dataesr/scientific-paragraphs-categorization huggingface dataset:
As the data availability for radar and lidar exhibits contrasting behaviour, it is likely that at least one of the instruments will provide Doppler velocity observations. Code and data availability. The code used for the analysis is available at https://doi. org/10. 5281/zenodo. 7234135 (Ritvanen, 2022). Numerical versions for the data availability curves in  Figs. 4, 6, 11, and 13 , and the observations from the FMI automatic weather station used in this study can be found at https://doi. org/10. 23728/fmi-b2share. 8353436be6e444dea496c0c01e6a3658 (Ritvanen et al. , 2022). Author contributions. JR, EOC, DM, RL, JT, and LT conceptualised the study. JR did the analysis, led the investigation, developed the methodology and software, and visualized and validated the results. JR and RL curated the data.
JR, RL, and JT acquired the funding for the project, and RL and JT managed the project. JT acquired the data storage, and RL and LT acquired the measurement instruments. EOC, DM, RL, JT, and LT supervised the study. JR, EOC, and DM wrote the draft and reviewed and edited the paper. Competing interests. Raisa Lehtinen and Ludovic Thobois are employees of Vaisala Oyj. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. The authors thank Pekka Rossi, for his input in early stages of the study. We also thank Sebastian Kauczok, for providing feedback that helped improve the article, along with the two reviewers.
Methods: We searched observational studies and clinical trials on Web of Science, Embase and Cochrane CENTRAL database between 2009/01/01 and 2020/10/01; as well as grey literature. We extracted safety (adverse events, grade 3 or 4 adverse events, treatment discontinuation) and effectiveness (CD4 cell counts and HIV viral load) outcomes. We estimated weighted summary pooled incidence with corresponding 95% confidence intervals. Results: Out of the 1,085 records screened, we included five studies (one comparative cohort, three single phase 2-3 trial arms, one retrospective cohort) reporting 975 children and adolescents, of whom 56% (544) received atazanavir. Three studies reported all-cause treatment discontinuation rates, yielding a pooled incidence of 19%  [15] [16] [17] [18] [19] [20] [21] [22]  at 12 months.
Quality Assessment and Data Extraction Reviewers (LSL, MHD, JJ and VL) were not blinded to the names of the authors, institutions, journal of publication or results of the studies. We used the Cochrane Risk of Bias tool version 2  (20)  for randomized clinical trials and the tool developed by the Clinical Advances through Research and Information Translation (CLARITY) group for observational studies  (21). Data extracted were related to study characteristics (first author, publication year, study design, source of data, country,.
Searches were conducted on Web of Science, Embase and Cochrane Central Register of Controlled Trials CENTRAL) databases for published studies. For the gray literature, we searched clinical trial registries updated in the past 2 years (ClinicalTrials. gov; WHO International Clinical Trials Registry Platform; EudraCT), the references from the most recent international guidelines on HIV treatment [WHO antiretroviral guidelines (6), US National Institutes of Health Guidelines for the Use of Antiretroviral Agents in Pediatric HIV Infection 2020 (15),.
Searches were conducted on Web of Science, Embase and Cochrane Central Register of Controlled Trials CENTRAL) databases for published studies. For the gray literature, we searched clinical trial registries updated in the past 2 years (ClinicalTrials. gov; WHO International Clinical Trials Registry Platform; EudraCT), the references from the most recent international guidelines on HIV treatment [WHO antiretroviral guidelines (6), US National Institutes of Health Guidelines for the Use of Antiretroviral Agents in Pediatric HIV Infection 2020 (15), Penta 2015, 2016, and 2019 guidelines (16-18)] and in conference abstract books (International AIDS Society Conference 2019 and 2020, Conference on Retroviruses and Opportunistic Infections 2019 and 2020, International Workshop on HIV Pediatrics 2018 and 2019, International Conference on AIDS and STIs in Africa 2019).
For computational efficiency, we recommend using a maximum of 2000 genome-wide independent variant-gene pairs for SURGE optimization. SURGE interaction-eQTLs SURGE optimization on a subset of genome-wide independent variant-gene pairs will result in approximations to the posterior distributions of the SURGE latent contexts (U) as well as eQTL effect sizes for each of the SURGE latent contexts for only the genome-wide independent variant gene pairs (V). It is of interest, however, to call interaction eQTLs with respect to each of the SURGE latent contexts for all variant gene-pairs, not just the subset of variant-gene pairs that are genome-wide independent and used for SURGE optimization. • 𝛽 ‚ƒ8 is the fixed effect of the interaction between the 𝑘 #n latent context and genotype We use the R package 'lme4' to quantify the significance of all K interaction terms: 𝛽 ‚ƒ" , … , 𝛽 ‚ƒ8 , … , 𝛽 ‚ƒ †.
Availability of data and materials SURGE software is available on github at https://github. com/BennyStrobes/surge. The GTEx v8 data  [5]  can be downloaded from the dbGaP website under phs000424. v8. p2 and on the GTEx portal (http://gtexportal. org/). PBMC single-cell eQTL  [18]  expression data are available in the Human Cell Atlas Data Coordination Platform and at GEO accession number GSE174188. PBMC single-cell eQTL  [18]  genotype data are available at dbGap accession number phs002812. v1. p1.
SURGE identifies 8 latent contexts, all of which result in hundreds of genes with at least one SURGE interaction eQTL, or variant whose effect on expression changed with the SURGE latent context (eFDR <=. 05, see Methods) (Fig. S3 , Fig. S4 , Table. S1). In this dataset, each RNA sample was extracted from a specific tissue, and while tissue identity information is not provided to SURGE, 6 of the 8 SURGE latent contexts capture differences in tissue type between the samples (Fig. 2A ). SURGE latent context 1 (latent contexts ordered by PVE, see Methods), for example, isolates RNA samples from Muscle-Skeletal tissue; RNA samples derived from Muscle-Skeletal tissue have an average latent context 1 value of -1. 82 (sdev 0. 342), while RNA samples from other tissues have an average latent context 1 value of -0.
The Limit of Detection (LOD) for each element was calculated from HEPA filter blank tests at the beginning and at the end of the campaign, estimated as three times the standard deviation. All hourly concentrations that were zero or below were replaced by 0. 5*LOD for data reporting. A detailed evaluation of the precision and accuracy of the instrument and as well an inter-comparison of the XACT measurements against other analytical methods can be found in  Furger et al. (2017)  and  Tremper et al. (2017). A further inter-comparison between the XACT measurements and the weekly samples was done. Weekly samples were performed on filters analysed by the ICP-MS methodology as described in  Goddard et al. (2015). The comparison showed slopes ranging from 0. 96 (Zn) and 1. 24 (Pb) with a slope of 1.
Acknowledgments The authors would like to thank  Martin Hooper  at  Neath-Port Talbot Council  for his help during the organization and deployment of the measurement campaign at Pontardawe; and  Simon Baldwin  from the  Welsh Government  for his contribution to the discussion of the results. This study has been partly funded by the  Welsh Government  under contract  C224/2015/2016. It used equipment funded by the  Natural Environment Research Council Traffic   Grant  ( NE/1007806/1 ). The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and READY website (http://www. ready. noaa. gov) used in this publication.
Scoring For each problem, the expert strategy -i. e. , a strategy that does not rely on preconceptions but requires categorizing the situation in the expert point of view -was defined prior to collecting the data (Appendix). Each expert strategy counted for 1 point. Calculation errors were not taken into account. For items involving more than one question, the answer to each question was given 1 point. Several scores were derived from the coding: • A global score (ranging from 0 to 18 points for 4 th graders and from 0 to 29 points for 5 th graders on the pretest and 40 points on the posttest) • A sub-score associated with each studied notion (see Appendix Tables  A. 1, A. 2, A. 3, A. 4, A. 5, A. 6, A. 7, A. 8, and A. 9)  To compare the pretest and posttest which did not contain the same items, a z-score per student at pre-and posttest, relative to the mean and standard deviation of the control group, was calculated  (Dillon et al.
Results at Global Level The data regarding student performance were not independent, since it was the classrooms that were recruited and not individual students Along with checking the equivalence of the two groups at pretest, this also required to check the variance explained by the hierarchical organization of the data (class clustering). At pretest, the z-score of the experimental group was equal to 0. 02 (SD = 0. 90) (Figure  3 ). A t-test comparison of the mean scores of the two population revealed no significant differences among them, t(587. 45) = -. 17, p >. 5. However, such a probabilistic approach is not sufficient to conclude that there is no difference between two groups. Therefore, we resorted to the Bayesian approach and calculated the Bayes factor with the BayesFactor package in R.
Boxplots of z-scores at Pre-and Posttest by Experimental Conditions At posttest, the average z-score of the experimental group was 0. 66 (SD = 1. 2) (Figure  3 ). To study if the improvement from pretest to posttest was significantly influenced by the intervention, a multilevel analysis was applied, since the data had a hierarchical structure, that considers the dependency of the students nested into classrooms. We first ran models using only the classroom as the random intercept, for the performance both at pre-and posttest. This model made it possible to quantify the intra-class coefficient (ICC). At pretest the ICC =. 421 and at posttest the ICC =. 387 indicated that there was substantial intra-class homogeneity. Hence, there was 41. 9% of the observed variance at the pretest and 38. 7% of the variance at outcome of the posttest which can be attributed to the effect of the classroom clustering.
Results by Grades Furthermore, the experimental conditions depending on the grade level (Tables  5  and 6  and Figure  4 ) were distinguish ed. Pairwise comparisons were conducted, with Bonferroni correction for p-values based on the retained M4 model using lsmeans function from the lsmeans package in R. At pretest, no significant difference between the control and experimental group in 4 th grade (β = -0. 0365, t = -. 324, p >. 05), nor in 5 th grade (β = -. 0365, t = -. 324, p >. 05). This result confirms the second part of Prediction 1.
Sample Collection The samples included in this study were obtained from patients attending the Assistance Publique des Hôpitaux de Marseille for diagnosis and treatment. The only inclusion criteria was the availability of a cytological sample for biomarker studies. This project was approved by the local ethics committee (PADS22-31). The conventional cytological diagnosis was performed by the Cell Biology Laboratory, and the molecular testing was performed by the Cell Biology Laboratory (ROS1 and ALK by ICC and FISH) and the Oncobiology Laboratory (NGS). NGS was performed from frozen cell pellets or, alternatively, stained slides if the entire sample was used for the conventional cytological diagnosis. Results of the molecular testing and clinical data were retrospectively analyzed.
The RNAs (ideally 10 ng) were then reverse transcribed into complementary DNA using the SuperScript IV VILO enzyme (Thermo Fisher Scientific). The detection for rearrangements were carried out using next-generation sequencing (NGS) technology on the Ion Torrent S5XL with the Oncomine Focus RNA assay Kit (Thermo Fisher Scientific) (see Table  S2  for the 5× Solid Tumor Fusion Transcript Panel). The analysis was performed with the Ion Reporter software (Thermo Fisher Scientific). A minimum of 50,000 mapped reads was required to allow interpretation of the result. For mutation screening, we used our custom panel Oncomine Solid Tumor and Oncomine Solid Tumor+ (OST/OST+), which includes hotspots of target regions of 28 genes of interest in oncogenesis (Table  S3 ). For all clinical samples, we performed sequencing with Ion Torrent S5XL (Thermo Fisher, Bourgoin, France) with a sensitivity of 5% for the minimum coverage of 500X.
Then, sequencing data were analyzed through two pipelines. The first pipeline was developed by Thermo Fisher on the Ion Torrent Suite + Ion Reporter. Ion Torrent Suite generates FASTQ data and ensures BAM (binary alignment mapping) alignment with the hg19 reference genome by using the TMAP (Torrent Mapping Alignment Program). Ion Reporter makes variant caller and variant annotations. The second pipeline was developed in our laboratory and runs open-source software such as BWA-MEM for alignment, SAMtools for mpileup, VarScan2 as variant caller and VEP Ensemble for annotations. All data are stored in our local MySQL database.
Statistical analyses. Statistical analyses were carried out using the SAS software (SAS Institute, Cary NC, New-York). First, statistical analyses were carried out within each muscle type using three-way ANOVA including hygiene conditions (Good or Poor), line (LRFI or HRFI), sex (male or female) and their interactions as main effects. Because there was no sex effect for the studied traits, the analyses were finally run using two-way ANOVA including hygiene conditions, line and the interaction between hygiene conditions and line. Pig was considered as the experimental unit. Initial BW was included in the model as a covariate to analyze growth performance. Least square means were compared using the LS means statement and the pdiff option of the GLM procedure. All data are reported in tables as LS means per experimental group, and a pooled SEM per trait.
Acknowledgements This study was funded by the  European Union's Seventh Framework Programme for Research, Technological Development and Demonstration  (Grant Number  613574 ,  PROHEALTH  project). The authors wish to thank  Nathalie Bonhomme ,  Sophie Daré  and  Christine Tréfeu  ( UMR PEGASE ) for laboratory analyzes and the staff of the  INRAE  experimental pig facilities for animal care ( UE3P ,  Pig Physiology  and  Phenotyping Experimental Facility , https:// doi. org/ 10. 15454/1. 55739 32732 03992 7E12 ). We are also very grateful to  Hélène Gilbert  ( Gen-PhySE ,  Université de Toulouse, INRAE, ENVT, 31320, Castanet-Tolosan, France ) for selection of the pig RFI lines.
Plaque size assays The size of plaques was determined as described previously  [43, 44]. CECs or CESCs were seeded in 6-well plates and infected with 100 pfu of indicated viruses. Four or six days later, cells were fixed with 4% paraformaldehyde, permeabilized, blocked and stained with either a cocktail of monoclonal antibodies (anti-gB, ICP4, VP22) or a polyclonal MDV-antiserum. At least 50 plaques were imaged and plaques sizes determined using the AxioVision (Zeiss) or Image J software (NIH).
Isoflurane anesthesia (1. 5 l/min O2, 3% isoflurane) was maintained over the course of imaging through a beak's cone. (v) Each image was first acquired with the automatic mode (2 min being the maximum of exposure time); When the signal was low, image acquisition was performed with the manual mode for 5 min exposure. (vi) The chicken was placed in a box until it woke up and subsequently either reintroduce into the isolator or directly PLOS PATHOGENS euthanized still under anesthesia. At each time point, one age-matched control chicken was imaged. Bioluminescence data were uniformly acquired and analyzed with the PerkinElmer Living Image software 4. 2, Perkin Elmer). Note that all tags on images are in total flux and all scales in radiance, as provided by the software. When the signal was quantified by zones (in average radiance or total flux) and plotted, the threshold of detection of bioluminescence was defined as the mean plus 2 standard deviations of the bioluminescence of the control chickens for the same zones.
5mg/mL in PBS) and immediately imaged with the IVIS Spectrum for bioluminescence. Bioluminescence data were uniformly acquired and analyzed as described above. In experiment 2, wings and feet were collected from rVP22-RFP-infected chickens at necropsy and frozen at -80˚C. Note that the feet were rinsed with water to remove dirt before freezing. The fluorescence imaging was performed on the "freshly" thawed samples (4 days of freezing). Detection of the RFP signal was performed using the spectral unmixing mode acquisition and analysis function of the IVIS Spectrum. Two simultaneous acquisition conditions with automatic mode were applied: (i) excitation at 535 nm (bandwidth of 30 nm), and emission at 580-600-620-640 nm (bandwidths of 20 nm), and (ii) excitation at 570 nm (bandwidth of 30 nm), and emission at 620-640-660-680 nm (bandwidths of 20 nm).
Spectral unmixing analysis was performed by Living Image 4. 7 software. RFP signals and tissue autofluorescence signals were assessed. The fluorescence quantification of several ROIs was performed on the image "RFP signal" minus "tissue autofluorescence signal". The results were expressed in Average Radiant Efficiency [p / s / cm 2 / sr] / [μW / cm 2 ]. In experiment 3, wings, feet and heads were collected from rTK GFP-SHA-infected chickens and imaged "fresh". Detection of the GFP signal was performed using the spectral unmixing mode acquisition and analysis function of the IVIS Spectrum. Two simultaneous acquisition conditions with automatic mode were applied: (i) excitation at 465 nm (bandwidth of 30 nm), and emission at 520-540-560-580 nm (bandwidths of 20 nm).
Acknowledgments We greatly thank  Dr Venugopal Nair  for providing the parental RB-1B BAC clone. We are grateful to  Ann Reum  ( FU Berlin ),  Sylvie Remy (INRAE)  and  Mireille Ledevin  ( INRAE ) for their technical assistance. We are grateful to  Anne Pinard ,  Noemie Perrot and Sebastien Lavillatte (PFIE, INEAE Centre Val de Loire ) for their assistance with the bird management and handling.
This work was supported by a  Transnational Access (TNA) grant  from the  VetBioNet INFRAIA program  ( EU H2020 N˚731014  to BK) and a  Lichtenberg grant  from the  Volkswagen Foundation  ( A112662  to BK). CM was supported by a  French MESRI PhD  fellowship. The funders had no role in study design, data collection feathers.
Data Analysis The video of each trial was recorded. Three reviewers examined the videos independently to determine the number of correct steps during each trial. A ''misstep'' was defined as any of the following: (a) the subject's stepping foot crossing over the plant foot; (b) the subject stepping to the side before completing the step; (c) the subject's stepping foot swinging in a wide, arcing path before stepping down; (d) a step duration greater than 3 s; or (e) a gap larger than 10 cm between the heel of the front foot and toe of the back foot when the step was completed  [14, 15]. Videos for all trials across all sessions for First, the subjects performed extra-or intra-vehicular activities (EVA, IVA) while visualizing the ISS in virtual reality (Visual 0 G) with the body unloaded 0% (1 G) or 100% (0 G).
Data Analysis The video of each trial was recorded. Three reviewers examined the videos independently to determine the number of correct steps during each trial. A "misstep" was defined as any of the following: (a) the subject's stepping foot crossing over the plant foot; (b) the subject stepping to the side before completing the step; (c) the subject's stepping foot swinging in a wide, arcing path before stepping down; (d) a step duration greater than 3 s; or (e) a gap larger than 10 cm between the heel of the front foot and toe of the back foot when the step was completed  [14, 15]. Videos for all trials across all sessions for a given subject were pooled, then the order was randomized to minimize reviewer bias based on their awareness of the session. After all reviewers completed their assessments, the median value was used to determine the percent of correct steps for that trial.
Thermofluor stability assay The fluorescent dye Sypro Orange (Invitrogen) was used to monitor the temperature-induced unfolding of TFAM WT , TFAM P178L and TFAM Δ25C. Proteins (2 μM final) were set up in 96-well PCR plates in reaction buffer (10 mM HEPES pH 7. 2, 50 mM NaCl, 1 mM DTT and 5 × SYPRO Orange). Differential scanning fluorimetry was performed in a C1000™ Thermal Cycler using the CFX96™ real time software (Bio-Rad). Scans were recorded using the HEX emission filter (560-580 nm) between 4 • C and 95 • C in 0. 5 • C increments with a 5 s equilibration time. The melting temperature (Tm) was determined from the first derivative of a plot of fluorescence intensity versus temperature  [47].
DNA binding and K d value determination For the determination of K d values, a dsDNA template (22 bp) containing the mtDNA HSP sequence was designed using two oligonucleotides (oligonucleotide 1: 5 ′ -GGT TGG TTC GGG GTA TGG GGT T-3 ′ , oligonucleotide 2: 5 ′ -AAC CCC ATA CCC CGA ACC AAC C-3 ′ ). Oligonucleotide 1 was labeled with [γ-P 32 ] ATP at the 5 ′ -end using T4 polynucleotide kinase (NEB). It was then annealed to the complementary ssDNA oligonucleotide 2 to obtain a labeled dsDNA template. The reactions were performed and analyzed as described above with the indicated TFAM concentrations (0, 1, 2, 4, 5, 10, 15, 20, 40, 50, 75, and 100 nM) per reaction. K d values were calculated using GraphPad Prism 8 software, using non-linear one-site specific binding analysis with a constraint of Bmax = 1.
On average, 20 single DNA tethers were measured simultaneously in a buffer containing 10 mM Tris HCl pH 7. 8, 75 mM NaCl, and 0. 25 % glycerol. A typical assay was performed as follows: (i), the r. m. s (rootmean-square) amplitude of the Brownian motion of the beads was measured for 20 min with bare DNA; (ii), force calibration was performed; (iii), stretching (force-distance) curves were generated. These steps with bare DNA constituted our control and were done for each assay. Proteins of interest were then flushed in the flow cell with a flow rate of 1 μL/min for 15 min, beads r. m. s was recorded for 20 min, and stretching curves were generated. Data acquisition was performed using AFS Tracking G2 and AFS Analysis G2 software (LUMICKS B. V. , Amsterdam, the Netherlands).
Analysis We used the extensible worm-like chain (eWLC) model  [25]  to approximate the dsDNA elastic behavior according to: x = L C [ 1 - 1 2 ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ( k b T FL p ) √ + F K ] , where x is the extension (end-to-end distance) of the DNA, L c is the contour length, k B T is Boltzmann's constant times absolute temperature, F is the force, L p the persistence length, and K the stretch modulus of DNA (set at 1200 pN). A custom-written MATLAB program was used to fit the stretching curves up to ~20 pN.