dataesr/scientific-paragraphs-categorization huggingface dataset:
It is possible that FE strains have enough contact with mammalian hosts that it remains advantageous for them to maintain their ability to grow at higher temperatures in rich nutritional environments. Psychrobacter spp. have been reported previously in the skin, respiratory, and gut microbiomes of several marine mammals, including whales, porpoises, seals, and sea lions; it could be argued that Psychrobacter presence is due to contamination from sea water. Polar bears hunt seals on sea ice and their diet during the summer includes bird eggs. Our data show that polar bears consuming seal meat have higher Psychrobacter abundance than those consuming eggs, which may result from their time on the sea ice, particularly since FE and RE were equally represented. Psychrobacter spp. grew to high densities when grown on amino acids, peptide mixtures, and fatty acids: all carbon sources that would be abundant in the gut of a polar bear eating fatty seal meat.
Acknowledgments. This work was supported by the  Max Planck Society. We thank  Jacobo de la Cuesta-Zuluaga ,  Sara Di Rienzi ,  Hagay Enav ,  Angela Poole ,  Jessica Sutter ,  Taichi Suzuki , and  William Walters  for discussions regarding project design and analysis, and  Andrea Belkacemi ,  Ilja Bezrukov ,  Pablo Carbonell ,  Silke Dauser ,  Julia Hildebrandt , and  Christa Lanz  for their advice on and assistance with sequencing. We also thank  Jürgen Berger  and  Katharina Hipp  for performing electron microscopy. We thank  Markus Dyck  and  Patricia Morin  for providing the Polar Bear Habitat samples,  Maria Frank  for samples from the  Metro Toronto Zoo ,  Daryll Hedman  and  Manitoba Conservation  for the sample from the  Polar Bear Holding Facility in Churchill , and  Sam Iverson  for samples from  Hudson Straight.
Data availability. Raw sequences for the Psychrobacter genome sequencing and polar bear feces 16S rRNA gene sequencing, as well as assembled Psychrobacter genomes, are available in the European Nucleotide Archive under the accession PRJEB40380. Annotated Psychrobacter genomes are available at ftp://ftp. tue. mpg. de/pub/ebio/dwelter. Raw data, R notebooks, and Python scripts for the analyses are available at https://github. com/dkwelter/Welter_et_al_2020.
Formally, multilayer networks are graphs where nodes are complex objects made of actors and layers. Therefore, in some cases the authors have used libraries for simple graphs to perform some operations on multilayer networks and implement some of the methods described in this article. However, none of the papers using this methodology provide a link to an open-source repository. General libraries. Some specific libraries are available providing native multilayer network objects and manipulation functions. In this section we focus on these libraries, and in particular multinet, 4  muxviz,  5  and pymnet. 6 election methods are not directly available as single software functions, but some of them can be implemented using other functionalities provided by the reviewed libraries. The multinet library provides a selection of centrality measures and relevance functions in addition to functions to remove nodes and edges, and their combination can easily be used to perform centrality-based and node-layer relevance filtering.
The pymnet library provides a function to produce induced subgraphs that can be used together with its functions to compute centrality measures and clustering coefficient. The muxviz library also provides the computation of some centrality measures, although the library is more oriented towards visual analysis than network manipulation. Model-based filtering and sampling are not currently supported, although simple sampling based on uniform probability or probabilities computed from centrality values can easily be implemented using multinet and pymnet writing some additional R, C++ (for multinet) or Python code. Support for aggregation is limited. Both multinet and muxviz provide a selection of community detection methods, but they do not provide a direct way to aggregate communities into single nodes.
A flattening function is implemented in multinet, both for weighted and unweighted multilayer networks. A method to aggregate layers, also mentioned in this survey, is available on muxviz, whereas pymnet provides an aggregation function to reduce the number of aspects. Multilevel partitioning, positional analysis, graph compression and coarsening are not supported. Concerning transformation methods, a previous version of multinet provided a projection function, not present in the current version. None of these libraries include embedding methods, which cannot easily be implemented just using the available functions because they require machine/deep learning engines. In summary, support for simplification methods is still very limited, although some of the approaches are available. Looking at some general libraries for multilayer network analysis, our general perception is that some of the approaches described in this article would require very little effort to be integrated in existing software, being based on basic functionality that is already available.
Our simulations show that differences in genetic loads in uniparental loci between the two hybridizing populations is a plausible driver of this phenomenon. Complete source code for data processing and simulations, as well as Jupyter notebooks with all analyses and results can be found at https://github. com/bodkan/archaic-ychr. All data is available from https://bioinf. eva. mpg. de/archaic-ychr. Complete source code for data processing and simulations, as well as Jupyter notebooks with all analyses can be found at https://github. com/bodkan/archaic-ychr. All data is available from https://bioinf. eva. mpg. de/archaic-ychr. We designed 52 bp oligonucleotide probes by tiling the identified 6. 9 Mb of target sequence with 52 bp fragments in steps of 3 bp. This resulted in 2,049,846 individual oligonucleotide probes.
Sequencing and data processing 3. 1. Newly generated archaic human Y chromosomes All captured libraries were sequenced on the Illumina HiSeq 2500 platform in a double index configuration (2x76 cycles)  (15) , and base calling was done using Bustard (Illumina). Adapters were trimmed and overlapping paired-end reads were merged using leeHom  (17). The Burrows-Wheeler Aligner (BWA)  (18)  with parameters adjusted for alignment of ancient DNA ("-n 0. 01 -o 2 -l 16500") was used to align the sequenced fragments to the human reference genome version hg19/GRCh37. Only reads showing perfect matches to the expected index sequence combinations were retained for subsequent analyses. PCR duplicates were removed using the bam-rmdup program, which can be downloaded in source form from https://github. com/mpieva/biohazard-tools.
For each Y chromosome BAM file, we performed a pileup of reads at each site (disabling base quality recalibration), filtering out reads with mapping quality less than 25, ignoring bases with base quality less than 20 and removing reads carrying indels at a pileup position. Then, under the assumption that alleles introduced due to DNA damage, sequencing errors, misalignments, or contamination will be in a minority at each site, we called the allele supported by 90% of the reads in a pileup as the haploid genotype for that site. For further analyses, we additionally restricted to genotype calls supported by at least three reads, as described in section 5. 3. This genotype calling procedure has been implemented as part of a functionality of a program which is freely available for download at https://github.
Inferring phylogenetic relationships To resolve the phylogenetic relationships of each archaic human Y chromosome to all other Y chromosome sequences, we merged the VCF files with genotype calls from each individual (including the chimpanzee) into a single VCF file and converted the genotypes to the FASTA format using a custom Python script (available on our Github repository: https://github. com/bodkan/archaic-ychr). To mitigate biases introduced by low coverage and characteristics of aDNA damage  (29) , we excluded all C-to-T and G-to-A polymorphisms and applied the same filters for each individual as for all other analyses in our study. Finally, we excluded monomorphic sites and sites carrying private changes on the chimpanzee lineage to reduce the size of the final alignment file. To construct a neighbor-joining phylogenetic tree (Figure  2A ), we utilized the functionality provided by R packages ape and phangorn  (30, 31).
these individuals. Crucially, however, despite the differences in bias between capture and shotgun sequences, both datasets lead to the same estimates of TMRCA with presentday human Y chromosomes(Figure S7. 11). Furthermore, although we see dramatically different phylogenetic relationships of Denisovan and Neandertal Y chromosomes with respect to modern humans (Figure2A), both groups of archaic human capture sequences display comparable magnitudes of both sources of bias (FigureS4. 5). Therefore, although undoubtedly present, capture and reference biases cannot result in the observed differences in divergence times between archaic human Y chromosomes and present-day human Y chromosomes (Figure2).
Acknowledgements We would like to thank  Svante Pääbo ,  Mark Stoneking ,  Benjamin Peter ,  Montgomery Slatkin ,  Laurits Skov  and  Elena Zavala  for helpful discussions and comments on the manuscript. Q. F. was supported by funding from the  Chinese Academy of Sciences  ( XDB26000000 ), and the  National Natural Science Foundation of China  ( 91731303 ,  41925009 , 41630102 ). A. R. was funded by  Spanish government (MICINN/FEDER) , grant number  CGL2016-75109-P. The reassessment of.
1. i Data sources. We use a combination of publicly available Scottish census data plus data on Scottish COVID-19 statistics provided by Public Health Scotland. All statistics on COVID-19 deaths are drawn from records held by Public Health Scotland managed by the Electronic Data Research and Innovation Service or 'eDRIS' made available at the data zone (DZ) level where DZs are population census units of approximately 500 to 1000 residents (here, recorded in the 2011 census). 10 We use publicly available census data on age demographics and movement to work patterns, available at the level of Census Output Areas (OAs), each of which contains approximately 20 households or 50 people. 11 12,13. We also use the census data on the 'Scottish index of multiple deprivation' or SIMD. If an area is identified as 'deprived', this can relate to people having a low income but it can also mean fewer resources or opportunities.
The SIMD is a relative https://www. nrscotland. gov. uk/files//geography/2011-census/geography-bckground-info-comparison-ofthresholds. pdf is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 27, 2020. ; https://doi. org/10. 1101/2020. 11. 25. 20144139 doi: medRxiv preprint measure of deprivation that considers multiple measures and combines them into a single value. 14 Deprivation data are not publicly available at the OA level, but instead are recorded in DZs. 15   To estimate reductions in mobility as a result of the restrictions of March 23 rd , we use publicly available estimates by Google. 16.
1. iii Modelling Lockdown We model the impact of lockdown using two effects. First, we model the reduction in longer distance activity by reducing the volume of commuter activity or the "mobility-reduction" scenario. We thin the number of movements to be consistent with the observed decline mobility, as measured through Google mobility data for Scotland 17  and social contact surveys  (Jarvis, et al. , 2020). We compared this assumption to an independent dataset to corroborate the reduction in rural areas, where data are few (see appendix V). Second, we model physical distancing measures as a reduction in the contacts (including both within the OA of residence, and the daytime OA that includes work locations). This directly influences transmission rates, therefore we call this the "transmission-reduction" scenario.
1. iv Influence of Health Index on Mortality. Based on NRS data on Covid-19 related deaths that were outside care homes, we examined the impact of different deprivation factors relevant to this period. We found that whilst deprivation overall (as measured by the SIMD) is significantly associated with increased Covid-19 mortality. This was further disaggregated as follows (see also appendix I): 1. Population level risk of Covid-19 mortality is associated with the SIMD indicator that describes (good) accessibility and orthogonally with the SIMD indicator that describes (poor) health, indicating that areas with poorest health and good access experienced higher Covid-19 mortality. 2. Risk of excess Covid-19 mortality (Covid-19 deaths as a fraction of all deaths) is most closely associated with the access indicator component of SIMD.
Methods The SIMD is based on a set of indicators at the data zone level (approximately 500 to 1000 persons per zone). The SIMD was estimated in 2016 and again in 2020, however, only the ranks are published for 2020 and we have been unable to access the methodology for estimating the raw SIMD scores for SIMD 2020. We therefore analyse here the 2016 data. The indicators that comprise the SIMD are broken down in Table  1  GitHub repository (https://github. com/TheDataLabScotland/openSIMD). This repository included the weightings and methodology for calculating the individual indicator scores and the overall SIMD score rather than ranks alone.
Statistical analysis We constructed two models: 1. A population level mortality model, and 2. An excess deaths model. The models were generalised linear models with binomial error structures at the local council level (N = 32). For the population level model the outcome variable was c(Covid deaths, population -Covid deaths). For the excess mortality model c(Covid deaths, all deaths -Covid deaths). To correct for overdispersion we fitted the model with an individual level random effect for each data point using the glmer function in the lme4 (1) package for R, in multivariable models variance inflation factors were checked using the car package (2) and overdispersion using the DHARMa package (3). We also checked the results against a model fitted using the quasi-binomial family. In both models we tested the following covariates in univariable analysis: • Population (form SIMD 2020)  is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 27, 2020.
The temperature measurement was conducted with a Pt100 sensor (4. 2 m a. g. l. ), and the global shortwave radiation measurement was performed with a Middleton SK08 pyranometer (125 m a. g. l. ). The horizontal wind measurements were conducted with Thies 2D Ultrasonic anemometers above the forest canopy (16. 8 to 67. 2 m a. g. l. ). The NO x measurements were performed with a TEI 42 iTL chemiluminescence analyzer equipped with a photolytic Blue Light Converter (NO 2 to NO), SO 2 was measured with a TEI 43 iTLE fluorescence analyzer, and CO was measured with IR absorption analyzers Horiba APMA 370 (until January 2016) and API 300EU (from February 2016 onwards). The NO x , SO 2 , CO, and proton transfer reaction quadrupole mass spectrometer (PTR-MS) sampling was conducted 67. 2 m a. g. l. The SO 2 , NO x , CO, PTR-MS (only 2012-2013), and meteorology data were uploaded from SmartSMEAR database (https://avaa.
Openair polar plots with ZeFir pollution tracker The wind direction dependence of different NR-PM 1 chemical species observed at SMEAR II were investigated with openair bivariate polar plots. Openair is an open-source, Rbased package described previously  (Carslaw and Ropkins, 2012). Briefly, the openair polar plots show how the pollutant concentration varies under different wind speed and direction. The calculation of the polar plots are based on binning pollutant concentration data into different wind direction and speed bins, followed by the concentration field interpolation. As the polar plots do not take the frequencies of the wind direction nor wind speed into account, they should be investigated together with a traditional wind rose, representing the likelihood of each wind direction and wind speed combination.
This could have led to efficient monoterpene oxidation towards condensable lowvolatility products. Furthermore, their condensation onto particles could explain the observed high organic aerosol mass concentration. The SOA formation enhancement as a function of temperature has also been investigated previously in a modeling study, where a significant global increase in monoterpene-derived organic aerosol concentration was projected to future, following different climate scenarios introduced by the Intergovernmental Panel of Climate Change (IPCC)  (Heald et al. , 2008). Kourtchev et al. (2016)  investigated the effect of high ambient temperature on biogenic SOA loading and composition utilizing measurement data from SMEAR II. They include the summers of 2011 and 2014 in their analysis, where summer 2011 represents a significantly colder summer (average ambient temperature was 8 • C less than in 2014).
The matrices are visualized with contour plots (contourf, MATLAB 2017a) except for Fig. 7h , due to the high noise level of the time trace. Neither the NR-PM 1 concentration nor its chemical species have large diurnal variability during wintertime (Fig. 7a ) due to low solar radiation and the lack of diurnal variability in ambient temperature (Fig. 1a  and b ) prolonging the lifetime of aerosols. Thus, the wintertime chemical composition of NR-PM 1 stays stable over the course of the day (Fig. 7b  and d ). As wintertime PM is presumably mostly long-range transport, its components' diurnal patterns are less obvious due their cumulative build-up in the atmosphere. For example, as sulfate aerosols, the most prominent inorganic species, are long lived due to their low volatility, we do not expect sulfate to have diurnal variation in wintertime because of the lack of major SO 2 sources in SMEAR II's proximity.
Importantly, much of these inorganic aerosol species were most likely from long-range transport. If emission regulations regarding SO 2 and NO x become stricter in the future in Europe, especially in Russia, the wintertime NR-PM 1 might decrease significantly at SMEAR II. In our understanding, this is the longest time series reported describing the aerosol chemical composition measured online in the boreal region. Long-term monitoring of changes introduced by emission regulations, together with the changes introduced by the changing climate, are crucial for understanding the aerosol sensitivity of the (boreal) climate. Thus, we will keep the ACSM measurements going at SMEAR II to obtain an even longer data set. The data presented here will be publicly available, and we welcome collaborative work in utilizing this information for broadening the understanding of the boreal environment.
Acknowledgements. First, we thank  SMEAR II  staff,  Petri Keronen ,  Erkki Siivola , and  Frans Korhonen , for measurement maintenance and support. We thank the  ACMCC, COST-COLOSSAL , and  Aerodyne Research  for the guidance in high-quality instrument operation. We thank  Otso Peräkylä  and  Jenni Kontkanen  for useful discussions. For financial support, we acknowledge the  European Research Council   Starting Grant COALA. We thank the anonymous reviewers for their expertise. Financial support. This research has been supported by the  European Research Council  (COALA, grant no. 638703 ). Open-access funding provided by  Helsinki University Library. Review statement. This paper was edited by  Maria Kanakidou  and reviewed by three anonymous referees.
Data availability. The ACSM data are available from EBAS database under EMEP ACTRIS framework as well as upon request from the corresponding authors. The trace gas and meteorology data are available at the SmartSMEAR data repository (https: //avaa. tdata. fi/web/smart, Junninen et al. , 2009). Contact of the original data contributors can be requested from atm-data@helsinki. fi. Other data are available upon request from the corresponding authors.
2019). Realtime genomic epidemiology data has proven to be useful to reconstruct outbreak dynamics: from virus identification to understanding the factors contributing towards global spread  (Grubaugh, Ladner et al. 2019). Here, we sequenced SARS-CoV-2 genomes from clinical cases sampled since the beginning of the syndromic surveillance in France. We used the newly generated genomes to investigate the origins of SARS-CoV-2 lineages circulating in Northern France and better understand its spread.
Results/Discussion We generated complete SARS-CoV-2 genome sequences from nasopharyngeal or sputum samples addressed to the National Reference Center for Respiratory Viruses at the Institut Pasteur in Paris as part of the ongoing surveillance (Figure  1A ). We combined the 97 SARS-CoV-2 genomes from France and 3 from Algeria generated here with 338 sequences published or freely available from the GISAID database, and performed a phylogenetic analysis focusing on the initial introductions and spread of the virus in France.
Because of this, and the scarcity of early sequences in many countries in Europe, country and within-country level phylogeographic estimations for the origin of clade G are also unreliable with the current dataset. Crucially, while all early symptomatic suspected COVID-19 cases were addressed to the NRC for testing, this was no longer the case as the epidemic developed (Figure  1A ). In addition, pauci or asymptomatic cases are scarcely represented here. As the earliest representative of clade G (HF1463) had no history of travel or contact with returning travelers, we can infer that the virus was silently circulating in France in February, a scenario compatible with the large proportion of mild or asymptomatic diseases  (Li, Pei et al. 2020) , and observations in other European countries  (Gudbjartsson, Helgason et al.
Genome assembly Raw reads were trimmed using Trimmomatic v0. 36  (Bolger, Lohse et al. 2014 ) to remove Illumina adaptors and low quality reads, as well as primer sequences for samples sequenced with the amplicon-based approach. We assembled reads from all sequencing methods into genomes using Megahit, and also performed direct mapping against reference genome Wuhan/Hu-1/2019 (NCBI Nucleotide -NC_045512, GenBank -MN908947) using the CLC Genomics Suite v5. 1. 0 (QIAGEN). We then used SAMtools v1. 3 to sort the aligned bam files and generate alignment statistics  (WysokerA, RuanJ et al. ). Aligned reads were manually inspected using Geneious prime v2020. 1. 2 (2020) (https://www. geneious. com/), and consensus sequences were generated using a minimum of 3X read-depth coverage to make a base call.
Phylogenetic analysis A set of 100 SARS-CoV-2 sequences generated in this study (97 from France, 3 from Algeria) was complemented with 338 genomes published or freely available sequences on GenBank or the GISAID database. From the latter, only published sequences were chosen  (Deng, Gu et al. 2020 , Fauver, Petrone et al. 2020 ) (Supplementary material, Table  S2 ). A total of 438 full genome sequences were analyzed with augur and auspice as implemented in the Nextstrain (https://github. com/nextstrain/ncov). Within the pipeline, sequences were aligned to the reference Wuhan/Hu-1/2020 strain of SARS-CoV-2 (GenBank accession MN908947) using MAFFT v7. 455  (Katoh and Toh 2010). The alignment was visually inspected and sequences from France were subset to analyze shared SNPs. No evidence of recombination was detected using RDP v4.