dataesr/scientific-paragraphs-categorization huggingface dataset:
Radiosonde of Guipavas Météo-France has a radiosonde station in Brest-Guipavas (less than 20 km from Brest harbor). The sonde data were retrieved from the University of Wyoming sounding archive. 6 The dataset is composed of twice-daily launched sondes measuring the water column in the troposphere. IWV values are computed by the integration of humidity profiles as proposed in  Bock et al. (2021). The radiosonde IWV value is extrapolated to the PANO antenna height as in  Bock et al. (2005). Then, the radiosonde IWV is also compared to the PANO IWV by matching the times.
Comparison of PANO IWV with ERA5 Reanalysis and Radiosonde The best computed IWV after Sect. 3. 2 is the parameterization 3 ı cut-off angle, square-root of a sine function for elevation weighting, and 3 mm h 1=2 random walk on the ZWD. The corresponding IWV dataset is compared to the ERA5 reanalysis and the radiosonde of Brest-Guipavas. Superimposing these timeseries with the BRST one provides the top chart in Fig. 3. This graph reveals a succession of wet (more than 20 kg m 2 ) and dry (less than 10 kg m 2 ) episodes for the region. These events are well described by the different techniques studied: radiosonde, ERA5 reanalysis from the ECMWF, BRST GNSS permanent station and PANO GNSS station. All over the period of study of nearly 50 days from April to May 2018, these IWV show a very good agreement with each other.
As such, rescuing the efficacy of existing  baumannii strain MS14413 treated with either PBT2 (64 mM); PBT2 (64 mM) and zinc (8 mM); or PBT2 (64 mM), zinc (8 mM), and tetracycline (0. 5 mg/mL) in CA-MHB. Genes with log 2 fold change of. 2 or ,22 and a P value of ,0. 05 are depicted by black dots; Feo system genes (feoA and feoB) are depicted by green dots; ferric acinetobactin ABC transporter system genes (bauC, bauD, and bauE) are depicted by red dots; multidrug efflux resistance nodulation division transport system genes (adeABC and adeFGH) are depicted by orange and blue dots, respectively; SMR family drug efflux system gene abeS is depicted by the purple dot. Data were collected from three biological replicates. (C) Transcript levels for selected genes measured by quantitative real-time PCR.
1. 0 (PMID 31661016). Pairwise alignments to MS14413 were visualized using the Artemis comparison tool (ACT) v18. 1. 0 (PMID De  Oliveira et al. 15976072 ). Whole-genome comparisons were then used to identify differences between wild-type and mutant MS14413 genomes. Insertion sequences were identified and annotated by a blastN comparison of draft genomes against the ISfinder database (PMID 16381877). Single nucleotide polymorphism (SNP) profiling and determination of core genome SNPs was undertaken by aligning the trimmed Illumina sequencing reads for each sample to the complete genome of wild-type strain MS14413 (GenBank accession CP054302) using Snippy v4. 4. 1 with a minimum read coverage of 10Â and minimum base quality of 20 that is required for a site to be considered. Growth analysis.
Transcriptome sequencing (RNA-seq) analysis. RNA-seq analysis was performed at the Australian Genome Research Facility. The cDNA library was prepared and assessed as described previously  (19). An average of 30 million reads per sample were generated, then trimmed using trimmomatic (v0. 36), and mapped to the reference genome (A. baumannii MS14413 accession number NZ_CP054302) using bow-tie2 (v2. 3. 4. 2) with default parameters (-local -very-sensitive-local). Read counts were determined using featureCounts (Rsubread v1. 28. 1), and differential gene expression was analyzed with edgeR (v3. 20. 9) and limma-voom (v3. 34. 9) with the counts per minute (cpm) threshold set at 2. 0. Figures were generated using ggplot2 (v3. 3. 0) and ggrepel (v0. 8. 2) packages in Rstudio  (61). Quantitative real-time PCR.
Permission was obtained from the University of Queensland ethics committee (SCMB/AIBN/144/17). Statistical analysis. All experiments were undertaken either in technical duplicate or triplicate, with no less than 3 biological replicates. Data are presented as means 6 SD. Bacterial dissemination data from in vivo studies are presented as geometric means. To compare means between more than two groups, a one-way ANOVA with post hoc (Tukey) was conducted. Murine survival curves were analyzed using the Mantel-Cox log-rank test. Statistical analysis was performed using GraphPad Prism software v9. A P value of ,0. 05 was considered statistically significant. Data availability. RNA-seq data sets generated and analyzed during the current study are available in the GEO repository (GSE175535). Genome data have been deposited to NCBI under BioProject PRJNA733838.
Raw Illumina sequence read data have been deposited to the Sequence Read Archive (SRA) under the accessions SRR14695606, SRR14695607, SRR14695609 to SRR14695613, SRR14695615, SRR14695617, SRR14695618, and SRR14695620 to SRR14695625. PBT2 Breaks Resistance to Tetracycline-Class Antibiotics ®.
Stimulus presentation and recording of the data were controlled by a PC running E-Prime software. The primes were presented over headphones at a comfortable sound level. At the end of the auditory primes, the targets were visually displayed in upper case at the center of the screen and remained until the participant's response. Participants were asked to make a lexical decision as quickly and accurately as possible with "word" responses being made using their dominant hand on an E-Prime response box that was placed in front of them. The computer clock was triggered by the presentation of the target and was stopped by the response. The prime-target pairs were presented randomly, and an inter-trial interval of 2000 ms elapsed between the participant's response and the presentation of the next pair.
Results and Discussion RTs to target words (available at https://osf. io/4spq3/; Open Science Framework;  Foster & Deardorff, 2017)  in the transposition condition were first analyzed using linear mixed effects models with participants and items as crossed random factors, using R software (R Development Core Team, 2016) and the lme4 package  (Baayen et al. , 2008; Bates & Sarkar, 2007). The RT analysis was performed on correct responses, thus removing 126 data points out of 1584 (7. 95%). For the model to meet the assumptions of normally-distributed residuals and homogeneity of variance, a log transformation was applied to the RTs  (Baayen & Milin, 2010)  prior to running the model. The model was run on 1458 data points. We tested a model with the variable Prime Type (transposed, control) as fixed effect.
0104, t = 2. 36, p<. 05). RTs on target words were 27 ms slower when preceded by transposed primes in comparison to control primes. The interaction between Prime Type and Target Frequency was not significant (b =-0. 0039, SE = 0. 0207, t = -0. 19, p>. 20). To evaluate the non-significance of this critical interaction, a Bayes Factor analysis was conducted using the lmBF function from the BayesFactor package  (Morey, 2015). The BF 01 value was 24 thus indicating strong evidence in favor of H0. The percentage of correct responses was analyzed using a mixed-effects logit model  (Jaeger, 2008)  following the same procedure as for RTs. Only the main effect of Target Frequency was significant (b =2. 3196, SE = 0. 5889, z = 3. 94, p<. 001) with more correct responses for high-frequency targets than low-frequency targets.
The original AIA data are enhanced using the noiseadaptive fuzzy equalization method (NAFE; Druckmüller 2013, see also Figure  2 therein ). NAFE 7  calculates a linear combination of a gamma-transformed image with a fuzzyequalized image, significantly enhancing local contrast of individual structures. The weakest but dynamic structures at the limit of detection by AIA were also enhanced using the running difference (RD) method, where one image in a series is subtracted from a preceding one using a predefined temporal delay. We employ the 72 s delay for 304 Å and 48 s delay for 131 Å. The shorter delay for 131 Å is necessitated by presence of fast-contracting flare loops and SADs (see Section 4). To suppress the otherwise dominant noise, three RD images are averaged in time and additionally smoothed by a 3 × 3 (1 8 × 1 8) boxcar.
To summarize, our observations strongly suggest that the rrrf (leg-leg) reconnection can be persistent throughout the peak and gradual phases of a flare and causes strong changes in evolution of the erupting filament, and presumably the CME. In the low corona, it also leads to occurrence of prominent supraarcade downflows (P-SADs). The authors thank the referee for comments that helped improve the manuscript. J. D. , J. K. , M. K. , and A. Z. acknowledge support from the grant No. 20-07908S of the Grant Agency of the Czech Republic, as well as institutional support RVO:67985815 from the Czech Academy of Sciences. G. A. acknowledges financial support from the French national space agency (CNES), as well as from the Programme National Soleil Terre (PNST) of the CNRS/INSU also co-funded by CNES and CEA.
To test this, all FIELDS DFB burst data recorded during the first eight solar orbits were examined for impulsive magnetic signatures associated with dust impacts, as in Figure  2 (e). All DFB burst captures containing both differential voltage and SCM data from this interval were considered, even those without associated long-duration potential perturbations. In total, 9849 burst captures were examined. Of these, 4651 events contained dust impacts, and 61 of those contained clear impulsive magnetic responses measured by the SCM. To estimate the distribution of ion cloud expansion velocities, each impact was assumed to occur at a distance of 6 m from the SCM. Dividing this distance by the measured delay times between the dust-impact preshoot and the magnetic impulse peak produces the distribution in Figure  4.
Debris-cloud Occurrence Rate via FIELDS The signature of debris-producing events in FIELDS DCcoupled electric field waveform data is distinctive: an abrupt spike followed by a >1 s time period when the spacecraft potential is disturbed: rising then and gradually returning to a pre-impact level. The FIELDS differential voltage data for the first eight orbits of PSP about the Sun (2018 October-2021 May) were searched for such signatures. The search was limited to times when the survey differential voltage data were sampled faster than 50 Sps, to ensure detection of the initial impact-plasma-cloud spike. Data at this rate or faster are recorded primarily when PSP is closer than 55 solar radii (R S ) to the Sun. The search algorithm used is purposely designed to be inclusive of signals other than debris-cloud events, because the time-series signals associated with dust-impact-plasma clouds are highly variable (Section 3.
2), and likely depend strongly on impact location (e. g. ,  Shen et al. 2021a ). The search method used is to algorithmicaly identify possible events, then subselect by-eye events with signatures similar to Figure  1(a). First, a 2 minute duration window of differential voltage data for a single antenna pair is selected. Times within 5 minutes of thruster firings or FIELDS bias sweeps are not searched. Using the data in a given window, the median value is subtracted, then the time-series data are integrated. If the data integrate to a negative value, the sign of all data is flipped to facilitate processing. The peak amplitude within the window is required to be >50 mV. The data are searched for a point after the peak when they fall below e -2 times the peak amplitude. If such a point exists, and is more than 1 s after the peak amplitude, then the time-series data from the interval is plotted for further examination by-eye.
Correlations between FIELDS and WISPR Debris-cloud Detection Figure  1  shows an individual event where FIELDS and WISPR detect the same debris cloud. Here, the 240 identified FIELDS long-duration potential perturbation events and the WISPR image QF database are compared to evaluate the correlation between FIELDS and WISPR signatures of debris clouds. The database includes 5521 images from WISPR-I and 3283 from WISPR-O. First, all WISPR images recorded <1200 s after a FIELDS long-duration potential perturbation event are identified. A time delay is calculated between the start of a FIELDS long-duration spacecraft potential perturbation event and the start of a WISPR image capture. The events are then organized into 120 s bins, and the average WISPR streak QF determined for each bin. Figure  10(a)  shows the number of WISPR images in each bin, and Figure  10(b)  shows the average QF in each time bin.
Long-duration spacecraft potential perturbation events (240) and star tracker demotions (12) are rare within the PSP data set, making the probability exceedingly low (unless they were correlated) that, of the 11 star tracker demotions with appropriate FIELDS data to observe such effects, eight occurred less than a few minutes after long- duration spacecraft potential perturbation events. Of the 12 star tracker demotions, 10 showed a short-duration FIELDS signature indicative of a dust-impact-plasma cloud within a few minutes prior to the demotion. This second observation is weaker evidence of a correlation between impacts and demotions, because the count rate for dust-impact-plasmacloud potential perturbations significantly exceeds one per minute during portions of the PSP orbit  (Malaspina et al.
2021a). Further, the response of the spacecraft body should be much reduced compared to that of the antennas for most events. This is because the debris cloud will generally expand perpendicular to the spacecraft body surface regardless of impact location, but a debris cloud created by an impact on the spacecraft body near the heat shield can travel parallel to one or more of the FIELDS antennas for an extended duration. To demonstrate the feasibility of this mechanism, the antenna charging model of  Shen et al. (2021a)  is applied to fit the data in Figure  1. For this comparison, a 7:1 scaled-up version of the spherical model utilized in laboratory experiments and numerical simulation by  Shen et al. (2021a)  is used. This model treats the PSP spacecraft bus as a 1 m sphere with four orthogonally mounted 2 m antennas extending from it.
Both monopole and dipole data are fit simultaneously, with equal weighting, to determine the best-fit solution. The best-fit solution is shown in Figure  12 (a), for monopole data, and Figure  12(b) , for dipole data. This solution has an impact location 30°away from antenna V 1 toward antenna V 3. This solution has a debris-cloud total charge of 1. 93 × 10 -10 C and a cloud radial expansion speed of ∼8 cm s -1. The model fit velocity is significantly lower than the fewmeters-per-second debris fragment velocity estimated using WISPR data in Figure  9 (c). However, there are a few possible reasons why this may not be a strong contradiction. First, the induced charge model fitting provides the radial group speed of the debris cloud away from the spacecraft in the plane of the four antennas. This direction is nearly perpendicular to the direction along which debris fragments must move to create well-defined streaks in WISPR.
These (and the vapor) will be too small (sparse) for WISPR's imaging capability. Solid fragments are predicted with 1. 5-15 μm diameter with an average fragment diameter of 5 μm, and velocities relative to the spacecraft of 1-600 m s -1. The number of solid fragments ejected is predicted to be less than ∼200. The predicted cone angle of the ejecta cloud ranges from near-zero to 30°. The fragment properties (diameter, velocity, number of large fragments) determined from the WISPR data analysis presented here are consistent with the low end of the predicted fragment properties.
Genotyping. We used multiple genotyping arrays (Illumina Infinium Global Screening Array, Human610-Quad BeadChip, and Infinium Asian Screening Array) for genome-wide genotyping in the four study samples (Supplementary Table  7 ). We conducted standard quality control at subject and SNP levels (Supplementary Notes). In brief, low-quality variants were removed, and subjects were excluded for the following reasons: (1) unintended technical errors or low genotyping quality; (2) estimated to be biologically related to other subjects and with lower call rates; (3) ancestral structure deviated from that of the underlying study population (Supplementary Fig. 5 ). To improve the density of genotypes and maximize the number of overlapping SNPs among samples genotyped by different arrays, we conducted imputation for each dataset with the same array.
We applied different imputation methods for non-MHC and MHC regions (29-34 Mb on chromosome 6 according to Homo sapiens genome assembly GRCh37). For non-MHC regions, we applied SHAPEIT (v2. 12) for phasing and IMPUTE2 for imputation using the 1000 Genome Phase III integrated variant set of the entire population as a reference panel. For the MHC region, we applied SNP2HLA for imputation, using the Han Chinese reference panel which includes data from 10,689 healthy individuals provided by the Beijing Genomics Institute (BGI). We further excluded variants with low imputation quality or abnormal allele frequencies (Supplementary Table  9 ). For non-HLA SNPs, we used the best-guess genotypes (maximum posterior probabilities exceeding a threshold of 0. 9) and applied plink to analyze these data in further analysis.
For HLA SNPs, we used the dosage data and applied R software using a logistic regression model in the analysis. After strict quality control and imputation, 4506 cases and 5384 controls with the corresponding numbers of SNPs in each study sample were included for further analysis (Supplementary Table  10 ). The SNPs were directly genotyped using the iPLEX Sequenom MassARRAY platform for the PRS model application. We used Sanger sequencing for cross-validation of the genotyping among different platforms (Supplementary Table  11 ). Polygenic risk score. A PRS for NPC was derived by integrating previously known  [19] [20] [21] [22] [23] [24] [25] [26]  and newly discovered genome-wide significant SNPs. A total of 12 independent variants were included in the PRS calculation based on the GWAS results (EPIC-NPC-2005, NPCGEE, SYSUNPC, and Hong Kong samples).
The age-specific NPC incidence rates for males and females were derived from the International Agency for Research on Cancer (IARC)'s Cancer Incidence in Five Continents, Volume XI (http://ci5. iarc. fr/CI5-XI/Pages/agespecific-curves_sel. aspx, shown in Supplementary Data 1). We projected the distribution of absolute age-specific cumulative NPC risks at different percentiles of the PRS  [48] [49] [50]. The current recommended starting age for NPC screening by the Chinese Ministry of Health was 30 years old. By setting a risk threshold as the average of the 10-year NPC risk for a 30-years old man (0. 30%) and woman (0. 09%), that is, (0. 30% + 0. 09%) / 2 = 0. 20%, we estimated the recommended starting age of first screening given the PRS. Statistical analysis. For the discovery samples, the per-allele ORs and standard errors (SEs) were calculated using logistic regression with PLINK software or R software based on the additive assumption in the discovery stage.
Acknowledgements This study was funded by the  National Key Research and Development Program of China  (grant number  2021YFC2500405  to W. H. J. ; grant number  2020YFC1316900  to Y. S. ), the  Basic and Applied Basic Research Foundation of Guangdong Province, China  (grant number  2021B1515420007  to W. H. J. ), the  Science and Technology Planning Project of Guangzhou, China  (grant number  201804020094  to W. H. J. ; grant number  201904010467  to J. B. Z. ),  Sino-Sweden Joint Research Program  (grant number  81861138006  to W. H. J. ), the  Special Support Program for High-level Professionals on Scientific and Technological Innovation of Guangdong Province, China  (grant number  2014TX01R201  to W. H. J. ),  National Natural Science Foundation of China  (grant number  81973131  to W.
Data availability The baseline patient information and genetic information data have been deposited in the Research Data Deposit public platform (www. researchdata. org. cn, accession number: RDDA2020001599). The raw genotype and phenotype data has been uploaded to The European Genome-phenome Archive (EGA) dataset (EGAS00001006062; EGAS00001006102). The summary statistics that support the findings of this study have been deposited in the NHGRI-EBI GWAS Catalog dataset [https://www. ebi. ac. uk/gwas/, accession number: GCST90093313]. Source data are provided with this paper.
Methods We conducted a cross-sectional single-center study during a period between February and April 2021 without lockdown but with very stringent socio-economic restrictions including curfew from 6 p. m. to 6 a. m. Recruitment took place at the entering of the Covid-19 outpatient testing center at Bordeaux University Hospital (southwest of France). Strict compliance with barrier gestures from investigators and participants was required. Patients between 18 and 45 years old who accepted to participate were included whether they were symptomatic or not, healthcare workers or not, in contact with a Covid-19 case or not. Following their test, participants answered an internet-based questionnaire on their sociodemographic data (age, gender, body mass index (BMI), profession and work schedules), their current symptoms and their medical background.
Statistical analysis The primary endpoint was the SARS-CoV-2 detection using RT-PCR on nasopharyngeal samples (positive/negative) performed in a hospital-accredited center following the recommendations of the French Ministry of Health. Socio-demographical data and sleepwake behaviors were compared between the Covid-19 group and the control group using a X 2 test for qualitative variables or the ttest for quantitative variables. Adjusted odds-ratio (OR), 95% confidence interval (CI) and p-value were then assessed using a logistic regression with age, gender, BMI, and work schedules as covariates. The threshold of 0. 05 was used to determine significance.
Population Out of 9419 patients (41% of men, mean age of 28 ± 8) tested in the center during the study period and meeting the inclusion criteria, 1014 participants were included since they were tested and interviewed during the work shifts of the student nurses assigned to the project (sampling rate: 10. 8%). The population interviewed was representative of the whole sample in terms of age and sex (39% men, mean age 28 ± 8). Out of the 1014 subjects recruited, 56 participants tested positive for Covid-19 (5. 5%). This positivity rate was similar to the department's data over the period (from 4. 3% on March 12 to 7. 5% on April 23). Table  1  describes socio-demographic and clinical patterns of the Covid-19 group and the control group. A total of 20. 6% of participants were overweight, including 6% obese, 27.
Acknowledgments We would like to thank the entire administrative and nursing team of the outpatient testing center who allowed us to carry out this study. Also, we would like to thank  Patrick Cassai  and  Luc Durand  for their support in writing the project and submitting it to the ethics committee, to  Marie-France Koltes  for welcoming and supervising the students and conducting the patient survey,  Aur elien Boiseau  for his involvement in data management, followup of participants and for having conducted the patient survey and finally  C ecile Klochendler  for the design and supervision of this research. Finally, we thank the students who help us by informing patients about the subject of our research.