dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). To make the most out of the existing COI sequences and to generate new ones, and at the same time accumulate reference sequences of other mitochondrial sequences (including 12S), the genome skimming technique is now a feasible alternative  (Coissac et al. , 2016)  that can be chosen as an alternative to COI barcoding with marginal extra cost, but with the advantage of obtaining whole cell organelle genomes. Adding just 116 new barcodes to the COInr-Med database, which is negligeable compared to the size of COInr-Med (0. 04%), still allowed to increase the resolution of 0. 6% (VTAM), 1% (RDP), 0. 8% (QIIME_SKLEARN) and 1% (QIIME_BLAST) of the ASVs, most often going from unassigned to species level. This is a clear improvement providing the good quality of the new barcodes. However, adding new barcodes also resulted in decreased resolution in a few cases, which is likely to be a sign of mislabeled sequences in the database and calls for database curation whenever it is possible.
CONCLUSIONS Our comparisons showed the importance of having reliable reference databases, filled with custom sequences, refined at a local study area and without the presence of unexpected taxa. Eliminating irrelevant reference sequences allows lighter and more adequate databases to be built that comes at the price of increasing the number of unassigned ASVs. However, this is not always a negative consequence. ASVs that became unassigned with a more specific database were generally assigned to a low-resolution taxonomic level and they had been very likely incorrectly assigned with the larger database. The use of a suitable database results in a cleaner and more accurate taxonomic identification. Creating a local database specific to the study, however, cannot compensate for the need of barcoding to fill the gaps in the taxonomic coverage of databases which should be done in association with expert taxonomists.
ACKNOWLEDGEMENTS This work is part of  Mugnai F. PhD  project on ''Monitoring of Mediterranean benthic biodiversity: an integrative taxonomic approach'' at the  Alma Mater Studiorum -Università di Bologna. This work was part of the European project  SEAMoBB: ''Solutions for Semi-Automated Monitoring of Benthic Biodiversity ''. Special thanks to  Dr. Kenan O. Matterson  for the field sampling and the molecular-related contributions, bioinformatics included. Thanks to  Marine Biology MSc  students involved in SEAMoBB project field-and labrelated activities during 2019 and 2020 campaigns. Thanks to  Dr. Barbara Mikac  for the identification of  Polychaeta in Ravenna laboratory. Thanks to all the diving centers for field activities support ( Rovinj Sub ,  Rovinji , Croatia;  Palinuro Sub ,  Palinuro, Italy ;  Accademia Blu Diving Center, Livorno, Italy) , and to the  CIBM (Centro Interuniversitario di Biologia Marina ed Ecologia Applicata ''G.
Funding This work was supported by the European project  SEAMoBB (Solutions for sEmi-Automated Monitoring of Benthic Biodiversity ), funded by  ERA-Net Mar-TERA  (id. 145) and managed by the  ANR  ( Grant  No. ANR-17-MART0001-02 , P. I. :  Anne Chenuil , Grant No. ANR-17-MART0001-03 , PI: STARESO) and  Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR)  Grant No. MIUR n. 728053 -SEAMoBB. The Spanish founder was  CDTI , and the project name  Soluciones Para El Monitoreo Semi-Automático De La Biodiversidad Bentónica  with Id number  SERA-20181031. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures The following grant information was disclosed by the authors: European project  SEAMoBB. ERA-Net Mar-TERA: 145. ANR :  ANR-17-MART0001-02 ,  ANR-17-MART0001-03. Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR) :  728053. CDTI. Soluciones Para El Monitoreo Semi-Automático De La Biodiversidad Bentónica :  SERA-20181031.
An ROI corresponded to one single field of view of the microscope (size 1. 3 × 1. 3 mm 2 ) and the positions of the ROIs were manually selected to be representative of the different areas of the sample. One static and one dynamic FF-OCT image were acquired at each ROI (see Sec. 4). During this study, FF-OCT imaging time totaled 30 min to 1 h per sample, including acquisition and transfer of the raw data needed for research purposes. Nonetheless, in a real clinical context, without recording the raw data, similar procedure could be achieved directly inside the operation room in <10 min. Once the imaging had been performed, the samples were placed in histology cassettes, were fixed in formalin, included in paraffin, sliced and H&E stained (see Sec. Manual correlation and alignment of the large-field static FF-OCT image with the histology image allows to establish good correspondence between the two images, as shown in Fig.
Feature Engineering and SVM Analysis of Static and Dynamic FF-OCT Allow an Automatic Cancer Diagnosis and Identification of Decisive Features for Interpretation The first automated approach was to measure features of interest in the dataset via image analysis and use these features in a multidimensional machine learning model, linear support vector machine (SVM), to separate healthy from tumoral ROIs. We first selected (see Sec. 4) only the images where both static and dynamic FF-OCT modalities were available on the same ROI [Figs. 4(a1) and 4(b1)]. When the number of ROIs was too small (<5), the images from the corresponding sample were removed from the dataset. This resulted in a dataset composed of 496 static and 496 dynamic images from 39 samples (30 tumoral and 9 healthy). In each image, we aimed to measure features describing the cells and the extracellular matrix organization.
We trained two random forest classifiers (one for static and one for dynamic FF-OCT) to segment individual cells and fibers [Figs. 4(a2) and 4(b2)] using the ILastik software 42 (see Sec. 4). The segmented images were then analyzed using a second filtering step (see Sec. 4) to exclude some misclassified pixels. We also calculated mesoscale features, by segmenting regions of highfiber, or high-cell, densities [Figs. 4(a3) and 4(b3)]. In total, combining static and dynamic FF-OCT images, we measured 44 features (listed in the Supplementary Material with associated references), including cell parameters (size and intensity), fiber parameters (density and organization), mesoscale organization, and fat content [Fig. 4(c) ] that were previously characterized as potential cancer biomarkers (see Sec.
4). For all 496 ROIs in the dataset, we therefore obtained 44 features that were used to classify each sample as compared to a ground truth defined as the histological diagnosis (see Sec. 4). We trained an SVM to classify each ROI [Fig. 4(c) ]. We used a fivefold cross validation and penalized by a factor 3 the possibility of having false positives (healthy samples found as cancerous) to balance the dataset inhomogeneity (See Sec. 4 and Supplementary Material). The training set was therefore composed of all ROIs from 80% of the samples for each fold (see Sec. 4). With a linear SVM, we achieved a mean accuracy of 88 AE 3% (sensitivity 89 AE 4% and specificity 86 AE 3%) and an area under the ROC curve (AUC) of 0. 90 AE 0. 03 at the individual image level over the five models (see Supplementary Material).
From the SVM model, a feature analysis allowed to measure the importance of each feature to separate the dataset and study the possibility of feature reduction. In total, we could reduce the number of features to 21 (see Supplementary Material for details and description of the kept features) without impact on the prediction accuracy of the network. The feature reduction had a marginal impact on the computation time and SVM performances but allowed for finding which parameters can better describe this specific dataset, hence helping to understand the measured changes in the tumoral samples. An important finding of the feature reduction is that the SVM approach needs features calculated from both static and dynamic images. Finally, we used the predictions obtained at the ROI scale to classify the sample from which the ROIs were taken.
Convolutional Neural Networks Combined with Dynamic FF-OCT Allow a Rapid Automatic Cancer Diagnosis and a Rapid Focusing on Potential Tumoral Regions Our second approach was to explore a purely data-driven approach facilitated by the deep learning paradigm. We chose to fine-tune a pre-existing CNN, namely the VGG16  43  architecture with weights pre-trained on the ImageNet dataset,  44  to directly classify each ROI from the D-FF-OCT images. The rich variability present in the ImageNet dataset has been proven countless of times to provide a better initialization than more specific but less abudant datasets. For example, initialization with ImageNet was proven to be about 15% more efficient compared with other specific datasets in the case of breast cancer H&E histology. 45 We modified the VGG16 to adapt its architecture to our problem [see Sec.
To further evaluate the potential of FF-OCT to be used as a perioperative tool, we additionally developed two methods of image processing and statistical learning and successfully brought more specificity and interpretability to these images. To our knowledge, this is the first time that machine learning algorithms are developed for D-FF-OCT. In the histopathology field, many machine learning algorithms have been developed on traditional staining, but applying them to FFOCT aims to facilitate the introduction of a high-potential imaging technique with a similar, but label-free and richer contrast mechanism in the field. In the end, although these results are preliminary and should be confirmed on bigger cohorts and different cancer types, we demonstrated that automatic diagnosis with FF-OCT compares well with manual diagnosis given by histopathological experts and should show increased performances as larger and more precise datasets will be generated.
Nonetheless, increasing the resolution decreases the field of view and thus increases the imaging time if a full sample is acquired. The best compromise between resolution (and more precise computation of cell metrics) and imaging time should be defined depending on the application. Our second strategy was based on CNNs. The CNN approach works without a priori knowledge of the tumor biology, and the same network structure can be applied to any other D-FF-OCT dataset as long as sufficient annotation is available. The CNN would reach better accuracy with a greater dataset size and label homogeneity. It would, therefore, be more recommended for large and well annotated datasets or when drawing the tumor margins with high resolution is required. Nonetheless, the CNN approach is less interpretable, and because it does not rely on any general feature of tumors, it is likely that a new network should be retrained from scratch for any new tumor type, whereas the FE approach could potentially identify general biomarkers.
Ground Truth Definition and Definition of Reduced Datasets for FE and CNN Approaches The performance of the pathologists' interpretation based on the FF-OCT images was measured as referred to the standard H&E histology-based diagnosis of the same sample. As explained hereafter, only subsets of the original dataset could be used for each automated analysis: • For the FE analysis, we included samples under three criteria. A sample was excluded if less than five ROIs had been acquired with complete data. Furthermore, for each ROI, one static and its corresponding dynamic FF-OCT image should be available (without translation), as well as the raw D-FF-OCT data. It resulted in 496 static and 496 dynamic FF-OCT images from 40 samples (31 tumoral and 9 healthy). In the FE analysis, the sample histological diagnosis is passed on to all the ROIs to define the ground truth for each ROI, resulting in a ground truth with some errors.
We emphasize that the first level of prediction is made at the ROI level so the dataset size here is 496 raw images. • In contrast, the CNN approach only relies on the dynamic FF-OCT images. Nonetheless, to obtain convincing results with this approach, histopathologist expert P2 was asked to label all the individual dynamic FF-OCT images based on his interpretation and correlation with the H&E histological slide. We converted the expert P2 annotation into a binary label indicating the presence of a tumoral part in each ROI. However, not all images could be labeled with high confidence, so the dataset was reduced to 373 dynamic FF-OCT images from 47 samples (34 with a tumor and 13 without). Here, we used data augmentation by replicating some raw images while changing the contrast and/or by applying geometrical transform (horizontal or vertical symmetries)-see dedicated section in Sec.
Segmentation of cells and fibers using iLastik The first step of the engineered features analysis on breast samples was to perform cell and fiber segmentation in the images, using iLastik, a free segmentation software. 42 Extensive description of parameters used in Ilastik as well as illustration of the segmentation workflow is proposed in the Supplementary Material. Briefly, iLastik is a relatively intuitive machine-learning tool based on random forest classifiers. For each channel of D-FF-OCT images, and for FF-OCT images, pixel neighborhood is characterized by a set of generic nonlinear spatial transformations calculated by the software and is compared with a training set of pixels, manually drawn on images from the dataset. For both FF-OCT and D-FF-OCT datasets, the training step was performed on eight images (five from cancerous samples and three from healthy samples).
Further image analysis and feature calculation The two sets of segmented images were then opened and analyzed using image processing (Matlab, MathWorks). From the D-FF-OCT images, we target individual cells, and some of their morphological parameters that we expect to be modified in a tumor, such as cell density,  60  cell size,  61  or shape. 62 We also measured their light scattering properties  63  and their motility. 62 From iLastik segmented images, we detected and characterized all eight-connected objects using the regionprops function (Matlab, Image Processing Toolbox), which we estimate to correspond to single cells. We obtained hundreds of groups of pixels, we will refer to as cells in the rest of the section, although there can be a few artifacts within these objects. We additionally filtered all cells with an area below 20 pixels (20 μm 2 , corresponding to a cell radius below 2.
SVM training From the previous section, we obtained a matrix with 44 features for all 496 images in our dataset. The idea behind the SVM classification is to find an (N -1) dimensional hyperplane that best separates the dataset between healthy and cancerous images. If none of these features alone can clearly separate the dataset, the SVM will find a linear combination of all these parameters that best separate the dataset. When a feature was missing (e. g. , if no region of high cell density was found in an ROI), the value 0 was attributed. For training the SVM, we attributed the histological diagnosis to all images of each sample. We could test several models (e. g. , non-linear models) to perform the image classification (Matlab Classification Learner toolbox, MathWorks) but decided to keep a simple linear SVM to reduce the risk of overfitting (given the small dataset) and to keep the model interpretable.
As mentioned in the main text, the dataset labeling is highly asymmetric; first, we have about three times more cancerous images than healthy images. Second, images from healthy samples should all be healthy, but images from cancerous samples can be either healthy or cancerous. In order to reduce overfitting (e. g. , always predicting cancer should give 75% accuracy), we made the SVM learning asymmetric by penalizing the false positives by a factor 3.
Feature reduction To learn which features are the most relevant for the segmentation of tumoral versus healthy ROIs, we apply a neighborhood component feature selection algorithm. It consists of learning a feature weighting vector from neighborhood component analysis (NCA)  66  by maximizing the expected classification accuracy with a tunable regularization parameter term. 67 This technique is more robust to the presence of irrelevant features and to dataset normalization and hence is frequently used for feature selection prior to SVM classification. A first step to learn the optimal regularization parameter by gradient ascent technique is used, and the NCA model is fitted to all data using this optimal parameter in order to obtain the feature weights. The obtained feature weights were ranging between 0 and 2, and we selected only the features with weight above 0.
Description of the CNN architecture The CNN architecture is similar to the VGG16  43  architecture with weights pre-trained on the ImageNet dataset  44  but with small modifications. We removed the classifier part and added a global average pooling (GAP) layer followed by a fully connected layer of 1024 neurons and an output neuron with sigmoid activation. The GAP layer allows network inputs of different sizes since it reduces each activation map to a single value, it results in a 512-dimensional vector bottleneck between the feature extracting convolutional layers and the dense classifier layers. Another advantage of GAP is the reduction of network parameters, which improves generalization and is particularly useful in the case of small scale data sets. Following the same reasoning, we chose only one fully connected layer.
With this, training lasts around 200 epochs and the optimal model is found somewhere between epoch 80 and 150 (depending on the data split and initialization, so depending on the random state). Training time is around 6. 5 min per epoch and 20 h per experiment. Thus, conducting a fivefold cross-validation experiment took around two and a half days (64 h); note that in these delays we also included the lag introduced by logging performance metrics, as well as the overhead introduced by reading the image batches from the disk, and not only training (i. e. , forward and backward propagation). Experiment tracking was made possible with the software Neptune (Neptune. ai), which helped organize and compare the performance of over 200 experiments conducted for this project, therefore, allowing us to choose the optimal hyperparameters in an exploratory fashion.
Data The network was trained on full resolution 1440 × 1440 × 3 RGB fields of view with binary labels indicating the presence or absence of tumorous tissue, obtained from the pathologist's refined annotation per ROI. An important detail is that in tumorous ROIs, there might be portions of the image resembling healthy tissue. The dataset partitioning into training and test sets was performed in a stratified manner with respect to the global per-sample diagnosis; in terms of size, 80% of the samples (n train ¼ 37, n train-tumor ¼ 27, n train-normal ¼ 10) used for training and the remaining 20% (n test ¼ 10, n test-tumor ¼ 7, n test-normal ¼ 3) for evaluating the performance. However, this stratification strategy does not ensure the exact same distribution of classes at ROI level, as each sample has a different number of acquired ROIs; this results in 286 fields of view for training (185 positive and 101 negative) and 87 for testing (60 positive and 27 negative).
4 and Fig. 5(a)]. The network was trained on full resolution 1440 × 1440 × 3 RGB D-FF-OCT ROIs. In this section, we used in total 373 individual ROIs from 47 samples from which frame by frame annotation was performed by the histopathologist P2 (34 samples with a tumor, 13 without; see Sec. 4 for extended dataset description and correlation with histology). We used the ROIs from 80% of the samples (37 samples; 286 ROIs, 185 positive, and 101 negative) for training the network and used data augmentation (see Sec. 4) to obtain a sixfold increase of the training set size. The 20% remaining (10 samples) were used to test the CNN (see Sec. 4 for details) with a fivefold cross-validation to test the entire dataset ultimately. The CNN outputs a probability for the ROI to be in the tumoral part, and we defined a threshold at 50% above which the ROI is considered as positive.
Acknowledgments The authors wish to thank the  Centre de Ressources Biologiques  and the technicians of the  Biopathology Department of Gustave Roussy  for the processing of histological samples. " HELMHOLTZ" (European Research Council (ERC)  (# 610110 ), PI  Mathias Fink and José-Alain Sahel) "OREO"  [ ANR-19-CE19-0023 ], ( PI Kate Grieve) ANRT  fellowship (grant  CIFRE 2018/0139 ). The  Institutional Review Board  from the  Institut Gustave Roussy (Villejuif, France)  approved the study, and informed consent was obtained from all patients.
Overall, 2,389 proteins were found in at least one dataset, and 1,742 proteins were shared (Figure  1B ). Moreover, we concatenated the relative quantification data for those shared proteins to increase the power of the analysis. 24 To visualize the resulting 20-dimensional dataset, we used dimensionality reduction by t-distributed stochastic neighbor embedding  25  (t-SNE) (Figure  1C ). The t-SNE plot revealed non-random distribution suggesting protein clustering based on fractionation profile and, hence, subcellular colocation. Using unsupervised clustering of the untransformed abundance distribution profiles by hierarchical density-based spatial clustering of applications with noise (HDBSCAN), we discovered twelve protein clusters (color in Figure  1C ). To identify secretory organelles, we explored the distribution of proteins predicted by Sig-nalP  26  to have an N-terminal signal peptide and mapped those onto the t-SNE plot (Figure  1D ).
A further 35 proteins were assigned to this cluster by Novelty-TAGM (Figure  2A ). They included multiple apple, EGF, Sushi, and chitin-binding domain proteins that likely share structural functions with COWPs and participate in assembling the multi-layered oocyst shell. 30 umerous enzymes were also found in this oocyst wall cluster (Table  S3 ). Three of them had a glucose-methanol-choline oxidoreductase fold, one was a glycosyltransferase, another a polysaccharide pyruvyl transferase, and many were annotated as peptidases and hydrolases. The transcripts for the proteins that make up this cluster are highly expressed in female gametes but generally absent in asexual stages (Figure  2B ). This is consistent with the observation that oocyst biogenesis is exclusive to female parasites, which assemble precursors in wall-forming bodies.
DISCUSSION Cryptosporidium occupies a central position in our understanding of the evolution of intracellular parasitism. Recent whole genome phylogenies place Cryptosporidium at the base of the apicomplexan tree, within or close to the gregarines, which parasitize invertebrates. 46, 47 Common with several gregarines, Cryptosporidium shows a profound reduction of the mitochondrion and loss of the relict plastid. 48 Our dataset experimentally validates the presence of a highly reduced mitochondrion in Cryptosporidium (see ''mitosome'' in Figure  1 ). The mitochondrial genome, and much of the mitochondrial electron transport chain, has been lost in Cryptosporidium,  49  but we demonstrate that multiple components of the mitochondrial iron-sulfur cluster assembly machinery including cysteine desulfurase, ISC (ironsulfur cluster) scaffold protein, ferredoxin NADPH oxidoreductase, chaperone hsp70, nucleotide exchange factor grpE, ISC transporter, and P loop NTPAse localize to the same organelle as the beta subunit of MPP and the alternative ubiquinol oxidase (Table  S3 ).