dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantifying Individual PM Exposure Relative to Mobility and Activities Individual exposure to PM was expressed as a concentration in µg/m 3. Mobility data were acquired through the Canarin II and tablet onboard GPS. In the results are displayed the spatial tracing of the PM 10 data acquired by one volunteer, a member of the POLLUSCOPE consortium, totaling six and a half days of data (156 h) from two sampling sessions in November 2019. This volunteer was selected as he is a collaborator working at the CEREMA Île-de-France and have agreed to publish his data. Both raw GPS and Canarin II data were used.
Statistical Methods We used Python's NumPy and Pandas libraries to produce the results provided in the tables. The Canarin data were downloaded from the POLLUSCOPE projects data hosting server. POLLAR's Canarin data were obtained from the Canarin project website  [50] , through a password-protected web page. Data produced by the polysomnography and Canarin deployment planning of the POLLAR study were provided through an Excel file. No post processing was applied to compensate for data loss or user errors; all figures and tables were produced using raw unprocessed data. Matplotlib and Seaborn were used to create the figures. Mobility data figures were produced using QGIS and an Open Street Map layer for understandability. In the study of mobility, the notion of points of interest (P. O. I. ) introduces two contrasting clusters with regard to individual exposure and device behavior.
Individual PM Exposure as a Function of Mobility (The POLLUSCOPE Project) The data presented below were acquired by the collaborator working at the CEREMA  3. The data are composed of two sampling periods of 8 days each (384 h in total). Table  2  shows, for each of the sampling periods, the average air pollution levels in µg/m 3 and the count of the total number of recorded samples. All sensors sampled air pollution once per minute. Over the course of each 8-day period, measurements were taken 6040 (period A) and 4227 (period B) times, representing approximately half and just under half of the potential maximum of 11,520 measurements, respectively (Supplementary S1). The volunteer was exposed to higher concentrations of air pollution during the second period (Table  2  Period. Activity data distribution in Figure  4  shows we lack activity for 55% of the acquired samples (Figure  4  "Period A + B").
Available activity data may contain mistakes and require post-processing. A higher percentage of activity data has been obtained from the user for the activities "Car" and "Office" (Figure  4  "Period B"). Any difference observed in terms of exposure to air pollution between the two periods could be explained by the lack of data, or by the over representation of specific activities. This underscore that sensors should ideally be never stopped during data logging, and how annotations that contextualize the sensor's data are mandatory to provide a trustworthy analysis. Figure  5  shows the two points of interest, circled in black, that we are going to focus on. The rest of the map displays the entirety of the spatial movement recorded by the GPS over the two periods. In the north-west, a single ride away from an urban area is visible, translated into lightly colored low PM 10 exposure.
Individual PM Exposure in Relation to Sleep Apnea POLLAR successfully showed, as a pilot study, how personal sensors can open new opportunities for public health research. The study faced technical difficulties during the first months; prototypes of the Canarin Nano were not equipped with flash storage, leading to some data loss when no 3G coverage was available. Keeping a locally stored, continually updated database in addition to the cloud service would be the optimal option, but was not technically possible at that time due to the hardware limitations of the prototyping board selected. Legal authorization to record geolocalized data was not obtained; therefore, GPS had to be remotely disabled. The study design to assess the personal exposition to air pollution during the days following the PSG screening made it impossible to investigate if pollution exposure days prior to the screening leads to a degradation of the sleep quality.
Suggestion of Sensor Architecture Design for Academic Research We saw how relevant information could be obtained directly from the user; inside the POLLUSCOPE project, we asked our volunteers, prior to delivering a set of sensors, to actively log their activity or pollution events during the following week of data acquisition. For this, however, we relied only on the users' awareness and good will. We are still missing crucial information, as the POLLUSCOPE participants were never aware when the sensors detected a drop or increase in pollutant concentration values. Switching from requiring the user to manually annotate events to automatically prompt the user for annotations, based on the sensor's live measurements, would improve considerably the quality and quantity of the available annotated data.
Both score above 2000 in CoreMark  [56] , a microcontroller-dedicated benchmark. A good candidate for our application would be the Portenta H7  [57]  from Arduino. It is equipped with a MIPI  [58]  interface, allowing the integration of high quality touch screens, has wireless connectivity with on-board Wi-Fi and Bluetooth modules, as well as an integrated battery charger for lithium polymer (Li-Po) batteries. These developing boards would provide enough processing power to display a graphic user interface (such as LVGL  [59] ), while performing continuous processing of both the live acquired data and previous user records. Cellular network modules, low power RF (radio frequency) modules (such as nRF24L01  [60] ) and other desired modules and components would need to be interfaced through a custom PCB.
Both the sensors and monitoring devices should be able to withstand daily use and keep functioning independently of their orientation, weather conditions and the user's activity (aside from swimming). Remote monitoring of individual sensors should be possible from a web service. Alerts could be sent to users or technicians if a dysfunction is detected. All logged data would be backed up locally and stored permanently on the remote server. Data should be both accessible directly from the monitoring device as well as on the web service (D). A reinitialization clearing the monitoring device's internal database should be performed by a technician for privacy concerns prior to delivering it to another user. Existing pollution sensors are dedicated devices, rarely monitoring parameters nonrelated to air pollution.
Author Contributions: Conceptualization, B. D. and I. A. -M. ; data curation and formal analysis, B. D. ; methodology, B. D. and I. A. -M. , supervision, I. A. -M. , G. P. and S. S. ; writing-original draft preparation, B. D. ; writing-review and editing, I. A. -M. , J. -L. P. and G. P. All authors have read and agreed to the published version of the manuscript. Funding: The POLLUSCOPE project was funded by the Agence nationale de la recherche (ANR) (Grant ANR-15-CE22-0018). The POLLAR project was funded by the EIT (European Institute of Innovation and Technology) Health (Grant ref: 18354-Impact of POLLution on Asthma and Rhinitis), Foundation 96 FMC-VIA-LR. Institutional Review Board Statement: The POLLAR study was authorized by the CPP and ANSM on 6 December 2018. Following several modifications (Amendments), this study was finally authorized by the CPP and ANSM on 4 of April 2019 and the first patient inclusion was done on 8 April 2019.
Acknowledgments: We thank the POLLUSCOPE (PI:  K. Zeitouni ,  UVSQ University Paris-Saclay ) and POLLAR (PI:  S. Zeng , ATOS) consortia. We acknowledge patients and the medical staff who produced the data in Grenoble. Our thanks to the  CEREMA Île-de-France  staff, who made VGP measurement campaign. Thanks to  Zeitouni K. , El Hafyani H. and Ktaish A. from  DAVID Lab-UVSQ University Paris-Saclay  for providing access to POLLUSCOPE project server. Our thanks to the  QGIS Development Team (2020). We are especially indebted to Aguiari D. 1 , who developed the  Canarin cloud infrastructure and Canarin  firmware, Naude J. M. 4 for conducting the VGP survey and authorizing the publication of his personal data and GPS traces and  Terrail R. from  Grenoble's Medical School , who ensured proper deployment of our sensors.
Model application to an in vivo goat study The experimental data used to calibrate the proposed model were taken from an unpublished animal study conducted by Materialise NV (Leuven, Belgium). Briefly, cylindrical porous titanium scaffolds were additively manufactured and three of them were inserted in each left femur and tibia of six goat models (Figure  2-A,B ). A regular porous structure (φ 8 mm, length 12 mm, porosity 71%) was built up of dodecahedron unit cell with thick struts (strut thickness 0. 50 mm and mean pore size 1. 77 mm) and covered with a solid cap to facilitate press-fit insertion into the bone (Figure  2-C ). The apparent stiffness of the scaffold was 2 GPa (Materialise data). Empty scaffolds were implanted, not pre-seeded with cells or filled with bone graft. Three animals were euthanized at 6 weeks post-operatively, while the other three were euthanized at 12 weeks.
Temporal evolution of bone ingrowth Bone ingrowth predictions were computed at each time increment of the numerical analysis and 470 the temporal evolution was defined for the whole period simulated (12 weeks). As for the in vivo data, bone ingrowth was assessed for samples located in the femoral epiphysis of all goats, while in the tibiae two scaffold were in contact with compact 475 bone and another one was in contact with trabecular bone (2-B,C)). In the in vivo data, bone ingrowth was quantified in both groups euthanized at different timepoints (6 and 12 weeks). Numerical predictions were computed for the experimental group euthan-480 ized at week 12, and the temporal evolution of the bone ingrowth was compared to experimental data at 6 and 12 weeks.
920 Furthermore, one additional limitation regards the in vivo data used to calibrate the model. The animal study was not designed to address the objectives of our work, thus relevant data that could strengthen the model calibration was never collec-925 ted, such as CT scans of animals euthanized 6 weeks after surgery or a larger number of samples and locations where bone ingrowth was assessed. On the one hand, to demonstrate an absolute relationship between mechanical stimulus and bone ingrowth 930 distribution, scaffolds should have been tested in the same implantation site while undergoing different loading conditions. On the other hand, only recent in vivo studies highlighted the importance of the local biomechanical demand and the individual 935 response for bone regeneration  [15] , as well as the role of bone mechanobiology in the maximization of bone ingrowth.
Data Availability Statement 1010 The results reported in this paper were generated as part of the CuraBone project (Marie Skłodowska-Curie grant agreement No. 722535). Please contact the corresponding author for further information on data access policies. The Python modules developed 1015 to pre-and post-process ABAQUS simulations are available under github. com/gabnasello/fempy and github. com/gabnasello/odbhelper.
And then released from FIGURE 1 | virB/D4 and homologues genes cluster coding the T4SS of Agrobacterium tumefaciens, Bordetella pertussis, Bartonella henselae, Helicobacter pylori and Vibrio tapetis and phylogenetic tree of these five strains. The phylogenetic tree was constructed using the gene virB4 and the software Phylogeny, "One click" mode  (Dereeper et al. , 2008). *This arrow represents 15 ORFs. a: bepA: bartonella effector protein. A detailed table (Table  S1 ) is available on Supplementary Material with the name of the sequences available on MaGe: MicroScope plateform  (Vallenet et al. , 2009)  and used to represent these cluster. pGEM-T using EcoRI and NheI (Promega, Madison) and ligated into the pLP12 plasmid digested with the same enzymes. The ligation mixture was transformed by heat shock into competent cells of E.
Bioinformatics and Biostatistics Handling of DNA sequences was performed by using the SnapGene software version 5. 1. 7. (Insightful Science). Phylogenetic analysis was also performed, based on the virB4 genes of 4 bacterial pathogens that carry the same type of T4SS than V. tapetis CECT4600: Agrobacterium tumefaciens, Bartonella henselae, Helicobacter pylori and Bordetella pertussis. This analysis was conducted using the software Phylogeny. fr (Dereeper et al. , 2008)  that used for alignment: MUSCLE, for curation: Gblocks, for phylogeny: PhyML and for graphic representation: TreeDyn. The Atlas T4SS tools available at www. t4ss. lncc. br/ (Souza et al. , 2012)  was also used to highlight the annotation corresponding to the sequence VTAP_v1_a3555, that might correspond to the virB5 gene of V.
Acknowledgements We would like to acknowledge the important theoretical contributions made by  Profs Yoshiki Kuramoto ,  Hiroya Nakao ,  Ryan Raut  and  Marcus Raichle. G. D. is supported Spanish national research project (ref. PID2019-105772GB-I00   MCIU AEI ) funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI) ;  HBP SGA3 Human Brain Project Specific Grant Agreement 3  (grant agreement no. 945539 ), funded by the  EU H2020 FET  Flagship programme; SGR Research Support Group support (ref. 2017 SGR 1545), funded by the  Catalan Agency for Management of University and Research Grants (AGAUR) ;  Neurotwin Digital twins for model-driven non-invasive electrical brain stimulation  (grant agreement ID:  101017716 ) funded by the  EU   H2020 FET Proactive programme ; euSNN  European School of Network Neuroscience  (grant agreement ID: 860563) funded by the  EU   H2020   MSCA-ITN Innovative Training Networks ;  CECH The Emerging Human Brain Cluster (Id.
001-P-001682 ) within the framework of the  European Research Development Fund Operational Program  of Catalonia 2014-2020;  Brain-Connects: Brain Connectivity during Stroke Recovery and Rehabilitation  (id. 201725. 33)funded by the  Fundacio La Marato   TV3 ; Corticity,  FLAG-ERA JTC 2017  (ref. PCI2018-092891 ) funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI). MLK is supported by the  Center for Music in the Brain , funded by the  Danish National Research Foundation  ( DNRF117 ), and Centre for Eudaimonia and  Human Flourishing  funded by the  Pettit and Carlsberg Foundations.
Genome Sequencing and Analysis Genome assemblies of both parent strains, S. cerevisiae A-81062 and S. eubayanus C-12902, were first obtained in order to create a reference genome to which sequencing reads from the hybrid strains could be aligned. A long-read assembly of S. eubayanus C-12902 was obtained from  Brickwedde et al. (2018). S. cerevisiae A-81062 has been sequenced previously by our group using an Oxford Nanopore Technologies MinION  (Krogerus et al. , 2019)  and with Illumina technology  (Krogerus et al. , 2016). Reads were accessed from SRR9129759 and SRR2911435, respectively. Here, the long reads were de novo assembled using the LRSDAY (version 1. 4) pipeline  (Yue and Liti, 2018). The initial assemblies were produced with smartdenovo (available from https://github. com/ruanjue/smartdenovo) using default settings.
The assembly was first polished with medaka (1. 2. 0; available from https://github. com/nanoporetech/medaka), followed by two rounds of short-read polishing with Pilon (version 1. 23;  Walker et al. , 2014). Alignment of long reads for medaka was performed with minimap2 (version 2. 17-r941;  Li, 2018). The contigs in the polished assemblies were then scaffolded with Ragout (version 2. 3;  Kolmogorov et al. , 2014)  to S. cerevisiae S288C (R64-2-1). Because of the relatively high heterozygosity of S. cerevisiae A-81062, two haplotypes were further produced through phasing in WhatsHap (version 1. 0;  Martin et al. , 2016). Short reads were first mapped to above scaffolds, and variants were called with FreeBayes (version 1. 32;  Garrison and Marth, 2012). Long reads were also mapped to the above scaffolds with minimap2, and the resulting VCF and long-read BAM files were then passed to WhatsHap.
Alignments were visualized with the "pafr" -package for R (https://github. com/dwinter/pafr). The tetraploid hybrid A-15225 and all derived spore clones and G10 isolates were sequenced by Biomedicum Genomics (Helsinki, Finland). The sequencing of A-15225 has been described previously in  Krogerus et al. (2017)  and reads are available from NCBI-SRA SRR5141258 (referred to as "Hybrid H1"). In brief, an Illumina KAPA paired-end 150 bp library was prepared for each strain and sequencing was carried out with a NextSeq 500 instrument. The newly described Illumina sequencing reads have been submitted to NCBI-SRA under BioProject number PRJNA357993. Pairedend reads from the NextSeq 500 sequencing were trimmed and filtered with fastp using default settings (version 0. 20. 1; Chen et al. , 2018). Trimmed reads were aligned to the concatenated reference genome described above using BWA-MEM  (Li and Durbin, 2009) , and alignments were sorted and duplicates were marked with sambamba (version 0.
7. 1; Tarasov et al. , 2015). Variants were jointly called in the 12 hybrid strains using FreeBayes (version 1. 3. 2; Garrison and Marth, 2012). Variant calling used the following settings: -min-base-quality 30-minmapping-quality 30-min-alternate-fraction 0. 25-min-repeatentropy 0. 5-use-best-n-alleles 70-p 2. The resulting VCF file was filtered to remove variants with a quality score <1,000 and with a sequencing depth below 10 per sample using BCFtools  (Li, 2011). The haplotype blocks in the meiotic segregants were obtained from the filtered VCF file by clustering consecutive reference (haplotype 1) or alternative (haplotype 2) allele calls using the vcf_process. pl script from https://github. com/wl13/BioScripts. Variants were annotated with SnpEff (version 4. 5covid19;  Cingolani et al.
, 2012). Visualizations were performed in R using the 'karyoploter' package  (Gel and Serra, 2017). Chromosome copy numbers were estimated based on the median coverage in 10 kb windows across each contig in the reference genome as calculated with mosdepth (version 0. 2. 6; Pedersen and Quinlan, 2018). Alignment of reads to the right arm of S. cerevisiae chromosome XV was visualized with samplot (https://github. com/ryanlayer/samplot). Structural variations in the S. cerevisiae A-81062 parent strain were identified using long sequencing reads. Long reads were first aligned to the de novo assembly produced above using NGMLR (version 0. 2. 7; Sedlazeck et al. , 2018) , after which structural variations were called from the alignment using Sniffles (version 1. 0. 12; Sedlazeck et al. , 2018). Variants were annotated with SnpEff  (Cingolani et al.
, 2012). Gene ontology enrichment analysis on the set of genes affected by heterozygous structural variants was carried out with YeastMine  (Balakrishnan et al. , 2012). Structural variations in the hybrid strains were estimated from split and discordant Illumina reads using LUMPY  (Layer et al. , 2014)  and genotyped with svtyper  (Chiang et al. , 2015)  as implemented in smoove (version 0. 2. 6; https://github. com/ brentp/smoove). Variations in all 12 hybrid strains were jointly called, and the resulting VCF was filtered to remove sites with an imprecise breakpoint or a quality score <100 using BCFtools  (Li, 2011).
Data and Statistical Analysis Data and statistical analysis on the fermentation and yeast data was performed with R (http://www. r-project. org/). Oneway ANOVA and Tukey's post hoc test was performed using the "agricolae" package  (De Mendiburu and Simon, 2015). Values were considered significantly different at p < 0. 05. Heatmaps were drawn with the "pheatmap" package  (Kolde, 2015).
Among the three single cell isolates of A235 that had undergone 10 consecutive batch fermentations in 15 • Plato wort, a total of 33 de novo SNPs were found. Only three of these SNPs were shared between all three single cell isolates. Of the 33 SNPs, three were missense mutations, one was a conservative inframe deletion, and one a conservative inframe insertion (Table  S3 ). The affected genes include PYC1 (YGL062W), encoding a pyruvate carboxylase. Of the remaining, 20 were intergenic and eight were silent mutations. Structural variations (SVs) in the S. cerevisiae A62 parent strain were estimated from the long reads using Sniffles. A total of 94 heterozygous SVs were identified, including 67 deletions, 27 insertions, 3 inversions, 1 duplication and 1 translocation (Supplementary Data 2).
These SVs affected 18 genes, and the following cellular component GO terms were significantly enriched among the list: extracellular region (GO:0005576; pvalue 1. 2e-5), anchored component of membrane (GO:0031225; p-value 6. 4e-4), fungal-type cell wall (GO:0009277; p-value 8. 2e-4) and cell wall (GO:0005618; p-value 0. 001). SVs in the F1 hybrid and derived spore clones were estimated from split and discordant Illumina reads using LUMPY through smoove. A total of 39 SVs were detected across the 12 strains (F1 hybrid, F1 spore clones, F2 spore clones, and G10 isolates), including 24 deletions, 2 duplications and 13 translocations (Supplementary Data 3). 12 deletion calls in the S. cerevisiae sub-genome of the F1 hybrid were supported by the SVs called for the A62 parent strain using the long reads.
FUNDING Research at  VTT  was supported by the  Alfred Kordelin Foundation ,  Svenska Kulturfonden-The Swedish Cultural Foundation in Finland ,  PBL Brewing Laboratory , the  Academy of Finland  ( Academy  Project  276480 ). Research in GL lab was supported by  ATIP-Avenir (CNRS/INSERM) ,  ARC  (Grant Number n • PJA 20151203273),  FP7-PEOPLE-2012-CIG  (Grant Number  322035 ), the  French National Research Agency  (Grant Numbers  ANR-13-BSV6-0006-01  and  11-LABX-0028-01 ),  Cancéropôle PACA  (AAP émergence 2015) and  DuPont Young Professor Award. JXY was supported by a post-doctoral fellowship from  ARC  ( PDF20150602803 ).
A KRAS and TP53 mutation frequencies in MPM from the catalogue of somatic mutations in cancer (COSMIC) stratified by histologic subtype (n = 775 patients). B Top 25 mutated genes from 10 molecular studies of human MPM (n = 838 patients). C-E KRAS and TP53 alterations in the cancer genome atlas (TCGA) pan-cancer MPM dataset (n = 86 patients). Shown are clinical and molecular data plot with alteration frequencies (C) and patients reclassified as KRAS-or TP53-altered (asterisks), copy number variation data summary (D), and segments of the KRAS and TP53 loci (E). Data information: In (A), data are presented as cumulative percentages of patients tested mutant respective to patients tested for every gene. P, overall probability, twoway ANOVA. In (B), data are presented as cumulative numbers (n; numbers above bars) and percentages (%) of patients with KRAS (red bar), TP53 (blue bar), and other (gray bars) mutations.
Based on our previous observation of a Kras G12C mutation (Kras, Mus musculus Kirsten rat sarcoma viral oncogene homolog) in an asbestos-induced murine MPM cell line  (Agalioti et al, 2017; Marazioti et al, 2018) , on published work that showed RAS pathway activation in MPM  (Patel et al, 2007) , and on the functional interconnection between mutant KRAS and TP53 signaling (Matallanas et al, 2011), we hypothesized that KRAS alterations are involved in MPM development, alone or in accomplice with TP53 alterations. Indeed, here we query the TCGA MPM dataset and employ sensitive methods in our own clinical cohorts to discover KRAS and TP53 alterations in a subset of patients with MPM. We further show that targeting oncogenic KRAS G12D alone to the murine pleural mesothelium causes MPM and, when combined with Trp53 deletion, triggers aggressive MPM with MPE.