selected and sequenced (Genewiz Europe, Leipzig, Germany). The nucleotide sequences were analyzed using BLASTN to identify the amplicons. The pDRAW32 software, version 1. 1. 101 (AcaClone Software; http: //www. acaclone. com) was used to analyze the nucleotide consensus sequences in virtual digestions, to define the accuracy of the determination and recognition of the different RFLP pattern types of vmp1 obtained in the PCR-RFLP analysis. The nucleotide sequences were submitted to the National Center for Biotechnology Information (NCBI) database.
Molecular Analysis According to the tuf Gene The presence of 'Ca. P. solani' was confirmed in about 50% of the 252 symptomatic grapevine samples, using nested PCR for the 16Sr gene and MseI PCR-RFLP analysis, which showed the characteristic pattern of the 16SrXII-A subgroup (data not shown). The positive samples were characterized on the basis of the tuf gene. In particular, the TufAYf/r primers yielded a specific amplicon of about 900 bp in 120 samples, and according to HpaII digestion, all of these samples showed the characteristic RFLP pattern of 'Ca. P. solani' tuf b type (Table  2 ; Supplementary Table  S1 ).
Molecular Analysis According to the tuf Gene The presence of 'Ca. P. solani' was confirmed in about 50% of the 252 symptomatic grapevine samples, using nested PCR for the 16Sr gene and MseI PCR-RFLP analysis, which showed the characteristic pattern of the 16SrXII-A subgroup (data not shown). The positive samples were characterized on the basis of the tuf gene. In particular, the TufAYf/r primers yielded a specific All 110 weed samples were collected from inside and outside the vineyard. Nine wild species were detected inside and along the borders of the vineyard (Table  2 ; Supplementary Table  S1 ). The dominant herbaceous plants were Convolvulus tricolor, C. arvensis, Solanum nigrum, and Helminthotheca aculeata. All herbaceous samples were asymptomatic, and the analysis based on the 16Sr gene detected 'Ca.
The molecular types V4 and V9 were recorded in all three groups of analyzed samples (grapevines, weeds, and insects) (Figure  2c ). Eight samples that were representative of the different RFLP patterns were sequenced and submitted to the NCBI database. The nucleotide sequences were compared with the NCBI database using Blast N analysis, with high identities (>97%) observed across all of these nucleotide sequences of the 'Ca. P. solani' strains that were already available in the database. The nucleotide sequences were used for virtual RFLP analysis, which supported the data obtained in the conventional RFLP (Figure  2a ).
Supplementary Materials: The following are available online at http://www. mdpi. com/2076-0817/9/11/918/s1, Figure  S1 : Map of the vineyard where the study was carried out in 2014 and 2015, indicating also the surrounding crops, Table  S1 : Detailed molecular characterization for the 16SrXII, tuf, and vmp1 genes. Funding: This research was partially supported by funding from "Approccio integrato per lo sviluppo di prodotti innovativi nei settori trainanti del comparto agroalimentare siciliano", Project: 2017-NAZ-0228-CUP B78117000260008, Ministero Sviluppo Economico, and by the project "Aetiology and management of grapevine phytoplasma diseases" funded by Marche Polytechnic University.
Acknowledgments: Authors thank  Giuseppe Bono ,  Servizio Fitosanitario Regione Siciliana , for his precious reporting, and  Azienda Liliana Ferrara. Authors thank  Xavier Foissac  ( INRA et Université de Bordeaux ) for providing positive control. Sincere thanks are also expressed to the three anonymous reviewers for careful evaluation of the manuscript and for the very constructive comments, which helped us improve our work, and will help the readers who view the paper.
Although these examples demonstrate the value of human genetics in drug development, important questions remain regarding strategies for identifying individuals with LoF variants in a gene of interest, interpretation of the frequency-or lack-of such individuals, and whether it is wise to pharmacologically target a gene in which LoF variants are associated with a deleterious phenotype. Public databases of human genetic variation have catalogued predicted loss-of-function (pLoF) variants-nonsense, essential splice site, and frameshift variants expected to result in a non-functional allele. This presents an opportunity to study the effects of pLoF variation in genes of interest and to identify individuals with pLoF genotypes to understand gene function or disease biology, or to assess potential for therapeutic targeting.
Although many variants initially annotated as pLoF do not, in fact, abolish gene function 6 , rigorous automated filtering can remove common error modes 7. True LoF variants are generally rare, and show important differences between outbred, bottlenecked 8 and consanguineous 9 populations  6, 10. Counting the number of distinct pLoF variants in each gene in a population sample allows the quantification of gene essentiality in humans through a metric named 'constraint'  [10] [11] [12] [13]. Specifically, the rate at which de novo pLoF mutations arise in each gene is predicted on the basis of rates of DNA mutation  10, 12  , and the ratio of the count of pLoF variants observed in a database to the number expected based on mutation rates-obs/exp, or constraint score-measures how strongly purifying natural selection has removed such variants from the population.
8% of their genomes autozygous. Here, the expected frequency of two-hit individuals is many times higher than in outbred populations, at five per million for the median gene (Fig. 2c ). These projections allow us to draft a roadmap for discovery of human knockouts across 19,194 genes (Fig. 2d, e ). Online Mendelian Inheritance in Man (OMIM) already describes human disease association for 3,367 genes (18%), although the discovery of LoF individuals in population databases will still be valuable for assessing penetrance and identifying LoF syndromes of known gain-of-function genes. Another 3,421 genes (18%) without known human disease association have two-hit pLoF genotypes reported in gnomAD  7  , ELGH  26  , PROMIS  27  , deCODE  28  or UK Biobank  29  , which suggests that this genotype may be tolerated.
2e ). In outbred populations, current sample sizes would need to increase by approximately 1,000-fold before ascertainment of a single two-hit LoF individual would be expected for the typical gene. By contrast, around a 10-to 100-fold increase from current consanguineous sample size, meaning hundreds of thousands of individuals in absolute terms, would identify at least one two-hit LoF individual for the typical gene. Among other simplifying assumptions (Methods), these projections presume that complete knockout is tolerated. When only one or a few two-hit individuals are expected in a dataset, the absence of any such individuals can be due to either early lethality, a severe clinical phenotype incompatible with inclusion in gnomAD, or simply chance. Thus, the ability to infer lethality of the two-hit genotype based on statistical evidence will lag behind the identification of two-hit individuals where they do exist (Fig.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-020-2267-z. 41  and those newly identified in gnomAD and in the literature (Extended Data Table  5 ). GPI, glycosylphosphatidylinositol. Detailed variant curation results are provided in Supplementary Table  1.
Calculation of pLoF constraint The calculation of constraint values for genes has been described in general elsewhere  10, 12  and for this dataset specifically by Karczewski et al. 7. Constraint calculations used LOFTEE-filtered ('high confidence') single-nucleotide variants (which for pLoF means nonsense and essential splice site mutations) found in gnomAD exomes with minor allele frequency <0. 1%. Only unique canonical transcripts for protein-coding genes were considered, yielding 17,604 genes with available constraint values. For curated genes (Extended Data Table  2 ), the number of observed variants passing curation was divided by the expected number of variants to yield a curated constraint value. For PRNP, the expected number of variants was adjusted by multiplying by the ratio of the sum of mutation frequencies for all possible pLoF variants in codons 1-144 to the sum of mutation frequencies for all possible pLoF variants in the entire transcript, yielding 6 observed out of 6.
Knockout roadmap For the knockout 'roadmap' (Fig. 2d , e), we classified genes according to the current status of human disease association and LoF ascertainment. Genes were classified as having a Mendelian disease association if they were present in OMIM with the filters described in Extended Data Table  1. Remaining genes were classified as '2-hit LoF reported' based on presence in one or more of the following gene lists: homozygous LoF genotypes in gnomAD curated as previously described  7  ; filtered homozygous LoF genotypes in runs of autozygosity with minor allele frequency <1% in canonical transcripts in the Bradford, Birmingham and ELGH 25 cohorts (total n = 8,925); observed number of imputed homozygotes >1 or number of compound heterozygous carriers where minor allele frequency <2% (for both variants) in deCODE  28  ; homozygous LoF reported in PROMIS  27  ; homozygous LoF with minor allele frequency <1% in UK Biobank  29.
Third and most importantly, a large fraction of reported pathogenic variants lack strong evidence for pathogenicity and are either benign or low penetrance  10, 41  , so without careful curation of pathogenicity assertions, summing the frequency of reported pathogenic variants in gnomAD will in most cases vastly overestimate the true genetic prevalence of a disease. Instead, we searched the literature and very roughly estimated genetic prevalence based on available data. In most cases, we took disease incidence (new cases per year per population), multiplied by proportion of cases due to variants in a gene of interest, and multiplied by average age at death in cases. In some cases, estimates of at-risk population or direct measures of genetic prevalence were available. Details of the calculations undertaken for each gene are provided in Extended Data Table  4.
Computation of P = 1 -sqrt(q) as described in the Methods is computationally expensive for downsampled datasets because it requires individual-level genotypes. Instead, this analysis uses 'classic' CAF, which is simply the sum of allele frequencies of all high-confidence pLoF variants each at allele frequency <5%, capped at a total of 100%, for both global and Finnish exomes. The results show that even when the sample size is held constant, the number of genes with zero pLoF variants observed is higher in a bottlenecked population than in a mostly outbred population. A constant y axis with no axis breaks is used in this figure to make this difference more clearly visible.
Acknowledgements This study was performed under ethical approval from the  Partners Healthcare Institutional Research Board  ( 2013P001339/MGH ) and the  Broad Institute Office of Research Subjects Protection  ( ORSP-3862 ) in compliance with all relevant ethical regulations; written informed consent was obtained from all research participants. We thank all of the research participants for contributing their data. E. V. M. acknowledges support from the  National Institutes of Health (NIH)  ( F31 AI122592 ) and an anonymous organization. gnomAD data aggregation was supported primarily by the  Broad Institute , gnomAD analysis was supported in part by  NIDDK   U54 DK105566 , and development of LOFTEE by  NIGMS   R01 GM104371. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH.
E. L. G. H. is funded by the  Wellcome Trust  ( 102627 ,  210561 ), the  Medical Research Council  ( M009017 ),  Higher Education Funding Council for England Catalyst, Barts Charity  ( 845/1796 ),  Health Data Research UK  (for London substantive site), and research delivery support from the  NHS National Institute for Health Research Clinical Research Network  (North Thames). N. W. is supported by a  Rosetrees  and  Stoneygate Imperial College Research Fellowship. The results published here are in part based upon data: (1) generated by The Cancer Genome Atlas managed by the  NCI  and  NHGRI  (accession: phs000178. v10. p8). Information about TCGA can be found at http://cancergenome. nih. gov; (2) generated by the  Genotype-Tissue Expression Project (GTEx)  managed by the  NIH Common Fund  and  NHGRI  (accession: phs000424.
4 , Supplementary Data 2). However, this deletion was not scored as an HAPM under our model. The frequency of stalk-deletion mutations is known to fluctuate in time and space among both LP and HP viruses  25. Although some deletions have been experimentally linked to increased virulence, their occurrence is likely associated with host switching (from wild bird to gallinaceous hosts), rather than with the emergence of the HP phenotype  25, 26. Our ancestral state reconstructions show that HAPMs occur on the ancestral nodes of HP clusters, or on branches within HP clusters, but are not observed on LP branches that are ancestral to HP clusters (Supplementary Data 2, Supplementary Figs. 5 6 7 8 and Fig. 3 ). Therefore, given the phylogenetic resolution of our datasets, it is not possible to determine if HAPMs appear shortly before, during, or after the emergence of the HP phenotype.
Interestingly, mathematical modelling has suggested that the sporadic population dynamics of HP strains may be determined, at least in part, by their linkage to one or more specific antigenic variants, whose frequencies are driven by the dynamics of host immunity, rather than by mutability  24, 60. Furthermore, the emergence of the HP phenotype has been often associated with virus adaptation from wild to domestic bird host populations  51. The strength of this association is difficult to assess in nature given the substantial bias (in both the literature and sequence databases) towards AIV infections and HP outbreaks in poultry versus those in wild birds. However, using our model of mutation-trait association (see 'Methods', subsection 'Testing for genotype-to-phenotype associations' and Supplementary Text 5), we found no association between any of the detected HAMPs (or any other mutation) and the host species (domestic versus wild bird).
Methods We developed an analysis approach to detect parallel genetic changes associated with the LP-to-HP evolution of AIV (summarised in Fig. 1 ). For convenience, we refer to these parallel genetic changes as HAPMs (HP-cluster-associated parallel mutations). We begin by identifying HP clusters from large-scale AIV phylogenies. Next, we use phylogenetically informed subsampling to create smaller datasets suitable for detailed analysis. The subsampled alignments are then analysed using phylogenetic ancestral state reconstruction to identify convergent mutations that might be associated with HP clusters. Associations between the mutations identified and the HP phenotype (i. e. HAPMs) are formally tested under our model. The reduced list of HAPMs is further analysed to assess their potential functional and selective relevance using the following criteria: (i) evidence of evolving under positive selection, (ii) predicted to be stabilising to protein structure, and (iii) known or predicted functional relevance derived from structural analysis and literature survey.