dataesr/scientific-paragraphs-categorization huggingface dataset:
We can now rely on the ITS we design along with the real datasets introduced in sections III and IV. Since traffic can greatly vary in time, we pick 200 random time intervals of one hour, and use the average number of messages per second during each of these intervals. More specifically, for each randomly picked interval, we count the number of messages sent by each device and infer the average number of messages sent per second by each device. By considering independent enough time samples, we are able to investigate the scalability of LoRa for these smart mobility services. We consider uniform grid deployments for LoRa gateways. To assess the scalability of such LoRa deployments, we however investigate several grid sizes, according to the density. Overall, we consider each borough separately since each area does not generate the same amount of traffic.
VI. CONCLUSION & PERSPECTIVES We define here a global smart mobility scenario, where several services cohabit. We then emulate the traffic generated by such scenario by using two datasets, collected respectively in NYC and Melbourne. In particular, we consider both eventbased traffic from static devices, and periodical traffic from mobile ones. Finally, we analyze how a LPWAN deployment (LoRa in practice) may be sized to support these mobility services. While our scalability analysis tends to indicate that relying only on LoRa seems enough and so conceivable for an unified ITS deployment, we note that the number of LoRa gateways should be carefully tuned according to the traffic. Overall, the network capacity is reached quite fast, and may create scalability issues for large-scale scenarios.
This early sample composed of 150 stars is used to perform a preliminary statistical analysis of the SHINE data, deferred to two companion papers presenting the survey performance, main discoveries, and the preliminary statistical constraints set by SHINE. Methods. Based on a large database collecting the stellar properties of all young nearby stars in the solar vicinity (including kinematics, membership to moving groups, isochrones, lithium abundance, rotation, and activity), we selected the original sample of 800 stars that were ranked in order of priority according to their sensitivity for planet detection in direct imaging with SPHERE. The properties of the stars that are part of the early statistical sample were revisited, including for instance measurements from the GAIA Data Release 2.
The SHINE database Over the past ten years we assembled a large sample of young stars with the main goal of preparing the SHINE survey. This work included the determination of several stellar properties, either from the analysis of new observations or from the literature. The database was also used to select samples for other programs, such as the NaCo Large Program for Exoplanet Imaging (NaCo-LP;  Chauvin et al. 2015) , the SPOTS survey for circumbinary planets (Asensio-  Torres et al. 2018) , the HARPS Large Program for planets around young stars  (Grandjean et al. 2020) , the search for planets around young stars in the framework of the GAPS program at TNG  (Carleo et al. 2020) , and several other programs. The determination of target parameters is mostly based on the methods described in the NaCo-LP target characterization paper  (Desidera et al.
Target priorities A well-defined priority scheme is necessary in order to run an efficient survey on a sample of several hundred targets because the potential for exoplanet discovery is very different from star to star, depending on age, distance, mass, and magnitude. Ideal targets are very young stars close to the Sun. Simulations described in Sect. 2. 3 were performed on this extended list in order to estimate the potential for planet detectability of each target and drive the final target selection. The priority assignment applied to the SHINE database is the following: 1. Build a FoM that allows the stars to be ranked according to the probability that they host planets potentially detectable by SPHERE. To estimate this probability, we used i) planet population models based on power laws in planet mass and separation, with cutoffs at large separations; combined with stellar ages this enables the planet luminosity to be estimated via suitable evolution models; ii) the MESS code (Multi-purpose Exoplanet Simulation System, see  Bonavita et al.
1 , right panel), under the assumption that any companion would be coeval with its host star, hence using the age of the star to select the appropriate evolutionary track. To take into account the fact that the detection limits are expressed in projected separation, while the models produces semi-major axis values, the code evaluates the probability that a companion with a given semi-major axis can have a projected separation which would put it inside the field of view (FoV). This is done assuming a random orbital phase and eccentricity drawn from a Gaussian distribution (see  Bonavita et al. 2012, for details). For each model we performed six MESS runs, using the list of 1244 targets from the SHINE database, but changing the following parameters: the value of the age used for the magnitude to mass conversion of the detection limits (the adopted age of each star, as well as the minimum and maximum values) in order to properly estimate the impact of the uncertainty on the age on the survey results.
To build the sample considered in the present paper, we included targets observed until February 2017 (the first two years of the survey), considering only the targets that are part of the statistical sample, as defined in Sect. 3. Targets observed in poor conditions (not validated following quantitative criteria of achieved contrast with respect to the expected level considering stellar magnitude and declination) were removed. Details on data reduction are provided in Paper II  (Langlois et al. ). We also removed targets identified as new close visual binaries from SPHERE observations in order to be homogeneous with the original selection of single stars or members of wide binaries 7. The new binaries will be presented in a dedicated paper  (Bonavita et al. 2020, submitted). We removed four targets (HIP 37288, HIP 39826, HIP 64792=GJ504, HIP 82588) which resulted to be older than 1 Gyr from the revised age analysis described in Sect.
Kinematics and moving group membership We exploited the updated kinematic data, particularly the high-accuracy parallaxes and proper motions from Gaia DR2, to evaluate the membership of our targets in young moving groups. We derived the space velocities U, V, W following prescriptions by  Johnson & Soderblom (1987) , and we exploited the BANYAN Σ tool 12 (Gagné et al. 2018a). Unambiguous assignment to groups or field can be made for nearly all targets thanks to the improved kinematics data and group definition. There are a few cases of different membership assignment with respect to the literature, mostly a few exchanges between Tuc-Hor and Columba and Carina or between Sco-Cen subgroups. A few targets classified in the literature as members of AB Dor MG have low membership probability according to BANYAN Σ.
Photometric variability and rotation period In our sample 103 stars have spectral type later than A. They are all young, and exhibit clear evidence of magnetic activity and photometric variability. The last property is exploited to make measurement of the stellar rotation period and of the level of magnetic activity. As the first step in our rotation period study, we explored the literature for all 103 stars and found existing measurements of the rotation period for 64 stars (62%). As the second step we explored the public photometric time series archives of all 103 stars and found (mostly in the TESS archive) suitable data for rotation period search for 58 stars (56%). Finally, we planned and collected our own photometric time series data for the remaining targets lacking suitable data and measured the rotation period for six of them.
01-1% had their rotation periods confirmed by independent measurements, for example from the literature. When data from more observation seasons (or sectors in the case of TESS) were available, we computed LS and CLEAN periodograms for each season(sector); the most accurate results were reported as adopted values, and they are shown in Figs. A. 1-A. 85. We followed the method used by  Lamm et al. (2004)  to compute the errors associated with the period determinations (see, e. g. ,  Messina et al. 2010  for details). To derive the light curve amplitude, we fit the data with a sinusoidal function whose period is equal to the stellar rotation period. As a result of our photometric analysis we obtained our own rotation period measurements and photometric variability amplitudes for 82 of the 103 target stars: 76 from data of one or more public archives and 6 from our own photometry.
In addition to the systematic uncertainties in the stellar models, possible biases of the isochrone method are linked to photometric variability of most of the late-type targets and to the possibility of unrecognized binaries. The first item is included in the error bar of the input parameters. For the second item, the sensitivity of our SPHERE observations to stellar companions, especially those bright enough to bias the photometry, allows us to rule out cases at separations larger than a few tens of mas. For closer companions we inspected the avail- 13 Version 1. 3 available at http://stev. oapd. inaf. it/ cgi-bin/param_1. 3 able data for spectroscopic binaries, although a significant fraction of the targets lack suitable RV monitoring. Finally, after Gaia Dr2, parallax is no longer the dominant source of uncertainty for isochrone age determination.
Acknowledgements. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France ),  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France) ,  INAF -Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zürich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France)  and  ASTRON (Netherlands)  in collaboration with  ESO. SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programmes as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for  FP6  ( 2004-2008 ), grant number  226604  for  FP7  ( 2009-2012 ) and grant number  312430  for  FP7  ( 2013-2016 ).
Based on data retrieved from the  SOPHIE archive at Observatoire de Haute-Provence (OHP) , available at http://atlas. obs-hp. fr/sophie/S. D. , V. D. , D. M. and R. G. acknowledge the support by  INAF/Frontiera through the "Progetti Premiali" funding scheme of the Italian Ministry of Education, University, and Research. AV acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 757561 ). AML acknowledges funding from  Agence Nationale de la Recherche (France)  under contract number  ANR-14-CE33-0018. JC was supported by  SC Space Grant and Fulbright Colombia. M. B. acknowledges funding by the  UK Science and Technology Facilities Council (STFC)  grant no. ST/M001229/1.
The development of high-throughput next-generation sequencing (NGS) technologies has enabled significant progress by quickly providing complete high-resolution genomes at relatively low cost  [36]. NGS has proven feasible as a complementary or alternative method for the characterization of transgene insertion events, having been applied in model plants and animals  [37, 38]  where the presence of DNA structural variations at the insertion site have also been observed  [39, 40]. Here, we report the use of Illumina resequencing combined with Oxford Nanopore technology (ONT) sequencing to produce de novo genome assemblies of five independent clonal transformant lines of Ostreococcus tauri strain RCC1115 in order to characterize insertional events. These data demonstrate that PEG-mediated transformation is a robust molecular tool leading to the integration of a single or few copies of transforming DNA that is stably maintained in the algal genome.
Analysis of Insertion Sites Using Illumina Short-Read Sequencing The quality of raw reads was checked using FastQC (v0. 11. 7). For each clonal line, the reads were mapped with the Burrows-Wheeler Aligner (BWA v0. 7. 17) using maximum exact matches (BWA-MEM) and a minimum seed length of 20 while keeping the other default parameters  [46]. Reads were mapped to the chromosomal sequences the Ostreococcus tauri RCC1115 (v1. 0) reference genome (BioProject number on NCBI: PRJNA337288), including the chloroplast and mitochondrial genomes (see Table  S1  for the GenBank contig identifiers and accession numbers), as well as the 6Kbp pOLK4 vector sequence. Throughout this study, the chromosome numbers we reported for RCC1115 were the equivalent chromosome numbers to the genome of O. tauri strain RCC4221  [47] , as whole-genome comparisons have revealed a low level of polymorphism (Φ = 0.
01) between these two strains  [48]  (Table  S1 ). Mapping results were visualized in the Integrative Genomic Viewer (IGV v2. 4. 16) implemented with the reference genome annotation available from the JGI website accessed on 15 March 2020 (https://phycocosm. jgi. doe. gov/Ostta1115_2/Ostta1115_2. home. html). To localize the insertion site, first all unmapped reads and mates were removed with Samtools (options: -view -F 12) software (v1. 10), then reads that spanned the junction between the chromosome and vector sequence were kept by manual methods, i. e. , reads that spanned the junction were manually parsed from the alignment file (in. sam format) by text-string searches for the vector identifier.
Analysis of Insertion Sites in ONT Long-Read Assemblies Analyses were carried out on assemblies from both assembly methods (Flye and Masurca), but only the results from the hybrid assembly are presented in view of its better assembly quality at the integration site, which was confirmed by PCR resequencing (see Section 2. 7). Assembly quality was also assessed by remapping the Illumina reads against the de novo ONT contigs using BWA-MEM (v0. 7. 17) (options: -k 20). The visualization and the analysis of assemblies was performed using Geneious software (v11. 0. 3+7). The contig containing the vector's sequence was identified by a BLASTn (v2. 10. 1+) search using the pOLK4 sequence as a query (accepting the top high-scoring pair with e-value < 1e-5 and at least 95% identity). The integration of the vector into the chromosome was visualized by dot plot using Mummer (v4.
PCR products were run on a 0. 8% agarose electrophoresis gel in 0. 5% TAE (Tris-acetate-EDTA) buffer and visualized under ultraviolet light after ethidium bromide staining. PCR products were purified using a Wizard ® SV gel and PCR clean-up system kit (Promega, ref A9281) and then sequenced by Sanger sequencing technology. Sequences were cleaned and analyzed with Geneious ® software (version 2. 1).
Bioluminescence Assay The cellular concentration for each culture was adjusted to 20 × 10 6 cells•mL -1 in a final volume of 200 µL in each well of a 96-well white plate (Greiner Bio-OneTM LUMITRAC, SAS, Les Ulis, France), then D-luciferin (Pierce, Waltham, MA, USA, ref 88293) was added to a final concentration of 100 µM. The plate was placed at room temperature in the dark for 10 min before measuring luminescence with a Victor Nivo 3F Microplate Reader Spectrofluorimeter (PerkinElmer Reader, Waltham, MA, USA, Ref: 396532, Dominique Dutscher) by digital photon counting. A bar plot was constructed using the ggplot2 data visualization package implemented within the RStudio environment.
Data Availability The parental O. tauri strain RCC1115 used for transformation and the seven clonal lines whose complete genomes have been sequenced in this study have been deposited in the Roscoff Culture Collection (http://roscoff-culture-collection. org/ accessed on 15 March 2021) with the following strain numbers: T3 (RCC7079), T6 (RCC7080), T12 (RCC7081), T14 (RCC7082), T16 (RCC7083), NT1 (RCC7084) and NT10 (RCC7085). The Illumina-ONT hybrid assemblies and raw Illumina and ONT reads of the O. tauri lines analyzed in this study are available from the European Nucleotide Archive (ENA) under project accession number PRJEB43294. The sequence of the pOLK4 vector is available under GenBank accession number MW598458.
Vector Insertions Occurred at a Single Genomic Location in Each Transformant The insertion events of the pOLK4 vector were characterized according to the bioinformatic workflow presented in Figure  2 , based on the strategies used in genetically modified crops  [37] , and also as described by Yang et al. in module 1  [49]. First, the quality of the raw read data was checked using the FastQC program (v0. 11. 5), confirming that the quality was sufficiently high to carry out analyses on the raw reads. Secondly, we mapped the shorts-reads from the transformed lines against the reference genome and the vector sequence with maximal exact matches. Next, we manually retrieved only reads of a pair mapping at one end to the reference genome and at the other end mapping to the vector sequence, which we called "junction pairs", to locate the vector insertion site.
In the T16 line, we observed a 10-bp deletion of genomic sequence and a 55-bp insertion of viral origin (a partial gene encoding an integrase Genbank protein ID: XP_022839977. 1) at the vector's 5 -end (Figure  4C ). In the T14 line, the integration of the vector was more complex than in the other lines, involving a 90-bp inverted duplication of the part of the ampicillin-resistance gene from the vector's 3 -end (position: 5828-5917 bp), and the insertion of the vector occurring between two 414-bp inverted repeat sequences of chromosome 19 origin (Figure  4D ). In the reference chromosome 19, these 414-bp inverted repeat sequences are separated by ~98 kb, and it is primarily this intervening sequence that has been deleted in T14. We observed the vector integration occurred adjacent to the telomeric sequence at the end of the contig, indicating that the integration locus is located at the chromosome end in T14 and the local structure of the vector insertion was resolved in the assembly.
Forward genetic experiments combined with a screen for sensitivity to mutagen treatments in Chlamydomonas have implicated the following genes in DDR: the REX1 gene  [63] , cytosolic thioredoxin h1 (Trxh1) (required for BER  [64]  and also observed in yeast  [65]  and mammalian cells  [66] , DNA polymerase zeta, DNA polymerase theta, SAE2/COM1 endonuclease, RaD17 and ERCC1  [32]. The RaD17 homolog in Arabidopsis was also associated with nonhomologous DSB repair  [67] , as was DNA polymerase theta in Arabidopsis  [68]. Other proteins known to play a role in NHEJ repair include the Ku70/Ku80 heterodimer and DNA ligase IV in plants  [69, 70]  and in yeast  [71, 72]. We confirmed by homology searches on the comparative genomics platform Pico-PLAZA3 [73] that O. tauri encodes four of the above-mentioned plant and algal gene families that could mediate NHEJ: the RaD17 complex (Genbank accession: OUS48190.
tauri remains to be elucidated, the combination of short-and long-read sequencing in this study has shed light on the genomic events involved. Our study suggests that DNA integration mediated by PEG occurred randomly, as none of our transformed lines shared the same integration site. This observation of random insertion is consistent with previous work  [15]  in which vector integration occurred in different chromosomes in 11 clonal transformants. In addition, vector insertions were accompanied frequently by structural variations at the insertion site. We found that most of the insertion events of nonvector DNA observed may have been produced by de novo synthesis from the cell's recombination/repair systems, since sequences large enough to be tested by BLAST (from 55 bp to 83 bp) bore no similarities to those in common public databases.
tauri RCC1115. Furthermore, it suggests that if antiviral immunity depends on the rapid generation of hypervariability in the SOC in the genus Ostreococcus, clones of RCC1115 would similarly display changes in SOC size. Indeed, an analysis of the karyotypes of seven transformed and three untransformed clonal lines of RCC1115 revealed genomic plasticity between all the lines independent of the transformation (data not shown); we could exclude the hypothesis that variations in chromosome size were caused by the PEG treatment, since the control (NT) lines were also affected. Moreover, the NGS data provide a clear description of rearrangements occurring in chromosome 19 leading to a large deletion of about 100 kb in line T14. Although we cannot go further in the characterization of the phenotypic effects correlated to each insertion event in the transformed lines, we could exclude a direct link between the vector insertion and the phenotypic instability of viral resistance/susceptibility during viral infection, which was better explained by a distinct mechanism of genomic plasticity.
Acknowledgments: We would like to thank the  Bio2Mar  platform for help with biomolecular analysis and BioPIC platforms for help with cytometry analysis. We would like to thank the Genotoul bioinformatic platform for access to the bioinformatic tools and the computing facility. Special thanks to  Vladimir Daric  and  Victor Loegler  for their help with bioinformatic analysis and the  Genophy group  for stimulating discussions and useful suggestions.
Cells 2021, 10 , x 14 of 22 Figure 5. 10225 Figure 5. Firefly luciferase assay of transgenic clonal lines. Emission of light measured expressed in relative luminescence units (RLU) from 19 transformed clonal lines (blue bars) and 4 untransformed clonal lines as a control (red bars). The stability of the vector within the host genome was analyzed in the 7 lines studied in this study by measuring the fluorescence emitted from samples (NT1D, NT10D, T3D, T6D, T12D, T14D, T16D) thawed 6 months after transformation. Clonal lines studied in this work are distinguished by filled black circles. Raw data are available in TableS4. Each sample was measured once.
The diversity and composition of rumen/fecal microbiota were studied using the high throughput sequencing Illumina MiSeq (Illumina, San Diego, CA, USA) method (2 × 250 nt paired ends). The primer sets used, and the rDNA regions targeted are indicated in the Supplementary Materials section, Table  S4. Libraries construction and MiSeq Illumina sequencing were carried out following the protocols validated by Novogene. The paired-end reads were merged and filtered, and the chimera were removed using FLASH, the Qiime quality control process and the UCHIME algorithm, respectively  [19] [20] [21] [22]. A sequence analysis was performed by the Uparse software with ≥97% similarity threshold  [23, 24]. For each OTU representative sequence, Mothur software was performed against the SSUrRNA database of the SILVA Database (Threshold:0.
8 ~1)  [25, 26]. Subsequent analyses of alpha diversity and beta diversity were performed with R studio software using Phyloseq and Microbiome R packages  [27, 28]  on normalized samples. The differential abundance analysis at OTU level was performed with the DESeq2 package  [29]. The measurement of the ferric reducing ability of plasma (FRAP) was completed by the assay based on the method of Benzie and Strain  [30]  but was slightly modified. Briefly, 30 µL of sample and 90 µL of water were pipetted in the microplate in triplicate. A working reagent (150 µL at 10:1:1 of acetate buffer 300 mmol/L pH 3. 6: FeCl 3 solution 20 mmol/l:2,4,6-tripyridyl-s-triazine solution 10 mmol/L) was added in each well, and the reaction mixture was incubated for 30 min at 37 • C. The absorbance was measured at 593 nm.