dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally we address the envelope of the sinusoidal interference pattern. The RIXS data cover about 20 Brillouin zones. Still the amplitude of the intensity modulation does not change strongly, at least for peaks α and β, allowing us to detect the interference pattern over a broad range of q. This is due to the nearly point-like nature of the 'slits' formed by the core hole in the intermediate states  [1] , i. e. , the 'slit width' is negligible. The q-dependent envelope rather contains valuable information on the dependence of the RIXS matrix elements on the scattering geometry and thereby on the precise wavefunctions.
RIXS measurements. RIXS measurements at the Ir L 3 edge were performed on the ID20 beamline at ESRF  [6]. We used an incident energy of 11. 2155 keV for M = Ce and 11. 2150 keV for M = Ti to maximize the resonantly enhanced RIXS intensity of the intra-t 2g excitations. An overall energy resolution of 27 meV was obtained by combining a Si(844) back-scattering monochromator and R = 2 m Si(844) spherical diced crystal analyzers  [28]. These analyzers make the RIXS setup partially dispersive, i. e. , one image on the pixelated detector covers an energy range of approximately 360 meV. Accordingly, the RIXS spectra of Fig. 3A  were measured by scanning the energy at constant q while the data in Fig. 3C  were collected by scanning q at constant energy. The data in Fig. 3B  were measured with a lower resolution of 0.
Acknowledgments. We gratefully acknowledge fruitful discussions with  M. Braden ,  I. Mazin , and  S. Trebst. This project was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Project numbers  277146847 247310070 -CRC 1238  (projects  A02 ,  B01 ,  B02 ,  B03 , and  C02 ) and  CRC 1143  (project  A05 ), respectively. The work of S. V. S. was supported by the  Russian Science Foundation  through the project  17-12-01207.
Expression and purification of recombinant PvRBPs The six PvRBPs used in this study were PvRBP1a (amino acid [aa] 160-1170), PvRBP1b (aa 140-1275), PvRBP2a (aa 160-1135), PvRBP2b (aa 161-1454), PvRBP2c Non-Binding (PvRBP2cNB, aa 501-1300) and PvRBP2P-2 (aa 161-641). Their accession numbers are PVX_098585, PVX_098582. 1, PVX_121920, PVX_094255, PVX_090325, and PVX_101590, respectively (www. plasmodb. org/plasmo). Methods for protein expression and purification of have been described elsewhere  [20, 21].
Funding: W. Q. H. is supported by both  Melbourne International Postgraduate Scholarship (MIPS) and Melbourne International Research Scholarship  (MIRS), C. T. F is under the support of  MIPS. S. K. is supported by an  NHMRC  fellowship ( GNT1141441 ). M. V. G. L was supported by  CNPq. W. H. T. is a  Howard Hughes Medical Institute-Conclusion/Wellcome Trust   International Research Scholar  ( 208693/Z/17/Z ) and funded by  National Health and Medical Research Council  ( GNT1143187  and  GNT 1160042 ). I. M. was supported by an  NHMRC   Research Fellowship  ( APP1043345 ). This work was supported by the  TransEPI consortium  (funded by the  Bill & Melinda Gates Foundation ),  National Institutes of Health  (Grant# 5R01AI104822 ), the  National Health & Medical Research Council  ( APP1102297 AND 1092789 ), and the  Victorian State Government Operational Infrastructure Support.
The HS-GC-MS conditions were as follows. C, respectively. Results were obtained in scan mode at 4 scans/s in the mass range (m/z) of 35-350 atomic mass units. VOCs were identified by comparison of GC retention times and mass spectra with those of the standard compounds (LGC Standards, France; Techlab, France and Cluzeau Info Labo, France). These standard compounds were selected based on a review of published data of VOCs detected in meat and seafood spoiled in the presence of B. thermosphacta  (Illikoud et al. , 2018a). Peak area (in arbitrary unit) was used as quantitative data to monitor the relative changes of VOCs over storage and, where appropriate, to correlate these findings with bacterial strain as well as with the studied food matrices. This relative quantification was scored from -(no detection) to +++ (highest detection).
The products were then purified and enriched with 15 PCR cycles. The final cDNA libraries were validated with a Fragment Analyzer (Advanced Analytical, Ankeny, IA, United States) and quantified with a KAPA qPCR kit (Kapa Biosystems, Wilmington, MA, United States). On one sequencing lane of a flowcell V4, the 12 libraries (2 strains × 2 juices × 3 replicates) were pooled in equal proportions, denatured with NaOH, and diluted to 12 pM before clustering. Cluster formation, primer hybridization and single-end read, and 50-cycle sequencing were performed on cBot and HiSeq2500 (Illumina, San Diego, CA, United States), respectively. Image analysis and base calling were performed using the HiSeq Control Software and Real-Time Analysis component. Demultiplexing was performed using Illumina's sequencing analysis software.
The quality of the data was assessed using FastQC from the Babraham Institute and the Illumina software SAV (Sequence Analysis Viewer). FastqQ Screen  (Wingett and Andrews, 2018)  has been used to search sequences from commonly sequenced genomes in the facility that produced the data, and potential contamination sources: Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Bos taurus, Gallus gallus, Caenorhabditis elegans, Saccharomyces cerevisiae, Candida albicans, Aspergillus niger, Escherichia coli, Streptococcus pyogenes, Bacillus cereus, Chlamydia trachomatis, Enterococcus faecalis, Propionibacterium acnes, Staphylococcus aureus, a set of Mycoplasma, and a set of rRNA from different species. The PhiX genome (used as illumina control sequences) and illumina adapters (TruSeq and Nextera) have also been searched.
Transcriptome Analysis Transcriptomic high-throughput sequencing data were analyzed using a bioinformatic pipeline implemented in the Microscope platform  (Médigue et al. , 2017). The current pipeline is a "Master" shell script that launches the various parts of the analysis (i. e. , a collection of Shell/Perl/R scripts) and controls for all tasks having been completed without errors. In a first step, the RNA-Seq data quality was assessed by including options like read trimming or merging/split paired-end reads. In a second step, the reads of each sample were mapped against the corresponding B. thermosphacta genome sequences: CD337 complete genome sequence ERZ500814 and TAP175 draft genome sequence ERZ500815  (Illikoud et al. , 2018b) , using the SSAHA2 package  (Ning et al. , 2001). This latter combines the SSAHA searching algorithm (sequence information is encoded in a perfect hash function), aiming at identifying regions of high similarity, and the cross-match sequence alignment program  (Ewing et al.
, 1998) , which aligns these regions using a banded Smith-Waterman-Gotoh algorithm  (Smith and Waterman, 1981 ). An alignment score equal to at least half of the read is required for a hit to be retained. To reduce the false-positive discovery rate, the SAMtools (v. 0. 1. 8) (Li et al. , 2009)  was then used to extract reliable alignments from SAM formatted files. The number of reads matching each genomic object harbored by the reference genome was subsequently computed with the Bioconductor-GenomicFeatures package  (Carlson et al. , 2011). If reads matched several genomic objects, the count number was weighted in order to keep the same total number of reads. Finally, the Bioconductor-DESeq package  (Anders and Huber, 2010)  with default parameters was used to analyze raw count data and test for differential expression between conditions.
Characterization of Differentially Expressed Genes RNAs were extracted at day 4, i. e. , approximately when strains reached the stationary phase and produced various VOCs. Cells were thus considered metabolically active. A total of 248,189,013 reads were generated using HiSeq 2500 single-end (Illumina, San Diego, CA, United States). For each transcriptome, more than 98% of the generated reads had a PHRED quality score higher than 30, meaning that the reads were of high quality (i. e. , base call accuracy higher than 99. 9%). Summary of data is given in Supplementary Table  S1. We searched for genes whose expression [log2 fold change (LFC 2 ) > 2; adjusted p-value (FDR) < 0. 05] was increased during growth in meat compared to shrimp juice. We focused first on the genes differentially expressed by both strains and then on those whose expression varied in only one of the two strains.
ACKNOWLEDGMENTS The LABGeM (CEA/IG/Genoscope and CNRS UMR8030) and the France Génomique National Infrastructure (funded as part of Investissements d'Avenir Program managed by Agence Nationale de la Recherche, contract ANR-10-INBS-09) were acknowledged for support within the MicroScope annotation platform. The French National Research Agency (ANR) was acknowledged for the financial support in the framework of the REDLOSSES project (ANR-16-CE21-0006) that contributed to the setup of the experimental procedure of volatilome analysis.
DNA ends were repaired, A-tailed, and ligated to adapters. Next, fragments were PCR amplified (8 cycles) and quality checked on a DNA 7500 chip on a 2100 Bioanalyzer (Agilent). Library sizes of ϳ800 and 1,200 bp were isolated, validated (Bioanalyzer), and titrated with quantitative PCR (qPCR). After denaturation, the libraries were seeded on a flow cell (MiSeq v2, 2 ϫ 150 bp) at a density of 16 pM. The sequencing rendered 3,963,212 (800-bp library) and 2,015,074 (1,200-bp library) paired-end reads. Data processing was done with default parameters. Adapters (Cutadapt  [7] ) and low-quality sequences (Sickle 1. 33  [8] ) were removed. After verifying the quality of the processed data (FastQC  [9] ), de novo assembly was performed (SPAdes v3. 9. 1  [10] [11] [12] [13] ). Three apparent extrachromosomal elements were detected with plasmidSPAdes (  13 ), (i) plasmid p576, (ii) bacteriophage phiX174 (added as a control for amplification and sequencing  [14] ), and (iii) sequences similar to part of the Brevibacillus laterosporus DSM25 bogC gene cluster.
ACKNOWLEDGMENTS We gratefully acknowledge  David Abia  and  Ramón Peiró-Pastor  for their help with the bioinformatics analyses. This work was supported by the  Ministry of Economy and Competitiveness of the Spanish Government  (grant number  BIO2016-77883-C2-1-P  to W. J. J. M. , which also funded J. V. -C. , A. M. -A. , and C. G. -C. ). Part of the economic support of the aforementioned grants was provided by the  Agencia Estatal de Investigación (AEI) ,  Fondo Europeo de Desarrollo Regional (FEDER) ,  European Union (EU). This research was also supported by institutional grants from the  Fundación Ramón Areces  and  Banco de Santander  to the  Centro de Biología Molecular Severo Ochoa. The funders had no role in the study data collection and interpretation, or decision to submit the work for publication.
Data availability. This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under accession number UWJF00000000. The version described here is the first version. Raw sequence reads have been submitted to the Sequence Read Archive (SRA) (accession numbers ERR2811649 and ERR2811650, corresponding to 2 ϫ 150-bp paired-end sequences of 800-and 1,200-bp fragments, respectively). The p576 sequences are available under DDBJ/ENA/GenBank accession numbers LR026976 (Sanger) and LR026977 (NGS).
Phylogenetic analysis of Anopheles APL1 gene copy number We confirmed by manual sequencing the existence of a single APL1 gene in Anopheles stephensi, as predicted by bioinformatic assembly and analysis short-read sequencing. Primers used for PCR sizing and for sequencing are indicated in Additional File 1: Figure  S1. The annotated genes in the VectorBase genome database (24) corresponding to A. stephensi APL1 are ASTE016290 in A. stephensi SDA-500 strain, and ASTEI02571 in A. stephensi Indian strain. The Vectorbase assemblies and annotations used, current as of January 2019 were: SDA-500 strain, assembly AsteS1, gene set: AsteS1. 7, dated 22 Oct 2018; and Indian strain, assembly AsteI2, gene set AsteI2. 3, dated 21 Feb 2017. For phylogenetic analysis of APL1 copy number as presented in Additional File 2: Figure  S2 , APL1C orthologues were obtained from Vector Base and sequence was extracted for +/-60,000 base pairs (bp) around the APL1C orthologue and its flanking sequence.
Mosquito mortality curves Mosquito mortality was monitored in cages of at least 50 mosquitoes, recorded every 2 days until all mosquitoes died. Treatment with dsRNA was done in 3 d old mosquitoes and recording of mortality began 4 d after dsRNA injection in 7 d old mosquitoes. Blood feeding with or without P. yoelii was done at 4 d after dsRNA injection in 7 d old mosquitoes, and recording of mortality began 3 d following the normal or infected bloodmeal in 10 d old mosquitoes. Beginning at adult emergence, mosquitoes were maintained with 10% sucrose, and in the case of antibiotic treatment, supplemented with penicillin 62. 5 µg/mL, streptomycin 100 µg/mL, and gentamicin 50 µg/mL. Two to three replicates were performed for each condition tested. A Cox proportional hazards regression model was fitted to the data using treatments as predictor terms  (34, 35).
Acknowledgments We thank the  Institut Pasteur core facility , the  Center for the Production and Infection of Anopheles (CEPIA) , for gametocyte culture and mosquito infection, and  Corinne Genève ,  GGIV Institut Pasteur , for rearing and infecting mosquitoes. We thank  Olivier Sylvie ,  Inserm  unit on Molecular biology and immunology of malaria liver infection, for provision of P. yoelii fluorescent strain delta-p230p-GFP.
Funding This work received financial support to KDV from the  European Commission ,  Horizon 2020 Infrastructures # 731060 Infravec2 ;  European Research Council , Support for frontier research, Advanced Grant # 323173  AnoPath; and  French Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID  and to MMR from  National Institutes of Health ,  NIAID  # AI121587. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Iodide in the sea-surface plays an important role in the Earth system. It modulates the oxidising capacity of the troposphere and provides iodine to terrestrial ecosystems. However, our understanding of its distribution is limited due to a paucity of observations. Previous efforts to generate global distributions have generally fitted sea-surface iodide observations to relatively simple functions of sea-surface temperature  (Chance et al. , 2014;  MacDonald et al. , 2014). This approach fails to account for coastal influences and variation in the bio-geochemical environment. Here we use a machine learning regression approach (Random Forest Regression) to generate a high resolution (0. 125 • ×0. 125 • , ∼12. 5 km), monthly dataset of presentday global sea-surface iodide. We use a compilation of iodide observations (Chance et al.
Similar to previous work, higher concentrations are predicted for the tropics than for the extra-tropics. Unlike the previous parameterisations, higher concentrations are also predicted for shallow areas such as coastal regions and the South China Sea. Compared to previous work, the new parameterisation better captures observed variability. The iodide concentrations calculated here are significantly higher (40% on a global basis) than the commonly used MacDonald et al. (2014) parameterisation, with implications for our understanding of iodine in the atmosphere. The global iodide dataset is made freely available to the community (Sherwen et al. (2019);.
Introduction Iodine in seawater exists in two major forms, iodide (I -) and iodate (IO - 3 ). Total inorganic iodine (I -+ IO - 3 ) remains approximately constant across most of the oceans, but the ratio of iodide to iodate varies has been shown to vary by  Chance et al. (2014)  with latitude, depth and oxygen level. A small amount of iodine (<10%) is thought to be present in organic forms in the open ocean (e. g. Wong (1991) ), however, this may be a larger fraction in coastal waters (e. g. Wong and Cheng (1998) ). The processes controlling the distribution of the ratio between iodide and iodate remain poorly understood  (Chance et al. , 2014). A reason for gaps in our understanding is that the observational dataset of iodide and iodate remains relatively sparse  (Chance et al. , 2014 (Chance et al.
, 2009)  and results in the emission of inorganic iodine (HOI and I 2 ) into the atmosphere  (Carpenter et al. , 2013) , which can subsequently catalytically destroy ozone  (Chameides and Davis, 1980). A number of model studies have discussed the impact of oceansourced iodine on atmosphere composition in the context of air quality  (Gantt et al. , 2017; Sarwar et al. , 2016; Sherwen et al. , 2017b) , climate  (Sherwen et al. , 2017b; Saiz-Lopez et al. , 2012) , aerosols  (Sherwen et al. , 2017a) , and stratospheric ozone  (Saiz-Lopez et al. , 2015). These atmospheric modelling studies have used relatively simple parameterisations for predictions of sea-surface iodide. Early parameterisations for sea-surface iodide were based on limited datasets, and used either an observed range of iodide concentrations  (Coleman et al.
, 2018)  and Gaussian Process emulation to explore model biases on a global scale  (Lee et al. , 2011; Revell et al. , 2018). Here, we use a recently expanded compilation of sea-surface iodide observations  (Chance et al. , 2019b)  to build a new seasurface iodide parameterisation using a data-driven machine learning approach. We choose to use the Random Forest Regressor (RFR) algorithm  (Breiman, 2001; Pedregosa et al. , 2011) , which is relatively simple and produces results that are also easy to understand. We aim to be able to predict global sea-surface iodide based on observations and ancillary physical and chemical variables (e. g. sea-surface temperature, depth, and salinity etc. ) from a number of publicly available sources. We first describe the input datasets we use (Sect. 2), then we explain the methodology taken (Sect.
3), and finally present the predictions at observational locations and globally (Sect. 4). We make the resulting high resolution, global, monthly dataset of predicted iodide available to the community  (Sherwen et al. (2019) ; DOI:https://doi. org/10/gfv5v3). When new observations become available, they will be incorporated into the model and updated versions will be provided through a "living data" model. et al. (2019b)  provides a compilation of the available 1342 sea-surface (< 20 m depth) iodide observations. The dataset is available from the British Oceanographic Data Centre (BODC,  Chance et al. (2019a) ; DOI:https://doi. org/10/czhx).
Chance It includes 45 % more data points, and has greater spatial coverage, than the previous compilation of 925 observations  (Chance et al. , 2014). Observations are categorised in  Chance et al. (2019b)  as "coastal" or "non-coastal", according to the designation of their static Longhurst biogeochemical province  (Longhurst, 1998). We adopt the same categorisation here. This sea-surface iodide dataset then forms the dependent variable for our regression. We require a number of physical, chemical and biological parameters as the independent variables in our regression models. Consistent in-situ measurement of these parameters are not available for the iodide observations. Thus we have used a number of ancillary datasets (Table  1 ) to provide this information. There are a number of criteria for these datasets: they need to be available at an appropriate resolution as a gridded product; they need to represent potential processes that could control iodide concentrations and they need to be in some way orthogonal to the other independent variables.
Gridded datasets of dissolved organic carbon (e. g. Roshan and DeVries (2017) ) and phytoplankton primary productivity (e. g. Behrenfeld and Falkowski (1997) ) may have some usefulness, but they themselves are built using statistical models with other variables and thus we do not use those here. The selected ancillary variables (Table  1 ) were first extracted from their native resolution using the nearestneighbour method, onto a consistent high-resolution monthly grid (0. 125 • x0. 125 • , ∼12. 5km). This horizontal resolution was used as this is the highest resolution of the current generation of global atmospheric chemistry simulations  (Hu et al. , 2018). We calculate monthly means because the chemical lifetime of iodide in the surface oceans is thought to be at least several months  (Campos et al.
A key difference of this approach is also that only a subset, the "training" set, is used to build the model and the rest (or "withheld" set) is then used to test the performance of the model. Here we use 80 % of the data for the "training" set and use the remaining 20 % as the "withheld" set (also commonly referred to as the "testing set"). To ensure that the models built are generalisable and mitigate overfitting, the Random Forest approach used here artificially increases the randomness within the forest  (Pedregosa et al. , 2011). This is done by randomly combining single decision trees by an approach referred to as "bootstrap aggregation" or "bagging"  (Breiman, 2001; Tong et al. , 2003). This additional "bagging" approach randomly samples observations within the training dataset and so mitigates over-fitting of the trees to the dataset  (Friedman et al.
, 2009). Furthermore, to maintain the statistical distribution between the training and withheld datasets and the dataset as a whole, a stratified sampling approach is used to randomly select data within the quartiles of the dataset. Machine learning algorithms can generally be tuned to increase performance using settings called hyperparameters. However, Random Forests are known to generally perform well without tuning. The default hyperparameters therefore were used here  (Pedregosa et al. , 2011) , except for increasing the number of trees ("n_estimatators") from 10 to 500. Mean Square Error (MSE) was used as the criterion for evaluating each split (also referred to as a "node"). The maximum number of "features" (the ancillary variables provided to the algorithm, such as temperature or nitrate concentration) considered when looking for the best split is set to the number provided to the algorithm.
Error and uncertainty calculations Understanding the errors and uncertainties in the global iodide distribution is important due to any sensitivities to this value within the modelled Earth system. We consider three sources of error in our predictions: the "dataset selection" error due to the splitting of the dataset into training and withheld parts; the "model selection error" due to the choice of dependent variables; and the "observational error" on the iodide measurements. To quantify the "dataset selection" error, we construct models from 20 pseudo-random splits of the dataset into training and withheld parts. The hyperparameters and input ancillary variables are kept the same for the generation of the 20 models, so that the only difference between the models is the training dataset. These 20 models are then used to predict the withheld data.
Performance metrics (Root Mean Square Error (RMSE) and average absolute prediction etc. ) can then be calculated for each model. This gives a range of 20 values, which can then be converted to a percentage range as the error. This is done by dividing the maximum within the range over the maximum value to give a maximum value and minimum within the range over the maximum value to give minimum value. Significant differences between model's performance metrics would suggest important sensitivity to the training / withheld dataset splits. We define the "model selection" error as the uncertainty resulting from the choice of input ancillary variables. A number of combination of input variables are possible in generating the models, and each will generate a different prediction. We quantify this error as the difference in performance against the withheld dataset and prediction value (e.