dataesr/scientific-paragraphs-categorization huggingface dataset:
Mosquito identification The mosquitoes used in this study were morphologically identified as belonging to  11   cox1 sequences were generated for all the 67 mosquitoes used to create the reference database. Anopheles bancroftii ITS2 spacer was also sequenced. The cox1 query and ITS2 sequences in the GenBank database allowed us to obtain reliable species identification for all the mosquito species of which reference sequences were available (Table  1 ); no cox1 sequences were available on GenBank for An. bancroftii, Cx. iyengari and T. melanesiensis. All sequences obtained in this study are available in GenBank database under the accession numbers MN733743-MN733814.
Number of required MSPs per species to obtain the best identification score Before the creation of the MALDI-TOF database, analysis was performed to determine the number of MSPs required per species for the optimization of LSVs. Three species were used for the experiment: Ae. aegypti; Cx. annulirostris; and Cx. quinquefasciatus. For all three species, the analysis showed that the LSV medians increased according to the number of MSPs used until they reached a plateau (Fig. 3 ). For Ae. aegypti, this plateau was reached after using 5 MSPs. Indeed, no significant difference was observed between the LSV medians when using 5-10 MSPs (Kruskal-Wallis test H-test: χ 2 = 0. 7, df = 5, P = 1). The plateau was reached when using 3 MSPs for Cx. annulirostris and Cx. quinquefasciatus: no significant difference was observed when using 3-10 MSPs (Kruskal-Wallis test H-test: χ 2 = 3.
Creation and validation of MALDI-TOF MS database Five MS reference spectra per species were used to create the database except for four species. Only 3 An. bancroftii MSPs were created due to the small number of specimens available (Table  1 ). Further MSPs were created for Ae. aegypti (n = 10), Cx. quinquefasciatus (n = 9) and Cx. annulirostris (n = 10). These were created during the previous step (i. e. determination of the number required of MSPs per species) and were not removed from the database during analysis. The visualization of the relationship between MSPs in the database displayed in a dendrogram showed that MSPs of each species clustered together, with distance levels under 500 (Additional file 2: Figure  S2 ). A total of 224 field-collected mosquitoes belonging to 11 species, consisting of 7-30 specimens per species, were queried against this database.
All these mosquitoes used to create and validate the MALDI-TOF database were analyzed no later than 5 months after sampling, except for Ae. scutellaris and An. bancroftii which were collected in 2016 and 2017, respectively (Table  1 ) and were preserved at -80 °C. Except for the 9 mosquitoes for which no spectrum was obtained, all the specimens were matched with MSP of the correct species in the database. Specifically, reliable matching was acquired for 96% (95% CI: 93-98%) of the specimens from each species during the blind test analysis (Fig. 4 ). Species in the same group matched also correctly including 100% of the Sitiens group (Cx. sitiens and Cx. annulirotris) and 100% of the Pipiens group (Cx. quinquefasciatus and Cx. iyengari). Other analyses were performed to determine the identification threshold value for these mosquito species and calculated MALDI-TOF MS sensitivity and specificity values.
The results showed that the optimal threshold value which maximized the MALDI-TOF MS sensitivity and the specificity ranged from 1. 48 to 1. 79 when only one species was used in the database (Table  2 ). When approximately half the species in the database were included in the analysis, this threshold value ranged from 1. 63 to 1. 7 (Table  2 ; Additional file 3: Figure  S3 ). High positive predictive values and negative predictive values were obtained (Table  2 ). High AUC (area under the curve) values (0. 85 to 1) were found regardless of the species used.
Discussion MALDI-TOF MS is an innovative tool for arthropod identification  [17]. To optimize its use for routine mosquito identification and entomological surveillance, some parameters must be taken into consideration. In the present study, the effect of trapping duration and three preservation methods on MALDI-TOF results were evaluated. To the best of our knowledge, the required number of specimens per species for MALDI-TOF MS database creation and the optimal threshold for mosquitoes' identification were determined for the first time. The first MALDI-TOF MS reference spectra for the identification of a selection of New Caledonian mosquito species is presented in this study. In entomological surveillance, traps are sometimes deployed for a long period in the field. Mosquitoes therefore remain trapped several days and exposed to uncontrolled temperature and relative humidity variations.
8 threshold. During an additional experiment, the spectra obtained from Ae. aegypti preserved at -80 °C during four years were also compared to MSPs of fresh field-collected mosquitoes (Additional file 5: Figure  S5 ). All these mosquitoes (n = 20) were correctly Cx. quinquefasciatus Fig. 3  Progression of log-score values according to the number of MSPs used in the analysis. Three mosquito species from the field were used, Ae. aegypti, Cx. annulirostris and Cx. quinquefasciatus. X-axis corresponds to the number of MSPs used in the database. Y-axis corresponds to the median of LSVs. Colors illustrate each species tested identified with LSVs greater than 1. 8. Storage at -80 °C is therefore compatible as a long-term preservation method before MALDI-TOF MS analysis. G G G G G G G G G G G G G G G G G G G G G G G G G G G G G 0 To the best of our knowledge, the required number of specimens per species needed to create a MALDI-TOF MS mosquito database was determined for the first time.
The assessment for three field-collected species showed that optimal LSVs were achieved when using three MSPs for Cx. quinquefasciatus and Cx. annulirostris and five MSPs for Ae. aegypti (Fig. 3 ); for optimal LSVs, at least   This MALDI-TOF database included only field mosquitoes identified into 11 species. Two of these mosquito species were recently introduced in New Caledonia: Ae. scutellaris, vector of dengue virus; and An. bancroftii, vector of Plasmodium sp. [14, 15]. Other species included in the database are also vectors: Ae. aegypti is the main vector of dengue, Zika and chikungunya viruses in the Pacific region  [37]  Using a cut-off distance level of 500 set by previous studies, all MSPs species in this database were reliably classified  [38]. During blind testing, with the exception of the nine specimens for which no spectrum was obtained, all mosquitoes were correctly identified to the species level using MALDI-TOF MS.
It also represents an interesting complementary method in mosquito identification, all the more that experts in systematics have decreased resulting from lack of expertise transmission. In this study, the optimal threshold for mosquito identification which maximized both MALDI-TOF MS sensitivity and specificity using ROC analysis was determined. The higher threshold value was found for Cx. quinquefasciatus (i. e. 1. 79). The optimal threshold value remained comparable to the results for each species when half of the database was included in the analysis (1. 63 vs 1. 70). High positive and negative predictive values at these thresholds show the ability of the MALDI-TOF MS to correctly differentiate mosquito species (Table  2 ). Based on the results and for optimal identification, the threshold value was rounded to 1.
This study may suffer from bias and limitations. The first possible limitation of the study is the low number of MSPs for An. bancroftii due to their insufficient number in the reference database. In spite of that, high identification scores for six of the seven An. bancroftii used for the blind testing were achieved. In the future, other samples should be added to the database to have at least five MSPs. Secondly, only legs were used for protein extraction during this study. These are more breakable during trapping or storage. As observed during analysis, the use of less than four legs for protein extraction could compromise MALDI-TOF identification. Another study has suggested the use of the thorax, which is not prone to degradation during sampling  [24]. The creation of another database using mosquito thoraxes for these species should be undertaken in the future to strengthen MALDI-TOF identification, in case few or no legs are available.
Thirdly, unlike other studies during which several spots per sample was realized with consideration of the highest LSV, one single spot for each sample was deposited in order to assess each parameter individually (trapping duration, preservation methods and duration, number of MSPs required for MALDI-TOF database creation and threshold value determination) without score improvement caused by depositing multiple spots. Consequently, the LSVs obtained are not optimized. Indeed, supplementary experiments showed that the LSVs improved when three spots of each sample were deposited onto the target plate with consideration of the highest scoring spectrum within the triplets (Additional file 6: Figure  S6 ). However, with one single spot, 92% of these mosquitoes identified showed an identification LSV that was higher than the threshold (1.
8) during the blind test analysis. In the future, at least two spots per sample should be deposited, especially when using MALDI-TOF MS to identify newly introduced species. Fourthly and finally, the optimal threshold was determined using the current MALDI-TOF database. The determination of this threshold score could depend on the richness of species diversity in the database. Adding more species in this database could potentially increase or decrease this threshold value. This value should therefore be revised when more species belonging to the same group become included in analyses.
Conclusions In conclusion, MALDI-TOF MS is a highly sensitive and highly specific method for mosquito identification in New Caledonia. This emerging identification method is a promising tool in public health for mosquito vector surveillance, allowing both accurate identification of mosquito species vectors in a region and rapid detection of introduced vector species. Other vector mosquito species recorded in the Pacific region including the other Aedes members of the scutellaris group are not present in this MALDI-TOF MS database. The inclusion of these species in the database is needed for MALDI-TOF to be used as an effective entomological surveillance and risk assessment tool in the Pacific region. Thus, this MALDI-TOF MS database will be able to provide a rapid response to vectors identification/introduction and help public health responses.
EV-miRNAs have been used as a novel diagnostic tool for a series of malignancies  [31, 50]. Our clinical data also support the role of plasma EV-miR-101 as a circulating biomarker for osteosarcoma. It has been reported that over 50% of EVs in serum are released by platelets during the process of clot formation  [51] , and thus, plasma was chosen, instead of serum, for the isolation of EVs in our study. To check for possible platelet contamination, routine blood testing was conducted for the plasma samples (obtained just before the step of ultracentrifugation to pellet the EVs), and no platelets were detected (data not shown). Recently, serum miR-101 expression has been proposed as a promising diagnostic marker of osteosarcoma  [52]. It is necessary to compare the effectiveness of miRNAs and EV-miRNAs as circulating biomarkers of cancers.
Next-generation sequencing Plasma EVs were isolated from one osteosarcoma patient (17 years old, female) and one healthy control (17 years old, female) using Ribo TM exosome isolation reagent (Ribobio). cDNA libraries were constructed with the EV-RNA, and then the sequencing was performed on the Illumina HiSeqTM 2500 platform (Illumina, USA). Clean reads were acquired after qualification of raw reads using FastQC software (www. bioinformatics. babraham. ac. uk/ projects/fastqc/) and then mapped to a reference genome by Burrows-Wheeler Aligner software (http://bio-bwa. sourceforge. net/). Then, the obtained sequences were aligned against the miRNA database miRbase version 21 (www. miRBase. org), and known miRNAs were verified using miRDeep2 software (http://www. mdc-berlin. de/rajewsky/ miRDeep).
Embedded cells were imaged with FIB-SEM (Figure  1A ) in automatic mode with a voxel size of 8 nm, to obtain complete datasets (i. e. stacks of 2D images, Supplementary Videos 1 to 6) with a workable (~1000-1200) number of frames. FIB-SEM datasets were further processed to obtain 3D models using open-access software (Figure  1 ). Frames were aligned using the template matching plugin implemented in Fiji (Figure  1A ). They were filtered to reduce noise while preserving cellular details. Based on the effectiveness in highlighting organelle boundaries, different filters were used for the different microalgae (Figure  1B ). A linear (Gaussian) filter followed by edge enhancement (sharpening) was chosen for Micromonas, Emiliania, Phaeodactylum and Pelagomonas, while a non-linear filter (median filter) was preferred in the case of Galdieria and Symbiodinium.
These choices reflect the different cellular features and biochemical composition of each taxon (e. g. the presence of a thick cell wall in Galdieria), which results in variable contrast. Segmentation of organelles, vesicular networks, vacuoles and storage compartments was carried out with 3D Slicer software  (Kikinis et al. , 2014)  (www. slicer. org, Figure  1C ), using a manually-curated, semi-automatic pixel clustering mode (3 to 10 slices are segmented simultaneously for a given Region Of Interest, (ROI)). This method is optimal in terms of image processing speed and allowed fast and accurate identification of subcellular structures when compared to a fully manual segmentation while avoiding possible artefacts (e. g. the presence of misclassified pixels). The segmented stacks, represented in memory as simple binary masks, were converted into 3D models using the "Model maker" module from 3D Slicer (Figure  1C ) , and then edited with the MeshLab software  (Cignoni et al.
, 2008; Ranzuglia et al. , 2012)  to eliminate 'isolated islands', which were erroneously annotated as ROIs. We also used MeshLab to simplify meshes ('mesh decimation', Figure  1D ), i. e. to reduce the model nodes and faces down to 25% of the original data without modifying morphometric values, such as surfaces and volumes (Supplementary Table  2 ). Every 3D model was imported into Paraview (Figure  1D )  (Ahrens et al. , 2005)  to visualise 3D objects and understand their relationship. Blender (www. blender. org) was used for object animation (Supplementary Video 7). Morphometric analyses were performed using the Python module numpy-slt (https://github. com/WoLpH/numpy-stl), which provided estimates very similar to those obtained with the geometry tool of MeshLab and 3D Slicer (Supplementary Table  3 ).
This Python package is faster than MeshLab, with obvious advantages in terms of analysis of large files (> 500 MB). Surfaces and volumes were computed using the discrete mesh geometry, surface being computed directly from mesh triangles, and volume being obtained from the signed volume of individual tetrahedrons, assuming a closed surface (watertight mesh Figure  1E ). Using the Trimesh Python module, the minimal distance between two meshes was calculated based on the closest points between two triangular meshes. Hence, the surface area contacts were quantified based on: i) calculating the minimal distance between each vertex of the plastid mesh to the mitochondria mesh (for 3 cells of every species), and then by ii) gathering mesh vertices according to a given distance threshold to generate contact surface.
Green: The median filter is less sensitive to edges. Image processing was done with 3D Slicer for segmentation (C), MeshLab and Paraview, for editing and visualisation, respectively (D). The STL and Trimesh python packages were used to quantify volumes, surfaces and distances (E). From a watertight mesh, surface is obtained by summing the surface of each individual triangle present in the mesh. Volume is computed with a volume integral formula discretized over tetrahedrons as obtained from  (Zhang and Chen, 2001). Distances between nearby point clouds of different objects within a cell were used to estimate organelle proximity. The whole process requires 10 to 15 days for a microalgal cell with a cell diameter comprised between 2 µm to 8 µm. Overall, this entirely free-access analytical pipeline meets all the quality criteria for selecting, filtering and classifying ROIs and for building optimal 3D structure models for morphometric analysis.
We interpret this conservation of organelle volumes and the variability of the other compartments as the signature of evolutionary constraints that preserve essential cellular functions (gene expression, energy production, anabolism/catabolism), while leaving metabolic flexibility to allow the storage of assimilated nutrients, particularly carbon. Thanks to the possibility offered by our approach to perform quantitative surface and volumetric analyses, we sought possible relationships between the organelles (Figure  4A ) in the different taxa, to reveal evolutionary preserved morphological characteristics. This analysis was initially biased by the presence of Symbiodinium (Supplementary Figure  2 ). These dinoflagellate cells, being much larger than the others, result in the clustering of data into two cells group (Symbiodinium on the one side and all the other cells on the other one) leading to the observation of apparent linear relationships between all the parameters considered.
A graded resin/acetone (v/v) series was used (30, 50 and 70% resin) with each step lasting 2 h at increased temperature: 30% resin/acetone bath from -30 °C to -10 °C, 50% resin/acetone bath from -10 °C to 10 °C, 70% resin/acetone bath from 10 °C to 20 °C. Samples were then placed in 100% resin for 8-10 h and in 100% resin with the accelerator BDMA for 8 h at room temperature. Resin polymerization finally occurred at 65 °C for 48 h. FIB-SEM acquisition imaging. Focused ion beam (FIB) tomography was performed with either a Zeiss NVision 40 or a Zeiss CrossBeam 550 microscope, both equipped with Fibics Atlas 3D software for tomography. The resin block containing the cells was fixed on a stub with carbon paste, and surface-abraded with a diamond knife in a microtome to obtain a perfectly flat and clean surface.
Due to its reduced cell dimensions, the voxel size was reduced to 4x4x4 nm 3 in the case of Micromonas, resulting in higher resolution datasets with approximately 350-500 frames/cell. Image processing. As a first step of image processing, ROIs containing cells were cropped from the full image stack. This was followed by image registration (stack alignment), noise reduction, semi-automatic segmentation of ROIs, 3D reconstruction of microalgae cells and morphometric analysis. Several problems may be encountered during these steps. Raw stacks consist of big data (50 to 100 GB for the whole imaged volume, containing several cells) that do not necessarily fit into the computer main memory (RAM). Moreover, cryo-substituted cells generate less contrasted images than cells prepared with chemical fixation.
Therefore, the first step in building a robust 3D model consists in 'isolating' a given ROI (e. g. an organelle) from other compartments, to obtain a smaller stack size that can be easily worked with (in practice, we worked with substacks that were around 10% of the original stack size). Single cells were isolated by cropping in 3-dimensions using the open software Fiji (https://imagej. net/Fiji). Image misalignment was corrected using template matching ("align slices in stack") option implemented in Fiji. This function tries to find the most similar image pattern in every slice and translates them to align the landmark pattern across the stack (https://sites. google. com/site/qingzongtseng/template-matching-ij-plugin) (Videos S1-S6). Aligned image stacks were filtered to remove noise using Python  (Oliphant, 2007)  and OpenCV (OpenCV.
2015, Open Source Computer Vision Library programming tools). Filtering techniques were chosen to highlight contours while removing noise in the images. Depending on the species, we found that the osmium staining was not homogeneously distributed. Therefore, it was not possible to filter raw datasets of every species with the same method. which is widely used and easy to implement  (Russo, 2002) , was used to process raw datasets of Emiliania, Micromonas, Phaeodactylum and Pelagomonas. However, this method was less effective when applied to raw datasets of Galdieria and Symbiodinium, where using the median filter turned out to be a better de-noising option. Segmentation. The filtered stacks were loaded into 3D Slicer to be processed. By selecting the model editor, we 'coloured' the ROIs using paint tools and adjusted the threshold range for image intensity values.
The ROIs were annotated and the corresponding label map was run into the model maker module, to generate corresponding 3D models that were exported in different formats  (. stl, obj, vtk, ply,. mtl). For further analysis, we used the. stl mesh, which proved to be more suitable for 3D analysis in our workflow (Supplementary Table  2 ). 3D reconstructed model. A 3D filtering process was needed to clean the model and reduce the size of the file (see Supplementary Table  2 ). In our case, 3D models generated by 3D Slicer were imported into the open source software MeshLab  (Cignoni et al. , 2008)  to clean the model by automatically removing isolated islands. We also performed a remeshing process to facilitate 3D modelling, visualization and animation. The edited model was then imported into Paraview software to capture a 2D representation of the 3D reconstruction.
We used Blender for model animation (see an example in Supplementary Video 7). Morphometric evaluations. Measurement of volumes, surface area, and the minimum distance between meshes) were performed using Numpy-STL (https://pypi. org/project/numpy-stl/) and TRIMESH (https://trimsh. org/trimesh. html) packages of Python (Supplementary Table  3 ). Surface and volume measurements. To compute the surface, we iterated over all the triangles of the mesh. The computation of the cross product between two edges of a given triangle gives a vector whose magnitude is twice the area of said triangle. Then, the sum of all these areas provides the total surface area of the mesh. We then computed the signed volume of all tetrahedrons, which goes from the origin (0,0,0) to each triangle present in the mesh.
Assuming a closed surface (watertight mesh), summing all those volumes give the volume of the mesh  (Zhang and Chen, 2001). A simple implementation of those algorithms is provided by the authors at https://gitlab. com/clariaddy/stl_statistics. Distance between organelles. Given two meshes, we computed the pointwise distance from the first mesh to the second one. For each vertex in the first mesh, we computed the minimal distance to the second mesh using the Trimesh Python module (https://github. com/mikedh/trimesh). Using this method, every vertex of the mitochondria model was compared to all vertices contained in the plastid model to calculate the intermesh distance. After having set a biological meaningful threshold distance (≤ 30 nm,  (Helle et al. , 2013 , Scorrano et al. , 2019 )), we generated subsets of plastid-mitochondria points that meet this criterion and reconstructed the matching surface using face data.