dataesr/scientific-paragraphs-categorization huggingface dataset:
[7]  dataset. This setup resulted in a total of 598 genotyped individuals across 13 populations and two time-points (2011 and 2016). We used E. Z. N. A HP plant DNA mini kits for leaf DNA extraction (Omega Bio-tek Inc. , GA, USA). We amplified nine microsatellites previously used for the I. glandulifera samples from 2011  [7]. Six of these microsatellites were developed by Provan et al. [39]  (IGNSSR 101, 104, 203, 210, 213 & 240) and three were developed by Walker et al. [17]  (A 2, 21 & 3). We constructed three multiplexes of three to five microsatellites in 10 μl reactions for amplifications. Each multiplex contained 1 μl template DNA, 1. 2-2. 0 μl of one of the multiplexed primer combinations (50-100 nM primer concentrations), 1. 8-2. 0 μl RNAse-free water and 5 μl Qiagen Multiplex PCR Master Mix.
The PCR cycling profile consisted of an initial denaturation (15 min) at 94 °C, 30 cycles of 30 s at 94 °C, 90 s at 55 °C and 60 s at 72 °C, and final extension (10 min) at 72 °C  [7]. After PCR, fragments were sized on a 3130xl Genetic Analyzer (Applied Biosystems, CA, USA) on a mixture of 1 μl PCR reaction, 0. 15 μl Applied Biosystems' GeneScan 500 LIZ size standard and 9. 35 μl formamide. The sized fragments were subsequently scored with GeneMapper Software v4. 0 (Applied Biosystems, CA, USA). Initial comparison of the allele identities and frequencies of the newly genotyped individuals, with those of the genotypes obtained by Hagenblad et al. [7] , suggested a consistent allele shift between the datasets, potentially due to the use of a different size standard (GeneScan 500 LIZ vs. GeneScan 600 LIZ respectively)  [40].
Ten individuals of the Hagenblad et al. [7]  dataset, selected to contain 85% of the observed alleles, were subsequently reanalyzed with the described PCR protocol using the original DNA extracts. This data indeed showed a consistent allele shift of two base pairs across all tested alleles, and was subsequently used to calibrate all genotype data to the original Hagenblad et al. [7]  standardized allele identities  [40]. Twenty individuals of the 2016 sampling year were furthermore genotyped twice, with an overall reproducibility of 98% of the genotypic allele patterns.
Data analysis Genetic diversity We used Micro-Checker to assess potential problems with scoring errors due to null alleles, stutter bands or large allele dropout  [41]. Although no stutter bands and large allele dropouts were observed, Micro-Checker did indicate a homozygote excess (potential null alleles) for six different loci in at least one, and up to eight, of the 13 populations in at least one of the two time-points. However, considering the overall low genetic diversity, we believe that these patterns are likely not caused by null alleles, except for marker IGNSSR101 & A2, which failed to amplify for all individuals of, at least, one population (populations Trondheim 1 and Trondheim 2, respectively). This is further supported by the observation of high estimated null allele frequencies (> 30%) for IGNSSR101 & A2, but not the other markers, following the Expectation Maximization algorithm for null allele frequency estimation  [42]  with the FreeNA software  [43].
Additionally, we calculated pairwise F ST values corrected for null-alleles using the ENA (excluding null alleles) correction method with the FreeNA software  [43]. We used paired t-tests to compare pairwise among population genetic differentiation (F ST , G' ST , Jost's D and null-allele corrected F ST ) between the 2011 and 2016 populations. Significance of these paired t-tests was assessed based on 9999 bootstraps, to overcome issues with the pairwise dependency of the data (SPSS Statistics 21. 0). We performed a hierarchical analysis of molecular variance (AMOVA) on pairwise F ST values (9999 permutations) with GenAlEx 6. 503  [44] , for each sampling year (2011 and 2016) separately. AMOVA portioned the total genetic diversity among the six study regions (among-regions), among populations within regions and among individuals within populations.
Genetic differentiation between populations was furthermore visualized using a covariance-based principal coordinates analysis (PCoA) based on the standardized F ST -matrix. To test for systematic changes in population-level genetic composition between the two sampling years we used the previously described repeated measures ANOVA design with the plot location on each of the first three PCoA axes as dependent variables (SPSS Statistics 21. 0). In these models, sampling year was included as a repeated measure factor and 2011 population size and change in population size between 2011 and 2016 as covariates. A similar PCoA and subsequent repeated measures ANOVA was subsequently performed on the null-allele corrected F ST values.
The Kolmogorov-Smirnov test was used to determine whether the conditional distribution of the residuals met the assumption of normality. It was confirmed that the Durbin-Watson (DW) values approached a value ≈2. 00 to rule out autocorrelation problems in the errors, and that the variance inflation factor (VIF) and tolerance parameters (T) did not exceed critical values, in order to prevent multicollinearity problems in the regression models  (Martínez-González, 2006). All tests were bilateral, with a significance level of p < 0. 05. There were no corrections for multiple comparisons given the highly exploratory nature of this study  (Feise, 2002). The SPSS v19. 0 statistical software package was used to perform the data analysis.
ACKNOWLEDGMENTS CIBERObn is an initiative of ISCIII. CIBERsam is an initiative of ISCIII. We would like to thank  Red de Investigación  en  Actividades de Prevención y Promoción de la Salud (Research Network on Preventative Activities and Health Promotion)  ( REDIAPP-G06-170  and  RD06/0018/0017 ),  Red de Excelencia (Excellence Network) PROMOSAM  ( PSI2014-56303-REDT ),  Dharamsala Institute of Mindfulness Psychotherapy of Zaragoza, Spain , and  Mente Aberta Brazilian Center for Mindfulness and Health Promotion of São Paulo, Brazil , for their support in the realization of this study.
14 MHz for 1 H and 100. 62 MHz for 13 C) and Bruker Avance III plus 400 (400. 34 MHz for 1 H and 100. 67 MHz for 13 C) NMR Melting points were measured on a Buchi Melting Point B-540 apparatus and temperature data are uncorrected. The X-ray crystallography studies were performed at the Service de Radiocrystallographie, University of Strasbourg and at the Karlsruhe Institute of Technology. At the University of Strasbourg crystallographic data were collected on a Bruker Kappa Apex II diffractometer using graphite-monochromated Mo-Ka radiation (λ = 0. 71073 Å). Crystallographic CIF data can be found deposited in the Cambridge Crystallographic Data Centre database under the CCDC 1897491 (L1 5OH 4Zn4) and CCDC 1897489 (L1 (COOMe) 2Zn3) codes. [18]trometers. All the collected spectra were referenced on residual solvent signal according to Nudelman et al.
Modelling rock glacier stability Modelling the rock glacier stability aims to identify the terrain attributes that may precondition rock glacier destabilization. The modelling followed a statistical approach similar to previous studies on landslides  (Goetz et al. , 2011)  and arctic permafrost slope failures  (Rudy et al. , 2017 ) that used the GAM with logistic link function (R package "mgcv"). The GAM was selected because of its flexibility in modelling non-linear interactions between dependent and predictor variables. The logistic link function allows us to model the occurrence of a categorical response variable as a function of continuous variables (predictor variables). All numeric predictors were represented using spline-based www. the-cryosphere. net/13/141/2019/ The Cryosphere, 13, 141-155, 2019  smoothing, for which we chose a maximum basis dimension of 4 in order to limit their flexibility and reduce overfitting.
Finally, the best model performance was estimated using the area under the receiver operating characteristic (AUROC)  (Hosmer and Lemeshow, 2000). The AUROC estimates the ability of the model to discriminate stable and unstable areas. The predictive power of the model was estimated with spatial cross-validation (R package sperrorest). The method selected was the k-means clustering, which consisted of dividing the mapped data in k spatially contiguous clusters  (Ruß and Brenning, 2010a). All but one cluster were used to train the model, while the remaining cluster was used to test the predictive power of the model. This process was repeated until each cluster was used at least once in both training and test sets. Here, we divided the database into k = 5 clusters of equal size per run and used 100 repetitions.
As surface disturbances were digitized as linear features, they were buffered and merged into an "unstable areas" polygon database. A buffer distance of 30 m was chosen. The model was found to be insensitive to changes in buffer size up to 90 m. All remaining areas within the polygons of stable and likely stable rock glaciers were used as "stable areas". Polygons of both unstable and stable areas were sampled using a 25 m × 25 m point grid in order to assign the response variable to the modelling database. The point values were then used as binary response variables with values of 0 for stable areas of (likely) stable rock glaciers, while 1 was assigned for unstable areas of potentially destabilized rock glaciers in the modelling stage. Since the rock glacier inventory counted a relatively small number of potentially destabilized cases (46 individuals), selecting only one point per rock glacier would have caused large uncertainty in the model outcome.
, 2017)  to act as a permafrost distribution map for the region. The PFI map was calibrated using active rock glaciers as evidence of permafrost occurrence, and it represents the permafrost conditions during the cold episodes of the Holocene, e. g. Little Ice Age (LIA). The climate warming between the www. the-cryosphere. net/13/141/2019/ The Cryosphere, 13, 141-155, 2019 Figure  5. Map of potential thawing permafrost (PTP) distribution in the Mont Cenis range, indicating the extent of the permafrost zone not in equilibrium with the present climate (red coloured areas). Temperature warming to compute the map is evaluated using HISTALP data  (Auer et al. , 2007)  between the end of the Little Ice Age (light blue shaded period in the temperature anomaly plot) and the current climate (red shaded period).
years  1850-1920 and 1995-2005  was determined using the HISTALP database  (Auer et al. , 2007)  over the region. A permafrost distribution map was then recomputed taking into account these temperature variations and represented the theoretical permafrost distribution in equilibrium with the current climate. By comparing this theoretical permafrost distribution and the PFI, a map of the potential thawing permafrost zone (PTP, i. e. the so-called "melting area" in  Lambiel and Reynard, 2001)  was obtained. In order to use the PTP as a predictor variable, it was represented by an index ranging between 0, i. e. no thaw expected, and 1, i. e. potential thaw. It should be emphasized that PTP is only a proxy of permafrost degradation, which occurs at all the elevations, while the PTP zone consists of a belt of 250 to 300 m in elevation that affects about 50 % of the lower margins of the permafrost zone (Fig.
Susceptibility modelling The model of rock glacier stability was also used to predict the occurrence of degrading permafrost over the French Alps by producing a susceptibility map (e. g. Goetz et al. , 2011). This was carried out using the R package RSAGA and the raster images of the predictor variable maps, which allowed extrapolation of the relationships between rock glacier stability and terrain attributes at the landscape scale. We would like to highlight that since the model is constructed using data on destabilized rock glaciers, the susceptibility map ap-plies mainly for processes relative to destabilization of icerich debris slopes. Therefore, in areas where creeping permafrost does not exist, the extrapolated susceptibility may have high uncertainty. The model predicted a DEFROST index, which was classified into five susceptibility zones using the 50, 75, 90 and 95 percentiles  (Rudy et al.
Acknowledgements. The present study was funded by the  region Auvergne-Rhône-Alpes  through the  ARC-3 grant  and by the  European Regional Development Fund  ( POIA PA0004100 ) grant. The Lanslebourg-Val Cenis municipality also contributed to the present study by funding internships within the  PERMARISK  project. We would finally like to acknowledge the two anonymous reviewers for their highly constructive feedback provided during the reviewing process. Edited by:  Moritz Langer Reviewed  by: two anonymous referees.
Statistical analysis All statistical analyses were performed using the IBM-SPSS version 24 (IBM Corp, Armonk, NY, 2015), with statistical significance set at 0. 05. Continuous variables were presented as the mean ± standard deviation (SD) or median ± interquartile range (IQR), while frequency distributions were used for categorical variables. For the assessment of different characteristics across the subject groups, participants were divided into three groups: HC, MD+AP, and MD/noAP. To compare variables across groups, chi-square tests were used to analyze categorical variables, and one-way analysis of variance (ANOVA) to analyze continuous variables. The Kolmogorov-Smirnov test was used to assess normality. In this regard, Mann-Whitney was used. Similarly, subject characteristics were compared across the SDN subgroups (<7 h, 7-8 h, and >8 h).
Acknowledgments We want to thank  Dr. Azza Al-Mujalli  for facilitating the recruitment of subjects from the primary care centers; and  Dr. Marjonneke Mook-Kanamori  for preparing the sleep questionnaire. This study was funded to  Dr. H Al-Amin  as a research grant from  Qatar National Research Fund  (QNRF) http://www. qnrf. org( NPRP 4-268-3-085 ). QNRF did not have any additional role in the study design, data collection and analysis, interpretation of data, decision to publish, or preparation of the manuscript.
An analysis of VOC concentrations and variation with NO y by Liu et al. 34  indicated a substantial increase in oxidant concentrations in the pollution plume, which, considering the work of Palm et al. 33  , suggests a significant role for anthropogenic control on SOA production. Statistical and cluster analysis of Aerosol Mass Spectrometer (AMS) and auxiliary datasets by de Sá et al. 35  at a surface site showed that the increase in OA ranged from 25% to 200% under polluted compared to background conditions, including contributions from both primary and secondary particulate matter. de Sá et al. 23  further showed that the relative contribution of the IEPOX-SOA factor to OA decreased significantly under polluted conditions. Liu et al. 36  observed that the afternoon concentrations of organic hydroxyhydroperoxides (ISOPOOH) decreased from 600 pptv under background conditions to <60 pptv under polluted conditions, suggesting important shifts in the gas-phase chemistry that could affect OA production.
Emissions of aerosols and gases from the traffic sector were included from a detailed high resolution 2 km × 2 km gridded emissions inventory developed for this region based on the methodology described in a previous study  70. We also included emissions of CO, NO x , SO 2 , VOCs, and particulate OC, BC, and SO 4 from power plants over the Manaus region including a mix of fuel oil, diesel, and natural gas used in 2014 for electricity generation and emissions from a large oil refinery based on a recent study  41. Emissions of CO, NO x , SO 2 , VOCs, and particulate OC, BC, and SO 4 from these point sources were included. Additional anthropogenic SO 2 and SO 4 area emissions were also included based on VOCA (http://bio. cgrer. uiowa. edu/VOCA_emis/) and the Emissions Database for Global Atmospheric Research (EDGAR v4.
Acknowledgements This work was supported by the  U. S. Department of Energy (DOE), Office of Science, Biological, and Environmental Research 's  Atmospheric System Research (ASR) program. Dr. Shrivastava  was also supported by the  U. S. DOE, Office of Science, Office of Biological and Environmental Research  through the  Early Career Research Program. The authors thank the G-1 flight and ground crews for supporting the GoAmazon 2014/5 mission. Funding for data collection onboard the G-1 aircraft and at the ground sites was provided by the  Atmospheric Radiation Measurement (ARM) Climate Research Facility , a  U. S. Department of Energy Office of Science user  facility sponsored by the  Office of Biological and Environmental Research. The  Pacific Northwest National Laboratory  is operated for DOE by  Battelle Memorial Institute  under contract  DE-AC06-76RL01830.
Data availability All data analyzed during the current study are included in this published article and its Supplementary Information. Aircraft measurements during the GoAmazon2014/5 field campaign used in this study are publicly available on the Atmospheric Radiation Measurement (ARM) website: http://campaign. arm. gov/goamazon2014/observations/. Model outputs from WRF-Chem that are used to generate figures in this study are available from the corresponding author on reasonable request.
pneumophila  (21) , was present in only eight (L. pneumophila, L. longbeachae, Legionella feelei, Legionella sainthelensi, L. santicrucis, Legionella shakespearei, Legionella quateirensis, and Legionella moravica) of the 58 Legionella species analyzed, suggesting that different effectors may compensate for RalF activity or that LCV biogenesis varies among different species (Fig. 2 ). One identified motif in Legionella was the ergosterol reductase ERG4/ERG24 (IPR001171) domain. Ergosterol is the primary sterol in the cell membranes of filamentous fungi, present in membranes of yeast and mitochondria  (22). Importantly, it is also the major sterol of amoebae such as Acanthamoeba castellanii and Acanthamoeba polyphaga, the natural hosts of Legionella  (23, 24). We found that 31 Legionella species encoded one or two proteins with the ERG4/ERG24 domain (Fig.
Evolutionarily conserved orthologs are found in Drosophila, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Dictyostelium, and plants  (28). The only Rab-like protein in a prokaryotic genome was reported in the L. longbeachae genome sequence  (16). However, upon analysis of our 80 Legionella strains, we identified 184 small GTPases, of which 104 could be classified with a very high confidence as Rho-, Ras-, or Rab-like proteins (1 Rho, 34 Ras, and 71 Rab domains) (SI Appendix, Fig. S3  and Table  S4 ). Blastp analysis of these proteins in the National Center for Biotechnology Information database revealed that 149 of the 184 small GTPases of Legionella were exclusively present in Legionella and eukaryotic organisms (Table  1 ). The Rab domain was localized to different parts of the effector proteins, and a subset of Rab proteins carried additional domains such as U-box domains, ankyrin motifs, or F-box domains (Fig.
Evolutionary History of Eukaryotic Domains and Eukaryotic Proteins. It is intriguing that Legionella species encode such a diverse repertoire of eukaryotic domains and eukaryotic-like proteins. To understand better this unique feature of the genus, we analyzed the evolutionary history of these proteins. After phylogenetic reconstruction of the genus Legionella based on the core genome (at least 50% identical) (Fig. 1A ), we analyzed the distribution of the eukaryotic motifs and the eukaryotic proteins with respect to the evolution of the genus. For most, we found patchy distribution, as the repertoire of these proteins is variable among the different Legionella species (Fig. 2 ). Such a distribution is indicative of gain and loss events during the evolution of the genus. To analyze further how these proteins may have evolved in Legionella, we selected 25 eukaryotic motifs representing 2,837 different proteins in over 800 orthologous groups and used the program Gloome to analyze the gain and loss events for these proteins.
Infections were carried out in duplicate or triplicate and colony-forming units were recorded at 24, 48, and 72 h postinfection. Levels of intracellular replication were compared with wild-type L. pneumophila strain Paris and an isogenic nonreplicating ΔdotA mutant as reference strains (Fig. 5  and SI Appendix, Figs. S12  and S13 ). Results were also compared with data previously reported for different Legionella species in THP-1, U937, A549, and Mono Mac 6 cells, mouse and guinea pig-derived macrophages, and in guinea pigs (SI Appendix, Table  S10 ). When results at 72 h postinfection were analyzed, 28 of the 47 species tested were impaired for intracellular replication, whereas nine species replicated similarly to, or better than, L. pneumophila Paris (Fig. 5 ). These nine species were Legionella gormanii, Legionella jamestowniensis, Legionella jordanis, Legionella like brunensis, Legionella maceachernii, Legionella micdadei, Legionella nagasakiensis, Legionella parisiensis, and Legionella tucsonensis.
Materials and Methods The materials and methods are described at length in SI Appendix, Materials and Methods. This includes sequencing and assembly, sequence processing and annotation, pan/core genome, ortholog and singleton definition, phylogenetic reconstruction and evolutionary analysis, phylogenetic analyses of Rab and eukaryotic-like proteins, infection assays, statistical analysis, and translocation assays. The raw sequence reads were deposited in the European Nucleotide Archive  (46). The sequences and annotations can be accessed at https://github. com/bbi-ip/Legionella_genus_proteins. git (47).
ACKNOWLEDGMENTS. We thank  Tim P. Stinear  for critical reading of the manuscript and helpful comments, and we acknowledge the receipt of 53 different Legionella strains from the Collection of the  Institut Pasteur. Work in the C. B. laboratory  is financed by the  Institut Pasteur , the  Agence Nationale de la Recherche  Grant  ANR-10-LABX-62-IBEID , and the  Fondation pour la Recherche Médicale  Grant  DEQ20120323697.
Background Global estimates for cholera annually approximate 4 million cases worldwide with 95,000 deaths. Recent outbreaks, including Haiti and Yemen, are reminders that cholera is still a global health concern. Cholera outbreaks can rapidly induce high death tolls by overwhelming the capacity of health facilities, especially in remote areas or areas of civil unrest. Recent studies demonstrated that stool specimens preserved on filter paper facilitate molecular analysis of Vibrio cholerae in resource limited settings. Specimens preserved in a rapid, low-cost, safe and sustainable manner for sequencing provides previously unavailable data about circulating cholera strains. This may ultimately contribute new information to shape public policy response on cholera control and elimination.
A similar study was performed in 2012 during a cholera epidemic in Sierra Leone, where watery diarrhea was directly sampled on filter paper without prior enrichment in APW, and stored for nearly three years at room temperature before successful MLVA genotyping  [13]. Given the limitations of MLVA in providing detailed high-resolution molecular epidemiological features, we sought to determine if the same spotted filter paper material could also be used to perform WGS. This is a proof of principle study demonstrating that DNA extracted from simple, low-cost, APW-enriched and culture isolate spotted filter paper can generate high-quality accurate sequence data that has the potential to inform public health decisions by providing essential information on cholera genotype and co-infection burden.