dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The Plankton Lifeform Extraction Tool is hosted by the Archive for Marine Species and Habitats Data (DASSH), which is accredited as the UK Node of the Ocean Biodiversity Information System (OBIS) and through the Marine Environmental Data and Information Network (MEDIN), the UK partnership of organizations committed to improving access to UK marine data, and core-funded by the Department for the Environment, Food and Rural Affairs (Defra) and the Scottish government. Lifeform data products can be generated at https://www. dassh. ac. uk/lifeforms/ (last access: 22 November 2021,  Ostle et al. , 2021). The PLET's lifeform data products are generated by applying the Plankton Lifeform Traits Master List trait look-up table (UK Pelagic Habitats Expert Group, 2020; https://doi. org/10.
(Glover et al. , 2005) : scores of zero are equal to the long-term mean, positive scores (in green/yellow) signify values above the long-term mean, and negative scores signify values below the long-term mean (in blue). Only those lifeforms that have been assigned a confidence level of "high" are shown (see Tables  2  and 3 ). Regions are defined in Fig. 1. sions of the integrated plankton datasets (see  Table 1 for details; CPR and Johns, 2019; MBA, 2019; PML, 2019; CEFAS, 2019; EA, 2019; MSS, 2019; SMHI, 2019; SEPA, 2020; SAMS, 2020). These time series may be updated in the future to include ongoing plankton monitoring, and more datasets may be added. Versions of several of these datasets are also available through other data repositories (e. g. institute-specific websites provided in Table  1 , or the Ocean Biodiversity Information System, OBIS).
However, PLET provides the first centralized database for all time series feeding into UK Marine Strategy and OSPAR biodiversity lifeform-based assessments and importantly a format and structure compatible with extraction of lifeform time series. By attaching DOIs to the underlying dataset versions held within PLET and the trait list, the tool provides full transparency and reproducibility for the generated lifeform outputs. Users of these datasets are encouraged to appropriately cite the data sources by means of their DOIs as well as this data paper, so that usage can be more easily traced. Doing so provides evidence of data uptake, enhancing the possibility of continued funding for valuable time series and for plankton indicator development.
As a transparent and accessible source of diverse plankton datasets, the PLET also facilitates exploration of associated research questions in an integrated way. Observations of changes in size-based plankton lifeform abundances  (Greenwood et al. , 2019; McQuatters-Gollop et al. , 2019; Bedford et al. , 2020a) , alongside methodological developments in measuring the complete plankton size spectrum  (Atkinson et al. , 2021) , provide an improved ability to understand what is driving changes in plankton size and species composition across the full spatial-temporal scales of the component datasets  (Schmidt et al. , 2020). Data from different data sources are not aggregated within PLET in order to maintain the scientific robustness of the outputs and incorporate a range of methodologies  (units, scale, fixedpoint, transect, etc.
). The data are outputted in a unified way from PLET to encourage comparison and interpretation of the changes in the plankton, if the user wishes to combine the outputs within a specified area and time period, a normalization technique can be applied; however care needs to be taken to ensure compatibility and coverage do not bias the combined results. For example, due to the differing taxonomic resolution and sampling methods across the various component time series, we do not recommend simple comparisons of indices of species richness or diversity. There is also the flexibility to improve confidence in low-confidence groupings and to potentially incorporate new types of plankton data into the tool in the future, such as the use of flow cytometry data. Time-series datasets are critical for identifying and assessing changes in the marine environment.
Given the expense and effort which goes into producing and maintaining these invaluable datasets, tools that make them more widely available, transparent, and accessible to the broader user community are needed. The PLET provides a centralized, easily accessible source for version-controlled time-series data and metadata and is an essential component of a robust assessment process as well as a tool to support the research, which is needed to underpin assessment. This includes exploring new ecologically relevant lifeform groupings and improving the understanding of lifeforms currently designated as medium and low confidence and which will in turn feed back into the process of assessing the health of the marine environment. The PLET is not a static resource; it is designed to readily accept additional datasets and be updated to support future assessments as the assessment procedure continues to evolve.
This is a critical step towards using multiple datasets collected with diverse methods to populate and assess a common indicator, allowing the assessment of pelagic ecosystems at regional scales. As the tool is expanded with additional datasets, its ability to detect change in plankton communities will increase, and the policy evidence it provides will continue to become more robust, providing decision makers with critical information to inform management measures. As pelagic habitat assessments continue to improve and adapt to the changing policy landscape  (Boyes and Elliott, 2016)  and to evolving plankton data availability, the PLET's flexibility will allow it to continue to underpin assessment.
C. Ostle et al. : The Plankton Lifeform Extraction Tool Appendix A Table  A1. Relevant trait in lifeform traits list. The trait list reflects the lifeforms and types of plankton data in the datasets used to date in lifeform-based assessment. The list is a living document, whose status reflects ongoing efforts to refine and improve the lifeform approach as well as a necessary compromise between focusing on traits which inform the lifeforms currently used for assessments and including additional information and traits where these are known and readily available. Trait groups for the plankton type "Protozoa" have recently been added to the trait list but are not shown here as they are not finalized and not used in any of the current lifeforms. The inclusion of "protozoa" and "ciliates" (which are protozoa) designation under "phytoplankton type" is to ensure these key taxa are captured in lifeforms.
Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets.
Deep-learning algorithms called Convolutional Neural-Networks (CNNs) have recently shown great promise for image classification and in particular semantic segmentation with reasonable computational power  (Van Valen et al. , 2016). Van Valen and collaborators (Van Valen et al. , 2016)  provided a general proof of concept that CNNs (DeepCell) could be used to segment both eukaryotic and bacterial cells in dense contexts with limited training datasets. However, while the study provides important tips toward the successful training of a CNN for a specialized cell segmentation task, it does not provide a trained CNN for the general segmentation of dense bacterial communities. Nevertheless, the approach provided promising perspectives to segment E. coli microcolonies on agar and confirming this, a CNN-based tool  (DeLTA, Lugagne et al.
, 2020)  was recently developed to detect and track E. coli cells immobilized in microfluidic devices (the so-called mother machine,  Lugagne et al. , 2020). Inspired by these methods, we decided to develop MiSiC (Microbial Segmentation in dense Colonies), a general CNN-based tool to segment bacteria in single or multispecies 2D bacterial communities at very high throughput. MiSiC is based on U-net, a CNN encoder-decoder architecture that has previously been applied for detection and counting of eukaryotic cells  (Falk et al. , 2019) , that relies on shape rather than intensity information and thus performs semantic segmentation of microbial colonies under any microscope modality. Thus, contrarily to most other softwares, MiSiC is insensitive to specific imaging conditions which often require tailored training data sets.
Operating MiSiC requires minimal parameter tuning and can be run with standard computational power, in a Napari Graphic User Interface (GUI, a complete user handbook for installation and use is provided) under Python (https:// napari. org/). Both semantic segmentation and instance segmentation (in which each cell is defined as a distinct object  [Jeckel and Drescher, 2021] ) masks can thus be easily obtained with minimal computational expertise, making MiSiC broadly applicable to the field of bacterial cell biology.
, 2019). Nevertheless, the labeled data must be representative of the broadly varying experimental conditions to produce reliable outputs: in our case, different bacterial species recorded under varying imaging modalities. A schematic of the training strategy is shown in Figure  1  and detailed in the Materials and methods section. Specifically, the U-Net was trained to segment cells by learning shapes of individual bacteria and patterns emerging from the tight contact between cells. Accordingly, we curated a hand-segmented dataset of 350 bright-field images (n = 34807 cells) of two rod-shaped bacterial species, Escherichia coli and Myxococcus xanthus. This annotated data was however insufficient for the robust segmentation of bacterial cells, thus it was further enriched with synthetic representations of rod-shaped bacteria of varying length but fixed 10 pixels width corresponding to ≈ 0.
Note that this problem may be resolved by post-processing, for example by applying a watershed algorithm to the MiSiC mask (Figure  2 -figure supplement 1), which effectively resolves additional undetected septa. Although MiSiC can thus detect a number of bacterial species, a current limitation           lies in its ability to segment round or oval cells. In fact, such shapes were initially omitted from the original training dataset to omit the spurious detection of round non-cell objects that are frequently observed in background images.
Dividing cells were therefore counted across the entire image (Figure  4i , Materials and methods) to determine where they localize spatially within the community. Figure  4j , shows that cell division is markedly increased in the prey area, demonstrating directly that Myxococcus grows during prey invasion. Thus, MiSiC is appropriate for the automated detection of cellular processes (detected at subcellular resolution) at community scales. Third, we explored how MiSiC could be further adapted to segment and classify multiple bacterial species intermingling and interacting in space; here Myxococcus cell groups invading the tightlyknitted E. coli prey colony. To segment each bacterial species directly from unlabeled phase contrastimages, new training datasets were produced and used to retrain the U-NET.
These labeled datasets were obtained by imaging GFP-labeled Myxococcus and mCherry-labeled E. coli (see Materials and methods). Images were captured for each channel (GFP, mCherry) and segmented separately with MiSiC to obtain masks for each species (Figure  5a ). These masks were used to retrain the U-NET, the predictive value of which was tested by deriving classification masks directly from a phase-contrast image of a mixed Myxococcus (GFP) -E. coli (mCherry) population (Figure  5 ). Comparison of the MiSiC classification to hand annotated GFP (Myxococcus) and mCherry (E. coli) images over selected fields where each species interact (n = 4, ie Figure  5b ) respectively yielded JI scores of 0. 95 ± 0. 036 (n = 200 cells) and 0. 89 ± 0. 047 (n = 545 cells), suggesting that the classification is highly accurate despite the tight interactions between Myxococcus and E.
coli cells. To further show that species classification is reliable in a large dataset, we next classified Myxococcus from E. coli cells directly in a phase contrast Bacto-Hubble image (840 tiles, 2. 1. 10 9 pixels). We could thus discriminate cells belonging to each species in the predation area (Figure  5c ) for a total detection of ~402,000 Myxococcus and ~630,000 E. coli cells in the entire image. Given the large size of the dataset, it would be impossible to test the accuracy of the classification procedure exhaustively by comparing it to the ground truth, instead, we tested whether the distribution of shape descriptors, such as the extent (E = area/bounding box area), solidity (S = area/convex area) and minor axis length, matched the distribution of these descriptors obtained from images of each single bacterial species (Figure  5d ).
5. 10 9 pixels) may be segmented in ≈ 50 minutes on a GPU-equipped (Quadro P5000) computer (RAM 64 Gb, Intel Xeon 2. 10 GHz). In addition, the Napari environment provides a simple GUI, allowing the generation of MiSiC masks from any image by any user without the need of computational expertise. MiSiC is therefore broadly applicable to diverse applications in bacterial cell biology and generates prediction masks that can be used with other available softwares. For example, Supersegger which corrects improper segmentation based on cell contours may likely be used to correct aberrant segmentations in a MiSiC mask, combining the advantage of SI-based segmentation and contour correction. Along similar lines, non-cell or improperly separated cell objects an appear in the MiSiC masks and while some can be removed by the introduction of noise, an easy way to do it is to apply a post-processing filter using morphometric parameters to remove objects that are not bacteria.
This can be easily done using Fiji, MicrobeJ or Oufti. MicrobeJ also contains a GUI and associated toolbox to cure improper cell contours manually. MicrobeJ and Oufti are especially useful from downstream analyses of the masks because they both allow cell tracking as well as determining protein localization at sub-diffraction resolution. Finally, we show that MiSiC can be further implemented for the semantic classification of bacterial cell types directly from phase contrast images. While the network developed herein works specifically for M. xanthus and E. coli discrimination, it provides a proof of concept that the approach can be extended to segment and classify any number of bacterial species provided that a dataset (i. e. fluorescence labeling) is available to train a U-Net with ground truth generated in MiSiC.
Given that these tasks must be tailored to specific applications, we did not implement them in this package but in principle, adapting MiSiC for such applications could be developed by any laboratory without deep expertise of CNNs. This is an exciting prospect at a time where tremendous efforts are injected to reconstruct micro communities in synthetic contexts for mechanistic studies  (Wolfe et al. , 2014). Although MiSiC is designed for the analysis of bacteria that develop in 2D, recent toolboxes for the study of micro communities in 3D are also emerging  (Hartmann et al. , 2021; Zhang et al. , 2020) ; we therefore foresee that quantitative microscopy approaches will profoundly impact microbiome research of health and environmental significance.
When grown in CYE rich medium, DM14 did not present any significant defect in growth rate or cell shape. DM14 cell size is not significantly different from that of the isogenic wild type DZ2 strain. DM14 cells were spotted on thin CF agar pads to follow FtsZ localization in axenic cultures and allowed us to confirm that cell cycle progression was accompanied with the relocalization of Ftsz-nG from being diffuse in the cytoplasm to forming a discrete fluorescent focus at mid-cell before cell septation as previously described  (Treuner-Lange et al. , 2013). To generate Dataset 2 (see below), strains of E. coli (EC500, Table  S1  Supplementary file 1) and M. xanthus (DM31, Table  S1  Supplementary file 1) expressing soluble versions of mCherry and sfGFP fluorophores respectively were used. To generate DM31, a plasmid allowing for the constitutive expression of sfGFP was built (pDM14 Table  S3  Supplementary file 3).
Microscopy and image acquisition All microscopy images were acquired with an inverted optical microscope (Nikon TiE) and a 100 x NA = 1. 45 Phase Contrast objective. Camera used was Orca-EM CCD 1000 × 1000 (Hamamatsu) camera mostly set with binning 2 × 2. Acquisition software was Nikon NIS-Elements with specific module JOBS. Fluorescence acquisition used a diode-based excitation device (Spectra-X Lumencore). The Bacto-Hubble image is a composite image of rasters of the entire area and requires that the scanning speed must be sufficiently fast to avoid image shifts due to ongoing cell dynamics. To minimize the shift in focus from tile to tile we used Nikon perfect focus system (PFS) equipped with servocontrol of the focus with an infrared LED. This is especially challenging because continuous focus alignment of the microscope slows down the acquisition times dramatically.
8 billion pixels, an acquisition-time up to 4 hr), enabling a continuous magnification display from eye visible structures to single-cells. Individual tiles for Bacto-Hubble images were acquired with the scan large field capabilities of NIS-Elements software. A key point for Bacto-Hubble large images was the quality of the sample slide mounting. The samples were placed on a glass slide with a thin doublesided sticky frame (in situ GeneFrame, 65 µL, ABGene, AB-0577). An agar pad was poured inside the frame and a microliter of cells was placed on it. The chamber was closed with a glass cover slide. This assembly allows very good flatness and rigidity.
Dataset 1 Combination of hand-annotated data with synthetic data provided the most accurate segmentation after training. When the network was trained with synthetic data only, segmentation was less performant presumably due to the fact that the synthetic data was generated by randomly throwing cellshaped objects onto an image, which does not capture the intricate patterns created by cell-cell interactions observed in real images of dense cell populations. This training dataset consists of three parts: a. A hand annotated dataset corresponding to: 263(training) +87(test) cases of Bright-field images of Escherichia coli and Myxococcus xanthus with segmented masks. This data set corresponds to images sizing from 157 × 157 to 217 × 217 pixels and containing a variable number of cells (up to 400 cells per image) for a total number of cells of 34,807 manually drawn cells.
b. 200 null cases that contain background images without bacteria taken from bright-field, fluorescence and phase contrast data. c. A synthetic data set consisting of 600 cases was generated, 30 % containing 16 cells per image (256 × 256 pixels) for sparse densities and 70 % containing a maximum of 327 cells for high densities per image. Furthermore, 200 null cases, generated by inclusion of random gaussian noise and circular objects, were also included in the dataset. The synthetic data was generated with a simple model for rod-shaped bacteria with a width ranging from 8 to 10 pixels. An overlap threshold of 2 % was used to obtain a dense cell population. The binary mask created was then smoothed with a Gaussian filter and Gaussian noise was added to emulate noise in real images. The ground truth in this training dataset (denoted as [X′,y ]) has two classes: one with the mask of bacteria and the other with the contour of the detected cell.
The test set consists of 87 cases of   S1 ). The fluorescence images were then processed with a gamma adjustment and segmented using MiSiC to produce clean masks and contours of two classes, namely, Myxococcus xanthus and Escherichia coli. Thus, y in dataset two contains two channels corresponding to a mask of each class. A total of 4,000 such pseudo-annotated images of size (256 × 256) were used for dataset 2. These images were obtained after random cropping of microscope images (1000 × 1000) and the number of cells varied from image to image. MiSiC, shape-index map-based segmentation σ The shape index (SI) map of an image, x, calculated over a scale , is defined as SI ( x, σ ) = 2 π tan -1 ( k2+k1 k2-k1 ) , where k 1 , k 2 (with k 1 > k 2 ) are the eigenvalues of the Hessian of the image, x , calculated over a scale σ  (Koenderink and van Doorn, 1992).
The SI map remains within the range [-1, 1] and preserves the MiSiC shape information while being independent of the intensity values of the original image. Using the Dataset 1, we pre-process the input images X′ to generate a train set: X train of size 1260 × 256 × 256 × 3. Each channel in X train is the shape-index map calculated at three different scales [1,1. 5,2] to obtain shape index information of the cells at various scales. An instance of the U-net is trained over this data set to produce a network able to map data represented by X train → y train. The network learns to reject the noise in the shape-index map and produces masks and boundaries of the cell like structures in the shape-index map. The trained network was tested over 87 cases of labelled bright-field images, that were previously unseen by the network leading to a segmentation accuracy of 0.
Parameters: scale and noise variance The dataset one used to train the MiSiC contains cells with a width in the range of 8-10 pixels. Thus, to obtain a satisfactory segmentation, the input image must be scaled so that the average bacteria width is around 10 pixels. However, the scaling often modifies the original image leading to a smoother shape index map. Since, MiSiC has basically learnt to distinguish between smooth curvatures with well-defined boundaries from noisy background. A smooth image without inherent noise leads to a lot of false positive segmentations. Therefore, counterintuitively, synthetic noise must be added to the scaled or original image for a proper segmentation. It must be kept in mind that the noise variance should not reduce the contrast of the edges in the original image while it should be enough to discard spurious detections.
Myxococcus xanthus and Escherichia coli classification A U-NET was trained on dataset two to segment a single channel phase-contrast image into an image containing semantic classification of each species. The probability map for each label is color coded (blue = E. coli. coli, orange = M. xanthus) such that each pixel has a probability value to be part of a given class. In rare instances, bi-color objects are obtained because in these cases the prediction is not homogeneous inside the predicted objects. For subsequent analysis, these objects were filtered for the morphometric analysis shown in Figure  5c. A trained model and a python script illustrating how it was used is available at: https:// github. com/ pswapnesh/ MyxoColi.