dataesr/scientific-paragraphs-categorization huggingface dataset:
Larval Biometry Here, ∼15 larvae per batch (3×) of each treatment for study 1 (algae/control) were randomly sampled on 4, 8, 12, and 14 dph, while ∼15 larvae per replicate (3×) and treatment for study 2 (food/control) were randomly sampled on 15, 16, 18, 20, and 22 dph. Larvae were anesthetized using MS-222 (Sigma-Aldrich Chemie, Steinheim, Germany) and photographed using a zoom stereomicroscope (SMZ1270i fitted DS-Fi2 Camera Head, Nikon Corporation, Tokyo, Japan) for assessment of larval standard length, yolk-sac, and oil drop area as well as total body area using the NIS-Elements D software (Nikon Corporation, Tokyo, Japan).
From the resulting total RNA, 680 ng were transcribed using the qScript TM cDNA Synthesis Kit (Quantabio, Germany) according to the manufacturer's instructions, including an additional gDNA wipe out step prior to transcription [PerfeCta R DNase I Kit (Quantabio, Germany)]. The expression levels of 11 target and 2 reference genes were determined by quantitative real-time PCR (qRT-PCR), using specific primers. Primers were designed using primer 3 software v 0. 4. 0 1 based on cDNA and predicted cDNA sequences available in Genbank databases (Table  1 ). All primers were designed for an amplification size ranging from 75 to 200 nucleotides. The elongation factor 1 α (ef1α) and 40S ribosomal S18 (rps18) genes were chosen as housekeeping genes since qBase+ software revealed that these mRNA levels were stable throughout analyzed samples (M < 0.
Nucleic Acid Analysis For this analysis, ∼10 larvae per batch (3×) from each treatment in study 1 (algae/control) were randomly sampled at 12 and 14 dph, while ∼10 larvae per replicate (3×) and treatment in study 2 (food/control) were randomly sampled on 16, 17, 18, 20, 22, and 24 dph. Larvae were immediately euthanized using MS-222 and frozen at -20 • C. Thereafter, individual larvae were homogenized using 250-300 µl of sodiumdodecyl sulfate Tris buffer (Tris 0. 05; NaCl 0. 1M; SDS 0. 01%; EDTA 0. 01; pH 8) and the tissue homogenates were treated using the method described by  Malzahn et al. (2003). Subsequently, fluorescencephotometric measurements using a specific nucleic acid dye [Ethidium bromide (EB), 2. 5 mg•ml -1 ] were used to determine   (Henkel et al. , 2012). The table also lists function, corresponding database and accession number of target gene sequences, as well as position of the primer on the referenced sequence.
Statistical Analysis All data were analyzed using SAS statistical software (version 9. 1; SAS Institute Inc. , Cary, NC, United States). Residuals were tested for normality using the Shapiro-Wilk test and homogeneity of variances was tested using a plot of residuals vs. fit values (PROC GLOT, SAS Institute 2003). Data were log 10 or arcsine square-root-transformed when data deviated from normality and/or homoscedasticity  (Zar, 1996). Statistical models were used to investigate effects of green-water and firstfeeding on larval biometry, gene expression, and nucleic acid (RNA/DNA) content. Here, we analyzed the data using a series of repeated measures mixed-model ANOVAs (PROC MIXED; SAS Institute 2003). Models contained treatment (algae/control or food/control) and age (4-14 dph or 15-24 dph) main effects as well as the treatment × age interaction.
ACKNOWLEDGMENTS We would like to thank  Maria Krüger-Johnsen ,  Peter Lauesen , and  Christian Graver  for collection of broodstock in Northern Ireland as well as  Johanna S. Kottmann ,  Elisa Benini , and  Valerio Cappadona  for help during experiments. Moreover, we would like to thank  Prof. Einar Eg Nielsen  and  Prof. Thorsten Reusch  for providing laboratory space as well as  Dorte Meldrup  and  Brian Klitgaard  for assistance during laboratory work.
FUNDING Funding was provided by the project Eel Hatchery Technology for a Sustainable Aquaculture (EEL-HATCH) supported financially by the Innovation Fund Denmark, Grant reference no. 5184-00093B. Moreover, Politis SN, received travel grants from the COST Office (Food and Agriculture COST Action FA1205: Assessing and improving the quality of aquatic animal gametes to enhance aquatic resources. The need to harmonize and standardize evolving methodologies, and improve transfer from academia to industry; AQUAGAMETE). Butts IAE, was partially supported by the USDA National Institute of Food and Agriculture, Hatch project 1013854.
Analysis of [ 14 C]-labelled sterols of the mutant and parental cell lines incubated with L-[U-14 C]-leucine showed that incorporation of radio-labelled carbons into sterols is abolished in both Δivdh cell lines. However, incorporation of L-[U-14 C]-threonine into fatty acids is not affected, which demonstrates that IVDH is involved in sterol biosynthesis from leucine (Fig  4C ). Interestingly, incorporation of L-[U-14 C]-threonine into sterols increased by 2-and 1. 5-fold in the Δivdh-C3 and Δivdh-C8 cell lines, respectively. The MitoProt program predicts with a high probability (0. 93) that the first 33 N-terminal amino acids of IVDH constitute a mitochondrial targeting signal. The intracellular localization of IVDH was investigated by immunofluorescence microscopy of PCF expressing the fulllength IVDH containing or lacking the first 33 N-terminal residues and fused with the EGFP at its C-terminal extremity (IVDH-1/412-EGFP and IVDH-34/412-EGFP, respectively).
Aliquots (10-50 μl) of Fractions 1 and Fractions 2 (FAMEs) were then loaded onto distinct HPTLC plates (Merck) developed in hexane/ethylether/acetic acid (90:15:2, v/v) to isolate and quantify sterols (R F 0. 20), and FAMEs (R F 0. 90), respectively. Sterols and FAMEs were identified by co-migration with known standards. Their radio-labelling was then determined with a STORM 860 (GE Healthcare). For the calculation of the number of nanomoles of precursor incorporated into fatty acids or sterols, we considered that one acetyl-CoA molecule is produced per molecule of acetate, threonine and glucose consumed. When all data were normalized to the results of the parental cell line set at an arbitrary value of 100, for the PCL samples, the differences between PCL samples did not exceed ±75% of values presented in Fig  2.
Statistical analyses Tsetse fly mortality and midgut infection rates were subjected to statistical analyses with the software XLSTAT Version 2016. 05. 34687 (Addinsoft) integrated to Excel 15. 31 (Microsoft). A two-way ANOVA test including 3 parameters (mortality rates, infection rates and number of dissected flies) was performed with intergroup comparisons by Tukey ad hoc post-tests with α = 0. 05. Infection rates were plotted as mean ± SD.
Acknowledgments We are grateful to  Dr Matt Gould  and  Pr Michael Boshart  ( University of Munich, LMU, Germany ) for providing us with the pEnT6P vector. The use of the facilities and expertise of the  Biocenter Oulu core facility , a member of  Biocenter Finland and Instruct-FI , is gratefully acknowledged. Lipidomic analyses were performed on the  Bordeaux Metabolome Facility-MetaboHUB  ( ANR-INBS-0011 ).
This research was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Universitede Bordeaux , the  Agence Nationale de la Recherche (ANR)  through grants  ACETOTRYP  (grant number  ANR-2010-BLAN-1319-02 ) of the ANR-BLANC-2010 call and GLYCONOV (grant number  ANR-15-CE15-0025 -to be the main source, if not the only one, used by trypanosomes in the infected insect vector digestive tract to feed the mevalonate pathway. Pasteur and by the  European Commission   FP7 Marie Curie Initial Training Network "ParaMet " (grant number  290080 ). EB was funded by a doctoral fellowship from  French National Ministry for Research and Technology  (doctoral school  CDV515 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
10 (15) and set in "paired end assembly, careful mode," yielded 91 contigs (Ն200 bp), which were arranged with BioEdit version 7. 0. 5 (  16 ) and analyzed with QUAST version 4. 6. 3 (17), set at "QUAST skip contigs shorter than 200 bp. " The genome total length was 6,766,660 bp, with a GC content of 62. 06% and an N 50 value of 146,305 bp. Its complete 16S rRNA gene sequence showed 99. 9% identity with more than 20 P. putida strains in the GenBank 16S database. A public gene annotation was provided by the NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAP) version 4. 1 (18). Annotation was also done with the Rapid Annotations using Subsystems Technology (RAST) server version 2. 0 (19) with the ClassicRAST annotation scheme. PlasmidFinder version 1. 3 (20) and plasmidSPAdes (21), both with default settings, did not detect any plasmids.
Data availability. This whole-genome shotgun project was deposited at DDBJ/ EMBL/GenBank under the accession number NFSB00000000. The version described in this paper is the first version, NFSB01000000. The 91 contigs have been deposited under the accession numbers NFSB01000001 to NFSB01000091. Raw sequencing data sets have been registered in the NCBI Sequence Read Archive database  (23)  under the accession number SRR5513018.
Strain genotyping with k-mers To genotype a training panel of n assembled bacterial genomes using k-mers, we first build a large matrix encoding the presence or absence of all k-mers of length k = 31 within each strain, using the Ray software  [22]. Considering k-mers of length 31 is a safe default choice in this context, offering in general a good trade-off between sequence specificity and computational efficiency  [18, 19]. Since k-mers occurring in too few or too many strains have a limited predictive power, we then discard k-mers found in one or two strains only, or in all the strains but one or two. Finally, since this k-mer based representation is highly redundant  [18, 19] , we define sets of equivalent k-mers from k-mers having the same presence/absence profile in all the strains. For the sake of model building, we can only keep one of them, which in practice dramatically reduces the number of k-mers to consider.
The function L is the logistic loss function, which quantifies the discrepancy between the true phenotypes y i of the strains and the predictions f (x (i) ) obtained by the model. The λ parameter achieves a trade-off between this empirical error and the Lasso regularization term, and is usually optimized by crossvalidation. Effectively tuning the Lasso regularization parameter is however a challenging problem. Cross-validation techniques usually succeed to build models of good predictive power, but the set of variables with non-zero coefficients is known to be unstable with respect to small variations in the training dataset or in the value of regularization parameter. Several resampling based strategies have been proposed to cope with this issue  [21, 23, 24]. We rely on the stability selection approach of  [21] , which is illustrated in Additional file 1: Figure  S1.
It consists in subsampling several times the entire dataset (step 1), solving for each subsample the Lasso-penalized logistic regression (step 2), and computing for each k-mer the proportion of models in which it was selected. This process is repeated for several values of the regularization parameter, which allows to define "stability paths" quantifying the probability of selecting each k-mer along the grid of regularization values (step 3). Instead of optimizing the regularization parameter λ itself,  [21]  propose to consider a threshold on the probability of being selected at some point on the regularization path: every k-mer whose stability path exceeds this threshold gets ultimately selected. Having identified stable k-mers, we finally fit a standard un-penalized logistic regression model that can be used to make predictions on new genome sequences (step 4).
We note however that the issue of optimizing the regularization parameter of the Lasso is cast with this approach into that of optimizing the stability threshold, which can be done by cross-validation. To do so, we repeat the whole process described in Additional file 1: Figure  S1  within each cross-validation fold, as detailed in "Models obtained" section. Using this optimized probability threshold, we repeat the process on the entire dataset to build the final model. In practice, we relied on the R software, and more precisely on the glmnet package  [25] , to compute regularization paths. Moreover, we considered subsamples involving half of the dataset and normalized each k-mer by its L 2 norm to ensure an homogeneous level of penalization, as suggested and discussed in  [21].
Model interpretation We aim to annotate the k-mers included in the model, to identify whether they fall within known genes or regulatory regions, and provide their putative function when possible. We consider the set of equivalent k-mers associated to each k-mer included in the model and try to reconstruct the longer stretch(es) of sequence(s) that they originate from. A set of equivalent k-mers usually corresponds to a larger sequence perfectly conserved across several strains of the panel, and sometimes to several such sequences in total LD. Following the terminology used by genome assembly algorithms, we assemble equivalent k-mers into longer unitigs, defined as the longest sequence(s) that can be obtained when they overlap by exactly k -1 nucleotides, using the bcalm2 software  [27]. A unitig is interesting to represent a set of equivalent kmers because it has the same presence/absence profile on the training genomes, but is in general longer that the individual equivalent k-mers, hence easier to annotate.
M. tuberculosis study Dataset constitution We gathered two datasets from previous studies. The training dataset was taken from  [19] , who recently made available a set of 1306 assembled M. tuberculosis genomes, together with binary resistance phenotypes relating to 7 drugs, namely ethambutol, ethionamide, isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin. The resistance phenotype of each strain was not always available for each drug, but for each drug the number of resistant and susceptible strains was reasonably high (Table  1 ). The validation dataset involved 1586 strains 1 that were previously used to validate the performance of the Mykrobe software  [7]. Binary resistance phenotypes were available for 5 out of the 7 antibiotics included in the training panel, but not for ethionamide and ofloxacin.
We used moxifloxacin as a proxy for ofloxacin resistance, since these two drugs belong to the same family and exhibit a very high level of cross-resistance  [28]. We note that a variable number of each phenotype was available for each antibiotic, with in particular only nine strains resistant to kanamycin and ofloxacin (Table  2 ). This made estimating the sensitivity of the predictive models for these drugs uncertain. We also note that we directly worked from raw sequencing data to obtain predictions, that is, without relying on a prior step of genome assembly. A total of 19,876,230 distinct k-mers of length 31 were obtained from the 1306 training (assembled) genomes. 5,113,633 k-mers remained after filtering those occurring in less than three strains or in all the strains but one or two, which approximately resulted in a fourfold reduction.
In both cases we considered a grid of 200 candidate values of the regularization parameter. It was defined by the glmnet software and ranged in a log scale from a maximum value defined as the smallest value ensuring that at least one variable is selected (i. e. , has a non-null coefficient), to a minimum value defined as this maximum value divided by 10 4. For the stability selection approach, we resampled 100 times the training dataset. Table  1  summarizes the predictive performance and number of k-mers selected. As expected from  [21] , the stability procedure led to sparser signatures than the classical L 1 penalization, especially for the ethambutol, kanamycin, and streptomycin antibiotics, for a slight decrease in terms of AUC. The predictive performance remained comparable, with the largest drop of 3 points observed for ethioniamide.
6. Middle : the regularization path obtained by fitting a L 1 penalized logistic regression model across the entire dataset, with k-mers colored according to the color code defined from the left panel. Right : number of k-mers selected by the stability selection approach for thresholds ranging from 0. 6 to 0. 8 met by modifying the decision threshold of the regression logistic model, which was set by default to 0. 5. This is illustrated in Fig. 2  for ethambutol, and in Additional file 1: Figure  S5  for the other antibiotics. The flexibility offered by the logistic regression model to control the trade-off between sensitivity and specificity can be useful in a diagnostics context to meet the target performance set by regulatory agencies. We noted finally that the sequencing depth greatly varied in the validation set, with a minimum value around 20 and a maximum greater than 700.
Model interpretation To interpret the predictive models obtained, sets of equivalent k-mers were assembled into unitigs, as described in "Model interpretation" section, and blasted against the H37Rv reference genome. The unitigs were highly conserved, with a coverage equal to 100% for all the unitigs and a minimum percent identify equal to 96. 7% (rifampicin model, unitig #3). Each set of equivalent k-mers corresponded to a single unitig whose length ranged from 31 (the size of the individual k-mers) to 61 nucleotides, with a median value of 44. 5 nucleotides (Additional file 1: Figure  S7 ). These unitigs were easier Fig. 2  Ethambutol ROC curve obtained using the k-mers based signature evaluated in the validation dataset. The orange dot represents the performance obtained by the Mykrobe predictor, and the blue one to our k-mer based approach when using the default threshold of 0.
Note that the fabG1 gene is located just before the inhA gene, which is one of the two main markers of resistance to isoniazid and ethionamide. Some mutations associated to resistance said to originate from the promoter region of inhA, as for instance in  [9] , could actually be considered to fall within the fabG1 gene. We noted that some k-mers were part of in several signatures. This was probably due in large part to the level of correlation between resistance phenotypes observed within the training panel (Additional file 1: Figure  S8 ). While in some cases such correlation is expected, for instance between rifampicin and isoniazid or between ethionamide and isoniazid  [12] , it may also be a consequence of a peculiar constitution of the training dataset due to a sampling bias, as discussed in "Conclusion" section.
There are at least two reasons for that. First, the library of mutations was obtained by compiling results of several studies, involving altogether probably a much greater number of strains than used here. Reproducing our approach on a more exhaustive dataset would most likely lead to selecting more k-mers. However, we noted that k-mers allowed to represent in a concise way complex patterns of genetic variations, as illustrated in Fig. 4 --> KAN -STR --> KAN -STR --> STR --> STR --> STR --> ISO -ETH --> ISO -ETH --> ISO -ETH --> EMB --> EMB --> EMB --> EMB --> ISO --> RIF --> RIF --> RIF --> RIF --> RIF --> OFL --> KAN Fig. 3  Signatures annotation at the unitig level. In total, 22 unitigs falling in 10 genes were retained. Known target / antibiotics association are shown on the right hand side of the figure.
S. aureus study We applied the same procedure to S. aureus. The training dataset was taken from  [6]. It involved 501 genomes and 6 antibiotics, namely ciprofloxacin, erythromycin, fusidic acid, methicillin, penicillin and tetracyclin. Three other antibiotics were not considered here because the number of resistant strains was too limited (7 and 3 out of 501 for rifampicin and gentamicin respectively, and 2 out of 176 for mupirocin). The validation dataset included 470 genomes and phenotypes, and was also used in  [7]  to demonstrate the performance of the Mykrobe predictor software. Additional file 1: Table  S1  gives the number of resistant and susceptible strains for these 6 antibiotics. Table  3  summarizes the results obtained. We first noted that the stability selection approach also led to very sparse models (1 to 5 k-mers per model), often sparser than the classical Lasso.
By selecting them in a discriminative fashion, our datadriven strategy allows to build complex prediction rules from a limited number of k-mers, as shown here for M. tuberculosis. A potential drawback of this approach, however, is that it intrinsically relies on the level of information provided by the training dataset. In particular, if the level of genomic variability around a causal determinant (e. g. , a SNP) is under-represented in the training data because of a sampling bias, our approach will lead us to build too large sets of equivalent k-mers, that will not be detected as such in a new strain showing a different genomic context around the determinant. The various strategies proposed to summarize equivalent k-mers may therefore be useful to cope with this issue. Moreover, the study led on M.
Coll et al. [9]  showed that specific mutations tended to co-occur among multidrug resistant strains, and evidence has been reported on other bacterial species that a mutation conferring resistance to a given antibiotic could also increase the level of resistance to other antibiotics  [30]. This observation also suggests, however, that explicitly learning jointly these predictive models within a multi-task learning framework may be a promising way to exploit such correlation patterns. Several extensions of the Lasso have been proposed to enforce tasks to share a common support, depending on their level of correlation  [31]. They could provide an interesting way to study and leverage such cross-resistance mechanisms. If however this correlation is specific to the training dataset, hence results from a sampling bias, it can clearly compromise the generalization of the model.
Drouin et al. [18]  and Davis et al. [19]  proposed different strategies to compensate for this correlation while learning predictive models, relying respectively on a subsampling of the dataset, or a post-processing of the list of k-mers selected. Within the penalized regression framework considered here, an alternative strategy could be to rely on multi-task appproaches enforcing tasks to have disjoint supports  [32]. More generally, correlation between phenotypes is an issue to establish causal relationships between genetic determinants and antibiotic resistance. It represents a confounding factor for bacterial genome-wide association studies that should be taken into account, as it is commonplace for population structure  [17]. The versatile framework of the penalized logistic regression offers many perspectives to further investigate bacterial genotypes/phenotypes relationships.
Fluorescence light was spectrally filtered with emission filters (ET700/75m, Chroma Technology) and imaged on an EMCCD camera (iXon X3 DU-897, Andor Technology). The microscope was equipped with a motorized stage (MS-2000, ASI) to translate the sample perpendicularly to the optical axis. To ensure the stability of the focus during the acquisition, a homemade autofocus system was built. A 785-nm laser beam (OBIS, LX 785-50, Coherent Inc. ) was expanded twice and directed toward the objective lens by a dichroic mirror (z1064rdc-sp, Chroma Technology). The reflected infrared beam was redirected following the same path than the incident beam and guided to a CCD detector (Pixelfly, Cooke) by a polarized beam splitter cube. Camera, lasers, and filter wheel were controlled with software written in LabVIEW  (40).