dataesr/scientific-paragraphs-categorization huggingface dataset:
It uses machine learning, combining classical approaches and Convolutional Neural Networks in a user-friendly way, to help ecologists, even those with no computer-science background, classify large numbers of images (typically >10,000/operator/day). It is meant to be collaborative, allowing an unlimited number of users to interact on the same dataset (all using the same taxonomic tree). Their work can be iterative (by correcting each other's mistakes) while always retaining the full history of identification for each object, and machine learning models can be built based on all classified images in the database. Currently (as of Jan 25th 2019), EcoTaxa hosts >72 million images of plankton (42% of which have had their identification validated by a human operator), collected with more than seven different instruments, over the world's oceans.
In 3 years, it attracted ∼550 registered users from ∼160 institutions. While functional, this prototype needs to be deployed more widely, on servers backed by academic institutions, and improved in various ways. For example, the database may not perform as well with billions rather than the current millions of images. The taxonomic back-end will benefit from the unification of phylogenies allowed by nucleic acid sequencing (e. g. , https:// unieuk. org). Finally, the machine learning back-end should benefit from various ongoing efforts to infuse more computer science knowledge into ocean sciences. The available database of images has already proved to be a good tool to foster such initiatives by providing a wealth of classified data  (Elineau et al. , 2018). Efforts are partly funded and underway to address the limitations outlined above.
EcoTaxa also includes a 'Particle module' (http://ecotaxa. obs-vlfr. fr/part/) intended to store, visualize, and export data originating from in situ instruments that quantify marine snow (UVP, LISST, LOPC). In this application, all data originating from the same sampling event (CTD data, marine snow abundance, and plankton identified from images) are gathered in the same dataset and can be downloaded by visitors. Data from any device counting and measuring particles can be easily integrated into this module.
Enhancing Capacity and Knowledge The community with the knowledge necessary to collect, quality control, analyze and interpret plankton data is small, and the current assets to sample the world's oceans are extremely limited given the task at hand (to monitor the distribution of planktonrelevant variables in time and space throughout the world's oceans). In addition, taxonomic expertise has declined, which severely limits many types of investigations. Significant efforts in education, collaboration (sharing of expertise, computer codes, instruments, annotated image databases, etc. ) and invention of sensors to be deployed for longer periods of time on autonomous platforms can help mitigate this limitation. In addition, the creation and adoption of best practices can be an important element in training to increase the uniformity and interoperability of measurements  (Pearlman et al.
We suggest that the following itemized strategies should be used to prioritize investments and provide below each some example activities (some may contribute to more than one priority): Promote free data and information sharing by using open access publication strategies of both articles and source data and following a "FAIR" principle  (Wilkinson et al. , 2016). Build robust distributed networks for collection, distribution and curation of data (like Argo and EcoTaxa) that do not depend on one country's funding and that serve the full scientific community worldwide (see  Tanhua et al. , 2019b). Channel the data to global and consistent public databases such as OBIS or other existing platforms in consultation with modelers. Make sure funding agencies that require data curation from funded PIs are consulted and in agreement with adopted approach.
Ensure there are experts available to assist the larger community with quality assessment and control (QA/QC) of plankton-relevant data collected by non-specialists. For imaging in particular, ensure there is ready access to taxonomic information/expertise, and find ways to reward people who serve the community (e. g. , encourage data publication with citable DOI for databases). Make lists and databases of taxa at different organization levels (for example, one with 5 taxa that are well identified and one with few tens of taxa when automatic sorting is followed by detailed human annotation). Work on best practice documents to ensure worldwide methodology is consistent  (Pearlman et al. , 2018)   Invest in efforts to homogenize plankton-relevant variables between national and international programs, and to intercompare and inter-calibrate methods to ensure measurements can be integrated into a global observing system and modeling framework.
ACKNOWLEDGMENTS Much of this manuscript flows from discussions of the authors with the members of SCOR working groups 150 (TOMCAT) and 154 (P-OBS) as well as discussions with the greater community in various GOOS workshops. We also thank  Mike Sieracki ,  Cabell Davis ,  Daniele Iudicone ,  Eric Karsenti ,  Sebastien Colin ,  Colomban de Vargas ,  Ulf Riebesell ,  Fabrice Not ,  David Checkley ,  George Jackson ,  Cédric Guigand ,  Ed Urban ,  Frank Muller-Karger ,  Sanae Chiba  and  Daniel Dunn , who contributed to the initial abstracts to OceanObs'19. FL is supported by the  Institut Universitaire de France. EB is supported by the  NASA   biology and biogeochemistry program. RKi and HH were supported by the  German Science Foundation  through the  Collaborative Research Center 754  ' Climate-Biogeochemistry Interactions in the Tropical Ocean '.
PALM takes advantage of photo activatable-/switchable-fluorescent proteins (FPs) along with stochastic activation of isolated fluorophores. Thousands of cycles of activation-excitation-bleaching followed by computational manipulation result in imaging with a resolution of ~10-30 nm  42. Commercially available PALM accompanied with dedicated software (for instance, N-STORM from Nikon) allows the detection and localization of single molecules for each excitation cycle and combines the localizations over thousands of images with user-defined parameters. In addition to obtaining intracellular structure at fine scale, cluster analysis has been applied in bacteria, which sheds light on dynamics and the mode of self-assembly of proteins along the cell  43. PALM has also been successful for tracking the trajectory of single molecules for short periods of time, to elucidate the diffusive state of bacterial proteins  44, 45.
cholerae with super-resolution PALM. To this end, we built a Matlab-based software Vibio, which combines PALM detected molecule lists with cell meshes which are drawn by MicrobeTracker. We show that using brightfield (BF) images are not sufficient for precise localization analysis. Therefore we present a novel cell outline technique in which the inner membrane or the periplasm is labelled with photo-activatable/switchable FPs. We also show that Vibio can distinguish inner and outer curvature of curved-rod cells. Altogether, we show that HubP is rather localized to the inner curvature from the tip of pole, while its interaction partners have distinct localization patterns. This new labelling method and localization software will provide a better landscape of localization for single molecules in populations of cells.
Incorporation of molecules into cells. In N-STORM, detected molecules can be visualized as crosses (see Supplementary Fig. S4a ) or rendered to Gaussian blur. While some analyses such as cluster analysis discussed above can be handled manually, others definitely require computational processing of data. As mentioned, however, no high-throughput software supports lists of molecules in x and y coordinates. Therefore, we had to develop a custom solution to integrate PALM results into the cell, with high-throughput capability. On this account, we developed Matlab-based software with a Graphical User Interface (GUI) (named Vibio) to combine molecules (in x, y coordinates) and cell shape information. For each cell outline, the corresponding molecular positions are extracted from the list and located accordingly in the cell.
All images were acquired with Nikon-Stochastic Optical Reconstruction Microscopy (N-STORM) at room temperature. The N-STORM was equipped with a CFI Apo TIRF SR 100 x oil immersion objective (NA 1. 49), Coherent lasers emitting at 405 nm (100 mW), 488 nm (150 mW), 561 nm (150 mW) and built-in Nikon Perfect Focus system. Raw data were taken in a field of 256 × 256 pixels (40960 nm x 40960 nm) with an Andor iXon Ultra DU897 EM-CCD camera at a rate of 55 frames per second. The camera and microscope were controlled with the NIS-Elements Advanced Research software (version 5. 01. 00). Transmission z-stack containing 32 BF images covering positions from 1. 6 µm below to 1. 6 µm above the focal plane were performed before and after the molecule detection. Sequential acquisition option (one frame activation with 405 nm laser and one imaging with 488/561 nm lasers, sequentially) used for molecule detection during 2500-5000 frames.
The cell shape was drawn for all the outlining's by MicrobeTracker 37 version 0. 937. Alternatively, conventional methods such as MicrobeTracker with BF images or contour detection script with z-stack of BF images  48  were also used. Molecule list of the protein of interest and the cell outline information were subsequently combined by Vibio for further analysis. Vibio is a Matlab based software with a graphical user interface. It was confirmed to run with Matlab R2015b and R2012b versions. The code is available in supplementary information (zip). Due to the death of CJM, however, it remained incomplete: e. g. the output of multifield data is not automatic but requires the manual integration of single-field data (which contains data from multiple cells). Combined data were visualized with Matlab (histograms) and Wolfram Mathematica 11 (scatter density plots).
The spatial coupling between two fluorescent molecules (Supplementary Fig. S4 ) was investigated by using the SODA 2D-STORM plug-in  59  in Icy. Briefly, dual color PALM data were exported into two files depending on the labelling (channel). 7-10 PALM images for each construction were analysed to calculate the number of isolated and coupled molecules as well as the average distance. To examine cell size and morphology of different strains, cells were grown in the M9 minimal media as for PALM imaging and 1 µL of culture was spotted on the agarose pad. Phase contrast images were taken with a Zeiss axio observer Z1 microscope, and Evolve EM-CDD camera (Roper) and Axio vision software. Image analysis was performed with MicrobeTracker.
Acknowledgements I. A. and Y. Y. express sincere condolence to C. J. M. , who passed away in October 2017. We are grateful to  Romain Le Bars  and  Laetitia Besse  at the  Imagif facility  for their assistance on PALM. We thank members of  Yamaichi group , especially  Mickaël Poidevin , for technical assistance and discussion on the project. We thank  Sahand Hariri  for Mathematica script and  Drs. Elena Espinosa ,  Debora Olivier ,  Nicolas Olivier  for critical reading the paper. This project was supported by  IDEX "Biologie Intégrative des Génomes ".
FTIR Spectroscopy FTIR spectra were recorded using a Bruker Vertex 70v FTIR spectrometer. Tissue specimens were applied to the attenuated total reflection (ATR) plate and mid-infrared radiation was passed through the sample using a single-reflection snap ATR crystal diamond (recorded at 1 cm -1 of spectral resolution, 32 scans). Spectra were recorded in the range of 800-3500 cm -1. As the samples were dewaxed, the air was measured as the background. All measurements were recorded in triplicate. Initial data analysis and baseline corrections were performed using OPUS 7. 0 from Bruker Optik GmbH 2011.
Statistical Analysis All models and methods used in the experiments were implemented in Python 3. 7 and R 3. 5. 0. Specifically, the Python package Pandas v0. 23. 0 was used to manipulate the data and Scikit-Learn 0. 20 was used to implement the machine learning techniques. Prior to analysis spectra were normalised using Scikit-Learn's StandardScaler, which removes the means and scales to unit variance. Spectra were smoothed with Savitzky Golay filter (SciPy). Survival analysis was performed with the R package 'Survival' version 2. 42-6. Two-sided p values of <0. 05 were considered statistically significant. All data reduction methods were used as implemented in Scikit-Learn.
Exploratory Data Analysis Spectra were mean-centred, scaled to unit variance, smoothed using a Savitzky-Golay filter and a linear detrend was applied. As expected, peaks corresponding to functional groups within nucleic acids, phospholipids, polysaccharides, proteins and remaining lipids are observable in the FTIR spectra of bone tissues (Figure  1 ). acids, phospholipids, polysaccharides, proteins and remaining lipids are observable in the FTIR spectra of bone tissues (Figure  1 ). We first considered whether it was possible to differentiate the treatment outcomes for the ES patients and turned in the first instance to unsupervised dimensionality reduction. Dimensionality reduction approaches are broadly based on the selection of the informative features, or the generation of variables, that retain the information present in the original dataset.
In principal component analysis (PCA), this dimensionality reduction is achieved by finding the linear combination of a set of variables that have maximum variance. When the spectral dataset was analysed by PCA (Figure  2a ), we saw some clustering of the patients who lived vs. those who died due to tumour progression, although with considerable overlap between these groups in the first two principal components. Other methods for dimensionality reduction, including non-linear methods, have emerged and we applied a suite of these, including both matrix deconvolution and manifold learning methods as implemented in the python library Scikit-Learn (Figure  2  shows the first two components of each method tested). Many of these methods afforded greater visual separation of the classes than PCA, although all tested methods resulted in some degree of overlap.
Of note were the kernel PCA methods (Figure  2f ,g), which resulted in a clear clustering of the spectra based on patient outcome. Kernel PCA methods differ from the more commonly used linear PCA in that they use an arbitrary function as opposed to a linear function  [23]. Scikit-Learn implements several functions for use in Kernel PCA including a sigmoid, polynomial (poly), cosine and a radial basis function (RBF). This analysis was repeated for the necrosis and relapse responses; however, none of the applied methods resulted in the visual separation of the classes (please see Supplementary Material). We first considered whether it was possible to differentiate the treatment outcomes for the ES patients and turned in the first instance to unsupervised dimensionality reduction. Dimensionality reduction approaches are broadly based on the selection of the informative features, or the generation of variables, that retain the information present in the original dataset.
Supervised Learning The goal of supervised learning is to find a model that will correctly associate the inputs with the outputs. In the case of Ewing sarcoma diagnosis, it would be clinically useful to be able to determine the outcome of treatment. It is not possible to tell a priori which machine learning method will be most suitable for a predictive task. Linear Support Vector Machine (SVM), Random Forest (RF) Decision Tree, Linear Discriminant Analysis (LDA) and Gradient Boosted Classifier (GDM) models were, therefore, trained on the reduced spectral feature sets. All classifiers were used with their default settings as implemented in SciKit-Learn. Table  2  lists the leave-one-out cross-validation accuracy for these models. We began by attempting to develop a predictor for patient death based on the reduced spectral dataset.
Of the tested classifiers a Linear Discriminant Analysis (LDA) classifier was able to predict patient death with an 81% accuracy using only feature sets based on preCTX spectra. Greater success was seen when the analysis was repeated on the postCTX spectra only with the random forest classifier able to predict patient death with better than 92% accuracy. Subtracting the preCTX and postCTX did not typically offer good predictive accuracy, except in the case of the k-nearest neighbours (KNN) classifier. We observed a general improvement in classification accuracy when both the preCTX and postCTX spectra are concatenated with one another. This resulted in an LDA model that was able to classify 92% of the patients correctly. This corresponded to the misclassification of only two patients. Prediction of patient relapse was more challenging with the postCTX reduced spectral dataset generally being more useful predictively.
A Linear SVM classifier trained on the preCTX spectral data set was, however, found to have the highest accuracy for relapse prediction (92%). Again, this corresponded to the misclassification of two patients. Concatenating or subtracting the preCTX and postCTX spectra did not improve classification accuracy as it did for mortality prediction. Finally, the prediction of patients whose tumours showed greater than 90% necrosis was more difficult. Specifically, KNN and linear SVM models were able to predict patient outcome in only 77% of cases from preCTX spectra alone. Accuracy could be improved, however, to 84. 6% by training a linear SVM model on the spectral changes due to CTX treatment. This is consistent with the work of Bergner et al. who found that, when using FTIR for spectral imaging, tumorous and necrotic tissue were difficult to distinguish and could be distinguished with a sensitivity of 75.
For the longitudinal analysis, hazard ratios (HR) and their 95% confidence intervals (CI) for death were estimated using Cox proportional-hazard ratios. Log-rank tests were used to estimate survival difference based on the model predictions and were calculated using the R package Survival. This analysis indicated that the survival times are indeed different with each predictive model. Specifically, the preCTX+postCTX LDA mortality model has a p-value of 0. 00023, the postCTX RF mortality model has a p-value of 0. 0002 and the linear SVM relapse model has a p-value of 0. 0004. In all except one case, patients who were predicted to die by either the LDA or RF mortality prediction model did so. LDA mortality model has a p-value of 0. 00023, the postCTX RF mortality model has a p-value of 0. 0002 and the linear SVM relapse model has a p-value of 0.
Conclusions The FTIR spectra from ES patients treated initially with VIDE-based CTX were analysed using a variety of machine learning approaches. This analysis demonstrates the possibility that such spectra may predict patient death or relapse with greater than 92% accuracy. Moreover, some of these data can be collected before beginning CTX, offering clinically useful information that might aid patient treatment. The significance of these results is limited by the small size of the study group, but we believe that these results point to the potential development of spectroscopic methods for the diagnosis, prognostication and treatment planning of paediatric Ewing Sarcoma. Supplementary Materials: Figure  S1. Dimensionality reduction methods applied to the FTIR dataset. Patients who suffer necrosis are coloured red whilst those who do not are coloured blue.
Matrix decomposition methods: (a). PCA (b). Factor analysis (c). Fast Independent Components Analysis (FastICA) (d). Incremental PCA (e). Truncated singular value decomposition (SVD) (f). Kernel PCA using a linear kernel (g). Kernel PCA using a sigmoid kernel (h). Kernel PCA using a polynomial kernel (i). Kernel PCA using a radial basis function kernel and (j). Kernel PCA using a cosine kernel. Manifold Learning methods: (k). Locally linear embedding. (l). Isomap m. Multidimensional scaling (MDS) (m). Spectral embedding and (n). t-distributed stochastic neighbour embedding (t-SNE). Figure  S2. Dimensionality reduction methods applied to the FTIR dataset. Throughout, patients who did not relapse are colored blue whilst those who did are coloured red. Matrix decomposition methods: (a). PCA (b).
Here we propose that using shotgun sequencing to examine food leads to accurate authentication of ingredients and detection of contaminants. To demonstrate this, we developed a bioinformatic pipeline, FASER (Food Authentication from SEquencing Reads), designed to resolve the relative composition of mixtures of eukaryotic species using RNA or DNA sequencing. Our comprehensive database includes >6000 plants and animals that may be present in food. FASER accurately identified eukaryotic species with 0. 4% median absolute difference between observed and expected proportions on sequence data from various sources including sausage meat, plants, and fish. FASER was applied to 31 high protein powder raw factory ingredient total RNA samples. The samples mostly contained the expected source ingredient, chicken, while three samples unexpectedly contained pork and beef.
Use of metagenomic sequencing to authenticate complex food types is a new approach that requires bioinformatic best practices to be developed. To advance this approach, public metagenome sequences and custom simulated in silico food sequence datasets were examined to develop and calibrate a food authentication pipeline that produces accurate eukaryotic species identification and relative quantification from high-throughput metagenomic sequencing reads, across plant and animal sources. This approach has many applications that include adulteration and hazard detection, quality control, e. g. , when working with new suppliers, and detection of anomalous samples which could indicate an issue in the food supply chain. In this paper, we describe a food matrix authentication bioinformatic pipeline, Food Authentication from SEquencing Reads (FASER), for use with high-throughput total DNA or RNA sequencing and demonstrate its applicability for use in food (Fig.
1 ). One key component of the pipeline is a comprehensive stand-alone BLAST  14  search index built on a whole genome reference collection containing 6,160 unique plant and vertebrate organisms (158 GB total size), which enables accurate species assignment directly from sequencing data. Together the database and bioinformatic analysis steps allow relative quantification of single and multi-ingredient samples across diverse plant and animal species. We applied FASER to 11 experimental and 5 in silico datasets with expected compositions and demonstrated accuracy achieving a 0. 4% median absolute difference between observed and expected relative proportions of the true positive species. On average, the observed combined relative proportion of true positive species per sample was over 99%. We demonstrated that utilizing as few as 150,000 paired end (100-150 bp length) sequencing reads was sufficient to achieve this accuracy on both in silico simulated sequence mixtures and publicly available experimental data.