dataesr/scientific-paragraphs-categorization huggingface dataset:
The 'Effective N' was subsequently used to calculate the eMAC (2 × minor allele frequency × 'Effective N' × imputation quality) for each variant. Variants were excluded when eMAC < 10, with the exception of the Newcastle 85 + (90th percentile cases versus all controls) and the RS (99th percentile cases versus all controls) data sets in which we excluded variants when eMAC < 25 due to the large imbalance between the number of cases and controls in these data sets (1:24 and 1:38, respectively) in comparison to the other ones (all < 1:10). For the CLHLS and LLFS data sets, we flipped the strands of several variants based on the discordance of allele frequencies with the reference panel. We only flipped palindromic variants with a MAF < 0. 4 and an allele frequency that differed from the reference panel by <10% after switching.
05 was considered significant  61. Colocalization of the tissue-specific eQTL results from the GTEx project and our longevity meta-analyses results was performed using the 'coloc. abf' function implemented in the R-package coloc  62. Genetic correlation analysis. To estimate the genetic correlation between the different phenotypes used in this study, we used LD score regression  24. The genetic correlation between the results from the 90th and 99th percentile cases versus all controls meta-analyses and 246 diseases and traits were estimated using the LD Hub web portal (http://ldsc. broadinstitute. org/ldhub/)  26. Since LD score regression is currently only possible with data from individuals of European ancestry, we used our meta-analyses results based on the cohorts from populations of European descent only.
Data availability The full meta-analyses summary statistics are available for download at www. longevitygenomics. org/downloads and through GRASP (https://grasp. nhlbi. nih. gov/ FullResults. aspx) and the NHGRI-EBI GWAS Catalog (https://www. ebi. ac. uk/gwas/ downloads/summary-statistics). All other data that supports the findings of this study are available from the corresponding authors upon request.
victoria transcriptome were assembled using Trinity  [27, 28]  either on a custom workstation in the lab or using the public Galaxy bioinformatics server  [29]. Read mapping was performed using bowtie2 alignment  [30] and RSEM  [31]  Putative FP-encoding transcripts were validated against raw read data and reconstructed as necessary (see below for detailed methods, results, and discussion). Sequence alignments were performed using Clustal Omega  [25]. For each avGFP homolog identified, the coding region was identified and a synthetic gene was designed to produce the encoded polypeptide sequence using codons optimized for both human and E. coli expression using an in-house BioXP3200 instrument (SGI-DNA, La Jolla, CA) or ordered as a gBlock double-stranded gene fragment (Integrated DNA Technologies, San Diego, CA).
Diffraction data collection. Diffraction data for AausFP1 were collected on beamline BL13-XALOC at the Spanish Synchrotron in Barcelona (Spain)  [37]  from a crystal flash-cooled at 100K in its mother liquor supplemented with 20% (v/v) of glycerol for cryoprotection. Diffraction data for AausFP2 were collected on beamline ID30B of the European Synchrotron Radiation Facility in Grenoble (France)  [38]  from a crystal flash-cooled at 100 K without addition of any cryoprotectant. Diffraction data were integrated and reduced using XDS and XSCALE  [39]. Data collection and reduction statistics are given in Supplementary Table  S2. Structure determination. A BLAST search (https:// blast. ncbi. nlm. nih. gov/) identified the fluorescent protein phiYFPv from the jellyfish genus Phialidium as the closest homologue of both AausFP1 and AausFP2 (sequence identities of 61% and 50%, respectively) with a known structure (PDB entry code 4HE4,  [40] ).
The structures of AausFP1 and AausFP2 were solved by the molecular replacement method using the 4HE4 coordinates as a search model with the program PHASER  [41]. The model was progressively and interactively modified in COOT  [42]  and refined with REFMAC5  [43]. The asymmetric units contain 4 molecules for AausFP1 and 1 molecule for AausFP2. Analysis of the interaction interfaces with PISA  [44]  strongly suggest that the AausFP1 tetramer consists of a dimer of a physiological dimer (interface areas of 1210 Å 2 vs. 360 Å 2 )for the third and fourth largest areas) while the AausFP2 monomer forms a physiological dimer with a symmetry-related molecule (interface area of 1290 Å 2 vs. 540 Å 2 for the second largest area). Structure refinement statistics are given in Table  3. Calculation of AausFP2 absorption maxima.
Acknowledgements We dedicate this manuscript to the memory of  Dr. Roger Y. Tsien  and  Dr. Osamu Shimomura , whose studies on A. victoria and avGFP continue to inspire us and to catalyze new technologies for biological imaging. This work was also made possible by the  Crystal Jelly exhibit at the Birch Aquarium at Scripps , highlighting the significance of this species in the history of biomedical research. This exhibit allows guests to visually observe the fluorescence in the jellies with the push of a button, and was the source of the A. victoria individual used in this work. We also wish to thank  Dr. Lauren M. Barnett  for aiding in the collection of A. cf. australis,  Wyatt Patry  ( Monterey Bay Aquarium ) for helping in species identification, and  Dr. Ute Hochgeschwender ,  Dr. Stephen R.
Obtaining modelled growth rates The resulting system of ODEs is numerically integrated using the odeint package from the Scipy library  (Jones et al. 2001)  in Python (version 3. 7) for the different constant or fluctuation treatments. Starting from low initial volume and low balanced reserve densities, the unsaturated increase in structural volume within the first 4 days yields an estimate for the somatic growth rate of juveniles that may be compared to experimental observations. Different parameter combinations, which represent limiting cases of (1) no reserves, no variable assimilation (null model), (2) no variable assimilation, (3) no reserves and (4) the full model with reserves and variable assimilation, give rise to submodels that combine the three important model components (i. e. Reserve, Acclimation, SU).
ACKNOWLEDGEMENTS We thank  Erik Sperfeld ,  Toni Klauschies , the editors and three anonymous reviewers for insightful comments on earlier versions of the manuscript and  Silvia Heim  for technical assistance. This study was supported by the  German Research Foundation  ( KO5330/1-1  to AMK,  WA2445/9-1  and  WA2445/15-1  to AW).
14  The Tas4TB team reaches even the most marginalised communities, thereby ensuring equity of access to early diagnosis and treatment. The Zimbabwean Tas4TB algorithm uses symptom inquiry about cough of any duration, fever, night sweats, haemoptysis and weight loss, together with a digital CXR, as screening tools. Patients with positive symptoms, abnormal CXRs or both are presumed to have TB: they are asked to produce sputum specimens, which the Tas4TB team delivers to the nearest district laboratory for Xpert testing, or for smear microscopy where Xpert is not available. Patients with bacteriologically confirmed TB, and some with clinically diagnosed TB, are linked with their nearest health facilities so they can access free treatment and care according to national guidelines. A web application is used for real-time data entry and updates a central electronic database where patient-level data at each stage of the screening cascade are stored.
Study procedure All the 230 identification numbers (IDs) for patients who were initiated on treatment on the basis of CXR only or CXR plus symptoms were selected from the MS Excel database (MicroSoft, Redmond, WA, USA). These patients had negative results on Xpert or on both Xpert and smear microscopy. Due to cost constraints, we consecutively sampled 168 images and stopped recruitment once the figure was reached. Four columns were added to the MS Excel file in order to capture ratings by Radiologist A, Radiologist B, Radiologist C and the final radiologist ratings. The digital CXR images together with a blank copy of the study proforma (with a filled out column for patient IDs, age and HIV status) were sent to Radiologists A and B for their scores. Both radiologists were blinded to the other's scores, the study objectives, the Xpert results and the field CXR scores.
Analysis and statistics Data on radiologist ratings were double-entered in two Microsoft Excel sheets (MicroSoft). The formula = IF(EXACT(SHEET1!A2,SH EET2!A2),0,SHEET1!A2&"/"&SHEET2!A2) was used to compare the Excel sheets. The final database contents (sociodemographic and clinical data and CXR ratings) were imported to EpiData v2. 2. 2. 186 (EpiData Association, Odense, Denmark) for analysis. Numbers (and proportions) were reported for patients who were over-treated. Two-by-two tables in OpenEpi software (Dean AG, Sullivan KM, Soe MM. OpenEpi: Open Source Epidemiologic Statistics for Public Health; http://www. openepi. com) were used to compare frequencies of CXR ratings by field medical officers and the radiologists. Percentage agreements and their 95% confidence in-tervals were calculated using sum concordances of 'suggestive of TB' and 'not suggestive of TB' as a fraction of total cell frequencies.
11, 12 he strengths of this study lie in the fact that we purposively selected expert radiologists, based on their experience in rating CXRs; also, study radiologists were blinded to the scores of the other radiologists and independent ratings to ensure credibility and accuracy of results. We facilitated their ratings by providing information on HIV status on the rating proforma. Data transcription errors were greatly reduced in two ways: sociodemographic data were extracted from an electronic database (with check functions) and data double-entered to capture CXR ratings. Finally, as digital CXRs images were used, the images were of high quality and may have reduced inconsistencies in reporting due to poor quality images. 16, 17 ur main limitation was a lack of culture results, the gold standard for TB diagnosis.
Datasets of the same geographic space at different scales and temporalities are increasingly abundant, paving the way for new scientific research. These datasets require data integration, which implies linking homologous entities in a process called data matching that remains a challenging task, despite a quite substantial literature, because of data imperfections and heterogeneities. In this paper, we present an approach for matching spatial networks based on a hidden Markov model (HMM) that takes full benefit of the underlying topology of networks. The approach is assessed using four heterogeneous datasets (streets, roads, railway, and hydrographic networks), showing that the HMM algorithm is robust in regards to data heterogeneities and imperfections (geometric discrepancies and differences in level of details) and adaptable to match any type of spatial networks.
Introduction The increasing development of geographical information systems (GIS), especially thanks to the growth of web technologies and collaborative tools, combined with the growing interest of disciplines connected to GIS such as archaeology, history, and urbanism, make it possible to easily access many heterogeneous datasets of the same geographic space, at different scales, temporalities, or spatial and semantic granularities. The integration of such various data is required by many research studies and applications, for instance, to design a representation of space and its changes over time in order to study its evolution, databases at distant times depicting a world that has changed. For more complex datasets, 1:M, N:1, or N:M links may occur when one or more features in A are matched with one or more features in B.
Among the large amount of existing matching algorithms, the overwhelming majority focus on road or street networks and have shown very good results with that kind of dataset. Nevertheless, only a few other approaches are tested with other types of networks, such as in  [7]  where a hierarchical process is proposed to match imperfect hydrographic networks. Only some of those papers consider networks with different level of detail  [2, 29, 30, 32, 36, 40]  or are adapted to manage geometric discrepancies  [27, 32, 36, 40, 46]. Furthermore, most of the algorithms use thresholds in the decision making processes  [2, 15, 26, 45, 51] , or numerous parameters  [13, 30, 32] , and a minority can detect N:M relationships  [2, 13, 30, 40, 46]. At last, the algorithms presented above are mainly geometrical.
Railways The third dataset consists of railway networks from the same sources as the second dataset. Hydrography Finally, the last dataset is composed of hydrographic networks from BD-Carto and BDTopo, another reference vectorial map produced by the French mapping agency at a lower scale (between 1 : 5000 and 1 : 50000) and higher level of detail than BDCarto. Table  1  summarizes the characteristics of our testing datasets.
A selection threshold to speed-up the matching process In their approach, Tong et al. [40]  consider all possible pairs of matching candidates in order to avoid the use of selection thresholds. With complex and large datasets, such as city street networks, a risk of combinatorial explosion arises. Moreover, there is obviously no need to consider matching candidates that are quite distant one from the other. For that purpose, before the calculation of emission probabilities we introduce a selection threshold to filter distant candidates and help reducing the running time of our algorithm. This threshold may be calibrated according to our knowledge about the geometrical accuracy of considered datasets. We insist that this is not a mandatory parameter, as all of the algorithm can be executed without selection threshold.
Qualitative matching results for several types of networks To run our tests, we arbitrarily used λ = β = 1 as parameters of the exponential distributions used as cost functions in the calculation of emission and transition probabilities. Qualitative analysis over a sample of matching results shows that the decision making process powered by the hypergraph assignment problem seems to produce better results in general, but differences are quite minimal. Figure  19  illustrates and example where the first decision making approach (double HMM) performs better at dealing with the differences of crossroads modelling (more detailed for Verniquet data) but also leads to false-positives (black circle). Red solid lines illustrate matching links between homologous edges. We examined matching links produced by our algorithm for the four datasets.
Globally, results are satisfying considering the facts that: 1. we use minimalistic, non-mandatory number of parameters to tune the algorithm: the selection threshold to filter matching pairs of candidates in order to speed up the process, and the type of the cost functions (exponential) for the calculation of emission and transition probabilities; and 2. we only make geometrical and topological considerations. Our approach succeeds at matching several kinds of spatial networks with different heterogeneities and imperfections. Figure  20  illustrates matching results on Paris street networks, and shows good quality of matchings, even for roundabouts 20(b) and despite geometrical discrepancies 20(a). Scores will be given for those datasets in the next section. Figure  21  highlights the robustness of HMM matching to deal with networks with heterogeneous levels of details (roads), whether between the two networks, or within one of them: matching is good in overall for countryside roads, and also for city roads and streets.
HMM matching also correctly deals with railways which are networks with different scales (see Figures  22  and 21 ). That involves a high selection threshold to take into account the potential large gap between candidates edges in the case of differences in the level of details of the datasets (i. e. , to consider 1:N and N:M matching links). It is relevant to note that those networks have the specificity of not being wholly planar. Finally, Figure  23  illustrates the good results of the algorithm for matching tree-like networks with very sinuous geometries. Classical false-positives that can be considered www. josis. org intrinsic to the approach are due to its tendency to over-match features (see Figure  21. b). Indeed, as we do not use thresholds for each geometric criteria in the calculation of emission probabilities, all edges of G 1 are possibly matched with one or several edges of G 2.
The F-score is a compromise between precision and recall. Table  2  illustrates precision, recall, and F-score calculated for Paris street networks using the HMM algorithm with both decision making process. The results are compared to Opt  [27]  approach which is a classical and performing algorithm for matching networks. We used optimal parameters for both algorithms, given by a calibration computed using the OpenMOLE platform  [33, 34] , such that F-score is maximized (see section 3. 7). First of all, even though additional evaluation on other datasets should be achieved, these numbers allow us to draw the following conclusions:.
The HMM algorithm seems to perform better for datasets with different levels of detail, such as differences in the representation of intersections, as shown in Figure  24. Nevertheless, the Fréchet distance might be insufficient in some cases to discriminate between several matching candidates. Figure  25. c shows the result of the HMM before the decision making process. In this case, as several matching sets are topologically possible, it is mostly the emission probabilities that lead to the given solution. This issue could be overcome by choosing other measures or combinations of distances for the computation of emission probabilities. One significant strength of our approach is scalability. As each path can be processed independently of the others, our algorithm can be easily parallelized.
Tasks such as ScalaTask, RTask, and NetLogoTask are specific to a programming language whereas CARETask and ContainerTask are designed to embed models written in other languages such as python or C. Once a model is embedded, OpenMOLE offers algorithms to design experiments. Such algorithms include sampling parameters, model calibration  [37] , incremental modelling  [9] , diversity search  [6] , and sensitivity analysis  [35]. Finally, the experiments designed with OpenMOLE can be delegated to a remote execution environment such as Slurm 2   [49] , HTCondor 3   [39] , or EGI 4   [16]. After integrating our algorithm into OpenMOLE (using a ScalaTask), we performed a calibration using the ground truth presented in section 3. 2. The results of this calibration (see Figure  26 ), using a multiple-criteria calibration with precision and recall, show that the algorithm searches for a compromise between these criteria.
It also correctly manages some major data heterogeneities (scale, level of detail, morphological evolutions) and imperfections (geometric accuracies). We also compared our results with those of Opt  [27]  and proved that the HMM algorithm performs slightly better in the case of Paris street network. The major weakness of the approach is its dependence on the topological quality of input networks. If the topology is broken (unconnected close nodes for instance), the algorithm will fail. There is nothing we can to to improve this except by preprocessing the input data as we did with the four datasets. The second intrinsic side-effect is that we need to filter final matching links because the algorithm is very optimistic by nature: as long as www. josis. org emission probability is positive, every edge is susceptible to be matched.
Acknowledments We thank the entire GeoHistoricalData team for their combined efforts in co-constructing open geohistorical data,  Romain Reuillon ,  Mathieu Leclaire  and the entire OpenMOLE team for their assistance in the exploration and calibration of the models used in the article. We also thank the anonymous reviewers for their feedback and gratefully acknowledge the support of the  French Agence Nationale de la Recherche (ANR) , under grant  ANR-18-CE38-0013  ( SoDUCo  project).
Code and data The code used in this article is accessible at https://github. com/GeoHistoricalData/ HMMSpatialNetworkMatcher for the proposed algorithm and at https://github. com/ GeoHistoricalData/nm for the OpenMOLE plugin and Opt implementation. The data, containing ground truth, used for comparison between the algorithms, is available at https://dataverse. harvard. edu/dataset. xhtml?persistentId=doi:10. 7910/DVN/CCESX4.
, 2009). This opens new avenues to fit nonlinear matrix models for multiple species, by considering other types of data than just counts of species, such as data on survival and development rates, as well as reproduction. One approach to incorporate such demographic datasets, used in plant community dynamics, is to fit separate models for reproduction and survival components of the demography  (Adler & HilleRisLambers, 2008; Chu & Adler, 2015) , and then simulate the community-level model thus created, to evaluate its prediction of the counts and spatial structure. While this approach is sound, it does not take full advantage of opportunities to combine vital rate data with counts, which might be problematic for small datasets. Capture-recapture and reproduction data can be combined advantageously with counts within the Integrated Population Modelling framework  (Besbeas et al.
, 2002) , which uses MPMs as its core for integrating over several datasets through products of likelihoods. This framework has recently been extended to density-dependent MPMs  (Abadi et al. , 2012). To the best of our knowledge, there has been only one comprehensive attempt to move integrated modelling from the population to the community level while including species interactions (i. e. , density-dependent links from species j density to species i vital rates), that of  Péron & Koons (2012). Other multi-species IPMs have been recently published, but they do not model interactions (e. g. ,  Lahoz-Monfort et al. , 2017)  or include interactions, but model the demography of one species only (e. g. ,  Saunders et al. , 2018). Although  Péron & Koons (2012)  did provide a proof of concept for community-level integrated modelling, their focus was on estimating parameters in a real two-species duck community in order to understand species coexistence, not performing a general assessment of the relevance of integrated community models (ICMs).
The capture-recapture study design implicitly assumes that it is easier to mark the predator, because they are less numerous than prey and the numbers of juveniles marked therefore represent a higher fraction of the predator population. The annual number of surveyed broods in set to 20 for the predator and 50 for the prey, hence, for both species, fecundity is sampled only for a fraction of the population. Table  5  describes all other models parameters that have been used for simulation. For scenarios 2 and 4, the fitted models are without capture-recapture (in scenario 4, only for the prey): we kept a probability of capture in the code but it has nearly equal priors and posteriors and cannot affect other estimates (it only enters the capture-recapture submodel, which is absent).