dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance of 3D software Complete rankings for each imaging modality and spot density are presented (Fig 2  , S13 ), together with summary information on all competition software (Table  S1 , Supplementary Note 1). As these data are continuously updated on the competition website, this resource provides microscopists with an easy quick reference for the current state of the art, including current best-in-class performers for each category. After assembling an overall summary of best performers for each competition category, we investigated the performance of software within each imaging modality.
Astigmatic localization microscopy Astigmatic localization microscopy is probably the most popular imaging 3D SMLM modality, reflected by the highest number of software submissions in the 3D competition (Fig  2 ). For astigmatism, we observed a large spread of software performance, even for the most straightforward high SNR, low spot density (LD) conditions (Fig 3A  -B , Table  S5 ). The best-in-class software (SMAP-2018) has significantly better localization error and Jaccard index performance than average (lateral RMSE 26 nm best vs 38 nm average, axial RMSE 29 nm best vs 66 nm average, Jaccard index 85 % best vs 74 % average). Clearly, the quality of the image reconstruction depends strongly on choice of 3D software. To investigate the reasons for software variation, we inspected plots of software performance as a function of axial position in the low density, high SNR dataset for best-in-class and representative middle-range software (Fig S6A ).
When we examined results for the low SNR, low density dataset (Fig 2B  , 3B ), we found an expected 2fold degradation in best-in-class RMSE (lateral RMSE 39 nm, axial RMSE 60 nm), due to the decrease in image SNR. However, the best-in-class software (SMolPhot) Jaccard index was effectively constant between the low and high SNR datasets (86 % vs 85 %), although the Z-range did drop at lower SNR (930 nm vs 1120 nm). The best astigmatism software packages were thus remarkably good at finding spots at low SNR, even away from the plane of focus. We analyzed how close software performance was to theoretical limits by calculating the Cramer-Rao Lower Bound (CRLB) as a function of axial position for each dataset and comparing it to the best-inclass software results (Fig S7, Fig S8). Close to the focus, best-in-class software was close to CRLB performance, but significant deviations for the CRLB limit occurred > 200 nm.
This could be due to the difficulty in actually detecting the spots away from focus. When we examined astigmatic software performance for the challenging high spot density datasets (Fig 2B  , 3 ), performance was reduced. For the high SNR high spot density dataset (best software, SMolPhot), localization error increased and Jaccard index decreased significantly compared to the low density condition (lateral RMSE best HD 51 nm vs best LD 27 nm, axial RMSE best HD 66 nm vs best LD 29 nm, Jaccard index best HD 66 % vs best LD 85 %). Inspection of the super-resolved images (Fig  4 ) nevertheless shows acceptable results for the HD dataset, particularly in the lateral dimension. In many circumstances, the performance reduction at 10x higher spot density should be acceptable for 10x faster, potentially live-cell-compatible, imaging speed.
We also observed a large spread of software performance for the high density datasets, probably because a significant fraction of the software packages were primarily designed for low density conditions. We observed poor performance for the most challenging low SNR high spot density astigmatism dataset (Fig 2, 3, S3, best software SMolPhot). Best-in-class localization precision and Jaccard index decreased significantly (lateral RMSE 76 nm, axial RMSE 101 nm, Jaccard index 58 %). These data suggest that low SNR high density 3D astigmatic localization microscopy entails a significant reduction in image resolution.
Double helix software performance decreased significantly for the low spot density low SNR condition (best software SMAP-2018), particularly in terms of best-in-class Jaccard index (66 % low SNR vs 77 % high SNR, Figure  3B , S3, S13A). DH Jaccard index was also significantly worse than astigmatism results at either high or low SNR (85 % high SNR, 86 % low SNR). This indicates that it was quite hard to successfully find localizations in the low SNR DH dataset, likely because the large size of the DH PSF spreads emitted photons over a large area, lowering effective image SNR. DH PSF designs with reduced Z-range but more compact PSF would likely be less sensitive to this issue  22. Double helix software performed poorly on the high spot density datasets at high SNR (best software CSpline), especially in terms of the Jaccard index (Fig 3B  , S13A , best lateral RMSE 67 nm, best axial RMSE 69 nm, best Jaccard index 46 %).
Performance of 2D software Alongside the 3D challenge, we ran a second edition of the 2D localization microscopy software challenge  7  to assess how the latest 2D software performed on more challenging, more realistic datasets, and to provide an assessment of how the field had progressed since the last challenge. We used the new simulation software, including an experimentally derived PSF and a realistic blinking model, and also simulated a very high spot density condition (5 molecules/ μm 2 ). We created a more spatially extended test structure, "pseudo-endoplasmic reticulum" (pseudo-ER), composed of 150 nm diameter hollow tubes, to avoid artefacts due to 1D simulated structures  27   S1 ). For the pseudo-ER dataset, at low density, best-in-class software (ADCG) performed well (Fig. S4, S5 ), with a Jaccard index of 90 % and lateral RMSE of 31 nm, substantially better than the class average (Jaccard index 72 %, lateral RMSE 36 nm).
Algorithms We identified several classes of algorithm participant software (Table  S1 ): 1) Non-iterative software tends to regroup the pixels in the local neighborhood of the candidates, like interpolation, center of mass (QuickPALM  28  ) or template matching (WTM  29  ). These (often older) algorithms are fast but tend to achieve poor performance (Table  S1 ). 2) Single emitter fitting software is usually built on a multi-step strategy of detection, spot localization, and optional spot rejection. The detection step finds bright spots in noisy images on the pixel grid. The selection of candidates is usually performed by local maximum search after a denoising filter. Others rely on more complex algorithms like the wavelet transform (e. g. , WaveTracer  30  ). We did not observe software ranking to depend significantly on the choice of optimization scheme, least-square, weighted least-square or maximum-likelihood estimator (Table  S1 ).
3) Multi-emitter fitting software groups clusters of overlapping spots, and simultaneously fits multiple model PSFs to the data. Typically, fitted spots are added to the cluster until a stopping condition is met  4, 5. This leads to improved localization performance at high spot density, at the cost of reduced speed. This class of software (e. g. , 3D-DAOSTORM  14  , CSpline  14  , PeakFit, ThunderSTORM  31  ) was amongst the top performers in each 2D and 3D competition category (Table  S1 ). As expected, single-and multiple-emitter fitting methods both performed well on low density data (Table  S1 ); apparently at the densities studied, exclusion of occasionally overlapping spots by singleemitter software is sufficient for strong performance; explicit multi-emitter fitting is not required.
Choice of PSF model Most software used a variant of Gaussian PSF model. A few participants designed more accurate PSF models (Table  S1 ). Either diffraction theory was used (MIATool 40 , LEAP  37  ) or spline fitting of an analytical function to the experimental PSF was adopted (CSpline 41 , SMAP-2018  20  ). Although simple Gaussian model PSFs were sufficient to obtain best-in-class performance for the 2D and astigmatic modalities (ADCG  35  , PeakFit, SMolPhot), top results for the more optically complex biplane and double helix modalities were exclusively PSF-modelling algorithms (SMAP-2018, CSpline, MIATool, LEAP).
The second major conclusion of the 3D challenge is that localization software that explicitly includes the experimental PSF in the fitting model gives a significant performance increase for 3D SMLM. For the more optically complex biplane and double helix modalities in particular, the best results were exclusively from software using PSF modelling approaches (SMAP, CSpline, MIATool). This result also highlights the need for experimental PSF modelling not only in SMLM software, but also emphasizes the high degree of experimental realism required of SMLM simulations. The clear performance advantage of experimental PSF modelling software in the 3D software challenge would have been entirely unobservable had it been run with a simple analytic PSF. Of the different algorithm classes, well-tuned single-emitter and multi-emitter fitting algorithms (each capable of dealing well with occasional molecule overlap) gave good results for low density 3D SMLM.
The performance of the best-in-class software at high spot density (ADCG  35  ) was only moderately decreased compared with the low spot density results, with nearly identical molecule detection performance, and a 30 % increase in localization error. Interestingly, the top three performers in the 2D high density condition were all compressed sensing algorithms (ADCG  35  , FALCON  34  , SMfit). In low density 2D conditions, the best single-emitter, multi-emitter and compressed sensing algorithms all gave comparable, excellent, performance. We speculate that performance in this category may now be near optimal levels. Rapid improvements in sCMOS camera technology mean that these cameras are rapidly becoming a major platform for single molecule localization microscopy  6. Therefore, a key future goal for SMLM software assessment should be to include sCMOS-specific localization microscopy software.
Furthermore, there remain two important classes of super-resolution microscopy for which software performance is crucial, but no broad software assessment has yet been performed: fluorescencefluctuation-based super-resolution microscopies (e. g. , 3B  44  , SOFI  45  , SSRF  46  ) and structured illumination microscopy  47. The results of this competition clearly demonstrate the formidable algorithmic performance of the best 2D and 3D localization microscopy software. However, a key outstanding challenge that often hinders adoption of new algorithms is that only a small subset of algorithms are packaged in, or compatible with fast, well-maintained, user-friendly software packages, which include all stages of the SMLM data analysis pipeline -analysis, visualization and quantification. One solution would be for the SMLM software community to collectively adopt both a standard data format and a single software platform for future software development, such as FIJI/ ImageJ  48.
CHALLENGE ORGANIZATION We first ran the 3D SMLM software challenge as a time limited competition, with a results session hosted as a special session of the 6 th Annual Single Molecule Localization Microscopy Symposium in August 2016. The competition has now been converted to a permanent software challenge accepting new submissions. Special mention to the software SMAP and 3D-WTM that participate to our eight categories (density x modality). The current list of participants is at: http://bigwww. epfl. ch/smlm/challenge2016/index. html?p=participants All datasets, methods, participations, and results of the challenge 2016 made available at http://bigwww. epfl. ch/smlm/challenge2016/. Software for simulation and analysis is hosted on the competition GitHub repository: https://github. com/SMLM-Challenge/Challenge2016/.
Structure The synthetic datasets were designed to be similar to images derived from cellular structures in real experimental conditions. We defined mathematical models for cellular structures that imitate cytoskeletal filaments such as microtubules and larger tubular structures such as the endoplasmic reticulum or mitochondria (Fig  S1A ). These structures have a tubular shape in the 3D space. Psuedomicrotubules are defined with their central axis elongating in a 3D space having an average outer diameter of 25 nm with an inner, hollow tube of 15 nm diameter. Pseudo-endoplasmic reticulum is defined as having a diameter of approximately 150nm. The underlying sample structure is formalized in a continuous space which allows rendering of digital images at any scale, from very high resolution (up to 1 nm/pixel) to low resolution (camera resolution: 100 nm/ pixel).
Experimental Point-Spread Function Model PSFs, stored as high resolution look up tables, were derived from experimentally measured PSFs. Although the algorithmic approach is distinct, this concept of accurately modelling the experimental PSF based on calibration data bears relation to the PSF phase retrieval approach previously employed by Hanser and coworkers  49. Images of fluorescent beads were recorded for each modality (Table  S4 ). Signal to noise ratio of recorded PSFs was maximized in all cases by maximizing exposure time and averaging over several frames to increase dynamic range. To acquire experimental PSFs, we took 100 nm Tetraspek beads (Invitrogen) adsorbed to #1. 5 (170 μm thick) coverglass, imaged in water. The excitation wavelength was between 640 nm and 647 nm, and a Cy5 emission filter was used.
Protocol Each localization file submitted by the participants was manually checked for erroneous systematic errors in the definition of the dataset coordinate system, such as offsets, XY axis flips or clear scaling errors. Datasets were then programmatically standardized into a consistent output format. All modifications are publicly available. If required, the modifications consisted of columns reordering, reversing axes, XY axis swap, and shifting the lateral positions by a half camera pixel. The assessment pipeline includes three main parts: localization processing, the pairing between true and estimated localization and the metrics calculations. The first one depends on the assessment settings. There are two switchable properties: photon thresholding and wobble correction. Their combinations yield four different assessment settings.
Up to 64 assessment runs per software were possible (i. e. , 4 modalities, 4 datasets per modality). For any setting, we excluded the fluorophores within a lateral distance of 450 nm from the border. This value corresponds to the radius of the largest PSF (i. e. , Double Helix). The activations too close from the border are more difficult to localise and could bias the results. The pairing between true and estimated localizations was performed frame by frame. The procedure matches two sets of localizations. We deployed the presorted nearest-neighbor search for its efficiency. The results are effectively similar to the computationally intensive Hungarian algorithm  7.
Photon thresholding A photon threshold was required primarily due to the use of a realistic fluorophore blinking model. Since a fluorophore could activate/ bleach at any point in a simulated frame, this led to many frames containing very dim, undetectable localizations, eg. where a molecule had been active for one or more frames previously, and then bleached during the first 5 % of a frame. These fractional localizations should also be present but practically undetectable in an experimental dataset. In order to focus the software analysis on the localizations where the molecule was active for the majority of a frame, which we decided was most consistent with experimental expectations, we implemented a photon threshold means where we kept the 75% brightest ground truth fluorophore activations.
• Astigmatism: Z = f(WX -WY) , where WX and WY are respectively an estimation of the size in X and Y. • Double-Helix: Z = f(θ), where θ is the angle formed the pairing of two close points. • Biplane: Z = f (Wleft -Wright), where Wleft and Wright are respectively an estimation of the size of the spots in left and the right plane. The Java code is available: https://github. com/SMLM-Challenge/Challenge2016 4 DATA AVAILABILITY 4. 1 Data availability statement Simulated competition datasets are available at http://bigwww. epfl. ch/smlm/challenge2016/, together with the parameters used to generate the data. The ground truth list of simulated molecule positions for each competition dataset remains secret in order to allow the software challenge to remain continuously open to new submissions. However, ground truth data is available for the simulated training datasets.
, 2014; Esser et al. , 2015). We therefore aimed at establishing a standardized workflow including fluorescence microscopy, automated signal recognition, and spatial statistics to investigate the arrangement of microbial rhizoplane colonization. The whole procedure has been compiled into imageJ and R scripts that are available online. We will present examples of analyzing the distribution of a putative nitrogen fixing bacterial strain on roots of wetland rice, one of the most important crop plants worldwide. Firstly, we describe the workflow including image acquisition, automated signal recognition, and statistical analyses of spatial patterns. Secondly, we present an application of the analysis pipeline on systematically obtained fluorescence micrographs to show the potential of such analyses in an ecological context.
SYBR Green I was excited at a wavelength of 489 nm and recorded from 500 to 540 nm. In the following, these settings will be referred to as the "dye channel. " Background autofluorescence of the root surface, giving information on root cell walls, was recorded via excitation at 565 nm and emission from 580 to 750 nm (in the following referred to as the "autofluorescence channel"). The following settings were used for each z-stack: image size: 184. 5 × 184. 5 µm, image resolution: 1024 × 1024 pixels, bit depth: 8 per channel, scan speed 100 Hz, line averaging: 4, frame averaging: 1, z-stack thickness: 10 µm, slice distance: 0. 5 µm, zoom factor 1, pinhole: 1 AU. Image stacks were stored as Leica *. lif files without post-processing. Two other z-stacks with similar acquisition settings were also acquired to test the generality of the data extraction procedure.
Image Analysis All microscopic images (raw *. lif files) were processed with Fiji  (Schindelin et al. , 2012)  to extract information on the spatial distribution of bacterial cells as well as properties of their environment (root cell walls). Extracted data was then loaded into R 3. 4 (R Development Core Team, 2017) and converted to spatstat objects  (Baddeley et al. , 2015)  for analyses. All codes used in this manuscript (Fiji macro and R codes) are available for download at https://github. com/xraynaud/microbial-celldetection. The overall detection procedure is shown in Figure  2  and specific details are given below.
Detection of Root Cell Walls The spatial organization of root cell walls was extracted from both autofluorescence and dye channels. Both channels were processed similarly. First, a local contrast enhancement filter was applied to all slices. Then a 3D Laplacian of Gaussian filter (radius 10 µm) was applied to all slices followed by an Unsharp mask filter. These procedures allowed for the removal of all small features from images (such as bacterial cells), retaining only the larger ones. From these processed images, linear structures were identified in each slice using the Fiji plugin ij-ridgedetection  (Steger, 1996; Wagner et al. , 2017)  to produce a binary 3D image set of root cell walls. 3D slices were then merged into a single 2D image, saved as TIFF black and white image, and converted to vector format (SVG) using the software autotrace (http:// autotrace.
Root Cell Walls Root cells are approximately rectangular in shape when viewed in microscopic images, where the longer sides correspond to the growing direction of the root. For each plant cell, cell walls perpendicular to the microscope focal plane (anticlinal cell walls) were generally identified as several small line segments. To obtain the orientation of plant cells from the image, the angles of each segment with the horizontal were calculated and a mixture of three von Mises distributions was fitted to this data (movMF R package,  Hornik and Grün, 2014). The total length of segments from the three identified sets was calculated and the orientation of plant cells, which correspond to the growing direction of the root, was assumed to correspond to the set having the greatest total length.
RESULTS In the main experiment, we acquired 27 individual z-stacks from three replicate rice roots that were colonized by K. sacchari for 16 days after inoculation. A representative example of the z-stacks obtained from the zone of cell maturation and data extracted thereof is shown in Figure  3. Z-projections of the dye and autofluorescence channels are shown in Figures  3a, b  along with the superimposition of both channels (Figure  3c ) and the extracted data (Figure  3d ). Images and data extraction for the other root regions are given in Supplementary Figure  S3  (zone of cell division) and Supplementary Figure  S4  (zone of cell elongation).
Imaging Procedure and Data Acquisition The aim of the paper was to provide a standardized method for analysing microbial cell distributions on the rhizoplane. The image analysis procedure was carried out using the Fiji  software. Fiji, and ImageJ, upon which it is built, are multiplatform (Windows, Apple, Linux), general purpose, open-source software. Its main features include the availability of numerous plugins (e. g. , ij-ridgedetection, which is used here), the possibility to develop new plugins for specific applications, as well as the automatized analysis of sets of images through the use of macros. Fiji is also able to directly import image stacks in the native Leica format that was used in this study. Other software such as daime  (Daims et al. , 2006)  and IMARIS (Bitplane AG, Zürich, Switzerland) could have been used as well.
The coordinates for most cells in the CARD-FISH image were correctly identified with the data extraction procedure. Despite varying signal intensities among target cells, a common feature of FISH applications to environmental samples  (Amann and Fuchs, 2008) , CARD-FISH is a valuable alternative to the presented approach when the aim is to detect specific target groups of microorganisms on roots. Avoiding the natural autofluorescence of biological specimens via signal amplification or other strategies is also a reasonable approach to increase signal-to-noise ratios  (Peredo and Simmons, 2018). In this study, however, we made use of the natural autofluorescence emitted by roots, a strategy that can be useful for the interpretation of confocal images in the context of plant-microbe interactions  (Cardinale, 2014).
Such clusters often appear as a bright blur in which the identification of single cells is difficult due to light scattering that causes fluorescence signals to smear around objects  (Sanderson et al. , 2014). This is one of the reasons the presented analysis procedure does not detect cells based on their sizes but rather on variations in signal intensity within high intensity signal patches. Consequently, this approach might miss some bacteria in dense clusters where variation in signal intensity is not sufficient to allow detection of single cells. Spatial analyses carried out on extracted data (e. g. , calculation of the pair correlation function) will underestimate aggregation so that the analyses presented here can be regarded as conservative. We believe, however, that this underestimation had a limited impact on our dataset because not all clusters were missed (see for example bottom of Supplementary Figure  S4 ) and these clusters did not occur frequently.