dataesr/scientific-paragraphs-categorization huggingface dataset:
This evolving framework provides researchers with the opportunity to quickly develop and optimize new algorithms based on existing TrackMate modules without the need of having to write de novo user interfaces, including visualization, analysis and exporting tools. The current capabilities of TrackMate are presented in the context of three different biological problems. First, we perform Caenorhabditis-elegans lineage analysis to assess how light-induced damage during imaging impairs its early development. Our TrackMate-based lineage analysis indicates the lack of a cell-specific light-sensitive mechanism. Second, we investigate the recruitment of NEMO (NF-jB essential modulator) clusters in fibroblasts after stimulation by the cytokine IL-1 and show that photodamage can generate artifacts in the shape of TrackMate characterized movements that confuse motility analysis.
Given all these hurdles, it is not surprising that only a few tracking algorithms exist  [5] [6] [7] [8] [9] [10] [11] [12] [13]  that are widely available and designed to be useful for the general needs of the greater Life-Science community. TrackMate is a plugin within the Fiji ImageJ distribution  [14]  for tracking developed with several concomitant goals. First, it focuses on usability by providing Life-Science researchers with a turnkey user-friendly tracking solution, fulfilling the recommendations of  [15]. It is openly available and well documented, and it houses several detection and tracking modules that allow combining manual and automated particle tracking approaches. TrackMate includes several visualization tools as well as other features that facilitate the export and exchange of data and results with other tracking tools and/or analysis applications.
Second, TrackMate has been designed for maximal flexibility. The capabilities of TrackMate can be tailored by the user through the addition of specific tracking, detection, visualization, or analysis modules. As a result, researchers can quickly develop a solution suited for their specific application, while taking advantage of existing modules, thus accelerating and facilitating development. Finally, TrackMate uses a data model that makes it a useful tool for a wide range of tracking applications, ranging from single-particle tracking of subcellular organelles to cell lineage analysis. Below we describe the features of TrackMate in more detail and illustrate their application towards three biological use cases that involve tracking and quantification of dynamic live-cell events.
Purpose TrackMate is a Fiji  [14]  plugin available through this software platform, and therefore benefits from the facilities offered by Fiji for image input/output and preprocessing. TrackMate's main entry point is an interactive plugin where tracking is performed using a wizard-like GUI (Fig. 1 ). The user is guided through several stages, each of which constitutes a step in the tracking process, choosing what algorithm to use for detection and tracking, and then configuring them. The result of each step is displayed immediately. This allows the user to readily navigate back to readjust settings should the process output be deemed unsatisfactory. Various tools allow for the inspection of intermediate results: a 3D view of the data and the results can be launched, and each particle or track can be annotated using specific colors to denote numerical values of interest, including e.
The accuracy of the particle-linking algorithms or spot trackers offered in TrackMate is documented in the Supplemental Information. To establish it, we relied on the ISBI Grand Challenge single-particle tracking dataset  [4]. Here, the user filters out spurious spots based on a quality threshold just after the execution of the detection step. Several filters can be stacked. Detection results, or spots, are represented as spheres with an initially constant radius. Here, the user chose a coloring scheme that reflects an estimate of the true spot radius. The bottom part of the GUI allows the user to navigate forward and backward through the tracking steps, to see a text log of the plugin activity and to save the data at anytime.
Using TrackMate in scripts and other applications TrackMate has a modular design that allows using its processing core without relying on the GUI, in scripts or other software. This allows TrackMate to be used for batch analysis, potentially running on a remote cluster over many images at once. The Supplemental Information details the core API of TrackMate and gives examples of scripts in Python and MATLAB. A subset of TrackMate capabilities can also be called using the ImageJ1 macro language.
Interoperability with other software TrackMate is deployed as a Fiji plugin, and therefore benefits from and interacts with the whole ImageJ ecosystem  [18] , whether for data import and preprocessing or export and scripting. Track-Mate focuses on single-particle tracking, and therefore its end results take the shape of tracks, collections of spots and links. It offers basic track analysis and visualization capabilities, but specific applications are likely to require subsequent, finer analysis, which requires TrackMate files to be interoperable with specialized software. The Fiji distribution of TrackMate ships the files required to import TrackMate results in MATLAB, documented in the Supplemental Information. A specific tool in TrackMate also allows the export of results to a format readable by the Icy software  [8].
Extending and reusing TrackMate TrackMate is modular in design. Each step in the tracking application corresponds to a module in TrackMate, organized by common interfaces. There are seven types of TrackMate mod-ules, including data visualization, particle detection, particle analysis, particle linking, link analysis, track analysis, and generic actions. This modular design is made to be extensible. External developers can create their own TrackMate module to fill their own needs. By extending common TrackMate interfaces, they will be integrated in the GUI without distinctions from native modules. Track-Mate discovers these new modules automatically thanks to the SciJava annotation framework, which considerably simplifies development. The new modules just need to be annotated with Sci-Java and compiled as java jar files.
TrackMate and multidimensional imaging TrackMate was designed for use with multidimensional light microscopy datasets from a wide range of modalities including simple 2D brightfield collection, 3D TIRF imaging, and 4D (space over time) laser scanning microscopy. The program was originally developed for C. elegans lineage analysis  [19] , but has been adapted and widely utilized for a number of other biological tracking applications. Given the wide number of current and possible dynamic imaging uses for TrackMate, in this manuscript we chose to focus on three applications that showcase the different strengths, features, and potential of TrackMate. In the first case study, we examine how laser scanning confocal microscopy (LSCM) induced phototoxicity affects cell division in C. elegans. By utilizing TrackMate-based cell lineage tracking we can determine whether different imaging doses or techniques can influence the normal cell lineage.
The formation of NEMO dots is a transient event that occurs rapidly and can be tracked in 2D (using SDCM). A key need for this application is the ability to use the tracking results in subsequent analyses using biophysics methods, revealing the key ability of TrackMate to interoperate with other analysis tools, such as MATLAB. The third example focuses on clathrin dynamics at the plasma membrane of Arabidopsis hypocotyl epidermal cells. In particular, we assess the ability of TrackMate to track the transient recruitment of fluorescent fusion protein tagged clathrin light chain (CLC-FP) into diffraction-limited structures at the plasma membrane and their subsequent release using Variable Angle Epifluorescence Microscopy or TIRFM. This example illustrates on an important trafficking process and the utility of TrackMate in both plant and mammalian models.
We investigate here whether specific cells of the lineage have different sensitivities to photodamage. This involves building comprehensive cell lineages, as the embryos sample different light doses. While this task is nowadays greatly facilitated by the pioneering work of other groups  [5, [25] [26] [27] , the tools they offer are unfortunately not applicable to our problem. Indeed, the images we generate have a quality that is not adjustable: it is the product of the sensitivity of the system tested and of the light dose probed. For a strong illumination power, the fluorophore quickly bleaches, while this does not happen or not as much for low incident power. A single dataset therefore may have movies with a very low and constant SNR for low light doses, and movies beginning with a strong SNR that quickly decays until the images are no longer exploitable for high light doses (Supplemental Movie 1).
To add to this quality discrepancy, a single image of the embryo also shows varying SNR as the Z position of cells vary. Indeed, as we use an oil objective, the image quality quickly deteriorates as the focus moves away from the coverslip. This problem is classically solved by varying the illumination power as the focus moves  [25] , but again this is not applicable to our study since we want to use a constant illumination power throughout a single experiment. It is important to note that the extremely high variability in image quality, both in space and time and within a single dataset, is there by construction. As the illumination power is an input variable in our experiments we do not have the liberty to tune it in order to reach a desirable image quality. Rather than develop a highly customized solution that would deal with this issue, we exploited the ability of TrackMate to combine automated and manual annotation approaches.
Results Two SDCMs were used to image human cells constitutively expressing GFP-NEMO, each with a different light dose range (low intensities: 14-40 lJ, high intensities: 500-700 lJ). Upon the addition of the IL-1 cytokine (10 ng/ml final), the NEMO superstructures appear as bright, punctate, diffraction-limited dots over a fainter background. These dots were tracked using TrackMate until they disappeared. Subsequently, the tracks were imported and analyzed in MATLAB. Their time-course is displayed on Fig. 4a. In the case of high intensities, fewer dots were observed per cell, but they remained for a much longer time. For low intensities, they appeared and disappeared in less than 15 min measured from the addition of IL-1, whereas for high intensities, they lasted for more than 30 min. Their motion characteristics also differed under high versus low intensity illumination.
Results Here, we employ TrackMate to analyze VAEM data of fluorescently tagged clathrin foci at the cell cortex to compare the software's particle tracking capabilities of manually chosen plasma membrane PM-associated clathrin foci with completely manually processed data. Representative images demonstrate CLC2-GFP foci labeling of PM foci and larger cortical Golgi structures (Fig. 5a  and b ) in Arabidopsis hypocotyl epidermal cells. The clathrin containing PM foci have a more uniform fluorescence and are diffraction limited in size compared to the larger CLC2-GFP labeled Golgi structures, displaying the characteristic fluorescence profile of sites of CME previously described  [21]. Three independent hypocotyls were imaged and time-lapse images from each were analyzed manually and semi-automatically with TrackMate (Supplemental Movie 4).
This suggests that the software's tracking capabilities are compatible with manual tracking of clathrin dynamics at the PM. A number of laboratories studying clathrin and clathrin accessory proteins involved in plant CME have found the average lifetime of clathrin-associated PM foci to range from 17. 7 s to 24 s, corroborating the average lifetime of clathrin foci at the PM found both manually and semiautomatically with TrackMate  [21, 38, 39]. Inevitably, TrackMate's ability to objectively track foci is dependent upon parameters defined by the operator thereby introducing some level of bias into the results. Nevertheless, these parameters can be universally applied across all samples, eliminating unintentional precision errors and eye-constrained selection of track termination. Taken together, this data demonstrates the utility of TrackMate in alleviating a previously laborious analysis of dynamic events in microscopy data.
This great breadth of applications highlights the vast applicability of TrackMate. Generality often has a toll on the ability to successfully handle very specific problems. As noted in  [4] , it is very likely that some tracking challenges will require the development of specific tracking tools. TrackMate positions itself not only as a tool for single-particle tracking, but also as a platform to facilitate and accelerate the development of such tools. Other developers can port their algorithm(s) to TrackMate as a module, reusing the facilities there that would otherwise consume countless and tedious time to develop (data model, visualization, etc. ). The SciJava annotation mechanism used for plugin discovery ensures that new modules can be developed and distributed without requiring any interaction with the TrackMate developers, ensuring full autonomy and independence.
As multiscale imaging methods are developed and deployed to look at process such as metastasis and cell growth, there will need to be corresponding modules that can track and analyze these processes over different spatial scales. As well, TrackMate needs to continue to evolve in its interoperability and ability to handle heterogeneous data, including nonimage data types. One strategy that has served TrackMate well that we will continue to leverage is to take advantage of existing software tools and also offer TrackMate as a flexible portable module. In future development, we hope to have TrackMate equally accessible not only as a Fiji plugin but as an Ops module  [18]. The ImageJ Ops initiative aims at producing a unifying library for scientific image processing, and makes it widely accessible to any software framework, not just ImageJ.
The Ops framework provides unified interfaces for basic image manipulations that can be called by any tool using this framework. This type of modularity has already shown considerable promise in the recent use of TrackMate in KNIME as a SciJava process  [59]. By having TrackMate in KNIME, it is possible to create data pipelines that could integrate Track-Mate with a number of other tools including statistical tools such as R. With the recently developed Ops framework being driven by the ImageJ2 and KNIME developer community, it should be possible to take TrackMate functionality even farther and to a wider audience. In this way, other programs such as databases and even other tracking programs could call TrackMate, much in the same way that programs invoke scripts and other algorithms. Recent developments in light microscopy, such as light sheet microscopy  [60] , have resulted in massive datasets that in requires large extensive annotations.
Acknowledgements The authors would like to extend their gratitude for financial support from the  European commission   FP7 ICT (project ' ' MEMI ", J-Y. T, S. L. S. ), the  Laboratory for Optical and Computational Instrumentation  at the  UW-Madison  and  National Science Foundation  award # 1121998  (J. S. , S. Y. B. , K. W. E). N. P. was a Pasteur visiting student thanks to funds provisioned by the  Stanford University. Track-Mate development has been and is supported by the  Fiji, ImageJ  and KNIME communities. Their invaluable help particularly that of Fiji maintainer  Curtis Rueden  and KNIME image processing developer  Christian Dietz  is acknowledged here. The authors also thank  Ellen T. Arena  for critical reading of the manuscript. J-Y. T thanks the members of the Imagopole of the  Institut Pasteur  and his family for support and patience.
Introduction In this paper, we report on the diversity of a large and non-taxonomically focused sample of terrestrial arthropods, collected on four islands of the Society Archipelago (French Polynesia) from 2005 to 2007. This sample was obtained as part of the SymbioCode initiative (coordinated by SC) in collaboration with the Moorea Biocode Project (http://biocode. berkeley. edu) (Check 2006). Although primarily focused on symbiotic bacteria and their flux across host species  (Bailly-Bechet et al. in press) , this dataset also offers an opportunity to complement biodiversity records from this region, which is the subject of the present paper. This study focused on four of the five main islands of the Society, situated along a 200 km northwest/ southeast axis, corresponding to the movement of the Pacific plate over a unique hot spot during the last three million years  (Guillou et al.
Photographs and morpho-species assignments Specimens showing no apparent morphological differences to a non-expert eye were attributed the same morpho-species numbers. Up to ten specimens per morpho-species were individually photographed (under binocular microscope or with a reflex camera depending on their size) and stored in 95% ethanol at -20°C before further processing. In total, 10 929 specimens were processed in this way (listed in the Supplementary file, sheet S3). All photographs can be accessed through the BOLD database (http:// www. boldsystems. org) (SYC project, DS-SYMC dataset, http://dx. doi. org/10. 5883/DS-SYMC) and the Table  1. Taxa that have been identified on the basis of morphology. For these groups, names of previously described species were used and new species are being described.
In case of conflict between the reads at a given position, the base used and the associated Phred score in the consensus were those of the highest quality read at the position. An ambiguous base call (N) was assigned to poorly supported positions (Phred scores below 20) in the consensus. High quality reads (i. e. , those carrying more than 80% nucleotides with Phred scores larger than 40), but not assembled to their reverse complement because of failure of one reaction, were also recovered. Consensus sequences and recovered single reads were then all aligned to an arbitrarily chosen reference CO1 sequence (GenBank KF226475, from the butterfly Hypolimnas bolina) in order to unambiguously identify and trim primer regions. Reads longer than 200 bp were then selected and the longest open reading frame was determined for all reads, yielding only 12 sequences with stop codons, which were discarded.
In addition, 18 sequences were identified as experimental contaminants (precisely matching another sequence in the dataset) and thus were excluded. All such contaminations occurred locally in DNA extraction or PCR plates: the contaminant well was never more than two wells away from the contaminated well. Notably, specimens were randomly distributed across 54 plates, so that specimens from the same morpho-species were not clustered, allowing cross contaminations to be detected when it occurred. We also excluded five natural contaminants, harbouring an insect parasitoid instead of host barcode. Sequences were aligned in Geneious using the predicted protein sequences.
Clustering of sequences within OTUs Of 3627 CO1 sequences, 3180 passed the BOLD database quality filters and were clustered using the Refined Single Linkage (RESL) algorithm into 1026 species-level groups or OTUs (operational taxonomic units), each identified with a unique Barcode Index Number (BIN)  (Ratnasingham & Hebert 2013). In brief, RESL employs a single linkage clustering procedure (producing clusters including all sequences connected by a genetic distance below a certain threshold) followed by a refinement step in which clusters including at least three sequences can be split if they show significant discontinuity in genetic distances. Of the 447 sequences that did not pass the BOLD quality filters, 314 were assigned to one of the BINs identified by RESL. The remaining 133 specimens were clustered in groups of sequences diverging by no more than 3% substitutions per site, resulting in an additional 84 OTUs also included as they represent significant information, although they should be considered more cautiously.
Taxonomic assignments With the exception of spiders, which were classified to the lowest possible taxonomic level based on morphology prior to DNA extraction  (Dierkens & Charlat 2009) , all specimens were only assigned the taxonomic rank of order or class upon morpho-species assignments. For barcoded specimens, the consistency of this assignment with the 1 st blast hit was verified (using blastn with default options against the NCBI nr database). Closer inspections of photographs or specimens allowed correction of any remaining inconsistencies. Such errors were either due to contaminations (as noted above) or, more often, to an incorrect initial assignment. A more accurate DNA barcode-based taxonomic assignment was then proposed based on the quality of the best blast hit, with the following categories: (1) species level assignment for at least 98% sequence identity over at least 80% of the barcode; (2) genus level for 95% sequence identity and 80% overlap; (3) family level for 90% sequence identity and 80% overlap.
In combination with blast, we developed a phylogenetic approach to propose a taxonomic assignment based on DNA sequence data. We developed R scripts to extract one representative CO1 sequence from each arthropod genus from GenBank (resulting in 20 350 sequences). For each arthropod order, we added these sequences to our own alignment using profile alignment in MAFFT  (Katoh & Standley 2013)  and constructed a tree for each order using FastTree  (Price et al. 2010). Poorly supported nodes (less than 80% aLRT support)  (Anisimova & Gascuel 2006)  were collapsed in Archaeopteryx  (Han & Zmasek 2009). We then inspected these trees in Dendroscope  (Huson & Scornavacca 2012) , with colour coding of tips based on family names, to refine and verify blast-based assignments. An improved classification of our specimen was then proposed using the "strict" criteria defined by  Wilson et al.
Characteristics of the sample In total, the SymbioCode sample includes 10 929 arthropod specimens that were assigned to morphospecies, individually photographed, and deposited in the BOLD database (http://www. boldsystems. org) (SYC project, DS-SYMC dataset, http://dx. doi. org/10. 5883/DS-SYMC) and the Moorea Biocode databases (http://biocode. berkeley. edu). The Supplementary file (sheet S3) provides a complete list of the specimens with a unique URL for each specimen. More than half (52%) of the specimens were collected at low elevations or near sea level (below 100 m elevation, Fig. 1 ) in potentially disturbed habitats, which are more likely to harbour non-native species than locations at higher elevations. Malaise traps were used only on the islands of Moorea and Tahiti, and mostly in remote locations at high altitude.
Biodiversity The CO1 sequences clustered into 1693 different haplotypes and into 1110 species-like groups or OTUs defined on the sole basis of molecular data. Of the 1110 OTUs, 1026 (92%) were defined using the RESL algorithm implemented in BOLD and thus received a unique identifier in this database (BIN); 642 of the BINs were new to the BOLD database. The remaining 84 OTUs comprised sequences that did not pass the BOLD quality filters, but were clustered in groups of sequences diverging by no more than 3% substitutions per site. Some specimens, from which DNA was not extracted, or for which CO1 sequencing failed, were nevertheless assigned on the basis of morphology (when possible to do so with confidence) to OTUs from this study (n = 4295 specimens) or to a named species not represented in the other OTUs (n = 411 specimens).