dataesr/scientific-paragraphs-categorization huggingface dataset:
As this cut-off was established at 35. 6 Ct value empirically, positivity of samples with Ct values slightly superior to 35 could not be excluded with certainty. However, for the proof of concept of this work, we decided to use mosquitoes for which the infection status was certain. Mosquito sampling was done 18 days after the infective blood meal to ensure that the parasite had already undergone several multiplications and had completed its migration towards the salivary glands  [29]. The in-lab MALDI TOF mosquito database was initially built using mosquito legs crushed manually with pestles  [12]. However, for determination of infection status in this study, analyses were performed on CTs, where parasites are located when the mosquito is infected  [5]. Moreover, standardization of the protocol required elimination of the manual sample preparation method and development of an automated crushing technique.
Sample crushing in water already showed MALDI-TOF results comparable to crushing in formic acid in previous entomological studies  [23]. However, the absence of acid impairs protein extraction and could explain the mild spectra variability that was observed within the same group, but also the poor quality of the spectra that were excluded from this study. Comparison of spectra from CTs crushed in water with acid/acetonitrile-derived spectra revealed high similarity of the profiles, with much higher quality and reproducibility for the samples crushed in acid solution. (unpublished). Here, spectra used to upgrade the database were thoroughly selected to be representative of the whole group, despite its variability. The single misidentification observed in this study could be attributable to the water crushing and its resulting variability.
Besides the two main limitations of MALDI-TOF technology, which are the cost of the device and the comprehensiveness of the database, this high-throughput technique allows analysis of a large number of samples at limited cost compared to current methods. This technique is already established in Africa, since bacteriology laboratories in Dakar, Senegal are now equipped with MALDI-TOF devices for clinical microbiology purposes. Productivity has consequently been increased there, and it has contributed to an intense development of these laboratories in the field of microbiology. With a running cost lower than $1 per sample, this technology remains less expensive than traditional methods, despite the high initial cost of the devices  [26, 27]. Also, as previously mentioned by Yssouf et al. [23] , the spectra files are available on request and transferable to any Bruker MALDI-TOF device, thus excluding any additional cost associated with the construction of this database (e.
g. , molecular biology). Since the application of MS to entomological studies does not lead to additional cost, the protocol described here remains entirely affordable. In fact, entomological applications have already been developed in Dakar, since MS identification of Culicoides was finalized there  [28]. This preliminary work remains to be validated with field samples. Adjustments and upgrading of our database will be necessary in order to include other reference spectra, such as Anopheles gambiae infected or not by P. falciparum. It would be interesting to assess the consistency of MS spectra from diverse mosquito populations, and also the performance of the identification associated with plasmodial detection for species belonging to the same complex. The detection of the same plasmodial infection in different mosquito species was not evaluated here, nor the detection of different Plasmodium species in the same Anopheles species.
Conclusion Matrix-Assisted Laser Desorption Ionization Time-Of-Flight appears to be a promising tool for the detection of Plasmodium spp. in mosquitoes, in addition to its ability to identify mosquitoes at the species level. This approach does not require knowledge of entomology or molecular biology once the database is comprehensive. However, any upgrade of the database will always require precise identification, initially provided by morphology and molecular biology. This high-throughput, fast and low-cost method makes it an appropriate technique for epidemiological studies and monitoring of Anopheles populations in Plasmodium-endemic areas, with the concomitant identification of mosquito species and their pathogens. The rapid identification of at-risk populations can provide a valuable advantage for vector control, which may include targeted installations of collective and individual protection against mosquitoes.
Acknowledgements We thank  George Grau  ( Bosch Institute, Sydney ) for providing the parasite strain and the  Pasteur Institute of Paris, France  for providing the strain of An. stephensi. We also thank  Marielle Bedotto-Buffet  for designing the 18S gene primers and probe used in the present study for the molecular detection of Plasmodium parasites, and  Constentin Dieme  and  Adama Diarra  for their help with the mosquito rearing.
Brain sections were examined using a light-field microscope. Finally, TH + cells were identified with the Image-Pro Plus software and visually counted. Statistical analysis. The Statistical Package for the Social Sciences (SPSS version 13. 0) was used for the statistical analyses. All data were expressed as mean ± standard deviation (SD) for three independent experiments, at least. The analysis of variance (ANOVA) was performed for all tests, followed by post hoc Fisher's LSD multiple comparison test. P<0. 05 was considered to indicate a statistically significant difference.
Subsequently, each Member State organised training, managed the visits and stored the national data in an online database. For the second year, at least one third of the total beekeepers from the first year was renewed and for comparison the same methodology was utilised. Population representativeness was achieved through random sampling of apiaries (primary units) and bee colonies (secondary units) in each Member State considered to be representative of the national beekeeping population  [20]. England & Wales (taken as one Member State) did not take part in the second year of the program.
Study population studied and retained variables To calculate the colony mortality rates, only the apiaries with three consecutive visits and consistent data needed for mortality calculation were included. Data consistency was tested through several checks applied to the dataset including some editing to retain as much data as possible for analysis  [25]. The consistency between various variables was checked; for exemple, the number of colonies randomly selected at the first visit and information reported at the second visit; the number of colonies randomly selected at the first visit and the size of the apiary; or the number of colonies owned by the beekeeper and the size of the apiary. After quality checking, 2 332 apiaries (out of 3 053) were retained for the analysis in the first year and 2 426 apiaries (out of 2 745) for the second year.
Statistical methods First, data sets from each year were considered independently and the 17 Member States were clustered according to honey bee colony mortality. Second, for each year considered again independently, the links between the seasonal and the winter mortalities were explored. To work with a complete dataset, missing data were completed using the data imputation method based on multiple correspondence analysis. Finally, risk factors of mortality were sought on the overall study including both years of data by means of generalised linear models (see below).
Clustering the Member States according to yearly mortality Yearly honey bee colony mortality was studied pooling together the winter mortality and the seasonal mortality on 2 332 apiaries for the first year and 2 426 apiaries for the second year. The 17 Member States were clustered according to mortality patterns (i. e. groups with high mortality rates versus lower mortality rates) using vectors composed of two values (for the winter mortality and for the seasonal mortality rates). The 17 vectors were subsequently analysed through the hierarchical clustering of observations on the main principal components function (HCPC) from the FactoMineR R package  [27]. This resulted in producing groups of Member States with similar annual mortalities for the first and second year separately.
Risk factors related to honey bee colony mortality (both years combined) Considering the missing data in data sets from each year, 1 139 (out of 2 332) apiaries should have been kept in the analysis for the first year, and 1 020 (out of 2 426) for the second year. To take into acount a complete dataset, an imputation method was performed to process the missing data separately for each year, based on multiple correspondence analysis (MCA)  [28]  through the functions estim_ncpMCA and imputeMCA from the missMDA R package  [29]. After imputation, data sets from each year were merged and the new global dataset was used in the following analysis. The winter and seasonal honey bee colony mortalities (response variables) were considered as count numbers with quasi-Poisson distributions. In the subsequent analysis, the models used were the quasi-Poisson generalised linear models.
Calculations of p-values (associated to the likelihood ratio) were implemented for the winter mortality and the seasonal mortality for each explanatory variable (glm function R package). The 0. 20 threshold was used to select the variables kept in the study: the ones with a pvalue superior to the threshold were removed from the study, the others variables being incorporated in the subsequent steps. To avoid multicollinearity in Poisson regressions, variables from each theme (e. g. , diseases, management) were summarized in a single categorical latent variable through MCA and classification using the FactoMineR package  [30]  as detailed in Jacques et al. [25]. A unique new synthetic explanatory variable resulting from clustering and composed of seven categories was included in the generalised linear model as a fixed effect.
The Country and the year variables were included as random effects. For each mortality, a mixed multivariate Poisson regression model was performed using the glmer function from the lme4 R package with a log link  [31]. Thereafter, contrasts were used to compare mortalities of each cluster of the synthetic explanatory variable to the other ones. At the end of the classification, the seven clusters of the synthetic variable were built according to eight criteria: the typology of the beekeeper (hobby, part time or professional), the size of the apiaries and of the colonies according to four classes: size 1 = operation having less than 50 colonies in total and apiary having less than 20 colonies; size 2 = operation having less than 50 colonies and apiary having between 21 and 50 colonies; size 3 = operation having between 51 and 300 colonies and apiary having more than 50 colonies; and size 4 = operation with more than 300 colonies and apiaries having more than 50 colonies.
Risk factors related to honey bee colony mortality After combining the complete dataset from both survey years, 33 variables were included in the analysis of the winter mortality and 28 for the seasonal mortality. The year effect was significant for both years of the study with a stronger effect on the winter mortality (p-value = 1. 10 -37 ) when compared to the seasonal mortality (p-value = 1. 10 -3 ). The new synthetic explanatory variable generated from the clustering was divided into seven categories (W1 to W7 and S1 to S7, for winter and seasonal mortality respectively). Mortality rates were calculated with the multivariate Poisson regression models for each cluster and for each mortality: winter (Table  1 ) and seasonal (Table  2 ). For the winter mortality, hobby beekeepers with a small number of colonies (size 1) were part of four clusters (W1, W3 to W5).
The Ms8h and Ms1w specific transcript sets (light green and orange, respectively) were once again the most divergent with 577 and 281 down-regulated transcripts, reinforcing thus the prominence of these two conditions in response to folivory. Finally, out of the 4,333 (2,588 + 1,745) transcripts whose expression was altered upon leaf herbivory, we were able to find homologies (Blastx, e-value < 1e-10) for 3,189 transcripts (73%) with Uniprot proteins. This mapping was used to retrieve PFAM and Uniprot keyword information in order to classify the functions characterizing the C. roseus responses to M. sexta. The next paragraphs focus on the main functions identified in the core sets   S1  and S2  respectively. Details for Uniprot keyword analysis are presented in Supplementary Table  3. of up-and down-regulated transcripts but also in specific transcript sets according to the Venn diagram analysis (Fig.
C. roseus leaf consumption induces a potential photosynthesis breakdown. Analysis of the core set of down-regulated transcripts revealed a strong representation of photosynthesis-related elements. This was the most striking feature of this set of genes. Indeed, many Uniprot keywords related to this process (e. g. Photosynthesis, Chlorophyll biosynthesis) were significantly enriched (p-value < 1e-10) within transcripts from this core set (Fig. 4d , Supplementary Table  S3  and Supplemental Figure  S3b ). Accordingly, the PFAM domain PF00504. 18 Chlorophyll A/B binding protein was also well represented (24 transcripts) although not significantly enriched (p-value = 0. 09; Supplementary Table  S4 ). Interestingly, a similar trend was also detected in transcripts specifically associated with Ms8h, MsDamaged24h and MsIntact24h (Fig.
Herbivory caused induction of PR-proteins, terpene synthases and JA metabolism. Within our defined core set of 813 up-regulated genes, many transcripts related to known defense processes were identified (Supplemental Tables  S3  and S3 ). For instance, it contained 17 transcripts related to PFAM domain PF00407. 16 (Pathogenesis-related protein Bet v I family, from the PR-10 family). In particular, transcripts homologous to Solanum tuberosum pathogenesis-related protein STH-1 and 2 (PRS1_SOLTU and PRS2_SOLTU) and a previously identified probable intracellular pathogenesis-related protein T1 from C. roseus (IPRT1_CATRO) were strongly induced in fed leaves (Figs  4c  and 5a ). A transcript encoding Pathogenesis-Related protein 5 (PR-5) was also identified. Interestingly, these genes types were also over-represented in specific gene sets and notably in the 24 h samples.
The Uniprot keyword 'Pathogenesis-related protein' was significantly represented in the MsDamaged24h specific gene set (p = 0. 0003) and the PFAM domain PF00407. 16 in the MsDamaged24h and MsIntact24h intersection set (p < 1e-05) (Supplemental Tables  S3  and S4 ). Interestingly, cysteine proteinase inhibitors homologous to CYT5_ARATH and CYSP_PEA known to be induced by herbivory and to inhibit insect proteolytic enzymes  60  were mostly found in these lists, in particular in the MsDamaged24h specific gene set but also in the core set of common transcripts such as the cystatin CYTI_VIGUN (Supplemental Tables  S3  and S4 ). This strong expression of defense proteins during herbivory indicated that C. roseus deployed defense responses complementary to MIA biosynthesis. Aside from these genes related to direct defenses, several up-regulated transcripts suggested a recruitment of indirect defenses through modulations of terpene metabolism.
thaliana Jasmonate O-methyltransferase (JMT_ ARATH) and A. thaliana MYB108 transcription factor (MY108_ARATH) (Fig. 5b ). In addition, 7 transcripts with a predicted Tify domain were also up-regulated (PF06200. 11; Fig. 5b , Supplemental Table  S4 ). Tify-domain containing proteins are known JA-related transcription factors like TI10A_ARATH, reinforcing the probable activation of the JA signaling pathway upon feeding  64. This was further illustrated by the presence of transcripts displaying putative AP2 domains and homology to the ORCA3 transcription factor, a key component of the JA-induced MIA biosynthesis  39. Interestingly, orthologs of the jasmonate biosynthesis genes AOC4_ARATH and LOX5_ARATH were also found in the 20 genes common to the five gene lists, thus highlighting the prominence of JA biosynthesis in the periwinkle response to herbivory.
Individual caterpillars were identified, initially weighted and laid on individual plants. Data were recorded 24, 48, and 72 h after the beginning of the experiment. For choice experiment, larvae were fed with 2. 2 cm-diameter leaf disks of C. roseus and N. tabacum placed on wet filter paper in 15 cm-diameter Petri dishes. Leaf disk consumption was monitored during 120 min and the experiment was repeated twice. RNA extraction and sequencing. RNAs were extracted from leaves with Trizol (Life Technologies) following the manufacturer's recommendations with slight modifications. After precipitating with isopropanol and washing with 70% ethanol, RNA pellets were re-suspended in 100 μ L of RNase free water and remaining sugars were precipitated by adding 10% ethanol (final concentration) and incubating 5 minutes at 4 °C.
For quantification of transcript accumulation, reads were pseudo-aligned on CDF97 reference transcript sequences  17  and counted with Salmon 55 in the variational bayesian optimized (-vbo) quasi-mapping mode with biase correction (-biasCorrect). The resulting quant. sf files were combined and processed with R. Differentially accumulated transcripts were identified by considering each experiment as unique (without replicate) and by fitting a linear model to each gene with the 'exactTest' function of the edgeR Bioconductor package  71. Biological variability was estimated by setting the square-root dispersion at 0. 4. To balance the absence of biological replicates, differentially expressed genes were set if the p-value in the test was below 0. 001. In addition, functions altered in response to folivory were focused on genes that were significantly modulated in at least two comparisons.
Annotation and term enrichment analysis. The CDF97 reference transcriptome was annotated with the Trinotate v3. 0 pipeline against Uniprot (Blastx and Blastp on Transdecoder predicted ORFs) and PFAM (hmmscan) databases. Uniprot keywords were retrieved by using Uniprot predicted homologs in CDF97. Enrichment tests of functional terms were performed by comparing effectives to a hypergeometric distribution (phyper function in R). All graphics were made with ggplot2 package. For promoter analysis, scaffolds and predicted CDS of C. roseus genome sequencing project  70  were retrieved from Dryad (http://datadryad. org/resource/doi:10. 5061/ dryad. hs593). CDS were mapped on scaffolds using megablast (BLAST + suite 2. 2. 29  72  ). When possible the 500 pb upstream the start codon were obtained for each CDS.
The MEME suite (v4. 11. 2) was used to detect new ungapped motifs with DREME program using default parameter  73. GOMO was next used to analyze the representation of candidate motifs in Arabidopsis genome. qPCR analysis. Targeted gene expression measurement was performed by qPCR using primers described in Supplemental Table  S5 , after cDNA synthesis as described previously  17. MIA quantification. MIAs were extracted from lyophilized samples (C. roseus leaves and M. sexta intestinal tracts) by grinding tissues with a mixer mill (Restch, MM 400) during 3 min at the maximal frequency. The resulting powders were incubated in 1 ml methanol (containing 0. 1% formic acid) and under vigorous shaking during 1 hour at 24 °C. After centrifugation (15,000 g; 15 minutes), supernatants were collected and used for quantification.
The MIA content of C. roseus leaves and of M. sexta intestinal tracts were determined using an UPLC-MS chromatography system coupled to a SQD mass spectrometer equipped with an electrospray ionization (ESI) source controlled by Masslynx 4. 1 software (Waters, Milford, MA). Analyte separation was performed on a Waters Acquity HSS T3 C18 column (150 mm × 2. 1 mm, i. d. 1. 8 μ m) with a flow rate of 0. 4 mL/min at 55 °C and the volume of injection was 5 μ L. The following linear elution gradient was used: acetonitrile-water-formic acid from 10:90:0. 1 to 50:50:0. 1 over 5 min. The capillary and sample cone voltages were 3,000 V and 30 V, respectively. The cone and desolvation gas flow rates were 60 and 800 Lh - Quantification of oxophytodienoic acid, jasmonic acid, and jasmonoyl-isoleucine. Samples stored at -80 °C were freeze dried and powdered for subsequent analysis.
Statistical procedures. Statistical differences between means were non-parametrically tested in R  75. The Wilcoxon rank sum test was used to avoid any conflict with the distribution of data. This was particularly the case for peak area in the metabolic analysis which displayed heteroscedasticity. P-values of tests were corrected with False Discovery Rate. Specific statistical procedures for RNA-seq data are presented above. Graphics were made with the 'ggplot2' package  76.
0 software  [16]. A first diversity tree was drawn up using data from the 2011 sampling operation in Ecuador. Diversity from Ecuador was evaluated in relation to the global diversity of the species. A global analysis, using data from the three sampling years, was performed to identify unique genotypes for future conservation and for breeding programs. In all, 1168 samples were kept for analysis, based on missing values (<8% not available, i. e. , one marker missing). A Neighbor-Joining (NJ) tree was built on the whole data removing pairs of data with more than 70% of missing values. To take into account the noise due to highly repeated genotypes, the max-length subtree procedure was used to eliminate redundancy and to identify the number of unique individuals without loss in the number of alleles.
The EETP Collection. Phenotypic analysis was performed at the EETP collection, using data from 2010 to 2012. The evaluation was carried out in 256 plants, corresponding to 16 accessions. Each accession contained different number of individual (between 12 and 20). A phenotypic dendrogram was performed by UPGMA clustering method using the Euclidean distance. Genotyping data were available for 146 plants. Tree diversity was determined for molecular data, and their relationship with phenotypic data generated by Plaza et al. [13]  was studied. Phenotypic data were measured between 2010 and 2012 for all the trees planted in 2007. During this time, the traits observed were the following: plant height (PH), stem diameter (SD), number of branches (NB), number of nodes per branch (NN), and internode length (IL).
Outlier data were removed whenever found and replaced the missing values. PCA analyses were performed using the dudi. pca function from the ade4 R package  [17]. Five principal components were chosen after observing the screen plot of The Scientific World Journal eigenvalues. Correlation circle was drawn using the s. corcircle function and the representation of individuals for the first two components was drawn using the s. class function. Two types of analyses were performed on these data. A clustering tree was first built on 249 plants using the dissimilar Euclidian distance evaluated by average linkage for the 17 traits observed across the three years. Another analysis was performed using a Principal Components Analysis (PCA) on five vigor traits (yield of cherry beans, number of cushion flowers, total of productive branches, total of branches per tree, and tree height) across three years observed on the 146 trees analyzed for genetic diversity.