dataesr/scientific-paragraphs-categorization huggingface dataset:
Physics of Reactors Transition to a Scalable Nuclear Future Proceedings of the PHYSOR 2020, Cambridge, United Kingdom reasoning applies to imposing a limit on the number of the time steps for the thermal-hydraulics. The fixedpoint is directly implemented in the calculation scheme, while Anderson is simply imported from the opensource Scipy-Optimize package  [16]. The only informatic development required was the design of a convertor that changes the format from dictionary of MED fields (SALOME standards) to one Numpyarray (Python standard). The convergence criteria have been determined for the L2-norm of the absolute residuals. The convergence of every variable is required as well as the internal convergence of every solver.
e. , k-means++) for clustering color distributions. In this paper, for simplicity, we apply k-means clustering  [2]  to the 1-D distribution of the hue values of the entire image, and we perform the above-mentioned procedures (i) and (ii) to detect the face region Ω hue S. Then we perform thresholding on the region Ω hue S for the saturation and value components of every pixel. We define the skin color region Ω S and the condition for the skin color at pixel p experimentally as follows: Ω S := p | p ∈ Ω hue S , s p ∈ [ s-0. 2, s+0. 2], v p ∈ [0. 15, 0. 95] ( 3 ) where s is the median of the saturation in the face area. The saturation and the value are normalized to [0, 1] and the threshold values are set to excellently extract the facial skin color region on our dataset, which is available on the project page.
Guide image filtering via optimization This section expresses our GIF, which uses a guide image to correct the input image, as an optimization based method. Our energy function actually resembles in part that of the image matting method  [17]  because we focus on and use its data fidelity term (in  [17]  that is also known as the local linear model ), but the design objective is different. Additionally, a similar method has been proposed in high dynamic range imaging  [25]. Our guide image filtering reconstructs an image x ∈ R 3N where the facial skin color region has a corrected color by using the input image y ∈ R 3N and the color grading result, where N is a number of pixels. In the RGB color space (R 3 ), let the pixel value at pixel j of the color corrected image to be solved be x j , the input image be y j , and the guide image be g j which are given by the color grading in Sec.
We implement the whole algorithm in MATLAB and OpenCV (C++), respectively, and the total execution time is within 11 sec. on a 3. 20 GHz Core i5 CPU, i. e. , face detection (Sec. 3. 1) is 5 sec. , facial color extraction (Sec. 3. 2) is 1 sec. , color grading (Sec. 3. 3) is 2 sec. , GIF (Sec. 3. 4) is 2 sec. , and matting (Sec. 4. 1. 2) is 1 sec. , respectively.
Experimental 1. General experimental details Ruthenium trichloride hydrate, ʟ-proline (ProH),trans-4-hydroxy-ʟ-proline (HypH),ʟ-serine (SerH 2 ), ʟthreonine (ThrH 2 ), ʟ-homoserine (HomH 2 ), 1,3,   13  C NMR resonances attributed to the minor isomer are italicized or, when possible, listed separately from those belonging to the major isomer. IR spectra of solid samples (650-4000 cm -1 ) were recorded on a Perkin Elmer Spectrum One FT-IR spectrometer, equipped with a UATR sampling accessory. IR spectra were processed with Spectragryph software. 47 UV-Vis spectra were recorded on an Ultraspec 2100 Pro spectrophotometer, using 1 cm PMMA cuvettes. CHNS analyses were performed on a Vario MICRO cube instrument (Elementar). pH measurements were performed with an Orion pH meter equipped with a Hamilton glass pH electrode, routinely calibrated with pH = 4.
Geophysical Data To assess the timing and location of regional TCs, particularly TC Pam, the IBTrACS  (Knapp et al. , 2010)  database (version 04r0)  1  was interrogated for TCs tracks; any portion of which occurred between longitudes 155 • E and 170 • W and latitudes of 30 • S and 10 • N and between the years 1979-2019 (the time period of the wave hindcast used in this study, see next section) were retained and defined as "regional" TCs. TC Pam's track is given in Figure  1. To assess impacts, three different categories of sea level information were used in this analysis: wind-waves, astronomical tides, and non-tidal sea-level. These are detailed in following sub-sections.
Wind-Waves Hourly significant wave height (H s ), integrated wave energy flux (C g E), peak wave period (T p ) and peak wave direction (D p ) for years 1979-2019 were extracted from the CAWCR Wave Hindcast, a validated global wind-wave hindcast with increased (approximately 7 km) spatial resolution around the Pacific island nations  (Durrant et al. , 2014)   2  and related collections. Previous global and regional comparisons with satellite altimetry and wave buoy data have found the overall hindcast H s bias and overall root-mean-square (RMS) difference to be on the order of -5% and < 10%, respectively, in the central Pacific  (Durrant et al. , 2014; Hemer et al. , 2016). However, in this study, a further verification of the hindcast's ability to estimate the extreme waves associated specifically with TC Pam was performed by comparing hindcast H s to calibrated H s measured by multiple satellite altimeters, provided via the Ribal and Young (2019) dataset (sourced from Australia's Integrated Marine Observing System) 3.
Non-tidal Sea-Level The computation of TWLs and associated extreme value analysis (see next) requires an uninterrupted time span of non-tidal sea-level data that corresponds to the wave hindcast  (years 1979-2019)  and the astronomical tides calculated in section "Astronomical Tides". This precludes using sea surface heights measured by satellite altimetry, which only start around 1992. We evaluated three global gridded sea level products, all available from publicly accessible data repositories, which meet this time span requirement: 1. NCEP Climate Forecast System Reanalysis Reforecast (CFSR) 6-hourly sea surface height  (Saha et al. , 2013) ; Severe: "worst hit" "80 per cent of wells were badly affected" "65 homes completely destroyed, 42 damaged," "inundations" Severe/Moderate: 60% households affected, although some discrepancy in reporting 1,2,3,4 Severe erosion on the western side, beach receded by 6-8 meters 5.
ACKNOWLEDGMENTS Many thanks to following:  Alec Stephenson  ( CSIRO ) who originated pieces of code used in the statistical analysis;  Claire Trenham  ( CSIRO ) who assisted in data identification and management;  Kathy McInnes  and  Julian O'Grady  ( CSIRO ) who provided comments on early versions of this manuscript;  Xuebin Zhang  ( CSIRO ) for recommending use of ORAS5 sea surface heigh; and  Tom Durrant (Oceanum)  for discussion of performance of WaveWatchIII under tropical cyclone conditions.
Study Population and Design This study was conducted in the neonatal intensive care unit (NICU) of Robert Debré University Hospital-Paris (France). Neonates receiving intravenous vancomycin as part of their routine clinical care were enrolled. Vancomycin dosing followed the individualized local guidelines routinely used in the unit  (Zhao et al. , 2013). A loading dose was infused over 60 min and followed by a maintenance dose administered as a continuous infusion over 24 h. Vancomycin routine therapeutic drug monitoring (TDM) samples were used for PK modeling. The following data were collected and evaluated as covariates with a potential influence on vancomycin PK: gestational age (GA), postnatal age (PNA), postmenstrual age (PMA, defined as the sum of GA and PNA), birth weight (BW), current weight (CW), SCr and c-reactive protein (CRP) concentrations (both collected within 48 h of TDM sampling), mechanical ventilation, and comedication of vasopressor agents or aminoglycosides.
Model Building PK data for vancomycin were modeled with the software package NONMEM; parameters were estimated by first-order conditional estimation with interaction (FOCE-I). As a first step, a structural model (without covariates) was developed. One-and two-compartment models were tested. As a second step, a stepwise covariate model building was performed applying a forward selection and backward elimination method  (Mandema et al. , 1992). The likelihood ratio test was used to test the effect of each covariate on model parameters. Power, exponential and linear models' functions were tested to describe covariate effects of continuous variables. In addition, effect of PMA on CL was tested by means of a sigmoidal maximum effect function  (Wang et al. , 2019). During the initial step of covariate model building, inclusion of a covariate required a significant (p < 0.
Model Evaluation The performance of developed model was assessed by graphical and statistical criteria. Goodness-of-fit plots were initially used for diagnostic purpose. The stability of the final model was also assessed using a nonparametric bootstrap analysis  (Parke and Charles, 2000)  with resampling and replacement (500 times). Values of estimated parameters obtained with the bootstrap procedure were compared with respective values estimated with original data set. Then, the final model was evaluated by calculating normalized prediction distribution errors (NPDE) (1,000 datasets were simulated with the final population model parameters)  (Comets et al. , 2008).
Only two of the SCr-based GFR-estimates developed for paediatric use were derived from data in preterm and term neonates  (Brion et al. , 1986; Wilhelm-Bals et al. , 2019). Their applicability is limited by the fact that SCr values were determined using the Jaffe method rather than the enzymatic one. None of the S-CysC-based GFRestimates reported to date has been validated in neonates. In our population, none of the three tested GFR-estimates was superior to SCr alone as covariate on vancomycin CL (Table  2 ). We were not able to test GFR-estimates based on height as height was not available in all study subjects. The vancomycin CL estimates based on both CR and CYS models are in accordance with values previously reported  (de Hoog et al. , 2004; Jacqz-Aigrain et al. , 2019). Although comparison of BIC values cannot be interpreted statistically, a drop of 2 is often admitted as a threshold for considering one model over another  (Mould and Upton, 2013).
The present study focused on 50 fields visited in 2018 (7-11 fields per site, 8. 33 on average, see Table  2 ). Each sample corresponded to one rice leaf per field and was collected between September and December 2018. In 40 out of the 50 fields (80%), we interviewed the farmer and asked for the rice cultivar grown. We also performed farmers' interviews at the same sites in the two previous years (2016-2017) and the subsequent year (2019)  [23]. A synthesis of obtained responses (annual frequencies of cultivars grown over the fouryear period) are reported in Figure  1b  and detailed information is available at https: //dataverse. ird. fr/dataset. xhtml?persistentId=doi:10. 23708/8FDWIE (accessed on 22 April 2021). In every case, we obtained permission from the farmers to work in their fields, and the management of the entire project followed the guidelines of the Nagoya protocol regarding access and benefit sharing.
Wet lab work (both DNA extraction and SNP genotyping) was performed at the Genotyping Services Lab at the International Rice Research Institute (IRRI). DNA fingerprinting approach used the Illumina Infinium rice 6K chip (C6AIR)  [24] , a set of SNPs designed to characterize the diversity within O. sativa species. This chip has already been used in rice diversity studies, for example, to characterize rice samples from Bangladesh  [25]. SNP genotyping data table was provided by the genotyping platform and used for subsequent analysis. In order to place the rice diversity from Burkina Faso in the global context of Asian rice diversity, we downloaded the 29 mio SNP datasets from the 3K genome data available at https://snp-seek. irri. org/ (accessed on 31 March 2021). Obtained PLINK binary files were converted to VCF using PLINK software v1.
9  [26] , enabling the keep-allele-order option. SNPs' positions corresponding to the C6AIR were extracted from the VCF file using bcftools v1. 9  [27] , then imported within the R software v4. 1. 0 [28] , as well as the genotyping table from accessions from Burkina Faso. Datasets from the chip genotyping and from the 3K genome data were merged prior to applying genomic filters. In order to keep best-quality SNPs, we applied the following filters SNPwise: less than 15% of missing data considering only the accessions from Burkina Faso, less than 10% missing data considering the whole dataset and an additional filter on heterozygosity, which removed positions with more than 45% heterozygosity. We ended up with a final dataset including 5247 SNPs. We first conducted a Principal Component Analysis (PCA) using LEA3.
1 R package  [29]. Graphical display of obtained PCA (Figure  2 ) was made using ggplot2 R package v3. 3. 5 [30]. Datasets were converted to the genind format from the adegenet R package v2. 1. 3 [31]  and analyzed both with adegenet and hierfstat R package v0. 5-7  [32]. A Discriminant Analyses on Principal Components (DAPC) was performed, using the 3K diversity groups as reference, to assign accessions from Burkina Faso to these groups (Figure  S1 ). We then focused our analysis on the accessions from this study (samples from Burkina Faso) and first computed a genetic tree within these samples (Figure  3 ). Genetic distances between the accessions were computed using the dist. gene function and the resulting Neighbor-Joining tree was computed using the ape R package v5. 5  [33]. Graphical representation was made using the 'fan' option of the ggtree R package v3.
1. 2 [34]. A PCA was then computed considering only the field genotypes (with or without a peculiar accession, Figures  4  and S2 ) with the dudi. pca function of ade4 R package v1. 7-17  [35]. Finally, basic population genetics descriptive statistics (gene diversity and populations pairwise F ST ), considering different levels of hierarchy, were computed using hierfstat (Tables 2, 3 and S1). Crops 2021, 1, FOR PEER  REVIEW 6  v2. 1. 3 [31]  and analyzed both with adegenet and hierfstat R package v0. 5-7  [32]. A Discriminant Analyses on Principal Components (DAPC) was performed, using the 3K diversity groups as reference, to assign accessions from Burkina Faso to these groups (Figure  S1 ). We then focused our analysis on the accessions from this study (samples from Burkina Faso) and first computed a genetic tree within these samples (Figure  3 ).
Genetic distances between the accessions were computed using the dist. gene function and the resulting Neighbor-Joining tree was computed using the ape R package v5. 5  [33]. Graphical representation was made using the 'fan' option of the ggtree R package v3. 1. 2 [34]. A PCA was then computed considering only the field genotypes (with or without a peculiar accession, Figures  4  and S2 ) with the dudi. pca function of ade4 R package v1. 7-17  [35]. Finally, basic population genetics descriptive statistics (gene diversity and populations pairwise FST), considering different levels of hierarchy, were computed using hierfstat (Tables 2, 3 and S1). Figure  3. Unrooted Neighbor-Joining tree based on distances computed from SNP data for the 77 samples from Burkina Faso. Samples from farmers' fields begin with two letters reflecting the sampling site and appear in blue when sampled in irrigated areas (BZ: Banzon, KA: Karfiguela, BM: Bama) and in red/orange from rainfed lowlands (SZ: Senzon, TG: Tengrela, BL: Badala).
Acknowledgments: This work was performed thanks to the facilities of the  'International joint Laboratory LMI PathoBios: Observatory  of plant pathogens in West Africa: biodiversity and biosafety' (www. pathobios. com,accessed on 30 October 2021; twitter. com/PathoBios). We are very grateful to  Abdoul Kader Guigma ,  Sylvain Zougrana ,  Yacouba Kone ,  Edouard Kabore ,  Daouda Hema ,  Momouni Traoré  and  Roméo Dabire  for their contributions to the fieldwork in Burkina Faso. We thank the rice farmers from  Badala, Bama, Senzon, Banzon, Tengrela and Karfiguela  for their kind collaboration. We are grateful to  Siaka Bagoyogo  for his help with registered cultivars,  Martine Bangratz  for testing some DNA extractions, and to  Maria Ymber V. Reveche  at  IRRI Service Laboratories-Genotyping Services Lab (GSL ) for her help in receiving the samples and genotyping.
Data Availability Statement: Field data are available on IRD platform DataSuds: https://dataverse. ird. fr/dataset. xhtml?persistentId=doi:10. 23708/8FDWIE (accessed on 22 April 2021). Genotyping data and R script used to analyze the data and perform the figures are available on GitHub: https://github. com/Africrop/divrice_bf (accessed on 31 July 2021).
Statistical Analysis Statistical analysis was performed with SPSS 23. 0 ® software (Armonk, NY, USA). Quantitative parameters were presented as mean, median, minimum, maximum and standard deviation (SD) or percentile  [5, 18]. The normality of data was tested by the Shapiro-Wilk test  [19]. To compare the spirometric parameters of each subject to its theoretical values, we have transformed theoretical values (expressed in %) into continued variables. A paired Student's t-test was used to compare measured and theoretical values. To build the flow-volume curve, we have used medians and the 5th and 95th percentiles. These parameters were also compared to theoretical values using a Wilcoxon rank test. The significant difference was determined at p < 0. 05. Stepwise multiple linear regressions were performed to identify the best predictions of lung function parameters in cyclists including lung volume as a dependent variable and anthropometric data (age, weight, height) as independent variables.
Demographic Data One hundred ninety-six high-level professional cyclists were eligible, but 51 of them (26%) were excluded for asthma or bronchoconstriction induced by exercise or allergy. Finally, 145 high-level professional cyclists were included. Their age was 23. 7 ± 4. 6 years, ranging from 18 to 38, their weight was 71. 0 ± 6. 9 kg, ranging from 59 to 102 kg, their height was 178. 9 ± 6. 0 cm, ranging from 168 to 197 cm and their body mass index (BMI) was 22. 1 ± 1. 6 kg/m 2 ranging from 18. 0 to 26. 3. None of them were smokers.
This parameter would have been relevant in order to research a difference between SVC and FVC of more than 200 mL, to exclude a potential dynamic bronchial compression  [28]. Reference values of SVC would also be interesting in this specific high-level sportsmen population. Furthermore, spirometric values and equations were not applicable to female high-level cyclists and to different ethnic groups because of different morphological characteristics  [4, 5]. In the future, a collaboration to obtain a French and European database of lung function could be of great interest in order to include more high-level cyclists, women and other ethnic groups. The statistical model of Cole et al. , could be used to establish new predictive values  [29].
The assembly contained 92. 08% of the conserved BUSCO core gene set and was estimated to cover 94. 65% of the genome. The obtained genes were compared with previously registered Scutellaria nucleotide sequences and similar regions using the NCBI BLAST service, and a total of 279 similar nucleotide sequences were detected. By selecting the 279 similar nucleotide sequences and nine chloroplast DNA barcode genes, primers were prepared so that the size of the PCR product was 100 to 1000 bp. As a result, a species-specific primer set capable of distinguishing five species of Scutellaria L. was developed.
tsusimensis (H. Hara) Ohwi and Scutellaria pekinensis var. transtra (Makino) H. Hara is commonly referred to as "skullcaps". These are subspecies with very similar phenotypes and are easily mixed and misused. For this reason, accurate identification and classification of each species is required not only in academia but also in industrial fields such as drug development using natural products  [4, 6]. However, genetic research on the genus Scutellaria is not active and only a small number of genes are registered in the NCBI GenBank database. Molecular markers have been widely applied as useful tools to assess genetic variation or to classify species and have improved the genetic analysis of plants  [7]. These molecular markers include random amplified polymorphic DNA (RAPD), ribosomal DNA (rDNA), simple sequence repeat (ISSR), sequence characterization amplification region (SCAR) and simple sequence repeat (SSR) markers  [8] [9] [10] [11] [12] [13] [14].
8. 24  [21]. To assess assembly quality, BUSCO  [22]  (based on evolutionarily informed expectations of the gene content of near-universal single-copy orthologs) analysis was performed using BUSCO 3. 0 with eukaryote odb version 9. Additionally, Illumina short reads were aligned against the gap-filled scaffolds to calculate the mapping ratio, mapping coverage and insert size for assessment. MAKER 3. 01. 03 [23]  was used to predict genes from the gap-filled scaffolds based on the SNAP model. For additional annotation, the consensus sequences were searched against the GenBank nonredundant (NR), UniProt, GO, InterPro, Pfam, CD, TIGRFAM and EggNOG 4. 5 databases  [24]  using blast  [25]  v2. 6. 0+.
Preparation of Species-Specific Primers Using Barcode DNA The chloroplast DNA (cpDNA) barcodes of five species of Scutellaria registered in the NCBI GenBank database were analyzed. Specific primers were designed by aligning each nucleotide sequence with CLC Sequence Viewer to search for nucleotide sequences (single nucleotide polymorphisms (SNPs), deletions, insertions) that appeared only in the relevant species. To increase specificity when preparing a specific primer, a SNP was placed at the 3 -end, and the third nucleotide sequence was mismatched to the 5 -side therefrom to prepare a specific primer. In addition, the nucleotide sequence obtained through WGS de novo assembly was matched with the previously registered nucleotide sequence of the genus Scutellaria using the NCBI BLAST service.
Blast Results After the complete genome or draft genome was assembled, BLAST analysis was carried out to identify to which species each scaffold showed similarity. The best hit and top five hit results were identified using the NCBI NT database. As a result of BLAST, 45. 03% of the samples had no hits and the Sesamum genus was the most matched, with a rate of 16. 31%. Ipomoea showed a rate of 8. 62%, Erythranthe a rate of 4. 59%, Scutellaria a rate of 1. 94% and other genera, a rate of 23. 50% (Figure  4 ).
Gene Prediction and Annotation Results After the complete genome or draft genome was assembled, the locations of proteincoding genes were predicted, and their functions were annotated. MAKER was used to predict genes in the draft genome, and the InterProScan, UniProt and EggNOG databases were used to annotate the functions of the genes. As shown in Figure  5 , a total of 23,814 genes were identified by MAKER. The numbers of identified CDSs, tRNAs and rRNAs were 22,788, 670 and 356, respectively (Table  6 ). In EggNOG functional classification, a total of 23,716 unigenes were classified into 25 categories. Among them, "function unknown" was the largest group  (12, 200 , 51. 44%). This was followed by "Posttranslational modification, protein turnover, chaperones" (1565, 6. 59%), "Transcription" (1500, 6.