dataesr/scientific-paragraphs-categorization huggingface dataset:
C. metallidurans was unharmed by the lincosamide clindamycin and the oxazolidinone linezolid and grew at the maximal tested concentrations of these antibiotics, but these resistances could not be inferred from the ARGs. Moreover, C. metallidurans was sensitive to all fluoroquinolones and β-lactam antibiotics besides Penicillin G in spite of possessing several efflux transporter genes from which a resistance against fluorquinolones can be inferred and also the β-lactamase ampC, which is a specialized cephalosporinase  Fig. 5  Tree of all isolates retrieved from sessions A-C. Circles on branches reflect the number of isolates obtained from the respective species. First dataset reflects the session number (A-C, blue), the locations where the isolates were retrieved from (Node (N) 1, 2, 3, Columbus (Col) and Cupola in different shades of brown), the enrichment conditions (low, high pH, oligotrophic conditions and heat-shock resistant; green) and the radiation dose (UVC, X-Ray; bars on the right; gray) which were used to pre-treat the samples and infers resistance against the tested cefotaxime and ceftriaxone.
ISS possibly harbors previously undescribed microbial species. The application of 21 different cultivation approaches resulted in a high diversity of microbial isolates from the ISS (see Supplementary Fig. 11 ), and 22 of the bacterial genera obtained during this study have not been isolated from ISS samples before,  Fig. 6  Resistances of selected microorganisms from ISS. ISS isolates (highlighted in brownish color) were compared with closely related isolates from ground controls (clean rooms, culture collections; white). Datasets reflect resistances to heat shock (filled circle: heat-shock resistance), temperature range of growth (red), pH range of growth (gray) and resistance toward antibiotics (dark red). Antibiotics were applied in maximal concentrations of 256 and 32 µg/ml, as indicated.
12  and 13 ). Under anoxic conditions, C. avidum formed a biofilm attached to the surface of both, untreated and anodized, aluminum alloys (Supplementary Fig. 14 ). Metagenomics reveals unique microbial composition and functions. We compared our shotgun metagenomics data with datasets available, i. e. , the Home Microbiome Project  28  and the Indoor Microbiology Project (ref. 5 ; both publicly available through MG-Rast). The Home Microbiome Project contains metagenomics datasets from indoor surfaces (e. g. , floors, door knobs, and light switches) as well as from human body sites (e. g. , skin, nose, and foot). The Indoor Microbiology dataset contains metagenomics data from controlled (e. g. , clean room) and uncontrolled (e. g. , public, private houses) indoor surfaces. The derived taxonomic diversity from the metagenomics dataset from our study showed the lowest diversity amongst all analyzed datasets (Shannon Index, p = 0.
After Sanger-sequencing (Eurofins, Germany) the obtained sequences were classified using the EzBioCloud platform at http://www. ezbiocloud. net/eztaxon  47. The ITS region of fungal isolates was sequenced using the primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS4 (5′-TCCTCCGCTTATTGATATGC-3′) and following cycling conditions: initial denaturation at 95 °C for 10 min, followed by 35 cycles of denaturing at 94 °C for 60 s, annealing at 51 °C for 60 s, elongation at 72 °C for 60 s, and a final elongation step at 72 °C for 8 min. The amplicons were Sanger-sequenced (Eurofins, Germany) and the obtained sequence was classified using the curated databases UNITE version 7. 2  48  and BOLD version 4  49. Fungal isolates of session A, B, and C were classified according to phenotypical characteristics Phylogenetic tree reconstruction.
For phylogenetic tree reconstruction, the forward and reverse sequences obtained from the isolates were merged to reach a minimum sequence length of 1000 bp. The phylogenetic tree was calculated with the Fast Tree program  50  and displayed with the Interactive Tree of Life online tool iTOL  51. DNA extraction of ISS wipe samples. After aliquots were removed for cultivation assays, the rest of the wipe solutions were filled into Amicon Ultra-15 filter tubes (Sigma Aldrich) and were centrifuged at 4000 × g for 10-30 min at 4 °C. The flowthrough was discarded and the remaining liquid in the filters was pipetted into 1. 5 ml Eppendorf tubes for DNA extraction with the modified XS-buffer method  52. Briefly, samples were incubated with XS-buffer (2 × , 20 ml stock solution: 1 M Tris/HCl (pH 7.
Sequence data processing and analysis. Demultiplexed, paired reads were processed in R (version 3. 2. 2) using the R package DADA2 as described  67. In brief, sequences were quality checked, filtered, and trimmed to a consistent length of ~270 bp (universal primer set) and ~140 bp (archaeal primer set). The trimming and filtering were performed on paired-end reads with a maximum of two expected errors per read (maxEE = 2). Passed sequences were dereplicated and subjected to the DADA2 algorithm to identify indel-mutations and substitutions. The DADA2 output table is not based on a clustering step and thus no operational taxonomic units (OTUs) were generated. Each row in the DADA2 output table corresponds to a non-chimeric inferred sample sequence (ribosomal sequence variants; RSVs)  67. In addition, the merging step occurs after denoising, which increases accuracy.
After merging paired end reads and chimera filtering, taxonomy was assigned with the RDP classifier and the SILVA v. 123 trainset  68. The visualization was carried out using the online software suite Calypso  69. For bar plots data was normalized by total sum normalization (TSS) and for PCoA by TSS combined with square root transformation. For the Shannon index analysis, sequences were rarefied as indicated in the Figure captions. Tax4fun was performed based on the Silva-classified OTU table, as described  70. Microbial community network. G-test for independence and edge weights were calculated on the RSV table using the make_otu_network. py script in QIIME 1. 9. 1  71. The network table with calculated statistics was then imported into Cytoscape 3. 7. 1  72  and visualized as a bipartite network of sample (hexagons) and RSV nodes (circles) connected by edges.
Raw fastq data files were uploaded to the metagenomics analysis server (MG-RAST)  74  and processed with default parameters. Annotations of taxonomy (RefSeq) and functions (subsystems) were then imported to QIIME 2 (2018. 11)  75 or Calypso  69  to calculate core features, alpha and beta diversity metrics, statistics, and additional visualizations of the data sets. Additional statistical analyses were performed using STAMP  76 76  Controls. Cultivation, extraction, PCR, and sequencing controls were processed and analyzed in parallel to biological samples. An unused wipe not taken out of its bag on the ISS was extracted for every sampling session, cut into pieces, placed on the different media and DNA was also extracted from the solutions obtained with the negative controls. All cultivation controls were negative (no growth of colonies).
Wipe solutions of the negative controls used for DNA extraction, PCR, and sequencing revealed a low number of ribosomal sequence variants. These RSVs were removed from all other datasets, if present in the samples. In parallel, we used decontam  77  to purify the RSV table from possible "kitome" signatures  78. Information on the decontam analysis and according results is provided in Supplementary Note 2, Supplementary Figs. 16-20, and Supplementary Data 5.
Data availability The assembled genomes are publicly available on the MicroScope platform  64  (Bacillus pumilus pH7_R2F_2_A; B. safensis pH9_R2_5_I_C; Bradyrhizobium viridifuturi pH5_R2_1_I_B; Cupriavidus metallidurans pH5_R2_1_II_A; Methylobacterium tardum pH5_R2_1_I_A; Paenibacillus campinasensis pH9_R2IIA) (http://www. genoscope. cns. fr/ agc/microscope/mage/; type strain abbreviation in "genome browser" search window, no log-in required; data can be downloaded using the "Search/Export" tool). Raw 16S rRNA gene sequences of the molecular approach are available in the European nucleotide archive (https://www. ebi. ac. uk/ena), study ID: PRJEB30994. Metagenomic data are publicly available via MG-RAST (https://www. mg-rast. org/; MG-RAST ID: mgm4821480. 3; no log-in required to access the data).
The partial 16S rRNA gene sequences of unique bacterial isolates obtained during the EXTREMOPHILES flight project and from the clean room in Kourou are available at GenBank via accession numbers LR215073 to LR215191. In addition, source data for analyses and figures are provided in Supplementary Data 1-5 as follows: Supplementary Data 1 (RSV table; Figs. 1 2 3 , Supplementary Figs. 1-3, 5, 10), Supplementary Data 2 (metagenomic-based taxonomic profile; Fig. 4 , Supplementary Fig. 7 ), Supplementary Data 3 (metagenomic-based functional profile; Fig. 9 , Supplementary Fig. 8 ), Supplementary Data 4 (Information on isolates, Fig. 5 ),.
Acknowledgements We are thankful for financial support by the  FFG (Austrian Research Promotion Agency , Project No. 847977 ) and thank the  European Space Agency (ESA)  for financing and realization of this space project as part of the  ELIPS program  (ILSRA-2009-1053 ARBEX). We are very grateful for the scientific and technical support by  Lobke Zuijderduijn  (ESA),  Stefanie Raffestin  (sampling in Kourou), the BIOTESC team ( Lucerne University of Applied Sciences ), astronaut  Jack Fischer  and all other involved members of space agencies and attached institutes. We thank  R. van Houdt  for providing a Staphylococcus arlettae isolate. The authors acknowledge the support of the  ZMF Galaxy Team: Core Facility Computational Bioanalytics, Medical University of Graz , funded by the  Austrian Federal Ministry of Education, Science and Research, Hochschulraum -Strukturmittel 2016 grant as part of  BioTechMed Graz.
Hybrid genetic background of fall armyworm population invading China. In order to identify the specific genetic background of the invading fall armyworm samples in China, 105 samples from 16 provinces were re-sequenced, as well as four samples from two countries in Africa (Zambia and Malawi). A total of ~10 G genomic Illumina data were generated for each sample, with a total coverage of about 25 × genome size. Firstly, we analyzed the whole mitochondrial genome sequences of all samples. A total of 208 different SNP loci were screened based on the published mitochondrial sequences of both the American R-strain (AXE) and C-strain (ASW). The original Illumina reads of each sample were mapped to the reference mitochondrial genomes, and the genotype of re-sequenced samples were identified according to these SNP loci.
Methods Genome sequencing and assembly. The fall armyworm samples were collected from maize fields in Lusaka, Zambia, in 2017 and reared to produce an inbred strain. One male moth, derived from seven successive generations of single-pair sib mating, was selected for extracting genomic DNA and constructing Illumina 350-bp and PacBio 20-kb insert libraries. Sequencing was performed on Illumina HiSeq 2500 and PacBio SMRT platforms, respectively. Two 3rd instar larvae were selected for Hi-C library construction and then sequenced on a HiSeq 2500 platform (Illumina). In addition, three 5th instar larvae, three pupae, three female moths and three male moths were used for RNA sequencing on an Illumina HiSeq 2500 sequencer. All the above samples were from the same inbred strain. The raw PacBio reads longer than 5 Kb were assembled into contigs using software wtdbg2  45.
Arow v2. 1. 0 software was used to correct assembly errors after comparing contigs with PacBio reads by pbalign v0. 4. 1. The Illumina raw reads were filtered by trimming the adapter and low-quality regions, resulting in high-quality reads. Then clean Illumina reads were aligned to the assembled contigs by BWA v0. 7. 17, and single base errors in the contigs were corrected by Pilon v1. 21. After removing adapter and low-quality sequences, Hi-C sequencing data were aligned to assembled contigs with Hic-Pro, then the unmapped paired, dangling paired, self-circle and dumped reads were filtered out (https://github. com/nservant/HiC-Pro). According to the restriction enzyme (DpnII) sites on contigs, we clustered the linkage group of contigs based on agglomerative hierarchical clustering method by using LACHESIS  54  (https://github.
com/shendurelab/LACHESIS), and then these contigs were clustered into chromosomes. shown in Supplementary Table  10  and the sample distribution in China is shown in Supplementary Figure  3. In addition, four fall armyworm samples from Africa were also used for resequencing, including two samples (AFR4-5) from the same inbred strain (AFR2017) as the genome sequencing in this study, and another two samples (AFR14-15) which were collected from maize fields in Bvumbwe, Malawi, in January 2019, which is also an inbred strain (AFR2019) reared in laboratory. After DNA extraction of each sample, a 350-bp insert library were constructed and paired-end sequencing was performed following the standard Illumina protocol. Sequence reads from microorganisms and host plants in raw data were removed before analysis.
A de novo repeat library of fall armyworm was constructed by RepeatModeler v. 1. 0. 4. TEs were identified by RepeatMasker v4. 0. 6 using both de novo library and Repbase library, and Tandem repeats were predicted using Tandem Repeats Finder 55 v4. 07b. The gene models were predicted by EVidence Modeler 56 v1. 1. 1, combined with ab initio predictions, homology-based searches, and RNA sequencing alignments. Predicted gene models supported by at least one of the annotations using UniProt datbase, NR database, and RNA-seq data were retained. Then gene functional annotation was performed by aligning the protein sequences to the NCBI NR, UniProt, eggNOG, and KEGG databases with BLASTp v2. 3. 0+. Phylogenetic tree construction and genomic comparison. Orthologous and paralogous groups of 10 species (Drosophila melanogaster, Plutella xylostella, Bombyx mori, Manduca sexta, Danaus plexippus, Heliconius melpomene, Operophtera brumata, Helicoverpa armigera, Spodoptera frugiperda, Spodoptera litura) with published genomes were analyzed by OrthoFinder v2.
In this study, the CF antibodies declined to the original levels within months, confirming that they are not persistent and thus, a useful indicator of recent exposure to virus. As no relationship between CF antibodies and protection has been proposed, an investigation of an outbreak of ERAV including the measurement of neutralizing antibodies would give useful insight into protection. In summary, this study demonstrates that routine serological monitoring of young racehorses by CF test revealed that ERAV is potentially an important contributor to training loss in young racehorses and merits further investigation with larger study populations and a more comprehensive testing regime. Unfortunately, only five nasopharyngeal samples were collected during this study, and all were negative for EHV1, EHV4, EI, EAV, ERAV and ERAB by real time PCR (data not shown).
e. , a "climate sensitivity bias";  (35) ] because a subset of sites was specifically sampled to maximize their sensitivity to a single parameter for climate reconstruction. To address this concern, we performed linear regression and analysis of covariance (ANCOVA), comparing the climate response of our network to that of two independent reference networks. These networks were collected within Canada's systematic national forest inventory  (36)  and within a European initiative to quantify historical forest biomass increment for carbon accounting and GVM evaluation (37) (data S3). As data collection was guided by ecological and spatial questions, rather than paleoclimatological principles, their representativeness for forests should be much improved compared with that of the International Tree-Ring Data Bank (ITRDB).
Trees in the Canadian and European networks are also younger (97 and 92 years, on average) and thus closer to the average forest age compared with the mean series length of 195 years in our network. Our analyses showed a similar climate response among the three networks (see Materials and Methods, fig. S7 , and table  S2 ). Hence, while parts of our dataset are likely subject to the climate sensitivity bias, the climate response of the overall network appears not to be much enhanced, allowing us to have confidence in the observed spatiotemporal patterns. The most pronounced changes in climate response occurred in the boreal zone, where decreasing T controls on canopy greenness have also been reported  (1). Boreal tree populations are adapted to cold environments, which reduces their responsiveness to T variability  (38).
The most pronounced changes (orange to red colors) occurred across cold-dry regions in Alaska, Canada, Finland, and Russia. Accordingly, the area where the temperature response (T int ) exceeded the precipitation response (P int ) of tree growth, i. e. , the blue area on the map, was reduced to cold-humid Alpine and coastal areas, as well as the northernmost taiga in Siberia. stabilized using adaptive power transformation prior to detrending. This procedure preserved interannual growth variability in the resulting tree-ring indices while removing long-term trends. The detrended treelevel series were averaged into site-level chronologies using a biweight robust mean. We estimated a common growing season for all sites based on the monthly course of GPP from 51 forested FLUXNET2015 sites that offer more than 5 years of data (Tier 1 data only; see data S4).
These sites represent the temperature domain covered by the tree-ring network well (fig. S2A ) and indicate a growing season between April and October (between October and April) for the Northern (Southern) Hemisphere when GPP is markedly positive (fig. S2B ). It has been hypothesized that the climate sensitivity of the ITRDB could be inflated because a subset of sites has been sampled for dendroclimatic purposes  (35). To evaluate this possible bias with respect to our study, we obtained two independent tree-ring datasets from Canada's National Forest Inventory  (36)  and from a European biomass network [(37); data S3] and detrended them in the same way as our global network. These reference datasets have not been collected for dendroclimatological purposes, and we used them to benchmark ITRDB's climate sensitivity.
We found that (i) the scatter of the three networks overlaps greatly, indicating that the ITRDB is not a marginal sample (fig. S7 ); (ii) ITRDB was not the most climate sensitive of the three networks; and (iii) effects of "dataset" in a series of ANCOVA analyses were mostly insignificant (table  S2 ). Hence, we consider our tree-ring network to be suitable for the purpose of this study.
Statistical analyses Climatic drivers of growth interannual variability We assessed the climatic drivers at each site during two 30-year periods between  1930 -1960 and 1960 -1990 CE. CE. Prior to 1930, the quality of the used CRU-TS3. 21 climate dataset  (41)  was insufficient at 301 remote sites in boreal North America and Asia and in South America. After 1990, the decrease in available tree-ring data toward the present (8) critically reduced the covered space. We obtained four gridded monthly climate parameters from CRU-TS3. 21: T, P, VPD, and SPEI. VPD was calculated as saturated minus actual vapor pressure, and SPEI was calculated using the R package "SPEI"  (42). We calculated regular and partial (data S1) Pearson correlation coefficients between the detrended tree-ring chronologies and monthly to seasonal climate from the corresponding CRU-TS3.
Projection of climate response into space In a second step of generalization, we projected the site-level climate response into growing season T and annual P space to achieve continuous coverage of the climatic domains occupied by extratropical forests. Problematically, the 0. 5°spatial resolution of CRU-TS3. 21 resulted in coarse representation of site climate, particularly in steep terrain. To correctly place sites in climate space, we obtained the long-term mean (1950-2000 CE) monthly T and P data for each site from the WorldClim database (43) at 1-km resolution. The monthly T and P data from CRU-TS3. 21 were scaled to WorldClim over 1950-2000 CE before calculating growing season T and annual P for both 30-year periods. Our cluster analysis (see above) showed no single season that explained interannual growth variability at global scales.
Acknowledgments: We thank  G. von Arx ,  R. Kostecki ,  A. Babst-Kostecka , and  A. Mazurkiewicz  for support. We thank  S. Klesse  and many others for making their tree-ring data publicly available. See data S4 for proper acknowledgment of the FLUXNET2015 data contributors. Funding: F. B. acknowledges funding from the  EU-H2020 program  (grant  640176 ; " BACI ") and the  Swiss National Science Foundation  (no. P300P2_154543 ). Author contributions: F. B. and D. C. F. designed the study with inspiration from O. B. F. B. homogenized the data, performed the analyses, and wrote the paper. All authors contributed to the discussion and the writing process. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
All raw and detrended treering data from the ITRDB (18) and the  European biomass network  [(37); data S3] are publicly available from the data portal of the  EU Horizon 2020 project  " BACI " (http://baci-h2020. eu/index. php/). All gross primary productivity data are publicly available from the FLUXNET data portal (http://fluxnet. fluxdata. org/data/fluxnet2015-dataset/). Custom code used for analyses is available in data S5. Additional data related to this paper may be requested from the authors.
We hypothesize that the effect of handling multiple EEG sessions might be highly different for subgroups with specific characteristics of age, sex, and neurological pathologies. Therefore, in order to appraise the performance metrics published in this field of research, one needs to consider the implementation of single vs. multi-session EEG datasets alongside the characteristics of the sample. A potential interaction between personal characteristics and the technical configuration with single or multiple sessions may significantly determine the conclusiveness of previously published research. In this work we aim to document the effect of selected characteristics of samples on the performance of biometric systems, along with appropriate (multiple-sessions) vs. inappropriate (single session) configuration of the enrolment and authentication scenario.
A. Feature Extraction We used a data sample as described in Section II-H with resting EEG recordings and 27 sensors, obtained during two sessions, separated by two weeks. In order to provide the same amount of data for each participant, EEG signals were shortened to the shortest available length across participants, which was 123 seconds. This signal was divided into 6 equalsized epochs of 20. 5 seconds length, which are considered as input samples to the system. Moreover, the reliability of EEG characteristics depends on the length  [59] , so that this length seems to be the best tradeoff between number of epochs and signal length. Thus, with two recording sessions, we obtained 12 epochs for each participant, and from each epoch we extracted 16 feature vectors. We estimated the power spectral density (PSD) as the single-sided amplitude spectrum from the Fast Fourier Transform of the signal.