dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this section, performances of all the scenarios are summarized in Table  5  and the results are discussed focusing on the impacts on the utility grid in terms of energy and power with/without considering the presence of PV panels. First of all, the charging of EBs shows that the determined average bus consumption in terms of kWh/km is consistent with the literature data  [4]  which validates the presented model. However, it should be noted that there is a potential for further improvement of the presented model, through an analysis of the GTFS and Open Street Map data, by defining classes of roads according to the number of curves, intersections, and traffic lights. Additionally, the obtained energy consumption is 36 kWh (9. 4%) less in scenario 3 (where a 70 kWh battery weighs 467 kg) compared to scenarios 1 and 2 (where a 422 kWh battery weighs 2.
Conclusions This study analyses the performance of various charging infrastructure placements. The proposed method considers a modelling of the bus network based on GTFS data and a sequential simulation of the bus operation. The method is applied to the operation of two buses on one bus line in Compiègne, France. Scenarios with charging stations at the depot, line terminals, and bus stops are compared. Results show the relevance of the method to estimate the impacts on both the transport delay and the power supplied from the grid. Charging at the depot implies having a larger on-board battery pack whereas on-route charging at bus stops and terminals helps reduce the battery capacity by 83%, and thus the bus consumption by 9. 4% due to a reduction in the bus weight. However, on-route charging with a high power can have an impact on the utility grid, especially during the peaks of charging power.
Visual observations Benthic surveys were conducted in B48 between 6-17 September 2018 onboard the 'Goldblatt Tide', a 70-m anchor handling supply vessel. During these surveys, the vessel was used as a platform for collecting sightings data to determine the occurrence of marine mammal and sea turtle species and vessels in the region. Two trained and experienced marine faunal observers (MFOs) conducted visual surveys during daylight hours in 'passing' mode (i. e. animals were not approached) when the vessel was on station (conducting benthic surveys), transiting between stations, and transiting to and from the port in Luanda, Angola. The vessel was relatively stationary during benthic surveys. Average transit speed was 6. 7 knots (3. 4 m per second). All observations were conducted from the bridge deck (∼12.
During visual observation sessions, one MFO scanned the water's surface for marine wildlife using binoculars and the naked eye, while the other MFO served as the data recorder and assisted in species determinations and photo-documentation of sightings. MFOs scanned 180°in front of the vessel during transit and 360°around the vessel when the vessel was relatively stationary at benthic sampling stations. MFOs rotated through these stations every 60 min to minimize fatigue. During periods of light rain and extremely strong winds/rough seas, the MFOs remained on effort and scanned for wildlife from inside the bridge. All sightings and environmental conditions data were recorded using WinCruz, a computer program developed specifically for marine wildlife surveys by the National Oceanic and Atmospheric Administration, Southwest Fisheries Science Center.
Environmental data, including BSS, wind speed and direction, swell height and direction, weather (e. g. rain, fog, haze), and visibility, were recorded every hour and as conditions changed. The position, course, and speed of the vessel were automatically recorded every 2 min in Wincruz via a connection to the vessel's GPS. A set of data fields were recorded for each marine mammal and sea turtle sighting: geographic position of the vessel (latitude and longitude), initial time of sighting, estimated bearing and distance of sighting from vessel, species identification, number of individuals (group size), behaviour of animals observed, the first cue (e. g. blow or splash) for the sighting, and platform activity during the sighting (e. g. transit, benthic sampling). When feasible, digital photographs were taken to confirm species identification.
Passive acoustic monitoring To complement the visual observations, PAM was used to attempt to capture evidence of the presence of species that may not have been sighted, particularly during the night when visual observations were not possible, and to potentially corroborate species observations through multiple data collection modalities. The PAM equipment was deployed and retrieved during benthic sampling at night when the vessel was relatively stationary. PAM equipment for this study included a C57 hydrophone and a TASCAM DR-100mkIII recorder set to a 96 kilohertz (kHz) sampling rate at a 24-bit resolution so that both low and high frequency marine mammal vocalizations could be recorded. Developed by Cetacean Research™, the C57 hydrophone is an extra rugged version of the popular C55 hydrophone.
Its shape allows it to be towed behind a vessel with less flow noise, a useful characteristic given the noise conditions within which the recordings were made. The C57 also utilizes a soft polyurethane encapsulation material intended to optimize underwater acoustic performance. All acoustic data were analysed after completion of the surveys. Analyses were manually accomplished through the use of Raven audio spectrogram software (Bioacoustics Research Program, Cornell Lab of Ornithology: https://ravensoundsoftware. com/). Each station was analysed separately two times; the full-scale data were viewed first followed by analysis of the 0-500 hertz (Hz) range. Specific analysis methodologies and results are described in detail in the Supplementary Information file.
, 1960; Dawson, 1991; Miller et al. , 1995; Dudzinski, 1996; Herzing, 1996; Blomqvist & Amundin, 2004; Lammers et al. , 2004; Simard et al. , 2008; Martin et al. , 2019). These potential odontocete sounds occurred sporadically and were only observed in data collected during a few survey days. For our analysis we selected eight representative click sequences, along with several instances of burst pulses and buzzes, from each of three separate acoustic data collection sessions recorded on 7, 8 and 14 September 2018, as evidence of the presence of characteristic odontocete acoustic activity.
Discussion Data from this survey provide a deeper understanding of marine megafauna occurrence in this region off western Africa. Results show deep-water (>2000 m) associations of six marine mammal species/groups and one sea turtle species. In addition to general distribution information, our sightings include noteworthy documentation of humpback and right whales far offshore of Angola. Although all of the marine mammal and sea turtle species identified during this survey have been recorded in Angolan waters previously (see  Townsend, 1935; Weir, 2007a Weir, , 2007b Weir, , 2010a Weir, , 2011)) , our probable southern right whale sighting is the first confirmed sighting of this species in Angolan waters since 1913. The very faint images observed within the 10-500 Hz audio recording range could perhaps be classified as humpback song sequences or possibly as right whale contact calls, both of which would likely be expected based on the characteristic vocal activity of these animals and the large number of humpback whale visual sightings recorded during the study.
In Angolan waters, recent cetacean occurrence data have been recorded opportunistically during geophysical surveys. For example, between 2004 and 2009, sightings were recorded in oceanic waters off Gabon and Angola  (Weir, 2007a (Weir, , 2010a (Weir, , 2010b)). However, no right whales were observed during these surveys. The most recent sighting of a right whale in the general vicinity of Angola in the eastern Tropical Atlantic was farther north off Gabon at '1 South and 8 30 ′ East' on 29 July 1999. This individual was designated as 'likely a southern right whale' (New England Aquarium, unpublished data). Our sighting was at 7°16 ′ S off the Angolan coast just north of the former south Angolan whaling ground of Tiger Bay, which lies at ∼16°37 ′ S. Based on current and historical information, it appears that our sighting is the first confirmed right whale sighting in Angolan waters since a whale was landed in southern Angola in 1913.
Acknowledgements. The authors gratefully acknowledge  TotalEnergies Corporate  environmental teams in Paris,  TotalEnergies Exploration & Production Angola , and  Créocéan  for providing this opportunity to collect critical data on marine megafauna during the benthic surveys. We would also like to thank the captain and crew of the ' Goldblatt Tide'  for their assistance and logistical support throughout the research expedition. Special thanks to the Créocéan benthic survey team for their planning, logistical support, and assistance with PAM deployments. Thanks also to  Kevin Knight  for providing GIS and mapping support and to the peer reviewers for helping to improve the quality of this paper.
Fluorescence was monitored as described in Materials and Methods. Data are normalized concerning the fluorescence of individual tetracycline derivatives at time zero. Data are represented as the mean of at least three independent experiments. A. U. , arbitrary units. The concentration of the tested tetracycline derivatives was 100 µM, which was not inhibitory for an OD 600 ~0. 6 during the experiment. The concentration of was added as indicated in Materials and Methods. Increased doxycycline uptake in P. aeruginosa PAO1 in the presence of NV716 has been reported and used as a positive control. Due to the difference in the y-axis scale, the uptake of 3c is shown in (A); 3u and 3j are shown in (B). Bacterial cells without treatment did not show any increase in fluorescence, indicating the absence of intrinsic fluorescence.
Fluorescence was monitored as described in Materials and Methods. Data are normalized concerning the fluorescence of individual tetracycline derivatives at time zero. Data are represented as the mean of at least three independent experiments. A. U. , arbitrary units. Molecules 2023, 28, x FOR PEER REVIEW 7 of 13 Figure  5. Tetracycline derivatives with different physicochemical properties interfere with protein synthesis in vitro. Inhibition of the synthesis of GFP as a reporter protein was monitored in an E. coli cell-free transcription-translation system. Doxycycline is a known inhibitor of protein synthesis and was used as a positive control. Data are represented as the mean of at least three independent experiments, and error bars represent the SD of replicates.
165305, Illkirch-Graffenstaden, France). An additional well contains bacteria alone. The fluorescence (A. U. ) of the tetracycline derivatives was immediately measured every minute for 30 min with a TECAN Spark microplate reader using specific wavelengths for excitation and emission as determined in Supplementary Figure  S1. The obtained data were then processed to obtain A. U. = f(t). The fluorescence of the cells alone measured at each time was subtracted for all the conditions tested during the same experiment. Finally, the fluorescence at time zero was subtracted for each time point of each condition. The uptake of tetracycline derivatives was quantified as the area under the curve (AUC) by using GraphPad Prism 6 software. Independent experiments were performed at least three times, and the results are shown as the means of the replicates.
Bacterial Cell-Free Expression Assays The effect of the selected tetracycline derivatives (i. e. , 3c, 3j, and 3u) on in vitro bacterial protein synthesis was tested using the Expressway Cell-Free E. coli Expression System (Invitrogen for Thermo Fisher Scientific, Illkirch-Graffenstaden, France). pEXP5-CT TOPO-GFP was used as a template for in vitro transcription-translation. The reactions were performed following the manufacturer's instructions and carried out in 50 µL including the addition of feed buffer at 30 min of incubation to support optimal protein synthesis. Reactions were initiated with 1 µg of plasmid DNA; mixtures were incubated at 30 • C with shaking (300 rpm), and fluorescence was measured after 3 h with a TECAN Spark microplate reader (λex = 475 nm; λem = 520 nm). IC 50 values were calculated using GraphPad Prism 6 software.
Acknowledgments: We are grateful to Nosopharm for providing the pEXP5-CT TOPO-GFP reporter plasmid and to  Isabelle Shalk  for gifting PAS263. We thank  Patrick P. Michel ,  ICM, Paris , for sharing Col-3 (3j) and 9-tert-butyldoxycycline (3x). We thank  Blandine Seon-Meniel ,  BioCIS ,  Université Paris-Saclay , for her help in managing and sending the different tetracyclines. We also thank all members of the  MCT laboratory  for valuable discussions and manuscript reviewing.
Data management and statistical analysis. This cross-sectional study, conducted on a sample, should make it possible to generalize the results to the entire target population. A margin of error on the estimate is defined to calculate the number of patients required, in addition to the expected proportion of the primary outcome. Thus, for an expected proportion of infectious diarrhea of about 80%  [2] [3] [4] [5]  , the inclusion of at least 97 patients would make it possible to obtain a precision of this proportion of ± 8%. Statistical analysis were performed with Stata (version 15; StataCorp, College Station, Texas, USA) and R 3. 5. 1 (http:// cran. r-proje ct. org/, accessed on February 16, 2021) software. All tests were two-sided, with an alpha level set at 5%. Categorical data were expressed as number of subjects and associated percentages, and continuous data as median [25th; 75th percentiles].
Results Population. During inclusion period, 18,378 children were admitted to the pediatric emergency department (Fig. 1 ). One hundred and fifty-four (0. 8%) patients were eligible but 59 did not have stool during consultation, thus 95 patients were finally included (75. 8% in 2019 and 24. 2% in 2020). Due to the low recruitment in 2020 and the lack of major differences observed between 2019 and 2020, the following data is provided for the two years combined. In total, 26. 3% of the cohort was recruited in both June and July, the highest inclusion rate was in August (31. 6%) and was then divided by two in September. Main socio-demographic characteristics, medical data and risk factors for contamination by enteric pathogens are presented in Table  S1. The median age was 22 months [11, 50], 82.
Common factors and patient profiles. No anamnestic, clinical or biological factor were significantly associated with the presence of a specific infectious agent associated with diarrhea (data not shown). To complete these analyzes, a MCA was performed. Among the 95 patients, 77 were included in the MCA and 18 were excluded because of data missing from any of the variables selected for the analysis (these variables are all presented in Table  1 ). The 18 patients excluded were comparable to the 77 included concerning the criteria relating to age, hospitalization, dehydration or month of inclusion (data not shown). The MCA revealed four groups of patients whose characteristics are presented in Table  1. The first group (n = 33) included all patients under six months of age and the majority of patients under two.
Single-cell RNA-seq data analysis Smart-seq2 single-cell RNA-seq data generated using malignant GBM cells were obtained from GSM3828672  43. Data normalization and pre-processing were performed using the Seurat workflow  72. Batch correction was performed using R package harmony  73. Cell-cycle scores of individual cells were assigned using Seurat::CellCycleScoring() based on the curated list Seurat::cc. genes. updated. 2019 comprising cell cycle markers.
Analysis of sequencing data FASTQ sequence files were analyzed with PinAPL-Py (http://pinapl-py. ucsd. edu/)  75. Briefly, sgRNA counts of PD20 samples relative to PD0 were compared for all experimental groups to determine sgRNA abundance changes that occurred over 20 population doublings. The SUMLFC (the sum of log fold-changes of all sgRNAs targeting the same gene, SigmaFC) algorithm was selected for gene ranking. The DDR pathways heatmap in Fig. 5D  was plotted by grouping the sgRNAs according to their corresponding DDR pathways (Supplementary Table  4 ). Log10 was applied to sgRNA counts to convert data sets to normal distribution. A Paired two-samples t test p value representing the drop out level of PD20 vs PD0 log10 (counts). -log10 (p value) was used to generate the heatmap (Supplementary Table  5 ).
We used the fmsb (version 0. 7. 3) package in R (version 4. 1. 2) to make the radar chart. For each cell line and each pathway, we individually performed a two-sided Z-test on the null hypothesis that the genes in the pathway are not on average different in dropouts. The test statistic was calculated as follows. First, using the normalized read counts from PinAPL-Py (Supplementary Table  2 ), we obtained the ratios between PD20 and PD0 for each guide and log2-transformed them. Then for each gene 𝑔, we constructed a two-sample Z-statistic comparing TMZ to DMSO as: 𝑍 ! = 𝑋 ! 𝑆 ! where 𝑋 ! is the difference in sample means and 𝑆 ! is the corresponding standard deviation. Finally, we combined the gene-specific Z-statistics in each pathway 𝑃 as: 𝑍 " = ∑ 𝑋 ! !∈" & ∑ 𝑆 ! $ !∈" which follows a standard normal distribution under the null hypothesis.
Genomic DNA from each clonal line was subject to whole-exome sequencing using NovaSeq 6000 PE150 (Novogene Co). For all FASTQ sequence files, we firstly discarded reads below the 50 bases long and reads with insufficient base qualities using Trimmomatic 0. 39  76. Next, the trimmed sequencing reads were mapped to the homo sapiens reference genome (assembly GRCh37/hg19) using BWA-MEM alignment algorithm in BWA package (version 0. 7. 17)  77. Then we converted the resulting sequence alignment map (SAM) files to binary alignment map (BAM) files using Picard 2. 23. 4 (http://broadinstitute. github. io/picard). After sorting the BAM files by SAMtools 1. 11  78  , we removed duplicates and validated the postprocessed BAM files using Picard again. To call single nucleotide variant (SNV), a base quality score recalibration was performed by using the tools IndexFeatureFile, BaseRecalibrator, and ApplyBQSR in GATK 4.
we compare the SNVs and indels to known polymorphisms (U373 MG glioblastoma) using the bcftools in SAMtools. Thus, we obtain the somatic mutations. In addition, we mapped the mutation calls in the filtered VCF files to the protein coding exon region database (https://earray. chem. agilent. com/suredesign/), and removed the mutations that are not overlapped with any regions in Agilent SureSelect DNA -SureSelect Human All Exon V6 (Supplementary Fig. 8 ).
To quantify CFP-RAD18/RPA32 percentage overlap, images acquired by an Andor Dragonfly Spinning Disk Confocal Microscope were analyzed using Imaris Microscopy Image Analysis Software (Imaris 9. 9). The percentage of surfaces overlap volume to surface of CFP-RAD18 channel was used to enumerate the CFP-RAD18/RPA32 overlap. For live cell imaging, the images acquired by Andor XD Spinning Disk Confocal Microscope were opened in Fiji, and foci-positive cells were manually counted using the cell counter plug-in.
ACKNOWLEDGMENTS The authors thank  Dr. Russell O. Pieper  from the  University of California San Francisco  for kindly sharing NHA and NHARas cell lines,  Dr. Pablo Ariel  for his assistance in microscopy image acquisition and data analysis. This study was supported by  National Institutes of Health  grants  R01 ES009558 ,  CA215347 , and  CA229530  (to C. V. ), The  University of the North Carolina Lineberger Comprehensive Cancer Center Tier   2 Innovation Award  (to Y. Y. ), The  University of North Carolina Translational and Clinical Sciences Institute  Award  UL1TR001111  (to Y. Y. ) and The  University of North Carolina Idea seed  Grant (to Y. Y. ). Microscopy was performed at the  Microscopy Services Laboratory at the University of North Carolina, Chapel Hill , which is supported in part by the  Cancer Center Core Support  grant  P30 CA016086  to the  UNC Lineberger Comprehensive Cancer Center.
Statistical analysis of the phenotypic data Data from the yield trials in Palmira (Pal13, Pal14 and Pal15), as well as the data from the BLCrV trials in Pradera (Pra17, Pra20M and Pra20P) were modeled with the mixed linear model (MLM) approach with spatial correction implemented in the R package SpATS (v1. 0-11)  (Rodríguez-Álvarez et al. , 2018). This model used the arrangement of plots in the field as a row-column cartesian plane. The genotypic term in the model was taken as a fixed effect factor to calculate the best linear unbiased estimators (BLUEs) for each trait and trial. In addition, the genotypic term was also taken as a random factor to calculate an estimate of the broad-sense heritability using the function 'getHeritability' of SpATS. To avoid biases in the estimation of heritability due to the presence of check lines in the Pradera trials (Pra17, Pra20M, Pra20P), the check lines EMP496 and PVA773 were taken as a separate fixed effect factor within the model.
Missing marker information was then imputed using Beagle (v5. 0)  (Browning et al, 2018) , setting the effective population size at 100 and using the genetic map reported by  Diaz et al, (2020). The process to construct a genotypic matrix was performed independently for each population. In addition, a joint matrix was produced combining the sequencing data from both populations and the available sequencing data of the P135-136 parental lines. The pre-breeding population P135-136 was genotyped using the DArTag targeted genotyping service offered by Diversity Arrays Technology (DArT PL, Bruce ACT, Australia). To select the SNPs to set up this platform, the sequencing data from 1,700 breeding lines and landraces that had been genotyped with GBS or whole-genome resequencing (WGS) at the CIAT Bean breeding program was assembled into a large genotypic matrix (see  Lobaton et al, 2018; Keller et al, 2020 , Diaz et al, 2020; Diaz et al.
2021a; Diaz et al. 2021b  for details about the sequencing data used). The variants in this matrix were filtered to remove SNPs and their surrounding 300 bp that fell within repetitive regions of the reference genome  (Lobaton et al, 2018) , and variants with a MAF below 0. 01. In addition, the predicted recombination frequency between SNP variants was calculated using the genetic map reported by  Diaz et al. (2020) , fitting a non-parametric Spline regression (implemented in R, v4. 0. 3) between the physical and genetic positions of the SNP variants. Using this information, one or two SNP variants per 0. 5 cM windows were selected across the reference genome. In addition, some markers that are regularly used in the breeding program at CIAT for MAS were included, producing a set of 1,861 SNP-containing target sites that were used for amplicon sequencing within the DArTag pipeline (Table  S2 ).
0. 3)  (Tello et al, 2019). This tree was visualized using Splitstree (v4. 15. 1)  (Huson and Bryant, 2006). In addition, a principal component analysis (PCA) was calculated on the SNP matrix using GAPIT (v3. 0)  (Wang and Zhang, 2021). A genome-wide association study (GWAS) was carried out independently on the AxM, Mesoamerican panel and P135-136 populations using the data from each of the six trials (Pal13, Pal14, Pal15, Pra17, Pra20M and Pra20P) and four evaluated traits (DF, DPM, Yd and BLCrV AUDP C ). These analyses were conducted using the BLINK algorithm  (Huang et al, 2019)  implemented in GAPIT (v3. 0)  (Wang and Zhang, 2021) , which uses a multiple-loci test method by combining a fixed effect model, Bayesian information criteria and linkage disequilibrium between markers. Significant associations were defined when the p value for each SNP marker was smaller than the Bonferroni-corrected threshold, which was calculated with a genome-wide type I error rate  = 0.