dataesr/scientific-paragraphs-categorization huggingface dataset:
At present, given the heterogeneity of data sources and collecting protocols, the available information is so variable that its integration for large-scale and trend assessments is still challenging. In Europe, the necessity of developing a collection system for compiling all the data in order to facilitate further analysis in the frame of the MSFD has been already recognised  (Galgani et al 2013a , Molina Jack et al 2019). The creation of a comprehensive database portal system requires further efforts of homogenisation of existing formats (Galgani et al 2018a), in-line with data regulations such as INSPIRE and the needs of environmental protection frameworks. At international level beyond the EU there is currently no agreed database format for enabling future large-scale assessments. This should be part of a global Integrated Marine Debris Observing System (Maximenko et al 2019) including modelling support in order to assess litter pathways and trends at large scale (see section 7).
and less than a handful of studies published on the South Atlantic and Southeast Pacific. The degree of pollution in the vast expanse of the open oceans is also largely unknown as most research focuses on coastal areas, European seas, North America, and the Western Pacific. JAMSTEC established the Deep-sea Debris Database (www. godac. jamstec. go. jp/catalog/dsdebris/e/; Chiba et al 2018), which provides access to georeferenced imagery of litter mostly from the deep Pacific Ocean taken during the institute's many submersible dives since the 1980s. Regional initiatives.
Acknowledgments Recognizing the need to expand the focus of seafloor investigations, the significance of seafloor macrolitter, and the potential benefits of moving towards harmonised litter monitoring at large scale, the  European Commission Joint Research Centre  and the  Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar-und Meeresforschung  organised a dedicated workshop. Discussions amongst experts in that field allowed identifying topics, needs, and possible ways forward towards better assessments of seafloor litter. This paper provides the outcome from that workshop, which was held in Bremerhaven, Germany, on the 30 and 31 of May 2018. Authors are grateful to  Martina Pierdomenico  and  Daniele Casalbore  from the  National Research Council (CNR) , and  Francesco Chiocci  from the  University of Rome La Sapienza, Italy , for providing the pictures in figure 1, and to  Caladan Oceanics  for providing the picture in figure 8(c).
Each grain was diluted in sterile water and serially diluted on Streptomyces isolation medium (4). The five selected strains had mycelial and sporulating phenotypes. After isolation and purification, spores were inoculated in Hickey-Tresner broth medium (30°C for 30 h), from which genomic DNA was extracted and purified (5). This procedure included a proteinase K treatment and a chloroform extraction step in order to remove the covalently bound terminal protein (6). An assembly was generated from Oxford Nanopore single reads using Canu (v1. 7. 1). Sequencing was carried out using the sequencing kits SQK-LSK109 and EXP-NBD104, the flow cell FLO-MIN106 (R9. 4. 1 revD) on the GridION system (v3. 1. 8), and the base-calling program Guppy (v2. 0. 8). When strains were multiplexed, Porechop (v0. 2.
Data for Illumina sequencing a Data for Oxford Nanopore sequencing b Replicon size (bp) Genome size (bp) Total no. of CDSs c Nanopore adaptor sequences are not available and were automatically trimmed during base calling. c As determined through RAST automatic annotation. CDSs, coding sequences. d C indicates the circular configuration of the replicon, as predicted by the assembling. GC No. of reads SRA No. of reads SRA content GenBank (coverage [؋]) accession no. (coverage [؋]) accession no. (%) accession no. RLA2-12 21,079,784 (83) SRX8039999 269,118 (175) SRX8039129 10,825,588 10,892,946 10,116 70. 3 JABAQG000000000 (pRLA2-12. 1 C ) d (67,358) RLB1-33 14,156,938 (50) SRX8040000 477,335 (222) SRX8039130 12,127,650 12,127,650 11,381 70. 0 CP050974 RPA4-2 19,808,024 (86) SRX8040001 231,331 (165) SRX8039131 9,856,149 9,856,149 9,287 70.
2b  and Supplementary Table  2 ). We next quantified the proportions of major leucocyte populations from thawed peripheral blood mononuclear cells (PBMCs) collected at the end of the dry season from children with or without subclinical P. falciparum (gating strategy in Extended Data Fig. 3 ). We observed that monocytes, T cells, B cells and natural killer (NK) cell sub-populations were not significantly different between children who carried (May + ) or did not carry (May -) P. falciparum (Fig. 2c  and Extended Data Fig. 4 ). To interrogate differences in cell function, we quantified intracellular cytokines, activation or cytotoxicity markers, transcription factors or exhaustion markers of freshly collected PBMCs from P. falciparum subclinically infected and non-infected children at the end of the dry season.
falciparum parasites and non-infected children at the end of the dry season. We found that the proportion of class-switched P. falciparum-specific MBCs (AMA1 + or MSP1 + IgG + IGM -MBCs) was significantly increased in subclinical carriers at the end of the dry season, whereas no such difference was found in the non-class-switched MBC population (AMA1 + or MSP1 + IgG -IGM + MBCs) (Fig. 2e ). Within the IgG + MBC sub-populations, we did not observe differences between P. falciparum carriers and uninfected individuals at the end of the dry season in P. falciparum-specific classical and atypical MBCs, but we detected increased P. falciparum-specific activated MBCs in subclinical carriers (Extended Data Fig. 4 ). Using another multiplex bead array, we quantified humoral responses of P. falciparum subclinical carriers and uninfected individuals, at the beginning Articles NaTurE MEDiciNE.
Study individuals and ethical approval. This study, registered at ClinicalTrials. gov (identifier NCT01322581), was approved by the Ethics Committee of Heidelberg University Hospital; the Faculty of Medicine, Pharmacy and Odontostomatology at the University of Bamako; and the National Institute of Allergy and Infectious Diseases of the National Institutes of Health Institutional Review Board. Samples and clinical data were obtained between 2011 and 2019 in a cohort study previously described  43  and conducted in Kalifabougou, Mali, where malaria transmission occurs from June through December. Access to antimalarial drugs was provided by a single clinic and pharmacy. A random sample of the village population was drawn in an age-stratified manner to enroll the study, including individuals aged 3 months to 45 years.
The sensitivity of the nested PCR is ~0. 5-1 parasites per μl  43. P. falciparum quantification. By flow cytometry, RBC pellets removed from CPT tubes after leucocyte isolation were washed and stained with 5× SYBR Green I (Invitrogen) and 7. 5 μM MitoTracker (Applied Biosystems) for 30 min at 37 °C. Cells were acquired using an LSR II and analyzed using FlowJo software 10. 2 or higher versions. By RT-qPCR, RNA was extracted immediately after thawing RBC pellets using TRIzol LS (Ambion) according to manufacturer instructions, and complementary DNA (cDNA) was synthesized using SuperScript IV VILO Master Mix with ezDNase (Invitrogen). RT-qPCR was run on a qTower (Analytik Jena) using Power SYBR Green PCR Master Mix (Applied Biosystems) with primers for the P. falciparum reference gene glycine-tRNA ligase (PF14_ 0198) (forward, 5′-TGAGTGATATGGATAATA TAAAGGAACAAA-3′; reverse, 5′-GGATGATATTTCACAA ACGTATCTTTCT-3′) and for human GAPDH (forward, 5′-ACAACTTTGGTATCGTGGAAGG-3′; reverse, 5′-GCCATCACGCCA CAGTTTC-3′).
Flow cytometry with P. falciparum-specific B cell probes. Recently thawed PBMCs were stained with biotinylated recombinant PfMSP1 and PfAMA1 and then coupled to fluorescently labeled streptavidin  21  , together with the following labeled monoclonal antibodies: CD3-BV510 (clone UCHT1), CD4-BV510 (clone SK3), CD8-BV510 (RPA-T8), CD14-BV510 (clone M5E2), CD16-BV510 (clone 3G8), CD56-BV510 (clone HCD56), CD10-BUV737 (clone Hi10a), IgD-BUV737 (clone IA-2), CD19-ECD (clone J3-119), CD21-PE-Cy7 (clone B-ly4), CD27-BV605 (clone M-T271), IgM-PerCP-Cy5. 5 (clone G20-127) and IgG-AlexaFluor700 (clone G18-145). Aqua Dead Cell Stain was added for live/ dead discrimination (Thermo Fisher Scientific). Stained samples were run on a LSR Fortessa X20 (BD), and data were analyzed using FlowJo 10. 2 or higher versions.
NaTurE MEDiciNE Msp2 fragment analysis by capillary electrophoresis. DNA was extracted from two 3-mm circular punches of blood spots on filter papers (Whatman 3MM), using the DNeasy Blood & Tissue Kit (Qiagen). Genotyping of msp2 was performed using a previously described nested PCR reaction  25. The first PCR amplified the outer msp2 domain (forward, 5′-ATGAAGGTAATTAAAACATTGTCTATTATA -3′; reverse, 5′-CTTGTACACGGTACATTCTT-3′), and the second PCR used fluorescently labeled primers to identify two msp2 allelic families: IC/3D7 (forward, 5′-AGAAGATGCAGAAAGAAKCCTYCTACT-3′ and reverse, 5′-GATGTAATCGGGGGATCAGTTTGTTC G-3′ VIC) and FC27 (FC27 forward, 5′-AATACAAGAGGTGGGCRATGCTCCA-3′; reverse, 5′-TTTTATTTGGTGCATTGCCAGAACTTGAAC-3′ 6-FAM). Fragment analysis was performed by capillary electrophoresis using a DNA sequencer (3730, Applied Biosystems) and analyzed using GeneMapper 5 software (Thermo Fisher Scientific), where a cutoff of 300 relative fluorescent units was set.
Raw reads were trimmed of adapter sequence and low-quality bases and filtered for low-quality reads using the FASTX-Toolkit v0. 0. 14. Remaining reads were mapped to the P. falciparum genome, build ASM276v1, using HISAT2 v2. 0. 4 (ref. 68 ). Reads mapping to genes were counted using htseq-count: v0. 6. 1 (ref. 69 ). For each sample, parasite age (hpi) was estimated based on gene expression using a previously described maximum likelihood method  28  , applying a script that was kindly provided by Chris Newbold (lemieux_et_al_pipeline_functions. r and lemieux_et_al_pipeline. r). Differential expression analysis was performed using the Bioconductor package DESeq2 v1. 26. 0 (ref. 70 ), with adjusted P values (P adj ) < 0. 05 considered significant. DEGs were analyzed for enrichment in Gene Ontology categories using DAVID  71.
RNA-seq data (normalized counts data and raw sequencing reads) are available at the National Center for Biotechnology Information's Gene Expression Omnibus (project ID no. GSE148125). Validating RT-qPCR was performed on the 24 above-mentioned samples and on an extra set of 18 samples: six dry season and 12 clinical cases during the wet season. VILO cDNAs were synthesized using the SuperScript VILO cDNA Synthesis Kit (Invitrogen) and purified according to QIAquick 96-well protocol (Qiagen) with a modified centrifugation protocol  72. Expression levels of eight transcripts (sir2, rex3, Pfsec23, PFB0100c/KARHP, PF07_0006/STARP antigen, PFB0900c/ PHISTc GEX20, PF08_0020/UBE4B and MIF) were determined by RT-qPCR using Invitrogen Express qPCR SuperMix with premixed ROX reference dye (Invitrogen) in 20-μl reactions.
All gene-specific oligo sequences were designed using Beacon Designer software (Premier Biosoft) and purchased from LGC, Biosearch Technologies, with double quencher BHQnova fluorescent probes owing to AT-rich P. falciparum sequences (Supplementary Table  7 ). In a multiplex format, we used reference gene glycine-tRNA ligase%2C putative (PF14_0198) and a standard made from pooled SPIA cDNA. RT-qPCR reactions were carried out at 95 °C for 2 min, 55 cycles of 95 °C for 15 s and 60 °C for 1 min. Data were analyzed using the 7900HT version 2. 4 sequence detection system software per the manufacturer's recommendations. Metabolite profiling. Plasma metabolomics was performed using both targeted and untargeted approaches, across several LC-MS platforms, for small molecules and lipids, to obtain full metabolite coverage.
Metabolite separation was performed as previously described  74  using a Waters Acquity UPLC CSH C18 column (100 × 2. 1 mm, 1. 7 µm). Ammonium formate and formic acid were added to the positive ESI solvents, and ammonium acetate was used for negative ESI. Targeted analysis of the Orbitrap data was performed as previously described  75. Untargeted analysis from the 5600 TripleTOF was performed using the default settings in MS-DIAL  76. The built-in databases were used for putative identification of metabolites at the MS/MS level. QA/QC samples were evaluated to minimize systematic and technical issues. All data were normalized to the total ion chromatogram and blank subtracted, to remove background noise, before statistical analysis. P. falciparum field isolates short-term culture. RBC pellets isolated from CPT tubes of RDT + samples in the dry season and of malaria case samples in the transmission season were cultured in fresh human O Rh+ erythrocytes at 7% hematocrit in complete RPMI supplemented with 0.
In the second method, the coverage over the middle of the LARSFADIG motifs (samtools depth) was addressed. These values were divided by the number of reads mapped on each assembly and multiplied by 10 million. As a complementary validation, a mapping approach was used: the sequencing reads were mapped using bwa mem, parameter -k 31 -a (to allow multiple hits), against a combined database of var genes from Otto et al. 31  (varDB. fulldataset. 1 kb. nt. fasta. gz) and the var genes from Otto et al. 82  , limited to var genes of the length of at least 3 kb. Next, the number of reads mapped with an AS score of at least 95 against the var genes from this combined database were counted.
Statistical analysis. Mann-Whitney or Kruskal-Wallis was used to test for differences between two or more groups, respectively. Differences between the number of clones and clone sizes were tested by Mood's median test. PfEMP1 slopes between the beginning and the end of the dry season of subclinical carriers and non-infected individuals were compared using a linear non-interaction model. Spearman's rank correlation between linear RT-qPCR normalized data and linear RNA-seq normalized data of each gene was obtained along with P values using GraphPad Prism 8. 0 software. Metabolite significant differences were determined by a two-way analysis of variance (ANOVA) corrected for multiple comparisons controlling for a false discovery rate of 0. 05%. Statistical significance was defined as a two-tailed P value of ≤ 0.
Acknowledgements We thank the residents of Kalifabougou, Mali, for participating in this study. We acknowledge the support of the  Flow Cytometry Core Facility of DKFZ in Heidelberg, Germany , and the  Immunology Core Lab of the UCRC in Bamako, Mali. We thank the  Metabolomics Core Facility at Penn State University  and  A. Patterson  and  P. Smith  from the  Penn State Metabolomics Core. We thank  Z. Bozdech  and the lab at  Nanyang Technological University, Singapore , for help and expertise with RNA extraction. This work was supported by the  German Center for Infection Research (DZIF) , the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Programme  (grant agreement  759534 ), the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) projektnummer240245660 SFB 1129 of the  German Research Foundation  and the  Division of Intramural Research, National Institute of Allergy and Infectious Diseases , of the  National Institutes of Health.
Data availability RNA-seq data (normalized counts data and raw sequencing reads) have been deposited in the National Center of Biotechnology Information's Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE148125. Metabolomics data are available at the National Institutes of Health (NIH) Common Fund's National Metabolomics Data Repository website, the Metabolomics Workbench, https://www. metabolomicsworkbench. org, where it has been assigned project ID PR000948. The data can be accessed directly via https:// doi. org/10. 21228/M8540T. This work is supported by NIH grant U2C-DK119886. The data file of assembled var gene fragments of all isolates is available at https://github. com/ThomasDOtto/varDB/tree/master/Otherdatasets/ Andrade_DryWet2020.
Data analysis Statistical analyses were performed with Graph Pad Prism versions 6. 0 or 8. 0, JMP 14. 0. 1, or R3. 5. 2 as detailed in the methods. FlowJo software 10. 2 or higher versions were used for flow cytometry analysis. RNA seq data was analysed using Hisat2 v 2. 0. 4, Bioconductor package Deseq2 v 1. 26. 0, Htseq-count v 0. 6. 1 and FASTX-Toolkit v 0. 0. 14. For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Research guidelines for submitting code & software for further information.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Data availability RNA-Seq data (normalized counts data and raw sequencing reads) have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE148125 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE148125 Metabolomics data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https:// www. metabolomicsworkbench.
Tiling splits a single pointcloud into a small number (1-8) of non overlapping pointclouds that together comprise the whole original pointcloud. Through tiling we enable viewers to save bandwidth by only downloading the relevant parts of a pointcloud, for example omitting invisible tiles. Subsequently each tile is downsampled into a small number (1-3) of resolutions. This downsampling is generally done through voxelization: overlaying a 3D cubic grid over the pointcloud space and replacing all points within each cube by a single voxel with the average position and color. The resulting pointclouds are fed into a lossy compressor which creates a linearized compressed data block for each tile at each resolution. The DASH sender is instantiated with a description of the number of tiles and resolutions and creates a manifest file based on this information, which is uploaded to the DASH server.
Subsequently, as compressed data blocks become available they are uploaded as DASH segments and the manifest file is periodically updated. The DASH server stores the manifest file and the segments, and serves these to the receivers over HTTP or HTTPS. We have opted for a simple DASH server, not an MCU with mixing and fusing capabilities because this will give us a baseline against which to compare if we later want to investigate cloud-based composition of pointclouds. The DASH receiver downloads the manifest file and makes the information on available tiles and compression levels available to the renderer. Based on view point, gaze direction and distance between viewer and subject the renderer selects the tiles it wants to receive and the quality level for each of those, and the DASH receiver starts requesting the segments for these tiles in parallel.
This is done in an eager fashion: the DASH receiver will attempt to request segments as soon as they are expected to be available. The per-tile compressed data blocks are fed to the decompressor which converts them back to pointclouds. The synchronization module is responsible for synchronizing the tile streams and the audio, but it has not been implemented yet because in practice it has not been needed yet: in our current experimental setup the network delays turn out to be fairly stable so the synchronization between individual tiles is good enough. For audio adding a simple fixed delay is applied. We are aware of the fact that this is not generally true, so our architecture does cater for a synchronization module. The renderer receives the self-view pointcloud from the capturer and a number of tiles per other participant and renders these in 3D space based on viewpoint and position of the participants.
IMPLEMENTATION The client-side modules (with the exception of the renderer) have all been implemented in a portable language-agnostic way, so they are usable from C, C++, C#, Python and probably others. They use a memory model with clear ownership of memory areas and explicit freeing via the same module that allocated the memory. The renderer module is in reality not so modular as depicted in figure  2  because it is an integral part of the Unity application that implements the whole VR experience. The client side modules are: • Point Cloud Capture (cwipc_realsense2) implements the capturer. It uses the open source Intel librealsense2 to capture RGBD data from one or more cameras and reconstructs a point cloud frame. • Codec (cwipc_codec) implements the encoder and decoder. It is based on the codec proposed by Mekuria et al.
Introduction According to the World Health Organization (WHO), the WHO China Country Office was informed of cases of pneumonia of unknown aetiology in Wuhan City, Hubei Province, on 31 December 2019  [1]. A novel coronavirus currently termed 2019-nCoV was officially announced as the causative agent by Chinese authorities on 7 January. A viral genome sequence was released for immediate public health support via the community online resource virological. org on 10 January (Wuhan-Hu-1, GenBank accession number MN908947  [2] ), followed by four other genomes deposited on 12 January in the viral sequence database curated by the Global Initiative on Sharing All Influenza Data (GISAID). The genome sequences suggest presence of a virus closely related to the members of a viral species termed severe acute respiratory syndrome (SARS)-related CoV, a species defined by the agent of the 2002/03 outbreak of SARS in humans  [3, 4].
Results Before public release of virus sequences from cases of 2019-nCoV, we relied on social media reports announcing detection of a SARS-like virus. We thus assumed that a SARS-related CoV is involved in the outbreak. We downloaded all complete and partial (if > 400 nt) SARS-related virus sequences available in GenBank by 1 January 2020. The list (n = 729 entries) was manually checked and artificial sequences (laboratory-derived, synthetic, etc), as well as sequence duplicates were removed, resulting in a final list of 375 sequences. These sequences were aligned and the alignment was used for assay design (Supplementary Figure  S1 ). Upon release of the first 2019-nCoV sequence at virological. org, three assays were selected based on how well they matched to the 2019-nCoV genome (Figure  1 ).
Chemical stability To exclude non-specific reactivity of oligonucleotides among each other, causing artificial fluorescent  The panels show six available sequences of 2019-nCoV, aligned to the corresponding partial sequences of SARS-CoV strain Frankfurt 1, which can be used as a positive control for all three RT-PCR assays. The alignment also contains a closely related bat virus (Bat SARS-related CoV isolate bat-SL-CoVZC45, GenBank accession number MG772933) as well as the most distant member within the SARS-related bat CoV clade, detected in Bulgaria (GenBank accession number NC_014470). Dots represent identical nucleotides compared with the WH_Human_1 sequence. Nucleotide substitutions are specified. Blue arrows: oligonucleotides as specified in Table  1. More comprehensive alignments can be found in the Supplement.