dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). In this study, we repeatedly produced holes of Ø10 lm to match the average junction size of the S180 cell doublets. We preformed cell doublets in an external chamber comprising an array of Ø50 lm round pits cast in agarose gel [supplementary material, Fig. 1(b) ]. The size of each antifouling pit was designed to accommodate only 2 cells. After mature contacts formed (4-6 h), we transferred the doublets at low density (1 Â 10 4 /ml) into the upper compartment of the stretching device. A custom LabVIEW program interfaces our channel to a Fluigent V R system (MFCS) to apply highprecision pressure gradients across the connecting through-hole and/or between the lower channel inlets/outlets. FIG. 2. Through-hole size customization for the channel cover layer. The dome-shape PDMS mold was pressed against a flat PDMS substrate with a precision pressor.
11, 15 We harnessed this stereotypical clustered-organization of junctional E-cadherin so that individual clusters served as fiducial points from which junction regions could be traced and correlated before and after the stimulation. Using a custom MatLab code that registered the locations of individual cadherin clusters (supplementary material, Fig. 4 ), we estimated the local recruitment of the protein at each location by quantitating changes in the total fluorescence in a 1 lm Â 1 lm Â 1 lm voxel centered over each cluster. Mapping the specific changes in protein recruitment under or in between each cluster i-avoided measurement artifact due to small rotation or deformation of the junction during the mechanical stimulation ii-allowed to directly compare the recruitment of protein under cadherin rich and cadherin poor regions along the junction.
Junction stress simulation Stress induced by deformation at the cell contact was simulated using the commercial finite element (FE) analysis software COMSOL Multiphysics. The entire geometry was constructed based on dimensions measured in the actual experiment. The channel had a cross-section of W40 Â H50 lm and 500 lm in length. The cell inside the channel was set to 15 lm in diameter and formed a Ø10 lm contact area with the channel top. It was modelled as a 0. 5 lm thick elastic shell (Young modulus: 2 kPa, Poisson's ratio: 0. 4). The liquid velocity field in the channel and the resultant drag force due to liquid viscosity on the cell surface were simulated for different flow speeds (supplementary material, Fig. 3 ). The force experienced by the junction was decomposed into pulling (or squeezing) force normal to the junction and shearing force parallel to the junction (Fig.
Imaging and data analysis For each S180 doublet properly positioned in SJS, a 10-20 lm-thick volume circumscribing the intercellular region was imaged using a spinning disk confocal microscope with 60Â magnification before and after the stimulation. The doublet adopted the same upright deformation-free configuration in both imaging events. The image stacks were then processed with custom Matlab codes to re-orientate the cell contact in a fixed referential. A 1 lm thick volume enclosing the junction was then isolated and integrated vertically to generate the junction top-view image (Fig. 4 ). Volumes of the same size but with an offset of 3 lm above and below the junction region were also analyzed as controls. The intensity change along the junction ring was determined based on the percentage difference between top-view images before and after stimulation.
Finally, all junction images within each experiment condition were overlaid and averaged to give a more conclusive presentation. Likewise, normalized intensity changes along the junction ring were also averaged. Because of plasma membrane fluidity, molecular patterns along the junction ring might be occasionally disturbed after the stimulation. To circumvent issues in junctional patternmatching during subsequent image quantification, a more elaborate signal analysis using Matlab was developed. Briefly, characteristic patchy distribution of E-cadherin puncta at the junction was translated into a circle of fiducial references, so that the junction molecular response could be tracked in a spatially aware manner. 3D coordinates denoting the locations of distinct Ecadherin puncta were determined.
Imaging and data analysis The image quantification method used in our equiaxial stretching experiments for the cellcell junction in the monolayer was based on Line-scan analysis. Briefly, volumes in Caco-2 monolayer containing the tight junction region were imaged using a confocal microscope with 63Â magnification. Cell-cell contacts were identified using the line-selection function in ImageJ. A line of 10 lm long and 1 lm thick was positioned orthogonal to, and centered on, each cell-cell contact according to the ZO-1 or occludin signal in every image. Measurements for the actin, E-cadherin, ZO-1 and occludin fluorescence intensity profiles along these lines were obtained using the custom Matlab code based on the ImageJ linescan function that averages the pixel intensity value along the line.
ACKNOWLEDGMENTS V. V. acknowledges support for the  NRF  Grant No. NRF-CRP11-2012-02. Work in Australia was supported by the  Human Frontiers Science Program  ( RGP0023/2014  to  S. Grill ,  Z. Bryant , and  A. Yap ), the  National Health and Medical Research Council of Australia  ( 1037320 ,  1067405 ), and the  Australian Research Council  ( DP150101367 ). A. S. Y. is a Research Fellow of the  NHMRC  ( 1044041 ).
Software Implementation The simulation and computation experiments were performed in the software "Gloups" developed under the Windows environment using MATLAB (The MathWorks, Inc. , America). The main procedures include one for simulation and another for calibration. In the simulation, the Monte-Carlo method is applied for stochastic development. The results are shown for different plant samples. A virtual target data file can be output. In the calibration, the analytical computation of the organic series is performed according to the initial parameter values, and the result is fit with the target data, either from simulation or from real plants. A simple tutorial is shown in Supplement E.
Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM).
We present AxonDeepSeg, a deep learning framework for robust and automatic segmentation of both axons and myelin sheaths in myelinated fibers. AxonDeepSeg features: (i) a CNN architecture for semantic segmentation of histological images; (ii) two ready-to-use models for the segmentation of scanning electron microscopy (SEM) and transmission electron microscopy (TEM) samples adapted to a variety of species and acquisition parameters; (iii) a well-documented training pipeline to generate models for new imaging modalities and (iv) free and open source code (https://github. com/neuropoly/axondeepseg). This paper is organized as follows. The Methods section lists the datasets used, details the architecture of the network and presents the validation methodology. The Results section presents axon and myelin segmentation results obtained on SEM and TEM samples, and shows an example application to extract morphological metrics from a full rat spinal cord slice.
Dataset Microscopy images used in this study were acquired with two different imaging techniques: SEM and TEM. Different acquisition resolutions were used, in order to increase variability and obtain better generalization of the model, with isotropic pixel size resolution ranging from 0. 05 to 0. 18 µm (SEM) and 0. 002 to 0. 009 µm (TEM). SEM samples were stained with 2% osmium, embedded in epoxy, polished and imaged with the same SEM system (Jeol 7600F). TEM images were obtained from mice brain samples (splenium), as described in  26. Additionally, a macaque sample of the corpus callosum was added to the test set. Preparation and imaging procedures are described in  7. 1 : List of datasets used for the experiments. For each sample, the following information is indicated: number of images used, species, tissue type, pixel size, field of view (FOV) and tissue preparation details.
Pipeline overview The pipeline of AxonDeepSeg is composed of four steps: data preparation, learning, evaluation and prediction. Figure  2  illustrates each step. In the data preparation step, raw microscopy images and corresponding axon/myelin labels are resampled to a common resolution space: 0. 1 µm per pixel for SEM and 0. 01 µm for TEM. These values are based on preliminary results and on the typical resolutions provided by each of these imaging systems. Resampled samples are divided into patches of 512×512 pixels due to memory constraints. This size was chosen to have around 15-75 axons per patch. Traditional pre-processing was applied patch-wise, including standardization and histogram equalization (not shown in figure  2  for clarity). For learning, the patches and corresponding labels were randomly split and then considered either for the training or for the validation sets (training/validation split of approximately 70/30%).
For evaluation, full test images were randomly selected. In the learning step, the training/validation dataset is fed into the network. Once the trained model is obtained, performance is evaluated on the test dataset (evaluation step). Finally, the trained model can be used for inference on new microscopy images (prediction step). The images are resampled to the pixel size of the model, divided into patches of 512×512 pixels, segmented, stitched to the native size, and resampled to the native resolution. Note that bilinear interpolation was used during the resampling steps.
Software AxonDeepSeg is coded in Python and based on the TensorFlow deep learning framework. It can currently run on Linux and Mac OS X systems. Segmentation inference can be done on standard CPU computers at reasonable computational time. For instance, the segmentation of the full rat slice (figure  6 ) took about 5 hours in a Mac laptop (2. 9 GHz). The code is available as open source in GitHub (https://github. com/neuropoly/axondeepseg) and an intuitive documentation is provided (https://neuropoly. github. io/axondeepseg/). A Binder link and a simple Jupyter notebook are available for getting started with AxonDeepSeg.
Acknowledgements The authors would like to thank  Ariane Saliani  and  Tanguy Duval  for helping with the acquisition of SEM data,  Dr. Hugues Leblond  for providing the human spinal cord sample,  Nafisa Husein  and  Harris Nami  for helping with the gold standard labelling of samples,  Drs. Adriana Romero Soriano  and  Yoshua Bengio  ( MILA -Montreal Institute for Learning Algorithms ), and  Dr. Robert Brown  ( McGill -Montreal Neurological Institute ) for fruitful discussions on the network design,  Drs. Nikola Stikov  and  Jennifer Campbell  for sharing TEM data of macaque, and  Dr. Els Fieremans  for sharing TEM data of mice. The authors would also like to thank  Compute Canada  and  Calcul Québec  for access to computation units and the " NVIDIA Corporation " for offering a Tesla GPU.
The number of 80-kN Equivalent Single Axle Loads (ESALs) imposed on the road structure by each vehicle class has been calculated directly from weighing station records. An input to this calculation is the structural number (SN), which has been estimated following the method outlined in HDM 4 Model Version 1. 3  (ISOHDM, 2001). Road structure data have been obtained from work contracts and supervision and evaluation reports on EDF-funded projects  (Dorsch, 2004; Commission Européenne, 2012 ). The  The Ministry of Public Works has defined 30 road sections on the Douala-Yaoundé-N'Djamena international corridor. Traffic counts have been performed in these sections from 2000 to 2014 per vehicle type (see Appendix B). For the purpose of this study, traffic counts in the two sections adjacent to Yaoundé (Yaoundé-Nomayos: 19.
However, there is no data to precisely estimate this effect. Fig. 2  shows the research methodology. First, ESALs, LEFs and average weights are estimated per vehicle type, station and year selected for analysis. These intermediary results feed the HDM-4 road deterioration model (RDM) and road user costs model (RUC). These models are run for all the overloading control scenarios, road sections and time periods. The results obtained are vehicle operating costs, travel time costs and estimation of the periodic maintenance and rehabilitation backlog. Finally, cost-effectiveness ratios of axle-load control are estimated for the overloading control scenarios.
Calibration of the models for estimating the effects of axle overload HDM 4 software for estimating road deterioration and road user costs has been calibrated and applied. Appendix C displays the main economic data for running the HDM 4 RUC model, which are vehicle fleet economic unit costs in Cameroon from 2000 to 2015. Appendix D shows the basic utilization parameters of vehicles and road section geometry, which have not substantially varied since 2000. Average vehicle fleet economic costs and basic utilization parameters have been obtained from a World Bank Road User Costs  Study (2006)  for African countries when they are not available at the national level or when average figures are difficult to estimate (passenger time, cargo delay, labour and overhead costs, kilometres and hours driven per year and vehicle, etc.
). Shadow prices for fuel and gasoline have been estimated, taking into account the annual product price structure. Ex-refinery prices, total storage, transport, stabilization and distribution costs are included. All taxes, custom duties and fees have been excluded on the basis of data from IMF country reports  (International Monetary Fund, 2014 and 2016). Regarding fleets, 10 vehicle types have been analysed: medium cars, pick-ups, small buses, medium buses, and trucks (P11, P12, S112, S113, S122, and S123). P13 and S111 trucks have been embedded into the P12 and S112 categories, respectively. In the Douala-N'Djamena corridor, the rate of empty-truck return trips has recently been estimated at 85% by a World Bank project appraisal document (2014). This average has been applied to estimate the LEFs for each vehicle type and section.
Axle load distribution for empty vehicles has been estimated using average observed weights and dimensions per vehicle type. Average operating weights have been obtained from weighing station records by applying the rate of empty-truck return trips. Average loads of heavy buses have been taken into account for ESAL estimation, even though their axle-load is not controlled in Cameroon. An average gross bus weight of 11. 9 tonnes has been applied (4. 4 tonnes for the front axle, 7. 5 tonnes for the rear single axle)  (World Bank, 2006). The HDM 4 model has been applied to three alternative situations: 2. Optimal scenario: zero overloading tolerance since 2000. This means that the 1996 legislation on road protection and road maintenance would have been strictly applied. Available data on axle loads from records at the ten weighing stations along the Douala-N'Djamena corridor have been modified to simulate strict application of axle-load legal constraints.
Acknowledgements We thank the  Government of Cameroon  for making available the data necessary to conduct this study, in particular the members of the  Inter-ministerial Committee  for the Follow-up of Weighing Operations (CISOP), the staff of the weighing stations and the private operators A2I Sarl and FOTI Sarl. We are particularly grateful to  Messrs' Godje ,  Tang, Kanga and Mbem  from the  Ministry of Public Works  and  Mr. Ekah  from the  Road Fund. We would also like to thank the  European Commission  and the colleagues of the  European Union Delegation in Yaoundé , as well as the transport sector officers of the  World Bank , the  African Development Bank  and the  Agence Française de Développement  based in Cameroon. We are appreciative of the very helpful comments and suggestions received from the two anonymous referees and the editor.
Followed by downstream modules leading to pH i , voltage and [Ca 2+ ] i fluctuations. The upstream module outputs were fitted to kinetic data on cGMP activity and early membrane potential changes measured in bulk cell populations. Two candidate modules featuring voltage-dependent Ca 2+ -channels link these outputs to the downstream dynamics and can independently explain the typical decaying envelop and the progressive spacing of the spikes. In the first module, [Ca 2+ ] i -spike trains require the concerted action of a classical Ca V -like channel and a potassium channel, BK (Slo1), whereas the second module relies on pH i -dependent, [Ca 2+ ] i -inactivated CatSper dynamics alone. The model predicts that these two modules interfere with each other and produce unreasonable dynamics when present at similar proportions, which suggests that one may predominate over the other in vivo.
Computational implementation and parameter estimations The systems of differential equations were solved numerically with Mathematica 11 (Wolfram Research, Inc. , Mathematica, Version 11. 0, Champaign, IL), XPPAUT 8  [25]  or R (using the package deSolve  [26] ). XPPAUT 8 was used for the bifurcation analysis (see below). Mathematica was further used to fit the model to experimental data using the Nonlinearmodelfit function with the optimisation method Differential Evolution with constraints. In the analysis, we defined several model variants based on subsets of the variables and equations presented above and in section A1. Some of these defined modules were used to estimate the parameters and some were used to analyse specific properties. The upstream module fitting was done in two parts: • The state space comprised by the variables {S, R H , R L , G}, which includes the reactions involving the SAP-receptor (eq.
6/43 • In one scenario, the model features the Ca V + BK module and has the state space ({S, R H , R L , G, f o kn , f o hc , V , f o cv , f c cv , f o bk , C}. The values of the Ca V channel parameters were adjusted based on the characteristics of low-voltage-activated T-type Ca 2+ channels  [32] , for which there is evidence suggesting their presence in sperm  [8, 10]  (Tab. 1). • In the alternative scenario, the model features the CatSper module and has the following state space {S, R H , R L , G, f o kn , f o hc , V , m cs , h cs , f a nh , C, H}. The CatSper gating parameters were set such that its Ca 2+ i and pH i sensitivity range is placed within the physiologically expected values in reported natural SAP responses (Fig. S3 ).
Mixed scenarios parametrisation We also analysed mixtures of Ca V +BK and CatSper modules, in the most extended model with state space {S, R H , R L , G, f o kn , f o hc , V , f o cv , f c cv , f o bk , m cs , h cs , f a nh , C, H}. We introduced a parameter θ to explicitly weigh the contribution of each combination of channels. The latter parameter modulates CatSper, Ca V and BK in the following way: I cs = (1 -θ) (g cs m cs h cs (V -E Ca )) (7a) I cv = θ (g cv f o cv (V -E Ca )) (7b) I bk = θ (g bk f o bk (V -E K )) (7c) From the above equations, we can express a ratio of effective maximal conductance of Catsper with respect to that of Ca V : r g = (1 -θ)g cs θg cv (8) Always taking the CatSper module as a starting point, the analysis of mixed scenarios was done by adding Ca V and BK channels in three ways: a) Ca V only, b) BK only, and c) Ca V + BK.
A modular signalling network The potential signalling network under study here is illustrated in Fig. 1. The signalling network is organised in three modules: an upstream module, which includes the ligation of the receptor by SAP and the early transient drop of membrane potential V generated by the cGMP response, and two alternative downstream modules with the potential to generate the Ca 2+ oscillatory response. The Ca V +BK module includes voltage-dependent Ca 2+ -channels and Ca 2+ -dependent K + -channel BK; while the CatSper module is composed of the voltage-, pH i -and Ca 2+ -dependent channel CatSper, the intraflagellar proton concentration and the voltage-dependent sodium-proton exchanger. The modular structure facilitated the analysis of the model and addressing the question on which sets of channels control the Ca 2+ -spike trains elicited by SAP stimulus.
In the upstream module (described by the variables S, R H , R L , G, f o kn , f o hc ) receptors (R) bind SAP molecules (S) and transit irreversibly through three states, each with less guanylate cyclase activity: High (R H ), low (R L ) and inactive (R i ); the cGMP synthesised by these receptors opens KCNG channels, (KCNG O ), which conduct a potassium current that hyperpolarises V (arrow downwards). The hyperpolarised levels of V promote the opening of spHCN channels (HCN O ), which exert the opposite action on V when conducting a cationic inward current. Two alternative modules are presented to explain the calcium oscillation trains: one that includes classic voltage-dependent Ca 2+ channels and BK channels, and another that considers CatSper, sNHE exchangers and proton concentration. Note in the module Ca V , that the calcium channel is purely voltage dependent, whereas CatSper has threefold regulation, namely Ca 2+ , H + and V.
Two candidate signalling modules can explain semiquantitatively the structure of the Ca 2+ -spike trains Two alternative signalling modules, labelled Ca V +BK and CatSper, can explain the characteristic envelop and increasing delay of the Ca 2+ -spikes when coupled alone to the upstream module (see Fig. 1 ). These modules are composed of specific combinations of ion channels and transporters. These combinations were selected by their capacity to produce a series of Ca 2+ -spikes that recapitulates semiquantitatively the ones observed experimentally, given the input cGMP kinetics used to calibrate the upstream module (Fig. 2 ). In other words, the numerical solutions of these modules lead to [Ca 2+ ] i -spike amplitudes and intervals between consecutive spikes that lie within the relative range of the spikes observed in individual sperm cells of S.
4 , to the asymptotic limit cycles for different fixed cGMP values in Fig. 5A ). It is important to note that at intermediate levels of cGMP, there is a range of values where limit cycles coexist. From what has been reported in the literature, only the dynamics compatible with the upper limit cycle has been experimentally observed. Presumably, even if the small oscillations of the lower limit cycle were present, they would be masked by measurement errors. CatSper module and its dynamical properties We now consider the alternative module whose central component is CatSper, the Ca 2+ channel that has recently been implicated in SAP signalling in sea urchin sperm. We assumed here, that CatSper opening relies on two independent gates: one modulated by both pH i and voltage, and the other one responsive to intracellular calcium.