dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This research was funded, in part, by  LYriCAN  ( INCa-INSERM-DGOS-12563 ),  LABEX PRIMES  ( ANR-11-LABX-0063 ,  ANR-11-IDEX-0007 ),  POPEYE  ( ANR-19-PERM-0007-04 ), the  NADIAM Pack-Ambition Rhône-Alpes region  project. A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.
The 3D environment can be easily modelled with a low set of surfaces for very simple configurations  [5]  or with Computer-Aided Design tools in case of more complex geometry  [6]. The radiation patterns of optical sources can be modelled by simple analytical models as the conventional Generalised Lambertian one  [7]  or by experimental datasets provided by numerical simulation or measurement  [8]. The Bidirectional Reflectance Distribution Function (BRDF) models the reflection properties of materials  [9]. It is defined at a surface point x with local normal vector n ! as the ratio between the reflected radiance dL r x; ω r �! À � in direction ω r �! over the received radiance L i x; ω i ! À � from direction ω i ! : R x; ω i ! ; ω r �! À � ¼ dL r x; ω r �! À � L i x; ω i ! À � ω i ! ⋅ n ! � � � � dω i ! : ð1Þ Hence, it gives the spatial distribution of the reflected optical power.
F I G U R E 2 5 Performance statistics according to the number of receivers (Hospital configuration-PCC). [19]. Both types of sensors could be mapped onto different 3D printed spheres distributed in the environment, similar to our first prototype shown in Figure  26. This experimental setup is realised as follows. Each sensor is mounted on a rectangular printed circuit board (PCB). The 3D model of the sphere carrying the sensors is first designed under a 3D modelling software like Blender. The positions of the sensors are calculated by the Fibonacci method presented in Section 4. 1. At these positions, the 3D model of the sphere is drilled in order to insert the PCBs carrying the sensors. These PCBs are screwed into the thickness of the 3D printed sphere so that the active surface of the sensor is flush with the outer surface of the sphere.
In brief 3D microscopy of entire cells presents an image complexity that challenges the computation and observation of measurements such as particle trajectories. Roudot et al. introduce u-track3D, a package that combines robust particle tracking with image navigation and track validation algorithms for the discovery and unbiased analysis of dynamic processes. u-track3D: Measuring, navigating, and validating dense particle trajectories in three dimensions INTRODUCTION Light-sheet fluorescence microscopy (LSFM) achieves threedimensional (3D) imaging with minimal phototoxicity, fast sampling, and near-isotropic resolution,  1, 2  allowing the study of dynamic intracellular processes in the entire cellular volume. [1] [2] [3] [4]  While computer vision techniques are well established for interrogating cell biological processes in 2D,  5  these tools do not translate to both the visualization of measurement results and their validation in 3D.
Several open-source  [25] [26] [27] [28] [29]  and commercial  [30] [31] [32]  software packages have been released to make volumetric rendering technology amenable to time-resolved 3D bioimaging data. Together they have proposed advances in accelerating sequence rendering,  26, 28  modularity and extensibility,  25, 27  annotation tools,  33, 34  or scalable raytracing for high-quality rendering. 29, 35  Those approaches provide an outside view of the 3D data and a range of tools to investigate the volume manually (e. g. , in focused regions of interest [ROIs] or slices). They also reveal many challenges associated with visual occlusions, 3D perception on a flat screen, interactions, and time-consuming volume exploration. BigDataViewer  36  has tackled the occlusion and perception challenges indirectly by focusing on the caching and rendering of 2D slices at arbitrary orientations.
The primary effect is to accelerate rendering at the expense of 3D context, but it also avoids distortion of pixel-level information. As such, the Mamut software has demonstrated that combining BigDataViewer and 3D rendering of vectorial information enables annotation in large and complex 3D trajectories. 37  The immersive quality of virtual reality (VR) headsets has also been exploited to better perceive and interact with complex 3D datasets. [38] [39] [40] [41] [42]  Finally, a complementary approach to full volume visualization is the presentation of software-defined ROIs. The embryoMiner package  43  implements this idea by building static ROIs from groups of trajectories The pipeline is here illustrated on lattice light-sheet imaging of HeLa cells undergoing mitosis labeled with eGFP-labeled EB3 (marking microtubule plus-ends, rendered in gray) and mCherry-labeled centromere protein A (marking kinetochores, rendered in red).
that are then used to systematically visualize the data in a series of 3D tiles. The approaches implemented in u-track3D also belong to this category; however, the ROIs adapt over time to the changing geometry of collective processes. Hence, u-track3D allows the inspection of specific particle dynamics in a crowded and dynamic surrounding. Considerably fewer works have been dedicated to trajectory validation. Previous work can be categorized into ground-truthbased approaches and error-inferencebased approaches. Within the former category, a time-consuming but widely accessible approach consists in using manual annotations to build a ground truth to be compared to the measured dataset. 24, 44, 45  Already in 2D, these techniques are subject to selection bias, as it is easier to annotate bright and well-separated objects.
While this confidence interval reflects the uncertainty about positions and velocities, the correctness of trajectory-tomeasurement matches is not evaluated. Our goal was to design an algorithm for tracking error inference that does not require annotation or specific simulations, does not rely on heuristics other than those considered by the tracking algorithm, can handle heterogeneous scenarios, scales with the computational complexity of the tracker itself, and provides an interpretable output for mis-matched, spurious, and missing trajectories comparable to conventional benchmarks. 7  Building upon our previous particle tracking work, 2 ,10,13 we thus designed the software package u-track3D to enable the measurement, observation, and validation of dynamic processes in 3D (Figure  1 ). u-track3D can detect and track morphologically and dynamically diverse cellular structures, including single molecules, adhesion complexes, and larger macromolecular structures such as growing microtubules.
The software design is open, allowing users to import the coordinate files from other detection routines and then apply the u-track3D framework only for trajectory reconstruction. We introduce a library for visualization and mapping of dynamic ROIs (dynROIs) that move with the biological structure under evaluation and enable an intuitive visualization of particle behaviors. Finally, we present a scalable approach for automatic assessment of trackability of each particle throughout the image volume by evaluating the stability of trajectory-to-assignment associations.
To support the concurrent tracking of objects of variable sizes, we implemented a multiscale particle detector equipped with a generalized adaptive thresholding approach (see section ''Multiscale particle detector'' in STAR Methods). DynROIs Moving from 2D to 3D images complicates the interaction of a human observer with both raw and derived data. Widely used global image projections, including maximum intensity projection (MIP), and other volume rendering techniques are limited by the overlap of many dynamic structures along the viewing axis. 6  However, detailed visualization of 3D images and trajectories in their local context is essential for a user to adjust software control parameters and to interpret the underlying biology. Projection approaches have to be tailored to emphasize a subset of selected voxel or aspects of highest interest.
Trackability score Validation of tracking results is crucial for proper parameter adjustment during image acquisition and analysis as well as the biological interpretation of integrated measurements. However, it remains an extremely challenging task in 3D datasets, particularly when the particle density is high. Contrary to a scenario in 2D where a single field of view presents a wide range of trajectories for visual inspection, dynROIs in 3D tend to capture only a few trajectories and cannot represent the heterogeneity of local image quality, particle density, and dynamic properties, which all affect the tracking accuracy. To solve this problem, we complemented u-track3D with an option to compute a local trackability score. We use Monte Carlo simulation to determine for every trajectory and every time point the confidence by which the algorithm was able to assign the chosen particle to the trajectory (see section ''stochastic programming for the evaluation of trackability'' in STAR Methods).
e. , they appear in an imaging volume as 3D point-spread functions. We used high-resolution diagonally swept light-sheet microscopy 2 to sample every second a full volume of puncta generated by the GFP-labeled AP2 subunit of the endocytic coat. U-track3D recovered the canonical lifetime distributions of abortive and maturing clathrin-coated pits,  52, 53  that is, an exponential decay for abortive pits and (legend continued on next page) Rayleigh-like distribution with maximal probability around 20 s for maturing pits (Figure  2C ; Data S1; see section ''clathrin-mediated endocytosis study on a glass coverslip'' in STAR Methods). While in 2D the identification of those two populations relied on extensive trajectory analysis to discount incomplete trajectories,  53  our u-track3D software achieves accurate trajectory classification directly by thresholding the maximum intensity of trajectories in 3D (Figures  2B  and 2C ).
Measurement of instability in microtubule dynamics across dense 3D mitotic spindles With limited sampling frequency in volumetric imaging, particle tracking can be improved by dynamic motion models through Kalman filtering. To assess the performance of a 3D implementation of previously published motion models for 2D tracking of microtubule polymerization dynamics,  54, 55  we imaged and tracked microtubule dynamics in HeLa cells by following GFP fusions of the microtubule plus-end tracking protein EB1 sampled at 1 Hz by lattice light-sheet imaging. 1  We quantified metrics such as growth rate, growth lifetime, and pause frequency (see section ''microtubule instability measurement'' in STAR Methods). The latter is a measure for the probability that a stalled or shrinking microtubule, which is accompanied by disappearance of the EB1 particle in the video, is rescued to renewed growth (see Figure  S1  and Data S1).
U-track3D leads the field of algorithms evaluated in the particle tracking challenge We evaluated u-track3D's competitiveness using the standard 3D test dataset of the particle tracking challenge. 7  We compared u-track3D against the top five approaches competing in the original benchmark as well as two tracking approaches that make use of recent advances in recursive neural networks (RNNs)  17, 18  (see Figures  3  and S2 ). The dataset is designed to mimic viral dynamics alternating between confined and directed displacements that are large enough to create significant ambiguities in the densest scenarios (Figure  3A ). The dataset includes 12 sequences representing four signal-to-noise ratios and three density levels (see Figure  S2 ). The challenge prescribes four metrics to compare the precision and accuracy of each approach.
Also, u-track3D ranks first or among the top three approaches for lower SNR data. Strikingly, deeplearning approaches are outperformed by conventional methods, except at the level SNR = 1. Of note, this scenario represents a breakpoint dataset that is not representative of the typical image quality targeted for a meaningful analysis of trajectory counts and lifetimes. At this low SNR, RNN-based approaches provide the best performance; however, they are nonetheless insufficiently robust to generate accurate trajectory estimates. While a precise ablation study is out of the scope of this paper, an analysis of the design of competing approaches can help in discussing these performances. First, the most competitive approaches in the original challenge  20, 23, 59  present differences in their design but they all rely on similar strategies: object detection is based on some type of adaptive thresholding, they use motion modeling for displacement prediction, they use a discrete optimization approach to assign predictions to measurements, and they employ various techniques to detect and correct for transient object disappearances.
In a nutshell, the first RNN technique uses a long/short-term memory (thus, in Figure  3  it is referred to as model-free LSTM) network to filter and predict particle position. The second, more recent, RNN technique by the same authors uses gated recurrent units (referred to as model-based GRU) and reduces the parameter space needed for prediction by learning the moment and the covariance of a normal distribution associated with the state of each trajectory. Both approaches are trained on a simulated dataset presenting a mixture of Brownian, directed, and heterogeneous motions. In the original paper,  18  the model-based approach performs better than both the conventional and the model-free RNN on the ''vesicle scenario,'' which simulates free diffusion. However, the difference is not as clear in the ''virus scenario,'' where the model-free approach often outperforms the latter.
DynROI applied to mitotic spindle dynamics Many cellular processes involve a largescale reorganization of macromolecular structures, which challenges 3D analysis. A good example is the vertebrate mitotic spindle. 61  Thousands of microtubules form a dense bipolar array while the two spindle poles move apart and rotate back and forth. Concurrently, spindle microtubules establish contacts with chromosomes at kinetochores and subsequently move chromosomes toward poles or the spindle center. This process is virtually impossible to understand by mere visual inspection of volume renderings. We therefore assessed how u-track3D and dynROIs may facilitate the analysis. The image dataset comprises dual-channel time-lapse sequences of GFP-labeled microtubule plus ends and mCherry-labeled centromeres protein A marking kinetochores in mitotic HeLa cells acquired at 0.
Trackability scores were consistently high in the quiescent dynROI, cyclically decreased in the fluctuating protrusion, and showed a sharp decrease where and when the fastest protrusion was located (Figures  7B  and 7C ). The score thus accurately reflects variations in particle trackability across space and time and detects time points of high ambiguity due to rapid movement. In a second experiment, we analyzed the spindle assembly dataset shown in Figures  5F-5H. The cumulative distributions of trajectory-averaged trackability scores showed that kinetochore trajectories overall are more reliably reconstructed compared to microtubule plus-end trajectories (Figure  7D ). The score also enables trackability analysis on a per-trajectory basis. Trajectories with a score near the 90th percentile of the cumulative distribution appear to be error free for both plus end and kinetochore channels (Figure  7E ).
DISCUSSION We describe a new version of the popular tracking framework u-track, which now enables the study of particle dynamics in 3D live microscopy and tackles key challenges in the exploration and analysis of those complex datasets. The u-track3D software is implemented in MATLAB and distributed with a user-friendly graphical user interface (GUI) and tutorial scripts. The GUI is designed for testing the software and for the interactive visualization of the particle detections, trajectories, and dynROI locations overlaid onto the raw data. In particular, both raw voxels and measurements can be observed using either slice-by-slice visualization or MIPs in the frame of reference of the laboratory or in a frame of reference of a dynROI. The scripts are primarily used for batch processing and analysis at scale, and they enable the systematic visualization of tracking results across a full dataset.
Our rendering engine is designed for automated and parallelized visualization of raw data and overlaid measurements, taking advantage of the asynchronous nature of processing jobs. Montages of raw and overlaid images can be easily specified and saved in a variety of formats (png, avi, and gifs). The script interface also provides a finer control of the shape of dynROIs than the GUI (cone, tube, rounded tubes, etc. ). Finally, both detection and tracking can be limited to a dynROI, enabling the rapid adjustment of algorithm parameters before processing a full dataset. Two datasets are provided to test the software, one extracted from the endocytosis imaging introduced in Figure  7A  and the other extracted from the mitosis imaging experiment introduced in Figure  5. Computation time and memory usage for the complete pipeline, including detection, tracking with trackability inference, and dynROI definition, has been benchmarked using the endocytosis dataset on a standard computing workstation and office laptop (see ''Benchmarking computational time and memory usage'' in STAR Methods).
27  They could complete the capabilities of our renderer with more advanced volumetric rendering (alpha, ray casting) as well as surface rendering. We thus introduce u-track3D as feature-complete software for the quantification and analysis of particle-like trajectories in 3Ds but also as a stepping-stone toward automated and unbiased exploration of any type of dynamic datasets. As we deliver the software to the community, we are continuously improving the software by fixing bugs and evaluating suggestions for improvements made by the community.
Microtubule instability measurement Cell preparation and imaging HeLa cells stably expressing EB3-EGFP alone or along with mCherry-CENPA were plated on coverslips mounted on the microscope in CO2-independent L15 medium containing 10% FBS, without phenol red, and maintained at 37 C for the duration of the experiment. All images were recorded with an Orca Flash 4. 0 v2 sCMOS camera (C11440-22C; Hamamatsu). Images were acquired in sample-scan imaging mode with a lateral translation of 0. 4 mm and subsequently deskewed in postprocessing. Final voxel dimensions for all lattice light-sheet image datasets were 104 nm 3 104 nm 3 210-217 nm. The microscope was controlled by custommade software. Acquisition frequency was adapted for each experiment and is specified in the main text. Plus-ends trajectory estimation Plus-ends, labelled through GFP tagging of EB1, were detected using a multiscale detector with the default p value (set to 0.
1S2 and EÀ709. SRG, Physik Instrumente) to acquire a three-dimensional stack of images. All equipment was controlled with custom LabVIEW software, which is available from UTSW upon completion of a material transfer agreement. Adhesion detection and elongation analysis Paxillin aggregates as a surrogate for adhesions were detected using the multiscale detector described in Section ''Multiscale particle detector'' based on a p value of 0. 001 and a scale ranging from 0. 3 to 0. 5 mm. The elongation of each detected adhesion is computed through a tubularity metric evaluated for each voxel and averaged across all the voxels associated to a single adhesion.
ACKNOWLEDGMENTS The authors are grateful to  Yuko Mimori-Kyosue  at the  RIKEN Institute  for the gift of the HeLa cells expressing eGFP-EB1 and fruitful conversations. We are also grateful to  Zhe Liu  at  Janelia Research Campus  for giving the ES cells. P. R. was funded by fellowship  LT000954/2015  from the  Human Frontiers in Science Program  and the  Investissements d'Avenir French government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University  -A* MIDEX. Work in the  Danuser lab  was funded by grant  R35GM136428. Maintenance and dissemination of the software was supported by grant  RM1GM145399. W. R. L. acknowledges support from the  Searle Scholars Program , the  Beckman Young Investigator Program , an  NIH New Innovator Award  ( DP2GM136653 ), and the  Packard Fellows Program.
3 mm and upper bound set at 0. 6 mm. The maximum gap length is set to 3 s (or frames) with a minimum length of 3 s for any track segment allowable to be connected by the gap closing algorithm. 10  Benchmarking computational time and memory usage Computational time and memory consumption have been tested on a computing workstation and office laptop (see specification below). The test includes detection, tracking with trackability and dynROI definition on the endocytosis test dataset (400 Mb) provided along with this manuscript. Runtime takes an average of 17. 3 s (s. d. 0. 2 s) seconds on the workstation and 2 min 29 s (s. d. 6 s) on the laptop. The maximum memory used by the pipeline was 12Gb on the workstation and 0. 5 Gb on the office laptop. Office laptop.
Narumiya RRID:CVCL_1922 Stable Embryonic Stem Cells Liu 56 N/A Software and algorithms u-track3D This paper https://doi. org/10. 5281/zenodo. 10055024 MATLAB 2015a-2023a Mathworks RRID:SCR_001622 Gmic D. Tschumperle/GREYC/CNRS https://gmic. eu/ AMIRA v6 Thermo Fisher Scientific RRID:SCR_007353 LabView Emerson RRID:SCR_014325 Other FCS Thermo Fisher Scientific A5256701 FBS U2OS Sigma F0926-500ML 5 mm diameter coverslips Thomas Scientific 64-0700 NA 0. 71 water dipping illumination Special Optics 54-10-7 objective 25X/NA 1. 1 water dipping detection Nikon Instruments CFI75 Apo LWD 25XY objective Camera ORCA-Flash 4. 0 Hamamatsu Photonics C11440-22C Bidirectional scan unit Cambridge Technology 6215 Remote focusing system Nikon Instruments CFI S Plan Fluor ELWD Achromatic doublet Edmund Optics 49-396 Filters Chroma Technology Corporation ZET405/488/561/640, ZT568rdc, ET525/50m, ET600/50m Piezzo Actuator Physik Instrumente P-603.
g. , an image containing an umbrella associated with the synset "artifact"). For the original IMAGENET  [24]  challenge, each input-output data pair consists of an input data point (an image from the IMAGENET database) and its corresponding output label (e. g. , an image containing an umbrella associated with the label "umbrella"). The idea is to take the knowledge gained from one task and transfer it to a different but related task by using the right training pairs to re-train the CNN; this method is called transfer learning  [26]. The advantage of using this method is that one can more easily explore the space of all possible architectures by adjusting the synaptic weights of the convolutional kernels, but also by testing the meta-parameters of the CNNs, such as the number of layers, the number of channels in each layer, or the coarsening of the visual information along the hierarchy  [27].
Main Contributions What distinguishes an image with an animal from an image without an animal? To answer this scientific question, our work proposes three major contributions. First, to define the psychophysical task, we built a script to build large, arbitrary datasets of images based on IMAGENET  [24]. It was defined by selecting labels according to a large semantic graph of English words: WORDNET  [33]  (see Figure  2 ). According to our scientific question, we first defined our task as the categorization of an animal in an image. As a control, we also defined an independent task consisting of detecting the presence of any artifact in the image (see Figure  1C, D ). Second, we re-trained the existing VGG 16 model on these tasks and compared its performance with experimental data. This allowed us to test the robustness of our networks to different geometric transformations and to compare their accuracy with that observed in the physiological data.