dataesr/scientific-paragraphs-categorization huggingface dataset:
He is the Chair of the Science Advisory Group of the GEROS-ISS mission. RENS BAGGEN was born in Geleen, The Netherlands. He received the M. Sc. degree from the Technical University of Eindhoven in 1992, with a focus on antennas and propagation. He was involved the fields of avionics and quality management with the Dutch Aerospace Laboratory (NLR), Amsterdam, in 1993. In 1995, he joined the Department of Antennas and EM-Modeling, IMST, and specialised in antenna theory, simulation methods/tools, waveguide antennas, metamaterials, and measurement techniques. He was the Coordinator of the FP7-Project MEMS-4-MMIC. He has been involved in many research and development projects in FP5, FP6, and FP7 and is currently involved in H2020 projects. Phased array technology is one of his main fields of expertise.
Estel Cardellach  (This work was supported in part by the  Spanish  under Grant  ESP2015-70014-C2-2-R  and Grant  ESP2015-70014-C2-1-R  and in part by the  Specific Expedition Budget of GFZ. The work of S. V. Nghiem was supported in part by the  NASA   Cryospheric Sciences Program  and in part by the  NASA Land Cover and Land Use Change Program. to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists.
Past global change may have forced animal-dispersed plants with megafaunal fruits to adapt or go extinct, but these processes have remained unexplored at broad spatio-temporal scales. Here, we combine phylogenetic, distributional and fruit size data for more than 2500 palm (Arecaceae) species in a time-slice diversification analysis to quantify how extinction and adaptation have changed over deep time. Our results indicate that extinction rates of palms with megafaunal fruits have increased in the New World since the onset of the Quaternary (2. 6 million years ago). In contrast, Old World palms show a Quaternary increase in transition rates towards evolving small fruits from megafaunal fruits. We suggest that Quaternary climate oscillations and concurrent habitat fragmentation and defaunation of megafaunal frugivores in the New World have reduced seed dispersal distances and geographical ranges of palms with megafaunal fruits, resulting in their extinction.
(a) Evolution of megafaunal palm fruits By compiling a comprehensive fruit trait dataset, we found that 12% (n ¼ 227) of all measured palms (n ¼ 1834) produce megafaunal fruits of 4 cm length (figure  1c ). Megafaunal fruits are, relative to small fruits (less than 4 cm length), more common in the New World (n ¼ 110 species, 16%) than in the Old World (n ¼ 117 species, 10%). Examples of typical megafaunal fruits are found in the New World genera Mauritia, Aphandra and Phytelephas, and in the Old World genera Raphia, Eugeissona, Hyphaene and Arenga (figure  2 ; see also electronic supplementary material, table  S1  for an overview of the biogeographic distribution of palm genera and their fruit sizes). Our reconstruction of ancestral trait states (figure  2 ) suggests that all palm fruits originated from a palm ancestor with megafaunal fruits (ca 110 Ma).
(b) Late Cenozoic extinction rates To test the hypothesis (H1) that extinction rates of palm lineages with megafaunal fruits have shifted in response to Quaternary climate change, we used time-dependent diversification models in a Bayesian time-slice analysis (see Methods). These models were fitted to the phylogenetic dataset with temporal cut-offs at 25, 20, 15, 10, 5, 2. 6, 1 and 0. 5 Ma, covering the Neogene and Quaternary. The geographical regions (global, New World and Old World) showed different results with respect to extinction rates (figure  3a -c ). Globally, we detected an exponential increase in extinction rates of palm lineages with megafaunal fruits from the onset of the Quaternary (figure  3a ). In contrast, palms with small fruits did not show such an increase (figure  3a ).
Methods (a) Palm data We used previously published phylogenetic data  [41] , which includes all 2539 palm species. All analyses were performed on 100 phylogenetic trees from the posterior distribution. For details on the phylogenetic inference, see electronic supplementary material. Fruit lengths for 1834 vertebrate-dispersed palm species were collected from published literature and were updated to the latest palm taxonomy. Species were classified into two main groups: small-fruited (less than 4 cm in length) and large, megafaunalfruited (4 cm in length) palms  [5, 6]. This classification was based on the seed dispersal ecology of the species and directly follows the classification of megafaunal fruits by Guimara ˜es et al. [5]. We note that we used fruit length rather than fruit diameter because data on fruit diameter were unavailable for 405 (out of 1834) palm species, and because fruit length strongly correlates with fruit diameter (see electronic supplementary material, figure  S3 ).
Palm species with megafaunal fruits rely on large animals (megafauna 44 kg) such as tapirs, elephants and extinct gomphotheres, ground sloths and glyptodonts for their seed dispersal, whereas palm species with small fruits are predominantly dispersed by birds, bats and non-volant, smaller-bodied mammals (figure  1 ). Dispersal by these different frugivore 'guilds' is expected to have contrasting effects on past extinction and transition rates of palms, thereby providing a solid comparative framework. Nevertheless, several palms ( particularly in subtribe Attaleinae) have very large, nut-like fruits without fleshy pulp  [37]. Nowadays, these species rely on dispersal by rodents rather than megafauna and may therefore not bear truly 'megafaunal' fruits  [5]. Although this fruit type could not be distinguished in our database, we evaluated the impact of this trait on the results by repeating the analyses excluding the Attaleinae (see sensitivity analyses below for details).
(d) Simulations on trait-dependent diversification The BiSSE model  [56, 57]  implemented in the 'diversitree' R package  [58]  was used to model speciation, extinction and transition rates of palm lineages with small versus megafaunal fruits. Recent criticism on trait-dependent diversification models  [59]  has encouraged researchers to perform simulations to test for type I and type II error rates in the data. We therefore performed three simulation studies. First, we randomly evolved a neutral binary trait on 10 empirical palm phylogenies under four transition rate ('q') scenarios (q ¼ 0. 01, q ¼ 0. 1, q ¼ 1 and q ¼ 10) (following suggestions by ref. [59] ), and also used our observed transition rates from the global dataset (q megafaunal to small ¼ 0. 017; q small to megafaunal ¼ 0. 006) on the simulation of a neutral trait on 100 empirical palm phylogenetic trees.
However, when modelling a transition rate shift, the extinction rate may also erroneously increase, although this erroneous correlation was not observed in the empirical data: New World palms with megafaunal fruits showed increased extinction rates without also increasing their transition rates (figure  3b, e ). In other words, the increased extinction in New World palms may be a true phenomenon, as it does not occur as an effect of increased transition rates. By including all extant palm species and their traits in a phylogenetic framework we were able to detect general, historical patterns. This dataset is therefore robust to issues related to biased sampling of taxa or traits (i. e. 12% of palm species have megafaunal fruits)  [60]. Furthermore, by including all palms we maximized the sample size that is required to estimate the parameters in diversification rate models  [56].
e. time independent) model (see electronic supplementary material, tables S3-S5 for model selection). We then used a step-wise model selection approach to fit up to 43 diversification models to the global (electronic supplementary material, table  S6 ), New World (electronic supplementary material, table  S7 ) and Old World (electronic supplementary material, table  S8 ) datasets (see electronic supplementary material, figure  S8  for an overview of the parameters in these models). These models contained different combinations of constrained and free parameters (i. e. speciation, extinction and/or transition rates were constrained to be equal for lineages with small versus megafaunal fruits, and/or for lineages evolving pre-Quaternary and Quaternary, or they were allowed to differ freely; electronic supplementary material, figure  S8 ).
We compared these models using likelihood-ratio tests (nested-models) and the Akaike information criterion (AIC) (non-nested models), and selected the best-fitting models given the fewest number of parameters without significantly affecting model-fit (DAIC , 2). Sampling fractions reflecting species and their traits (small or megafaunal fruits) sampled from the total were used (32% of species with small fruits and 18% of species with megafaunal fruits were not sampled in the global dataset). Sampling fractions were corrected for the New World and Old World datasets. A Bayesian Markov chain Monte Carlo (MCMC) was run for the best-fitting model for 10 000 generations on 100 randomly sampled palm phylogenies. We plotted the posterior distributions (95% Bayesian credibility intervals) of the parameter estimates for the extinction and transition rates (figure  3 ), and the speciation and net diversification rates (electronic supplementary material, figure  S9 ) through time.
These results suggest that our classification of palms with megafaunal fruits 4 cm, and the inclusion of palms with large, nut-like fruits, are not driving the overall diversification patterns obtained in this study. Data accessibility. The phylogenetic data and the fruit size data that support the findings of this study are available from the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. cm4nm. The palm species distribution data are available from the World Checklist of Selected Plant Families (http://apps. kew. org/wcsp). The R script and program to perform the simulations are available from the rspb. royalsocietypublishing. org Proc. R. Soc. B 285: 20180882 electronic supplementary material. Additional data files are available from the authors on request.
Funding. This research was funded by the Netherlands Organization for Scientific Research (824. 15. 007) and the University of Amsterdam (starting grant) to W. D. K. , the SNF Early Postdoc. Mobility grant (P2ZHP3_161991) to R. E. O. , a grant from the Garfield Weston Foundation to the Global Tree Seed Bank Project at the Royal Botanic Gardens, Kew to W. J. B. , the Agence Nationale de la Recherche (ANR-15-CE02-0002-01) to T. L. P. C. , and the Carlsberg Foundation Semper Ardens project MegaPast2Future (CF16-0005) and the Villum Investigator project 'Biodiversity Dynamics in a Changing World' funded by Villum Fonden (16549) to J. -C. S.
Growth Curves Bacterial growth was evaluated in presence and absence of gentamicin (64 µg/ml). Ninety-six-well plates were filled with 200 µl of minimal medium and inoculated with 10 3 cells per well. Growth curves were performed in a Tecan Infinite M200 spectrophotometer with incubation, at 37 • C, with linear shaking, for 48 h. Growth rates were determined using GrowthRates program  (Hall et al. , 2014). Growth curves were evaluated in triplicates for each strain.
Genome Sequencing Whole genome sequencing of all selected strains and their ancestors was carried out using the pair-end protocol in an Illumina HiSeq2000 Sequencer. More than 1,000,000 sequences, between 35 -82 bp, were obtained per sample, and their quality was analyzed through FASTAQC. Sequences were aligned with BWA  (Li and Durbin, 2009) , using "bwa aln" and "bwa sampe" commands. Genome sequence of MG1655 was obtained from Genebank Database (NCBI): https://www. ncbi. nlm. nih. gov/nuccore/U00096. 3. Alignment files were transformed to BAM format, sorted and indexed through samtools  (Li et al. , 2009). snpEff  (Cingolani et al. , 2012)  was used for the annotation of gene polymorphisms. For mutations in upstream and downstream regions, 200 bp were considered. Results were filtered and analyzed in an interactive manner using a visualizer developed by the Computational Genomic Service of the National Center for Biotechnology (CNB-CSIC) in Madrid, Spain.
Evolutionary Trajectories Evolutionary trajectories of the common resistance determinants were determined by analyzing the time of apparition of mutations along the experiment. For this purpose, two single colonies were randomly selected from chemostat populations at different gentamicin concentrations. Only clones from populations obtained at 0. 5, 2. 0, 4. 0, and 8. 0 µg/ml gentamicin were selected, as isolates from higher gentamicin concentrations showed highlevel gentamicin resistance. For instance, gentamycin MIC of isolates from 8. 0 µg/ml ranged from 16 to >128 µg/ml (data not shown). We chose five loci (atpG, cyoA, fhuA, fusA, and potA) as representative of the five common genetic elements that were repeatedly targeted by selection to be subjected to PCR amplification and Sanger sequencing.
ACKNOWLEDGMENTS We thank people from  Victor de Lorenzo  and  José Luis Martínez  groups for helping with some experiments. Supported by  Plan Nacional de I+D+i   2013-2016  and  Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Economía, Industria y Competitividad ,  Spanish Network for Research in Infectious Diseases  ( REIPI RD16/0016/0009 )-cofinanced by  European Development Regional Fund A way  to achieve Europe,  Operative program  Intelligent Growth 2014-2020 and grant  FIS PI13/00063  from  Instituto de Salud Carlos III. CI-Q was supported by grant  Becas Chile Doctorado en el Extranjero (72110933) ,  Consejo Nacional de Investigación Científica y Tecnológica, Chilean Government , and  CIDI-DIUV 4/2016.
IDENTIFIED STUDIES Of the screened publications, a total of 19 relevant articles were identified (Fig. 1 ). Seven of these were sourced from the database searches (PubMed [n = 6], Google/Google Scholar [n = 1]), and a further seven were identified from a recent systematic literature review. 9 The remaining five articles were identified during data extraction (n = 2) and data analysis (n = 1), and as a recommendation by one of the investigators (MD) (n = 2). Of the 19 relevant articles, three were excluded (duplicates removed [n = 2], judged by investigators to not report relevant data [n = 1]), leaving a total of 16 unique studies (Fig. 1 ). These studies reported associations between device errors and clinical outcomes, and were included for discussion in the review. Details of the included studies are shown in Table  1.
DISCUSSION The majority of studies identified from the literature reported an association between inhalation errors and worse disease outcomes in patients with asthma or COPD, and the magnitude of effect was similar across the different endpoints studied. Most of the studies were cross-sectional and were conducted in pulmonology clinics/outpatient departments. A few prospective, longitudinal studies, and some database analyses, were also identified. There are advantages and disadvantages to each study design that should be considered when interpreting the literature. For example, it is difficult to infer causality using a cross-sectional approach, due to lack of temporality. This approach can also be subject to recall bias (especially for HRU studies). In general, outcome data (e. g. , number of hospitalisations, ER visits etc.
) in the identified studies relied upon retrospective recollection by patients, and were often limited to events in the recent past (1 -3 months). Furthermore, while database analyses provide readily available data, few databases capture data on device errors, and as a result, associated information on inhalation technique. Prospective studies can overcome the issue of temporality by providing more reliable data on outcomes, especially those related to HRU. However, they are operationally more challenging to conduct, and do not provide results as quickly as cross-sectional or database analyses. Longitudinal studies, on the other hand, can evaluate the long-term impact of errors and causality, and are able to address a variety of research questions. For example, longitudinal studies can be used to measure the effects of interventions that target inhaler errors.
9 Most of the errors in the CritiKal study were generic to the type of inhaler (DPI or MDI) rather than device specific. 19  limitation of this systematic review was that a meta-analysis of the literature could not be performed due to heterogeneity in study designs, outcomes, and patient populations studied, although these may become viable in the future as study designs become more uniform. Published data from studies in asthma and COPD demonstrate the presence of an association between inhalation errors and outcomes, with an apparent relationship between a reduction in errors and improvement in outcomes, irrespective of endpoint. The magnitude of effect is similar across the different endpoints. Therefore, investment of time by healthcare professionals is vital to improving inhalation technique in asthma and COPD patients in order to improve health outcomes.
ACKNOWLEDGEMENTS This review was funded by  GSK. The authors thank  Saifuddin Kharawala  and  Gavneet Kaur  from  Bridge Medical , who performed the original literature searches and data extraction. Editorial support (in the form of writing assistance, collating author comments, assembling tables, grammatical editing, fact checking, and referencing) was provided by  David Mayes , MChem, of  Gardiner-Caldwell Communications (Macclesfield, UK ) and was funded by  GSK. Autohaler and  Easi-Breathe  are trademarks of  Teva Pharmaceutical Industries Ltd. Turbuhaler is a trademark of  AstraZeneca. HandiHaler and Respimat are trademarks of  Boehringer Ingelheim. Diskus is a trademark owned by or licensed to the GSK group of companies.
2014 28 Clinical cross-sectional Brazil OP Adults 268 MDIs, DPIs Clinical Correct ( < 2 errors) vs. incorrect inhaler technique ( ≥ 2 errors) Giraud, Allaert & Roche 2011 29 Prospective clinical (1 month follow-up) France Pharmacy Adults 727 pMDIs, breath-actuated MDIs Clinical Optimal use (no errors) vs. non-optimal use ( ≥ 1 critical or non-critical error) Yildiz et al. 2014 30 Prospective clinical, Turkey OP Adults 572 pMDIs, DPIs Clinical Patients making 0-1 basic errors vs. patients making > longitudinal ( ≥ 6 months 1 basic error follow-up) Harnett et al. 2014 31 Prospective clinical, Ireland OP ≥ 16 years 40 pMDIs, DPIs, soft-Clinical, QoL Optimal users (no errors) vs. misusers ( ≥ 1 of 10 steps longitudinal (3-4 months mist inhaler performed incorrectly) follow-up) Levy et al. 2013 32 Retrospective, database UK -All ages 3981 MDIs, DPIs Clinical Patients with correct vs.
incorrect technique (incorrect (IMPACT), cross-sectional technique = failure of one or more of: inspiratory flow between 10-50 L/min; correct flow for ≥ 1. 5 s post- actuation; post-inspiration breath hold for ≥ 5 s) Price et al. 2017 19 Retrospective, database Australia, -≥ 16 years 3660 pMDIs and DPIs Clinical Frequency of specific errors and device-specific errors (iHARP), cross-sectional Europe COPD only Molimard et al. 2017 33 Clinical cross-sectional France OP > 40 years 2935 pMDIs, Respimat, Clinical, Patients with absence of error vs. presence of critical DPIs economic error Both asthma and COPD Melani et al. 20118 Clinical cross-sectional Italy OP > 14 years 1664 MDIs, DPIs Clinical, Inhaler misuse (patients with presence of error or economic critical error) Maricoto et al. 2015 34 Clinical cross-sectional Portugal OP > 12 years 62 MDIs, DPIs Clinical Patients with ≥ 1 error, number of errors committed (0-4) Roggeri, Micheletto & Roggeri 2016 35 Clinical cross-sectional Italy OP > 14 years 400 NR Economic See above (Melani et al.
Weight Selection & Resolution Aspects The above problems (Equations (  11 ) and (  12 )) are strongly non-convex and cannot be solved with the standard H ∞ control optimization tools, which provide unstructured full-order controllers and are unable to optimize separate channels on multiple models. However, using specialized non-smooth optimization techniques, some efficient algorithms have been proposed in  [10]  and then improved when they were implemented in the HINFSTRUCT and SYSTUNE routines of the MATLAB R Robust Control Toolbox  [16]. In this application, the HINFSTRUCT routine was used on an augmented design model with a block-diagonal structure including 16 (2 × 4 × 2) models associated with.
Inter-individual host diversity is often ignored when pooling together several individuals, or underestimated by insufficient sampling in the population, and intra-host variability is rarely considered, but these two levels are essential to infer the evolutionary dynamics of host microbiota interactions  [12]  and to better link microbiota diversity with associated phenotypic changes in the host  [13]. Next generation sequencing techniques can provide whole genome sequencing data of communities of organisms. Some host sequencing projects contain microbe-related reads that are often considered as "contaminant" in the analysis of the host genome. These datasets can actually be analyzed and provide meaningful insights about organisms seen as holobionts. Shotgun metagenomic sequencing has several features which enables high-resolution analysis of taxonomic and genetic diversity associated with holobionts.
On the other hand, metagenomic profiling when based on reference databases is either restricted to few marker genes  [19, 20]  or can perform strain-level assignation only for model systems or very well studied organisms for which many strains are already characterized (for instance for biomedically important pathogens  [21, 22] ). Finally, reference-free metagenomic profiling approaches, also called binning approaches, are often based on previous assemblies that have already discarded polymorphism information  [23, 24]  or, when using co-abundance signals, may lead to incorrect binning when conserved and variable regions of a same species are sorted in different bins  [25]. Overall, one of the main pitfalls of current holobiont analyses is the characterization of microbes at strain/genotype level.
Apart from model communities for which comprehensive strain databases are available, fine variations in symbiont genomes are not accurately addressed by the current metagenomics-dedicated methods. Then, a basic but efficient strategy consists in converting the problem into several non-metagenomic ones, namely analyzing each symbiont and its corresponding read subsets independently using classical genomic variation methods. The major difficulty remains to be able to partition unambiguously the read datasets, and this is definitely easier when disposing of good reference genomes for all the symbionts. In the present paper, we present a framework designed to recover strain-level genomic variations from metagenomic reads preliminary mapped on reference genomes. When a given symbiont lacks a good reference genome, it is then built de novo from the metagenomic datasets.
It is located at a sweet spot of complexity, with a symbiotic community of moderate size and with various modes of transmission of symbionts between hosts. It offers an interesting case of diversity partitioning between host populations through genetically and ecologically differentiated biotypes, and it is a species for which ample genomic resources are available for both the host and its associated symbionts. In this paper, we analyzed metagenomic data from a large dataset of pea aphid-resequenced genomes to explore the extent and partitioning of microbial diversity at the different scales presented above. By mapping the reads on a set of reference genomes, we assigned the majority of the reads to microbial taxa associated with the pea aphid complex. This enabled a high-resolution inventory of the genomic diversity of bacterial symbionts found in the pea aphid complex.