dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). To test this prediction, we relate the observed differences in performance between central and peripheral populations with the actual differences in climate. We searched the scientific literature for peer-reviewed publications published by 23 rd May 2018 using keywords related to CP comparisons of population performance, retaining papers that provided data for at least two populations from the range centre and two populations from one latitudinal range margin (HLM or LLM) in the species' natural environment (Supplementary Material S1). We only considered primary papers reporting demographic performance metrics that could clearly be identified as estimators of individual fecundity, survival, or lifetime fitness. We identified 42 papers that fulfilled our criteria, involving 96 CP comparisons (HLM: n = 58, LLM: n = 38) and 623 populations (Fig.
Materials and Methods Meta-analytical models -Our dataset had a hierarchical structure as some primary papers contained several case studies. We accounted for this potential non-independence of cases by estimating model heterogeneity from multiple sources: (i) among true effect sizes, (ii) among CP comparisons stemming from the same primary papers (by computing the variance-covariance matrix among all effect sizes) and (iii) among groups of moderators. This was done using multilevel error meta-analysis  82  with the rma. mv function of the R package metafor v. 2. 0-0  83, 84. Primary paper identity was declared as a random factor and individual CP comparisons were nested as random factor within primary papers. We estimated variance components for primary papers (σ 1 2 ) and case studies (σ 2 2 ) together with intra-class correlations (ρ), that is, correlations between true effect sizes from the same study (such that ρ= σ 1 2 / (σ 1 2 + σ 2 2 )).
g. publication bias, balanced representation of moderators, etc. ) (Fig. S5, S6 ). Collection of climate data -We gathered the geographical coordinates of all populations included in the meta-analysis from the primary papers (n = 623 populations; see map in Fig. S1 ). For each population, we calculated the average annual temperature between 1990 and 2013 (when most studies were performed) based on monthly temperature data, from CRU TS 3. 22  87  for terrestrial species and HadISST 1. 1  88  for marine species. We then aggregated populations to calculate average temperatures for each combination of study, species, performance variable, and region (either central, HLM, or LLM). We could then relate each comparison of performance between a margin (HLM or LLM) and the central range (i. e. , Hedges' d) with the difference in average temperatures between both regions.
Analysis of relationships between climate and population performance -We decided to compare average long-term temperatures among regions, rather than warming trends, as the former can be estimated more accurately and precisely at the scale of this study. Similarly, although precipitation might also be an important climatic variable for some terrestrial species, we decided to focus on temperature only due to the limited sample size available to fit our models. To assess the relationship between the differences in performance and the differences in climate between marginal and central populations, we used additive mixed models (function gam in the R package mgcv, version 1. 8-17  89  ) using the temperature differences as predictor, and the study as random effect (to control for lack of independence).
S2. Bibliographic compilation We searched the ISI Web of Science (WOS) and Scopus until 23 rd May 2018 for papers containing adequate data for our study. The terms "centre AND periphery", "central AND marginal", "abundant centre" and "latitudinal cline" were introduced in combination with performance related terms including "fecundity", "survival", "recruitment" and "population growth rate". We restricted our search to the categories: Environmental Sciences-Ecology, Plant Sciences, Zoology, Entomology, Marine and Freshwater Biology, Biodiversity Conservation, Agriculture, and Forestry in WOS ("Theme" as the search field) and Agricultural and Biological Sciences and Environmental Science in Scopus ("Article title, abstract and keywords" as search fields). After discarding papers that were clearly out of scope, we retained 54 papers from WOS and 41 papers from Scopus.
e. , true replicates); and (3) we only considered papers that provided sufficiently clear criteria for the definition of central and peripheral range parts relative to the global range of the target species. Finally, we searched the text of the selected papers and came to a final set of 42 papers that provided data amenable to meta-analysis, either primary data or data extracted from figures (see S1). These papers were then classified in two major kinds. First, papers including explicit centre-periphery comparisons of mean performance values from populations described as central or peripheral in the text. Second, papers based on latitudinal clines. In this later case, from each region we used the three most central and the three most extreme populations along the gradient (or more when several populations were located closely together; see S1).
S3. 1. Is the dataset subject to publication bias? Publication bias occurs when a dataset lacks disproportionately many case studies with either positive or negative effect sizes, that is, when some tendency has been more likely to be published (publication bias) or retrieved (dissemination bias) than others. We used four complementary approaches to estimate whether publication bias was likely to occur in our dataset: (1) visual inspection of a funnel plot, (2) the calculation of a fail safe number, (3) a correlation between reported effect sizes and the impact factor of source journals, and (4) a cumulative meta-analysis to test for time-lag bias. (1) Funnel plot. Funnel plots probe whether studies with little precision (small studies) give different results from studies with greater precision (larger studies).
Asymmetry in the funnel plot is often interpreted as a sign of publication bias (i. e. , the decision of authors or editors to publish or not a given result) or dissemination bias (i. e. , small studies tend to be published in poorly accessible or indexed journals). On the contrary, the funnel plot we constructed from our dataset was symmetrical, indicating that small and large studies, as well as studies reporting negative, positive or close to zero effect sizes were equally likely to be published. The utility of funnel plots in the context of multi-level meta-analysis remains a matter of debate, because sets of points may be clustered together as a result of statistical dependencies. However, there was no evidence for such clustering, and data points corresponding to HLM and LLM case studies were fairly well distributed.
This observation further supports our conclusion that publication bias was unlikely in our dataset. (2) Fail safe number. Fail safe numbers (FSN) estimate how many studies with effect sizes averaging zero should be added to negate the significance of the grand mean effect size (or to reduce it to a specified minimal value). Among the various available metrics, Koricheva et al. 81  recommended the use of Rosenberg's FSN  90  , a weighted metric that is tested against a normal distribution. Rosenberg's FSN was 257 (P < 0. 001), indicating that "publication biases (if they exist) may be safely ignored"  90. (3) Correlation between effect sizes and the impact factors of the reporting journal. Publication bias is likely to occur if higher impact journals tend to publish papers with stronger results, whereas results reporting weaker or no empirical support for hypotheses are more likely published in lower rank (and maybe less accessible) journals (or not published at all).
23, P = 0. 030). The fact a top-rank journal such as Nature published results with small effect sizes is, however, a clear indication against publication bias in our dataset. (4) Cumulative meta-analysis. Temporal trends in effect sizes may affect the generality (and stability) of conclusions drawn from meta-analyses. Temporal trends may result from changes in methodology, technology or dominant paradigms. We assessed the temporal stability of the grand mean effect size (both for the complete dataset and for HLM and LLM separately) by conducting a cumulative meta-analysis. This analysis calculates the grand mean effect size of a successively accumulating subsample of the global dataset to which case studies are sequentially aggregated in their order of publication (i. e. , from the oldest to the most recent publication year).
S3. 2. Accounting for potential non-independence of case studies drawn from the same primary paper Some primary papers contained more than one measure that we could use for our metaanalysis (e. g. , reporting different performance estimators for the same species or the same estimator for different species). Such measures could be mutually non-independent, leading to pseudo-replication in the dataset used for the meta-analysis. We used two complementary approaches to account for potential non-independence of case studies stemming from the same primary paper. (1) We used multi-level meta-analysis where we specified two random factors with the rma. mv function of the R package metafor  83  : the identity of the case study and the identity of the primary paper; the first factor was nested within the second.
(2) We ran a sensitivity analysis to assess the robustness of the main result (i. e. , margins differ in relative population performance) against the non-independence of case studies from the same primary paper. Sensitivity analysis: we created a subsample of our global dataset that contained only one randomly selected case study from each primary paper. We then performed a mixed-effect metaanalysis on this subsample to test for the existence of a margin (HLM vs. LLM) effect with the rma function from package metafor  83. This procedure was repeated 1000 times, each time with a newly created test dataset (which is analogous to bootstrap procedures using random drawing with replacement). Among the 1000 models, 63. 5% supported a significant difference between HLM and LLM. The mean (± 95% distribution) of random samples for HLM (-0.
Acknowledgments We are grateful to  Amy L. Angert  and  Sergei Volis  for supplying unpublished information and to  Pedro Jordano  for insightful comments on the manuscript. JK, FV, ASJ, JE, JCS, BC, FRS. Software: BC, FRS. Supervision: FP, AH. Visualization: BC, FRS. Writing original draft: AH, FP. Writing (review and editing): FP, AH, BC, FRS, JK, FV, JE, ASJ, JCS.
Funding This study was funded by  NordForsk  grant no. 80167  to the  NORA consortium (Nordic Network for the Study of Species Range Dynamics ,  2009-2012 ), by projects  POPULIM  ( CGL2010-22180 ) and PERSLIM ( CGL2010-18381 ) of the  Spanish MICINN , the  EU BiodivERsA  project  BeFoFu  ( NE/G002118/1 ) and the  INRA   ACCAF  project  FORADAPT. FRS was funded by a postdoctoral fellowship from the  Spanish Ministerio de Economía y Competitividad  ( FPD2013-16756 ). JCS considers this work a contribution to his VILLUM Investigator project (VILLUM FONDEN, grant  16549 ) and his  European Research Council  project ( ERC-2012-StG-310886-HISTFUNC ).
Islands have remarkable levels of endemism and contribute greatly to global biodiversity. Establishing the age of island endemics is important to gain insights into the processes that have shaped the biodiversity patterns of island biota. We investigated the relative age of monocots across islands worldwide, using different measures of phylogenetic endemism tested against null models. We compiled a species occurrence dataset across 4,306 islands, and identified 142 sites with neo-, paleo-, mixed and super-endemism. These sites were distributed across the world, although they tended to be more common at low latitudes. The most frequent types of endemism were mixed and superendemism, which suggests that present-day island biodiversity has frequently been shaped by processes that took place at different points in times.
Tasmania is a nice example of how geological history and island age estimate may influence phylogenetic endemism. Tasmania's biota date back to Gondwana (i. e. about 80 My BP), although the island was only recently separated from Australia  35. Consequently, Tasmania was classified as an area of paleo-endemism despite its relatively recent isolation. This is a well-known example, however, for many islands, information on the geological age is lacking. This highlights the need to synthesize the different parameters that are used to estimate island age and facilitate their integration in a database. In this study, certain islands were found to belong to different categories depending on whether we considered expanded or restriced phylogenetic endemism (PE E and PE R ); this may be significant for understanding the "relictualization" process.
Another bias is the human tendency to pay more attention to rare organisms  46. Although this tendency would increase the diversity estimate of a region, by encouraging the sampling of rare and endemic taxa, it could impact our analysis by inflating estimates of endemism. Nevertheless, as most of the largest herbaria of the world are nearly completely digitized  47  the number of available recorded occurrences is huge (we used more than 2 million). We performed several procedures to reduce potential biases (i. e. crossing occurrences with the eMonocot database, using an index of geographic coverage to account for spatial gaps). In addition, considering endemism at the genus rather than the species level is also likely to lessen this bias: when a collector visits an island he/she is more likely to come across all the genera rather than all the species present.
Methods Phylogeny. In this study, we used the monocot phylogeny of Tang et al. 53  , a dated phylogeny with 20 fossil and/or molecular calibration points. It is the most comprehensive monocot phylogeny to date and is well resolved for nearly 70% of the 2,823 recognized genera. Plant occurrences. Plant occurrence points were downloaded from the Global Biodiversity Information Facility (GBIF) portal (www. gbif. org), and cross-checked in the eMonocot database (http://e-monocot. org/). We extracted all records, except fossil specimens, from the GBIF portal and selected monocot taxa by using all names found in the eMonocot database as queries. The eMonocot database provides a comprehensive list of synonyms for the great majority of species, distribution verified by experts within polygons at the TDWG level 4 scale  54  , and native or non-native species status.
Both the GBIF and eMonocot are continuously updated and cover a time span of at least 500 years. We used the eMonocot database to (i) check for synonymy (ii) verify the native and non-native status of GBIF occurrences, and (iii) check for discrepancies in distribution records, and we consequently excluded data from GBIF that deviated inexplicably from the eMonocot data. Regarding (ii), we aimed to keep only native genera (Supplementary Fig. S2 ) and proceeded as follows. First, we compared native genera found in the eMonocot database with the occurrence data extracted from GBIF. As different spatial scales for islands and polygons were used in eMonocot and GBIF, this procedure was only valid for a small set of islands. For these islands, we kept only genera that were present in the eMonocot database and identified as native (Supplementary Fig.
S2 ). All other islands were associated with a higher scale polygon defined in the eMonocot database, i. e. TDWG4 polygons. When a genus was identified as non-native to this polygon, the occurrence was discarded. However, as information about native and non-native status was sometimes missing in the eMonocot database, we employed a second test to identify genera occurrences found outside their native range (referring to points (ii) and (iii)). This was done by building polygons delimited by the maximal and minimal latitude and longitude of their known native ranges and discarding all occurrences outside these limits. Islands. We used the Global Island Database provided by the United Nations Environment Programme  55  , which comprises information on 180,495 islands worldwide, as the basis of our island dataset.
In the end, we were able to include 2,556,584 occurrences representing 15,964 species from 1,524 genera found on 4,306 islands. Crossing data from three large databases -the GBIF, eMonocot and UNEP-WCMC databases (2007), allowed us to compile, to our knowledge, the most comprehensive dataset comprising information on the largest number of islands used in island biogeography research. Metrics. We used the measure of phylogenetic endemism (PE) of Rosauer et al. (2009), with some adjustments to accommodate for certain features of our dataset, such as presence only data, and the aim of the study (Table  2 ). PE departs from the traditional definition of endemism as it does not represent the confinement of a species to a discrete geographic unit but measures the geographical concentration of evolutionary history compared to a broad landscape where diversity is distributed to varying degrees  17, 19, 20.
Patients and patient material Personal communication with colleagues following the initial report,  7  searching the DECIPHER database,  14, 15  and using GeneMatcher  16  enabled us to collect clinical and mutational details on 39 individuals with variants in CTCF. Testing (for individual methods see Table  S1 ) in collaborating centers was performed either in the setting of routine diagnostic testing without the requirement for institutional ethics approval or within research settings approved by the ethical review board of the respective institutions (University Erlangen-Nuremberg, University of Tartu, UZ Leuven, Northern Ostrobothnia Hospital District, Cambridge South REC, Republic of Ireland REC; see also Table  S1 ). Informed consent for publication of patient photos and for testing and for publication of mutational and clinical data was obtained from the individuals, or their parents or legal guardians.
Transcriptome analysis Quality control of RNA samples was performed using a Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA). Library preparation was performed using the TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA). Libraries were subjected to single-end sequencing (101 bp) on a HighSeq-2500 platform (Illumina). The obtained reads were converted to. fastq format and demultiplexed using bcl2fastq v2. 17. 1. 14. Quality filtering was performed using cutadapt v. 1. 15; 17 then reads were mapped against the human reference genome (Ensembl GRCh37, release 87) using the STAR aligner v. 2. 5. 4a,  18 and a STAR genome directory created by supplying the Ensembl gtf annotation file (release 87). Read counts per gene were obtained using featureCounts program v. 1. 6. 1  19  and the Ensembl gtf annotation file.
Subsequent analyses were performed using R version 3. 5. 0. 20 n particular, differential expression analysis was performed with the DESeq2 package v. 1. 20. 0. 21 Additionally, the lfc shrink function of the apeglm package was used to control for expression changes in lowly expressed genes. 22 Gene lists were filtered for protein coding genes with a base mean count of at least 10. Differentially expressed (DE) genes were identified by filtering for padj ≤0. 01. DE genes with a log2foldchange >0 or <0 were considered as "upregulated" and "downregulated," respectively. Enrichment of GO Terms among DE genes was analyzed utilizing the PANTHER version 14. 0  23  enrichment analysis tool on the Gene Ontology  24, 25  homepage with the following settings: test: "Fisher's Exact"; correction: "Calculate false discovery rate"; and GO-Slim terms as annotation data set.
DISCUSSION By assembling data on additional 36 individuals with pathogenic or likely pathogenic variants in CTCF we considerably contribute to the delineation of the mutational and clinical spectrum of CTCF-associated NDDs. The phenotype associated with CTCF aberrations is highly variable. All individuals presented with developmental delay and/or a variable degree of learning or cognitive difficulties, extending into (low) normal IQ ranges. The mild end of the phenotypic spectrum is reflected by two familial cases in our cohort. As also true for other, potentially mild NDDs, autosomal dominant inheritance has to be considered, particularly in trio exome approaches when primarily searching for de novo variants. Our observations might also imply that due to a possibly very mild phenotype or incomplete penetrance, very rare presence of a variant in public databases such as ExAC or gnomAD is not necessarily an exclusion criterion for its pathogenicity.
URLs Gene Ontology http://geneontology. org/ Homologene https:// www. ncbi. nlm. nih. gov/homologene OMIM https://www. omim. org/ PANTHER http://pantherdb. org/ Primer3 http:// primer3. ut. ee/ SysID database https://sysid. cmbi. umcn. nl/ UCSC Genome Browser https://genome. ucsc. edu/.
ACKNOWLEDGEMENTS We thank all individuals and families for participating in this study. We especially thank  Laila Distel  and  Christine Suchy  for excellent technical assistance;  André Reis ,  Arif Ekici , and  Fulvia Ferrazzi  at the next-generation sequencing core facility at the  Institute of Human Genetics in Erlangen ; and  Felix Engel  for help with the confocal microscope, which was supported by the  German Research Foundation  ( INST 410/91-1 FUGG ). C. Z. is supported by grants from the  German Research Foundation  ( ZW184/1-2 ,  ZW184/3-1 , and  270949263/GRK2162 ) and by the  Interdisciplinary Center for Clinical Research in Erlangen  ( E26  and  ELAN-Fonds). H. V. E. is a clinical investigator of  FWO Vlaanderen. K. Õ. and S. P. received support from  Estonian Research Council  grants  PUT355 ,  PRG471 , and  PUTJD827.
M. H. W. is supported by  T32GM007748. This study makes use of data generated by the  DECIPHER community. Funding for the project was provided by the  Wellcome Trust. The DDD Study presents independent research commissioned by the  Health Innovation Challenge Fund  (grant number  HICF-1009-003 ), a parallel funding partnership between  Wellcome  and the  Department of Health , and the  Wellcome Sanger Institute  (grant number  WT098051 ). The research team acknowledges the support of the  National Institute for Health Research , through the  Comprehensive Clinical Research Network. The  Broad Center for Mendelian Genomics  ( UM1 HG008900 ) is funded by the  National Human Genome Research Institute  with supplemental funding provided by the  National Heart, Lung, and Blood Institute  under the  Trans-Omics for Precision Medicine  ( TOPMed) program  and the  National Eye Institute.
Results Reconstructing the nanoscale structure from the diffraction data. We modeled the PMN structure at 300 K and 200 K using atomic configurations of 40 × 40 × 40 perovskite unit cells (320,000 atoms, 16 × 16 × 16 nm), which sample interatomic distances up to 8 nm (see the Methods section). The atomic coordinates in these configurations were varied according to an RMC algorithm  24, 25  while simultaneously fitting the neutron and X-ray powder total-scattering functions; their respective realspace Fourier transforms, which represent atomic pairdistribution functions (PDF); and 3D distributions of X-ray diffuse intensity measured using a single crystal. In addition, we included the extended X-ray absorption fine structure (EXAFS) data for Pb and Nb to provide chemical resolution. The refinements were enabled by several breakthrough developments in the RMC software and methodology which permitted efficient treatment of large atomic configurations, accurate fitting of the total-scattering data to long distances, and fitting of 3D diffuseintensity distributions over an extended range of reciprocal space (see Methods).