dataesr/scientific-paragraphs-categorization huggingface dataset:
The data from lot 3, allowed to test the model ability to describe pearl oyster performances at different life stage and latitudes and longitudes far from the pilot site of Ahe, since Gambier island is located by 23°S. Conversely, Tapakoto atoll is only 115 km away from Ahe. Validation simulations were run in the conditions of food and temperature that were monitored simultaneously, along with the respective biological dataset. The parameter XK was readjusted for each atoll according to the description of the food conditions and/or the age of the oysters. Several missing values of chl-a concentration during the 2017 Ahe study (Dataset 4) were filled using Fourier series (Curve fitting Toolbox 3. 5. 6 Copyright 2001-2017 The MathWorks, Inc. ) fitted on the chl-a data available at each station.
simulations usually remained in the range of the observed values except for the large underestimation of the flesh dry mass between February and May (Figure 6 A; R 2 = 0. 561; p-value = 0. 0277) and an overestimation of the shell length from June to August (Figure 6 B; R 2 = 0. 904; p-value = 0. 001). Le Moullac et al. , (2012) suggested 3 spawning events (Figure 6 blue arrows) in December, March and June. Here, these conclusions are not confirmed by the simulations that suggested one spawn per individual occurring asynchronously throughout the year (sharp decrease in the thin lines). Dataset 4Environmental conditions from stations 1 & 3 (Figure1), are shown Figure7A & B respectively. Time-series of gameto-somatic index variations are plotted on Figure7C & D. No readjustment of Xk from the value calibrated in laboratory (Xk = 0.
Acknowledgements This study was co-funded by the  Institut français de recherche pour l'exploitation de la mer (IFREMER)  and the  Direction des Ressources Marines et Minières de Polynésie française (DRMM). Support was also provided through the  ANR-16-CE32-0004   MANA (Management of Atolls)  project. The authors thank the IFREMER and DRMM teams who participated in the experiments conducted at Vairao. A special thanks to  Caline Basset  for her invaluable help during the acquisition and the processing of the field data as well as  Augustin Mata  and  Judy Mata  for having welcomed us on the beautiful atoll of Ahe and allowed to work in the best conditions.
ACKNOWLEDGEMENTS We thank  P. Seńat ,  D. Duet  and  J. -C. Tack  for the technical support, and are grateful for fruitful discussions within the Quantronics group. We acknowledge IARPA and  Lincoln Labs  for providing a JTWPA used in the measurements. We acknowledge support from the  Horizon 2020 research and innovation program  through grant agreement No. 771493  ( LOQO-MOTIONS ), and of the  European Union  through the  Marie 365 Sklodowska Curie Grant  Agreement No. 765267  (QuSCO), and of the  Agence Nationale de la Recherche (ANR)  through projects  QIPSE, MIRESPIN  ( ANR 19 CE47 0011 ), and the  Chaire Industrielle NASNIQ , and of the  UK's Engineering and Physical Sciences Research Council  through a  Doctoral Training Award. T. S. was supported by the  U. S. Department of Energy  under Contract No.
Simulation environment A simulation environment is created to evaluate the proposed model. This simulation is carried out with the AnyLogic simulator, which is a simulation environment for transportation systems (Abu-Taieh and El Sheikh 2007). AnyLogic is a Java-based development environment that includes graphical model editor and code generator. We use the jFuzyLogic library to implement the fuzzy inference system. The travel time was chosen to evaluate the performance of the system. The journey time (JT) of priority vehicles is the time between the start point and the arrival point. The journey time obtained in our approach is compared with the journey time in perfectly stationary conditions (JTP) (Figure  8 ), with the priority vehicles evolved between the start and stop points at a fixed speed without stopping at the traffic lights signals, and with journey time in a regulation system without priority (Figure  9 ).
Most fundamental studies addressed the 'isolated vortex merger' problem, omitting the influence of neighbouring eddies, large scale currents, or boundary, topographic, and planetary effects  [19] [20] [21] [22] [23] [24] [25]. Efforts to include more physical effects in studies of vortex merger  [26] [27] [28] [29]  were often impaired by the lack of general observations in the global ocean, or by the complexity of the resulting dynamical system. Despite recent trials 30 and pointwise observations  12, [31] [32] [33]  , a global description of vortex merger in the ocean is lacking. In this paper, we present an analysis of merging events in the global ocean, using a global 1/12° re-analysis dataset. This study was conducted over a five-year period, in five domains in the middle of each major oceanic basins -these domains a priori respect the hypothesis of isolated vortex merger studies: they are far away from the coastlines, topographic features, and strong currents, and are located at mid-latitudes (see Fig.
1 ). From 5,867 detected merging events, we infer the actual characteristics of mesoscale eddies that merge, and test the isolated vortex merger problem hypotheses. Further, we compare this analysis with the outcome of 1,600 idealized numerical simulations. The latter rely on a 3D primitive equations model, both on the f-plane and on the β-plane, using a realistic open ocean parameterization (e. g. a realistic stratification, and numerical parameters used in regional numerical simulations  34, 35  ). To allow for a direct comparison, we use the same merging event detection algorithm in both datasets. Here, we show that in the five study areas, vortex mergers are not isolated; they are influenced by neighbouring eddies as well as by the β-effect. Paradoxically, the merging process in the ocean is well represented by very simple simulations of Gaussian eddies a thousand meters deep on an f-plane, at least as far as the critical distance of merging is concerned.
Results Vortex merging analysis in the ocean: are interacting vortex pairs isolated from the surrounding ? 5,867 merging events were detected, viz. 605, 605, 1,797, 937, and 1923 in the NP, SP, NA, SA, and IO areas, respectively (Fig. 1 ). They were identified and extracted over a five year period, from the 1/12° HYCOM re-analysis dataset (hereafter 'HYCOM dataset' , see 'Methods' section for the dataset, the detection algorithm, hereafter AMEDA, and Supplementary Fig. 2A  and 9  for an example of merging event detection). The dataset relies on the assimilation of altimetric and in situ data in a mesoscale resolving numerical model, ensuring realistic surface fields with a satisfying horizontal resolution. 1,031, 1,037, 2,647, 1,445, and 2,779 different eddies were involved in the detected merging events, out of 4,051, 4,641, 6,079, 4,222, and 6,131 detected eddies (i.
These results provide important information to test the validity of usual assumptions used in isolated vortex merger studies. First, the ratio of the Ro of the two merging eddies (Fig. 2D ), a proxy of the symmetry of the system, shows that a significant number (>10%) of merging events occur with one eddy having a Rossby number 1. 5 times larger than the other eddy. Most of the merging events studied here are nonetheless symmetric, as about 45% of merging events have a Ro ratio lower than 1. 2. This may also be related to the resolution of the HYCOM dataset, which does not allow for extreme asymmetric events (e. g. with Ro ratio > 2). Second, the Coriolis parameter varies by about 3-5% (Fig. 2E ) around eddies: meridional asymmetry is intrinsic to the system. Third, the vorticity variance (Fig.
2H ). Merging eddies are thus constrained inside a sea of eddies. Note here that AMEDA does not take into account 3-eddy merging events. It thus underestimates the number of merging events, and maybe overestimates the number of like-signed eddies around the two merging eddies. In summary, the usual hypotheses for symmetric, isolated vortex merger -i. e. two identical eddies on the f-plane, isolated from the surrounding-are not verified in the HYCOM dataset. Comparing the merging distance between the global ocean and idealized simulations. In the HYCOM dataset, the average normalized merging distance d m is of 3. 04 for AEs and 3. 09 for CEs (Fig. 2I ). This distance is obtained on the first day of interaction between the merging eddies, when they start to influence each other (see 'Methods' section).
In such simulations, merging eddies can exchange more vorticity as the wall prevents them to drift far away. This increases the merging efficiency as well as d m i. The merging distances observed in the HYCOM dataset are reproduced by the idealized simulations on the f-plane: the values of d m i match well the observed d m for all Ro and Bu studied (in particular for simulations with H = 1000 m and α = 2, as expected -in the ocean, eddies are mostly Gaussian  37  , and about 1000 m deep  [41] [42] [43] [44]  ). With the β-effect, if eddies are not constrained by a disk, d m in the HYCOM dataset is significantly larger than the d m i values observed in idealized simulations, especially for small Bu and Ro. In this parameter range, β-disk simulations with H = 1000 m and α = 2 are found closer to the measures made in the HYCOM dataset.
This other kind of events should therefore play an important role in the ocean energy budget as well, opposite to the role of the vortex merger, as it transfers heat and energy toward small scales. A similar study as the one presented here could thus be conducted, in order to describe the modalities of the splitting process in the global ocean. In particular, the AMEDA algorithm used here is suitable for such a task, as it allows to get the historic of splitting events that eddies went through, exactly similarly as for the merger  37. To accurately describe the vortex splitting, as well as to extend the present study to smaller vortices (i. e. for larger Ro and Bu), a higher-resolution dataset should be used. Indeed, with a higher resolution, smaller vortices are detected. The study of smaller structures could allow to describe the interaction regimes between eddies at horizontal scales below the mesoscale.
NCODA uses the 24-hour HYCOM forecast  57  as a first guess in a 3D variational scheme and assimilates available satellite altimeter observations, satellite, and in situ sea surface temperature as well as in situ vertical temperature and salinity profiles from XBTs, Argo floats and moored buoys. Surface information is projected downward into the water column using Improved Synthetic Ocean Profiles  58. We focus on areas respecting the hypothesis of isolated vortex merger studies. First, areas are far away from the coastlines, topographic features, and strong currents (to avoid coastal, topographic, or current/eddy interactions). We also exclude areas close to islands -this explains blank areas e. g. in the northern part of the SP area around Marquesas Islands. Second, the five areas are located at mid-latitudes (to avoid equatorial dynamics and ice related phenomena).
5, 3. 0, 3. 5, 4. 0], and we run simulations on both the f-plane and the β-plane. Hence, we are able to know the maximum value of d/R (with an accuracy of ±0. 25) for which eddies with given initial parameters merge (this value is noted d m i in the text). We scan 400 different sets of parameters, and a total of 1600 simulations. We downgrade the resolution of the outputs to 0. 08°, and we consider one output per day to have a dataset consistent with the HYCOM outputs. To study the vortex merger in a context where eddies are not isolated but packed, surrounded by neighbouring eddies, we perform a few simulations with a boundary around the two main eddies. The boundary is a circular wall with a radius of 4. 5R from the center of the domain. These simulations are referred to as β-disk simulations in the text.
The radius of the disk is chosen as the median distance between the merging eddies centroid and the closest neighbouring eddies edge (see Fig. 2H ). A free-slip condition is chosen such as no frictional effect are induced by the boundary. Our definition of vortex merging events with AMEDA. To identify the merging events both in the HYCOM dataset and in idealized simulations, the Angular Momentum Eddy Detection and tracking Algorithm (AMEDA  37  ) is used. One of the benefits of AMEDA is that it does not depend on arbitrary thresholding, which would require a fine-tuning of geometrical parameters. Also, the algorithm is robust with respect to the grid resolution and can thus be applied to a wide variety of velocity fields (experimental, numerical, altimetry. ). This algorithm has been used and validated in previous studies in the past few years  36, 37, 44, 68  , see also an example of application of AMEDA in https://www1.
This method provides the characteristics of each detected eddy from its generation (or first detection) to the last time it was detected (either its death due to destabilization or interaction with broader scale structures (topography, currents. ), or until it merges with another eddy): the position of the eddy center, its shape, its polarity, its radius R (defined as the mean radius of the maximum velocity contour), the mean maximum velocity along this contour V, and the ID of eddies that would have merged with it. Example of merging events are shown for the HYCOM dataset, an f-plane simulation, and a β-plane simulation in Supplementary Figs. 2A-C  respectively. The definition of merger is the same for both the HYCOM dataset and the idealized simulations. As discussed in previous studies  21, 25  , defining whether merging occurred may be tricky, mainly for three reasons.
Indeed, as this one increases, the smallest eddy is too small to be detected, and AMEDA indicates a merging event. We do this in both HYCOM dataset and in idealized simulations, thus giving a consistent definition of merging in the two cases. In the case of the idealized simulations, if eddies have not merged during the first 50 days of simulation, they are considered as independent. This duration has been chosen knowing that the median lifetime of eddies before merging in the HYCOM dataset is 40 days. Note that in the HYCOM dataset, to be sure that we consider merging events of two stable vortices, we only consider eddies with a lifetime larger than 10 days. Common definitions: dimensionless numbers and distance of merging. The definitions of R and V in both the initialization of idealized simulations, and AMEDA detections in the HYCOM dataset are consistent.
From R and V, one can compute the Rossby and Burger numbers of the merging eddies. They are respectively defined as Ro = V/(f 0 R) and = Bu R R ( / ) D 2. Ro is a proxy of the surface intensity of the dynamic core 1 ; Bu indicates the importance of stratification against rotation. The values of R and V chosen for the initialization of the idealized simulations give a range of Rossby numbers varying between Ro = 0. 019 and Ro = 0. 3, and Burger numbers between Bu = 0. 32 and Bu = 1. 65. This covers the parameter space of eddies which merge in the HYCOM dataset (see Fig. 2B ). Note that for the study of eddies detected in the HYCOM dataset, R is in fact the average of the R values between the first detection of the eddy and the merger. This average is done because the radius of eddies before the merging may vary substantially due to the strong interactions between eddies (see e.
g. Supplementary Fig. 2 ). To compute Bu for each detected eddy in the HYCOM dataset, we interpolate the value of R D from the ref. 59 's global estimation at the position of the detected eddy. The normalized distance between eddies when they merge in the HYCOM dataset d m is defined as the Euclidean distance between the two eddy centers (Fig. 2I ) when AMEDA detects the merger (on the first day of interaction between the two merging eddies). It is normalized by the mean radius of the two eddies. Since the two radii may differ, and because their value is impacted by the merging events, d m is subject to variations. Thus, we present also in Fig. 2I  the distributions of d m , for which R is equal to the mean value of R in each bin, plus or minus the standard deviation of R in this bin. They are respectively the dotted and the dashed step lines.
d m is defined such as it can be compared with the maximum initial distance between eddies, for which they are observed to merge in idealized simulations d m i (for a given set of parameters). Other quantities used to describe merging events in the HYCOM dataset. From the eddy characteristics extracted by AMEDA in the HYCOM dataset, we compute quantities to describe the merging events occurring in the global ocean (Fig. 2 ). In the following we denote by • 1 and • 2 the quantities corresponding to the two eddies implied in a specific merging event. We compute the ratio of the Rossby numbers for each pair of eddies (Fig. 2D ) as Ro Ro max( , ) 1 2 / Ro Ro min( , ) 1 2. Thus, for each pair, this ratio is always greater than 1, with 1 corresponding to eddies with equal Rossby numbers. The orientation of the two eddies with respect to the meridional direction (Fig.
Acknowledgements This work was funded by the  Direction Générale de l' Armement (DGA)  via a full grant for  Charly de Marez 's PhD. This work is also a contribution to PRC 1069 'Meso and submesoscale vortices in the Atlantic and  Indian Ocean ' , funded by  CNRS  and  RFBR. Simulations were performed using the  HPC  facilities  DATARMOR  of  "Pôle de Calcul Intensif pour la Mer" at Ifremer, Brest, France. AMEDA has been developed under the funding of the  ANR - Astrid  Project  DYNED-Atlas  ( ANR 15 ASMA 0003 01 ). AMEDA is available online, https://github. com/briaclevu/AMEDA. Global HYCOM re-analysis were downloaded from the dedicated repository https://www. hycom. org/dataserver/gofs-3pt1/reanalysis. Model outputs are available upon request. We gratefully acknowledge  C. Vic  for his helpful comments on the manuscript.
Overview of experiments, context, and findings In this study, spheroids (consisting of 5-10×10 3 cells) were embedded in dense 3D collagen matrices. Upon completion of this embedding, time-lapse imaging was begun immediately in order to monitor all subsequent spheroid invasion and collagen deformation/remodeling via our computational 4D-OCM imaging methods (detailed in the Methods). Imaging was performed at 40-minute intervals for a total duration of 2 days, after which custom data processing routines (detailed in the Methods and Supplementary Note 3) were used to reconstruct 4D image data and visualize/quantify the biophysical interactions between cell collectives and 3D collagen substrates. Our computational 4D-OCM methods (and subsequent data visualizations) for imaging cell invasion and collagen deformation/remodeling are detailed in the remaining sections of this manuscript, whereas biological findings arising from the application of these methods to study the role of obesity-associated adipose stromal cells (ASCs) in cancer progression can be found in a companion paper  31.
Elastic image registration reveals space-and time-varying collagen displacements Time-varying deformations of the collagen substrate were observed in conjunction with the previously described changes in spheroid morphology and collagen degradation/remodeling. Unlike standard TFM methods which track the motion of embedded fiducial marker beads, here, the motion of collagen fibers was tracked directly (analogous to a previously reported TFM method based on confocal reflectance microscopy  27  ). Elastic image registration (via the built-in MATLAB function imregdemons) was employed to estimate the time-varying 3D displacement field which would map the original collagen substrate at time t=0 to the deformed collagen substrate observed at any given time-point during the first 24 hours of imaging.
Our use of temporal speckle contrast also substantially increases the amount of raw data that must be acquired by nearly an order of magnitude. However, the ability to generate synthetic imaging channels from label-free, long-term OCM image data may outweigh this cost. Nevertheless, future work which seeks to reduce the amount of data that must be acquired would be of great value. If large quantities of data are acceptable, and high-speed OCM imaging is available, the utility of temporal speckle contrast may be further enhanced by enabling the generation of multiple synthetic channels which correspond to different structures and/or levels of metabolic activity  34. Although we were able to quantify the displacement of collagen fibers via elastic image registration (using the MATLAB function imregdemons), these measurements cannot be expected to be reliable very close to the cellcollagen boundary.
S x y z t S x y z t S x y z t N         (2) where   , , , S x y z t is defined as in Eqn. (1). Similar to our previously reported method 30 , this technique leverages temporal speckle contrast to distinguish rapidly changing structures (primarily cells) from static background structures (here, primarily collagen), resulting in a synthetic 'cell' channel. 'Cell' channel images were downsampled by a factor of 768:400 along the lateral dimensions, and by a suitable factor along the axial dimension, via the MATLAB function imresize3 to yield a final voxel size of (1. 88 μm) 3 and (2. 34 μm)  3  for images of monoculture spheroids and co-culture spheroids, respectively. This down-sampling was used in order to perform smoothing and reduce computational load. These images exhibited a depth-dependent additive 'background'.
Collagen displacement tracking Time-varying collagen displacements were computed across the first 24 hours of images using elastic image registration via the built-in MATLAB function imregdemons (which uses Thirion's 'Demons Algorithm', an iterative, large-deformation registration method inspired by the optical flow equations  37, 38  ). Specifically, this function was used to compute the 3D displacement field required to register the 'collagen' channel image of the first time-point (t=0) to the 'collagen' channel image of any given time-point acquired during the first 24 hours of imaging. The imregdemons function was called using the following input parameters (as defined on the documentation page for the function 47 ): pyramid levels = 3, iterations per pyramid level = 100, and accumulated field smoothing = 2.
5e, f ), indicating that ATR is essential to allow cells to sustain the harsh mechanical environment imposed by blood flow and extravasation. ATR interactors known to influence nuclear mechanoresponse. To identify potential ATR interactors and targets contributing to nuclear mechanics and dynamics, we performed high-resolution mass spectrometry screens (IP-liquid chromatography (LC)-MS/MS) in exponentially growing U2OS cells expressing GFP-ATR. Combining data from three SILAC (stable isotopes labeling with amino acids in cell culture) quantitative approaches (Supplementary Fig. 4a ), we obtained 479 unique interactors of ATR (analysis details in "Methods" and Supplementary Data 1). Our ATR interactome exhibited a 55% enrichment (265/479) for proteins with (S/T-Q) motif, a potential targets of ATR phosphorylation, while such proteins represent only 22% (5137/17522) in the total phospho-proteins of the PhosphoSitePlus database  34  (Supplementary Fig.
b Cell death measured as the percentage of engaged cells that burst at the constriction (n = 122 and 88 cells for shCtrl and shATR1; data pooled from three independent experiments). c Quantification of 53BP1-GFP foci generated due to constriction in HeLa cells expressing 53BP1-GFP in the presence of DMSO or ATR inhibitor, VE-821 (n = 24 and 17 for DMSO and ATRinh; pooled from two independent experiments). Cells that undergo cell death in the constriction are highlighted in red (for ATRinh). d, e FRET signal measurements of cells engaged in constrictions. d Images of FRET signal at various stages of migration through the constriction and measurement of signal ratio between front (leading half of the nucleus) and back (lagging half of the nucleus) of a nuclei at various stages of migration (n = 11, 10, 10, and 6, respectively).