dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow-through Strategies PlanktoScope v. 1 is equipped with a peristaltic pump module (Figure  1A6 ) composed of a stack of 5 acrylic layers forming a closed chamber inside which 3 "rollers'' can spin around the motor axis compressing a tube along the internal wall. The speed of the motor and the diameter of the compressed tube determine the flow rate which is about 3 ml/min at maximum speed. The compact PlanktoScope v. 2 uses off-the-shelf peristaltic pumps for flow. Many are available in a 10mm x10mm form factor. Common 12V versions provide reliable flow rates of several ml/min. Several models can be easily incorporated by small modifications to the laser cut mount on the 3D model and connected to the other port of the Adafruit Stepper Motor HAT controlling the stage. In both designs, a continuous flow mode and a stop-flow mode can be used.
Stage and Focus The PlanktoScope v. 1 includes a module combining the focusing and exploring functions (Figure  1A4 ). This linear delta design, used in some 3D printers, uses 3 vertical independent linear stepper motors that hold a platform, each with 2 arms. Each stepper is driven by an A4988 driver powered with 9V and controlled by a common Arduino mini pro present in the module. To control the location of the sample maintained by the platform, an inverse kinematic is necessary to transform an X/Y/Z desired displacement in a delta motion. Here, the code embedded in the Arduino was simplified to control the focus by moving the three stepper motors simultaneously. This Arduino has a defined I2C address allowing the Raspberry Pi to iteratively set a new focal position. The platform made of two separable magnetic bodies can host a broad range of sample holders: a slide, a petri dish, an optical chamber, or a flow cell.
Illumination In the PlanktoScope v. 1, the illumination module is built of 5 concentric rings composed of 1, 6, 12, 24, and 32 white ultrabright LEDs having a narrow-angle of 17°. The light intensity of each ring can be tuned separately to offer a broad range of illumination modes. Two main modes are (i) pure dark-field where the two external rings are used (Supplementary Material Figure  5A ) and (ii) pure bright-field where the most central LEDs are used (Supplementary Material Figure  5B ). In the following results, we opted to use the maximum light intensity of the central LED to maximize the depth of field in the flow cell. The compact PlanktoScope uses a single ultra-bright LED at a constant intensity with a narrow angle of 15° enabling bright-field illumination and providing a nearly collimated light source.
Optical Modules The optical train is defined by two inverted S-mount lenses (M12 lenses) that are both encapsulated in different detachable modules. The two modules have been designed to enable a rapid change of each M12 lens used as a couple. The alignment is set by the insertion holes cut and positioned by the laser cutter machine. The distances of the M12 lenses to each other and the sensor are defined by rotating the M12 lenses in the holes tapped using an M12x0. 5 hand thread tap from Thorlabs. This optical train remains the same on both versions of the PlanktoScope.
Power, Computational, and Sensor Modules The PlanktoScope v. 1 is directly powered through one multifunctional module dedicated to the computation and sensor (Figure  1A1 ). It receives 12V either by a regular AC power adapter for lab experiments or a battery for field deployment. A custom BUS made of 6 electronic wires dedicated to power the other modules provide 12V, 5V, and Ground wires. The three other wires consist of the I2C, SDA, SCL, and a dedicated Ground enabling the exchange of data between the different modules. The camera sensor is a Pi Camera v2. 1 embedded in the module. It is positioned facing up to collect the image coming from above. Under this module the user on one side of the PlanktoScope are 3 suction cups allowing the user to fix the instrument on flat surfaces and improve its vertical and horizontal stability for field experiments (e.
User/machine Interface and Software Architecture By utilizing the headless configuration for the Raspberry Pi, we removed the need for a dedicated monitor, mouse, and keyboard, enabling control of the instrument from any device able to access a web browser over a WiFi connection (Figure  1F ). This strategy enables any user to immediately interact with the device without OS or software compatibility issues. The user can then access a browser-based dashboard powered by Node-RED for remote control of the system; acquisition settings, interactive collection of the metadata, as well as rapid state modification of the actuators. The software architecture (Supplementary Material Figure  3 ) is based on existing programs and python libraries, such as Node-RED (https://nodered. org/) for the Graphical User Interface and the first layer of the programming interface, MorphoCut (https://github.
com/morphocut/morphocut) for handling the image processing from the raw images to the online platform, and EcoTaxa (https://ecotaxa. obs-vlfr. fr/) for plankton images classification and annotation. The back-end of the GUI is also based on Node-RED, a flow-based development tool for visual programming which is provided by default on any Raspberry Pi software suite. Node-RED provides a web browser-based flow editor, which can be used to create JavaScript-based applications. Elements of applications can be saved or shared for re-use. The strategy makes it more accessible to those with limited experience in scripting. This visually modifiable program can easily be shared through a JavaScript Object Notation (. json) text file. 5A, B, 6 ), the optical configuration was a 16 mm focal length for the tube lens and a 12 mm focal length for the objective lens.
Image Processing The raw images are stored on the Pi after collection and can be automatically processed on the Planktoscope by MorphoCut, a python-based library designed to handle large volumes of imaging data (https://github. com/morphocut/morphocut). Several operations are applied to the raw images (Figure  2A1 ) acquired in fluidic mode. MorphoCut first applies a running median to approximate the background image (Figure  2A2 ) based on 5 frames, a Canny Edge Detection via OpenCV is performed, followed by dilation, closing, and erosion functions (Figure  2A3 ) also from OpenCV. From the binary image, MorphoCut extracts the region of interest (ROI) for each present object (Figure  2A4 ). MorphoCut then extracts, using Scikit-image (van der Walt et al. , 2014) 32 keys mathematical image descriptors.
Each ROI is then stored, along with contextual metadata defined by the user on the Graphical User Interface (GUI). This way, large data sets can be compressed at sea by storing only relevant ROIs and data tables. Finally, all data outputs are zipped in a compressed file and formatted for being imported to the EcoTaxa server (Figure  2B ). Ecotaxa is a web-interfaced database, which combines supervised machine learning with collaborative visual inspections/classification by taxonomy experts to classify and assign taxonomy to plankton from environmental plankton image datasets. This creates a uniform data format already utilized by plankton researchers worldwide.
An Open, Modular, and Miniaturized Imaging Platform for Plankton Ecology The PlanktoScope was developed in two configurations: v. 1, a modular, compartmentalized configuration maximizing multifunctionality and adaptability, and v. 2, a compact version designed for rapid assembly, portability, and standardization. Both versions achieve an optical magnification of 1. 3X and a pixel size of 0. 9μm/px. The travel distance of the specimen stage is about 3. 2cm with a step size of 0. 15μm to comply with a large range of lens working distances and sample mounting strategies. For a framerate of 8 frames per second and a 500μm thick flow cell, we can image a volume at 0. 1ml/min. The components are off-the-shelf and readily accessible from numerous vendors at a low cost to enable replication. The corresponding open-software strategy utilizes existing libraries for image processing and a flow-based visual programming platform to allow users to rapidly customize acquisition and processing steps.
Image segmentation can be toggled for automatic processing after an acquisition sequence, allowing the user to efficiently inspect objects extracted from large volumes, even at low abundance. The fully modular PlanktoScope v. 1 is based on six triangular units (Figure  1A ), each being a separate functional layer that couple together through shared optical and electronic paths: 1) a single board computer coupled to its camera sensor, 2-3) two reversed M12 lenses (an objective lens and a tube lens) separated in two modules, 4) a motorized stage and focus delta platform for sample manipulations, 5) independent programmable rings of LEDs for sample illumination, and 6) a peristaltic pump. The modules are connected mechanically, electronically, and optically, enabling simple re-configuration (Figure  1B ).
In the modular PlanktoScope v. 1, three magnets are incorporated into the corners of the interface between modules (Figure  1A ) enabling both proper alignment of the six units and quick reconfiguration. The microscope can be used in vertical or horizontal configurations, placed upright or inverted, depending on the need or constraints of the experimenter. For example, a vertical mode enables manual exploration of a static sample that can be placed on a glass slide, flow-cell, petri dish, or optical chambers (Figure  1B ). The delta stage enables tracking of an organism with high precision or a quick survey of the sample holder area. A horizontal mode allows automated, continuous imaging of liquid samples passing through the flow cell at a predefined rate (Figure  1C ). On the other hand, the compact, flow-through PlanktoScope v.
2 uses a minimal structure to position and align the components, to increase robustness and stability for field deployment or in-situ installation. Modularity is still maintained by allowing the lenses and flow-cell to be quickly interchanged. While the modular version requires 10 h for the machining, soldering, and assembly, the compact version drastically reduces the build complexity enabling a complete machining/assembly in less than 4 h. Both prototypes are based on a Raspberry Pi singleboard computer that controls the electronics, acquires and processes the images, and serves as the user/machine interface (Figure  1F ). The magnetic coupling of the modular PlanktoScope enables electronic connectivity through the contact of copper ribbons that connect each module at their interface to form a custom BUS for Inter-Integrated Circuit (I 2 C) connection and power.
The different independent microcontrollers, here Arduinos, receive queries as actuators and send logs as sensors back to the Raspberry Pi. The compact version utilizes Pi HATs (Hardware Attached on Top) for both assembling and deploying code. The Pi HATs enable the rapid addition of numerous off-the-shelf specialized boards. Three HATs are utilized to serve different functions: one for cooling the CPU of the Raspberry Pi and providing visual feedback, one for controlling the focus stage and the pump, and a third HAT supporting a GPS for geolocalization of the images. Thanks to the massive community built around Raspberry Pi, hundreds of other possibilities exist for new modules and more functions built on top of this platform. Both instruments can be powered through either standard wall Alternative Current (AC) power or from battery cells for field deployment (Figure  5A ).
We then attempted to extract geometrical characteristics from that dataset that could be ecologically informative. By measuring the ratio between the maximal and minimal length of detected objects (i. e. , the aspect ratio, quantifying elongation) across plankton size fractions and depth (Figure  5A ), we observed a relation between the increasing number of detected objects and their aspect ratio. Most of the detected objects collected below the pycnocline and with an equivalent diameter between 36μm and 64μm had an aspect ratio of around 4, indicating the presence of a large population of elongated objects. By further exploring the vertical stratification of objects within the size fraction 53μm to 55μm, where the aspect ratio is on average about 5. 9 at 6 meters, we found many elongated plankton (Figures  5B, C ).
These same metagenomic studies have unveiled the massive biodiversity of micro-eukaryotes  (de Vargas et al. , 2015)  with mostly unknown functions  Carradec et al. , 2018  in planktonic ecosystems. These organisms, essentially protists, are often more complex than metazoans in terms of cell structures, symbiotic interactions, and behavior  Gavelis et al. , 2015 , (Vincent et al. , 2018) , properties that cannot readily be inferred from genomic data Keeling, 2019. To quantify and understand the role of microeukaryotic complexity in ecosystem functions, it is critical to develop instruments allowing their high-throughput imaging worldwide. The PlanktoScope is a low cost, versatile, and high-resolution digital microscope designed to enable professional and citizen scientists to perform large scale surveys of planktonic life.
We have demonstrated here its capacity to monitor morphology and physiology of eukaryotic cells in culture, or to quantify fundamental features of micro-plankton communities in a coastal water column directly in the field in Chile, a country where single blooms have created losses of over 800 million dollars locally, leading to major public health crisis  (Mardones et al. , 2021). Further demonstration of the PlanktoScope v. 2's efficiency for quantitative imaging is presented in the same issue  (Mériguet et al. , this issue) , showing how the PlanktoScope and Flowcam provided comparable data while sampling along a basin-scale transect of the Tara schooner from Lorient (Britanny, France) to Punta Arenas (Chile). The current version of the PlanktoScope is limited in the plankton size range (50-200µm) it can recognize and quantify, however its fundamental modularity and relative simplicity make it possible to implement future new modules to analyze smaller or larger plankton.
The foundation of PlanktoScope lies in the principles of open-source hardware and software, combined with an open yet cohesive community of engineers, makers, researchers, and citizens in daily contact with sea-water (i. e. , seatizen of the 'Plankton Planet' initiative, see  de Vargas et al. , 2022). Current trends in affordable electronics and distributed manufacturing, together with computer vision and automated image processing, make it possible to put instruments' manufacturing and data collection in the hands of thousands of users across the planet. Therefore, we have also launched web tools to share, develop, and replicate the PlanktoScope globally. Instructions to order and/ or manufacture the different components and assemble them into a functional instrument are available @ www. planktoscope.
ACKNOWLEDGMENTS We acknowledge all members of  PrakashLab and Plankton Planet core team , as well as  Romain Bazile ,  Anna Oddone ,  Simon-Martin Schröder ,  Daniel Elnatan ,  B. B. Cael ,  Rainer Kiko ,  Marie Walde ,  Guillaume Bourdin ,  Aurélie Labarre , and  Flora Vincent  for insightful comments and suggestions throughout this work. We thank members of the EMBRC platform PIQv for image analysis, as well as  Jorge Mardones ,  Lara Zamora  ( IFOP, Chile ), and  Gurudeep Rastogi  ( Wetland Research and Training Center, Chilika Development Authority ) for support during field testing in Chile and on the lake Chilika. This article is contribution number 2 of Plankton Planet.
FUNDING This work was initially funded by the  'Plankton Arts ' grant from the  Fondation d'Entreprise Total  to CV and MP. Additional funding came from the  Simons Foundation (AL) , the  Schmidt Futures, Moore Foundation ,  NSF Center for Cellular Construction (NSF   STC award   DBI 1548297 ),  CZ BioHub , and  HHMI-Gates Faculty Fellows Program (MP) , the  New Zealand Royal Society Strategic Seeding Fund  ( 16-CAW-008-CSG ), the  Institut Universitaire de France (FL) , and the  French Government "Investissements d' Avenir" program OCEANOMICS  ( ANR-11-BTBR-0008 ) (CV, SC) and  EMBRC-France  ( ANR-10-INBS-02 ).
Consequently, mitochondrial structure and protein content differ between cell types (4) and we want to understand how mitochondria adapt to their cellular environment. mitoXplorer version 1. 0 (mitoXplorer 1. 0) was developed as a visual data mining platform that allows us to analyse the dynamics of gene expression and mutations of all genes with a mitochondrial function (mito-genes)  (1). It contains the most complete and up-to-date mitochondrial interactomes for four different model species (human, mouse, Drosophila melanogaster and budding yeast) as well as four different visualization interfaces that enable users to mine expression and mutation data in a comparative manner, as well as from a single dataset. mitoXplorer 1. 0 was the first platform available to mine expression dynamics of Nucleic Acids Research, 2022, Vol.
50, Web Server issue W491 mito-genes in different conditions. However, it fails to integrate the mitochondrial interactome into the cellular context and thus to help identify potential signalling and transcriptional regulators that adjust mitochondria and their functions to their cellular environment. We here introduce mitoXplorer version 2. 0 (mitoXplorer 2. 0), in which we have added integrative data analysis functions to help address these questions. MitoXplorer 2. 0 contains several new functions for data analysis, such as an enrichment function for mito-processes, a function to explore time-series data, the possibility to compare datasets across species and two novel data integration functions that aim at identifying mitochondrial regulators: first, a function to identify potential transcriptional regulators of mito-genes with a similar expression profile (co-regulated mito-genes) by making use of our recently published AnnoMiner web tool  (5) ; and second, a function to identify potential signalling pathways from and to mitochondria, by embedding the mito-interactome in the cellular interactome and subsequently exploring the network neighbourhood of a selected mito-gene based on integrating the network with differential expression data  (6).
Novel functions in mitoXplorer 2. 0 Mito-process enrichment analysis. In order to identify enrichment of our 38 manually curated mito-processes in a dataset, we implemented a gene set enrichment analysis (GSEA) function. This function helps guide users to focus on important mito-processes of a dataset for subsequent deeper analysis with other functions available in mitoXplorer 2. 0. Mito-process enrichment analysis was coded using the GSEApy Python library  (7, 8). When P-value and log 2 FC are both present, genes are ranked by the combined score as defined by Xiao et al. (9) : combined score = abs (log 2 FC) × -log 10 (P-value). If no P-value is provided, genes are ranked by the absolute value of log 2 FC. The gene set enrichment score is tested against a null distribution of enrichment scores generated from 100 permuted gene sets composed of randomly selected genes from the input dataset.
The results of the GSEA analysis are shown in the form of an interactive dot plot, as well as a bar plot (see Figure  2A  for an example). In the dot plot, mito-processes are represented by bubbles, whereby the size reflects the normalized enrichment score and the colour represents the combined score. The Y-axis shows the significance of the test (-log 10 FDR), while the Xaxis shows the different mito-processes. By clicking on one of the mito-process bubbles, all its values associated with the GSEApy analysis are displayed in the information panel. In the bar plot, mito-processes are ranked by combined score, whose value is also given in parentheses next to the mitoprocess. Time-course visualization. In mitoXplorer 1. 0, only six datasets could be compared to each other with the comparative plots.
To overcome this limitation and to effectively visualize an increasing number of available time-course data, we implemented a time-course visualization interface. With this function, we allow users to visualize up to 20 time points of a temporal dataset. The structure of this visualization interface is similar to the comparative plots, with all its interactivity, except that genes can be individually selected from the sortable heatmap for display in the line plot by clicking on the gene name. Time points on the Xaxis are connected via a line to visualize expression profiles over time. The user can thus visualize the temporal gene expression profile of a single or multiple mito-genes. Colours are assigned automatically to help visualize individual genes (see Figure  2B ). This visualization was developed using the D3.
js library  (10)  in order to produce a fully interactive visual interface. Cross-species analysis. mitoXplorer 2. 0 offers the possibility to compare datasets across species. Cross-species analysis is possible with the comparative plots or the heatmap function. This will help unravel similarities and understand differences in mitochondrial dynamics between species. Orthology information has been included in the backend MySQL database of mitoXplorer by adding orthology ID tables for each model organism. These tables contain dictionaries, having as key the ID of the gene of the model organism of interest and as values the orthologous gene IDs in the other species available in mitoXplorer 2. 0. Orthologs between the four model species offered by mitoXplorer were initially collected using the last release of BioMart  (11).
We manually curated these datasets and only 1:1 relations were kept. The user has to first choose 'cross-species' from the 'Data Mining' drop-down menu and subsequently select the species to analyse. Only the comparative plot and the heatmap are offered for cross-species analysis. In the resulting plots of both visualization interfaces, the IDs of the first organism selected are used to label genes. However, the user can retrieve the ID of the ortholog of the second organism together with underlying information, by clicking on the respective cell in the heatmap or the bubble in the scatterplot. Integrative analysis functions. In mitoXplorer 2. 0, we also wanted to provide the possibility to identify mitochondrial regulators. More specifically, we wanted users to be able to find potential transcriptional regulators of co-regulated mito-genes, as well as identify potentially active signalling pathways from or to mitochondria.
The interactivity of mi-toXplorer allows one to directly select the gene(s) for both new integrative analysis functions starting from the com- TF-enrichment. Identifying potential transcriptional regulators is done by performing a TF-enrichment analysis via connecting mitoXplorer to the AnnoMiner web server  (5)  and through direct upload of data from mitoXplorer to An-noMiner. In brief, AnnoMiner seeks enriched peaks of transcriptional regulators from the ENCODE  (12) , modEN-CODE  (13)  and modERN  (14)  databases in the promoter regions of a user-provided gene set. This set of genes for an AnnoMiner TF enrichment analysis can directly be selected in mitoXplorer 2. 0 and uploaded to the AnnoMiner web server via the TF-enrichment function (see Supplementary Figure  S2E  for the TF-enrichment menu).