dataesr/scientific-paragraphs-categorization huggingface dataset:
For this work, we parameterized simSPT to consider that two subpopulations of particles diffuse in a sphere (the nucleus) of 8 µm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 µm), with an axial detection range of ~700 nm, centered at the middle of the HiLo beam. Molecules are assumed to have a mean lifetime of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. The localization error was set to 25 nm and the simulation was run until 100,000 in-focus trajectories were recorded. More specifically, the effect of the exposure time (1 ms, 4 ms, 7 ms, 13 ms, 20 ms), the free diffusion constant (from 0. 5 µm²/s to 14. 5 µm²/s in 0. 5 µm²/s increments) and the fraction bound (from 0 % to 95 % in 5 % increments) were investigated, yielding a dataset consisting of 3480 simulations.
More details on the simulations, including scripts to reproduce the dataset, are available on GitLab as detailed in the "Computer code" section. Full details on how the simulations were analyzed by Spot-On, vbSPT and MSD i are given in Appendix 1. "Motion-blur" bias. Constant excitation during acquisition of a frame will cause a fast-moving particle to spread out its emission photons over many pixels and thus appear as a motionblur, which make detection much less likely with common PSF-fitting algorithms. In contrast, a slow-moving or immobile particle will appear as a well-shaped PSF and thus readily be detected. (B) Tracking ambiguities. Tracking at high particle densities prevents unambiguous connection of particles between frames and tracking errors will cause displacements to be misidentified.
8 The SPT data was simulated and plotted using simSPT (available at https://gitlab. com/tjian-darzacq-lab/simSPT). (Persson et al. , 2013)  and MSD i (Appendix 1), we analyzed 3480 simulations (described in Figure  3  -Figure Supplement 2) using these methods. In (A), the heatmaps show the relative error and in (B) the heatmaps show the absolute error. In a few rare cases (marked by black squares), the MSD i -method failed such that no estimate was possible. Note that whereas Spot-On would always underestimate D FREE inside a small 4 µm radius nucleus, when the confinement is largely relaxed by considering a 20 µm radius nucleus, Spot-On now shows essentially no bias in its D FREE -estimate. FIGURE LEGENDS (MSD i (R 2 >0. 8)). (A).
Legends for Figure Supplements Cumulative distribution function (CDF) plots and summary tables of the relative error in the D FREE -estimate (C) and the absolute error in the F BOUND -estimate (D). Bias refers to the mean error, "std" is the standard deviation and "iqr" is the inter-quartile range (difference between the 75 th and 25 th percentile). As can be seen, correcting for defocalization bias slightly improves the D FREE -estimate, but is essential for an accurate F BOUND -estimate. As expected, the longer the lag time, the more important it is to correct for defocalization bias. As can be seen, Spot-On is not very sensitive to how many TimePoints were considered. However, when working with small datasets of experimental data, if there is not enough data at higher TimePoints values, noise can make the estimates unreliable.
All 3480 simulated datasets are also available (see Data Availability section). Briefly, we parameterized simSPT to consider that particles diffuse inside a sphere (the nucleus) of 8 µm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 µm), with an axial detection range of ~700 nm with Gaussian edges, centered at the middle of the HiLo beam. Molecules are assumed to have a mean lifetime of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. The localization error was set to 25 nm and the simulation was run until 100,000 in-focus trajectories were recorded. More specifically, the effect of the exposure time (1 ms, 4 ms, 7 ms, 13 ms, 20 ms), the free diffusion constant (from 0. 5 µm²/s to 14. 5 µm²/s in 0. 5 µm²/s increments) and the fraction bound (from 0% to 95% in 5% increments) were investigated, yielding a dataset consisting of 3480 simulations.
The advantage of simulations is that the ground truth is known. For more specific simulations, extra parameters were varied, such as the width of the axial detection range (Figure  3   In the case of the main 3480 simulation SPT datasets, we analyzed the data using the Matlab version of Spot-On (either using JumpsToConsider=4 or all), MSD i (either R 2 >0. 8 or all) or vbSPT. We describe the analysis in details below.
Nevertheless, several papers assign diffusion constants to individual trajectories based on a MSD-fit. While the exact method differs somewhat from paper to paper, the most popular approach is to set a threshold of a certain number of localizations per trajectory (most commonly 5; though we note that some reports explicitly attempt to compensate for the bias introduced by setting such a threshold  (Zhen et al. , 2016) ). Each trajectory with at least 5 localizations are then fit, often using the Matlab library MSDAnalyzer  (Tarantino et al. , 2014) , and thus assigned an apparent diffusion constant. An additional threshold is then applied: only if the fit to the MSD curve is judged sufficiently good, is the diffusion constant then used. Otherwise the trajectory is ignored. This fitting threshold is frequently set based on the coefficient of determination as R 2 >0.
We refer to this whole procedure as MSD i (R 2 >0. 8). Examples of fits are shown in Figure  3  and Figure  3  -Figure Supplement 3 and the Matlab code to perform the fitting is available together with the data (see "Data availability'). 5 parameters were estimated in the fit. Performance evaluation: Overall, MSD i (R 2 >0. 8) generally performs reasonably well when it comes to estimating  FREE , but extremely poorly when it comes to  BOUND and  BOUND. The mean error (bias) for estimating  FREE was 8. 0%, the inter-quartile range (IQR) was 4. 9% and the standard deviation 28. 5%. For estimating  BOUND the bias for MSD i (R 2 >0. 8) was -20. 6%, the IQR 32. 1% and the standard deviation 26. 4%. We note that since  BOUND necessarily has to take a value between 0% and 95% in the simulations and since half the simulations have  BOUND <50%, a mean error of -20.
6% is actually quite large. Although the bias for  FREE is much smaller, in ~5% of all cases, the error in estimating  FREE is bigger than 2-fold. Moreover, in a few very rare cases, not a single trajectory out of the 100,000 simulated trajectories pass both thresholds (R 2 >0. 8; at least 5 frames). Why is MSD i (R 2 >0. 8) fitting so unreliable? It is instructive to consider an example. In the example dataset provided with the MSD i code (simulation with  FREE =2;  BOUND =0. 75; 1 ms frame rate), the estimated  FREE =2. 06 is very good, but the estimated  BOUND =0. 16 is extremely poor. Even though the simulation dataset contains 100,000 simulated trajectories, only 3726 of them actually pass the threshold (R 2 >0. 8; at least 5 frames). Thus, MSD i (R 2 >0. 8) only uses around 4% of the data.
Since the tiny fraction of the dataset that is used for analysis is chosen based on how well it fits an MSD-curve and since displacements of bound molecules are dominated by localization errors and therefore generally poorly fit by MSD-analysis, the procedure enriches for the free population, which is why the estimated bound fraction (16%) is so much lower than the true bound fraction (75%). Additionally, we note that MSD i -based analysis is extremely sensitive to the fitting threshold: if instead of R 2 >0. 8, all trajectories had been used the estimated bound fraction would be 87% instead of 16%. In conclusion, MSD i (R 2 >0. 8) is unreliable for estimating  BOUND when short trajectories are at stake, which is the usual case when performing intracellular SPT of fastdiffusing molecules. MSD i (R 2 >0.
8) most likely fails due to a combination of the following reasons among others. First, it poorly handles localization errors, which dominate the displacements of bound molecules. Second, by only considering trajectories of a certain length (normally at least 5 frames), it only analyzes a small subsample of the dataset. Third, there is no correction for defocalization bias. Since fast-diffusing molecules move out-offocus and thus have shorter trajectories, the 5-frame threshold introduces a large bias against freely-diffusing molecules. Fourth, the fitting threshold (R 2 >0. 8) is relatively arbitrary and the results of the analysis is extremely sensitive to this threshold. Accordingly, in these simulations MSD i (R 2 >0. 8) only analyzes a small fraction (~5%) of all the trajectories; note that this bias against the bound population provides a compensatory bias against the bound population to account for the bias against the free population due to defocalization bias.
As for MSD i (R 2 >0. 8), examples of fits are shown in Figure  3  -Figure Supplement 3 and the Matlab code to perform the fitting is available together with the data (see "Data availability'). In the case of MSD i (all), the main reason for the unreliable estimates is due to defocalization bias. Since fast-diffusing molecules move out-of-focus and thus have shorter trajectories, the 5-frame threshold introduces a large bias against freely-diffusing molecules. Overall, consistent with previous benchmarking efforts on membrane proteins  (Weimann et al. , 2013) , MSD i (all) performed least well among the tested methods. vbSPT Rational and parameters: vbSPT performs single-trajectory classification using Hidden-Markov Modeling (HMM) and Bayesian inference  (Persson et al. , 2013)  and can assign different segments of a single trajectory to different diffusive states, each associated with a particular diffusion constant.
vbSPT was initially designed for SPT of diffusing proteins in bacteria  (Persson et al. , 2013) , where defocalization biases are virtually nonexistent since the axial dimension of most bacteria are generally comparable to or smaller than the microscope axial detection range. Furthermore, vbSPT does not explicitly model the localization error. It is then expected that the software performs poorly when the localization error is high, as can be expected when imaging intranuclear factors. In practice, the following parameters were used to assess vbSPT performance. The software was run on the full set of 3480 simulations. The priors and optimization parameters were left as default and the scripts to perform the analysis are provided together with the experimental data (please see Data Availability section): dim = 2; trjLmin = 2; runs = 3; maxHidden = 2; bootstrapNum = 10; fullBootstrap = 0; init_D = [0.
JumpsToConsider Users can either choose to use all displacements from all trajectories (set "Use all trajectories" to "Yes" in the web-version of Spot-On or "UseAllTraj=1" in the Matlab version of Spot-On) or to use only a subset by controlling the JumpsToConsider variable. For example, consider a trajectory consisting of 6 localizations and without gaps. If JumpsToConsider=4 and TimePoints=6, then this trajectory will contribute 4 displacements to the 1∆ histogram, 4 displacements to the 2∆ histogram, 3 displacements to the 3∆ histogram, 2 displacements to the 4∆ histogram and 1 displacement to the 5∆ histogram. Thus, even though the trajectory contains 5 1∆ displacements, only the first 4 will be used for analysis if JumpsToConsider=4. Why would we want to limit the number of jumps that were used? Since freely-diffusing molecules move out-of-focus, almost all very long trajectories will be bound molecules.
PDF or CDF fitting Although for large datasets PDF-and CDF-fitting perform similarly as shown in Figure  3  -Figure Supplement S9, CDF-fitting tends to provide more reliable estimates of  FREE and  BOUND when the number of trajectories decreases, likely because PDF-fitting is more susceptible to binning noise. Thus, for quantitative analysis we always recommend CDF-fitting, though PDF-fitting can be convenient for making figures since most people find histograms more intuitive.
Choosing allowed ranges for diffusion constants Spot-On comes with default allowed ranges. For example, for the 2-state model,  FREE = 0. 5; 25 and  BOUND = 0. 0001; 0. 08. These ranges are generally reasonable, but may not be appropriate for all datasets. Whenever Spot-On infers a diffusion constant that is equal to the min or max, caution is needed and it may be necessary to change these limits. In particular, a molecule is bound to an unusually dynamic scaffold,  BOUND =0. 08 µm²/s is almost certainly too high. Thus, we recommend imaging a protein that is overwhelmingly bound, such as histone H2B or H3, fitting the histone data with Spot-On and then use the inferred  BOUND for histone proteins or a slightly larger value as the maximally allowed  BOUND value.
, 2014). Nevertheless, even with paSPT it is still necessary to decide on an optimal density. The key parameters are size of the ROI (ideally the whole nucleus for studies in cells) and D: a large nucleus and a slow D can support a higher density than can fast-diffusing molecules in a small nucleus. As a general rule of thumb, we recommend a density of ~1 fluorescent molecule per ROI per frame. This will keep tracking errors at a minimum and still support rapid acquisition of large datasets. All data acquired for this study was acquired at approximately this density. In practice, keeping an optimal density will require some trial-and-error optimization of the 405 nm photo-activation laser intensity. 405 nm excitation does contribute background fluorescence, so we prefer to pulse the 405 nm laser during the camera "dead-time" (~0.
5 ms in our case) to avoid this. Moreover, this also makes it easier to keep the photo-activation level constant when changing the frame rate. However, the optimal photo-activation power will depend on the expression level of the protein, protein half-life and the dye concentration and will therefore have to be optimized in each case. We recommend recording initial datasets and then analyzing them using Spot-On which reports the mean number of localizations per frame and then using this information to determine the optimal photo-activation level. However, even then some cell-to-cell variation may be unavoidable: especially in transient transfection experiments where there is large cell-to-cell variation in expression level or when studying proteins expressed from stably integrated transgenes (e.
g. Halo-3xNLS and H2b-Halo in our case). In these cases, some cells will likely exhibit too high a density. To deal with this, Spot-On includes the option to analyze datasets from individual cells first and then excluding a cell with too high a density before analyzing the merged dataset. Which datasets are appropriate for Spot-On? In the sections above, we have discussed how to minimize common experimental biases in SPT experiments and proposed spaSPT as a general solution. However, many 2D SPT datasets recorded under different conditions are also appropriate for Spot-On. For example, SPT experiments without photo-activation or with continuous illumination may also be appropriate for analysis with Spot-On. But since Spot-On uses the loss of fast-diffusing molecules over time to correct for bias and to estimate the free population, it is essential that all trajectories are included in Spot-On for analysis.
Acknowledgements We are very grateful to  Davide Mazza  who inspired this work and provided invaluable comments on Spot-On, to  Florian Mueller  for suggestions on the web-application,  Christophe Zimmer  for insightful discussions,  David McSwiggen  and  Sheila Teves  for kindly providing cell lines,  Carolyn Elya  and  Chiahao Tsui  for the name " Spot-On ", and to members of the  Tjian/Darzacq labs  and  Maxime Dahan  for discussions. We also thank  Astou Tangara  and  Anatalia Robles  for microscope maintenance. ASH is a postdoctoral fellow of the  Siebel Stem Cell Institute. This work was supported by  NIH  grants  UO1-EB021236  and  U54-DK107980  (XD), the  California Institute of Regenerative Medicine  grant  LA1-08013  (XD), by the  Howard Hughes Medical Institute  ( 003061 , RT) and used the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris.
J. P. M. was funded by  MRC/DFID  Concordat agreement Grant number  MR/P000959/1. J. C. H. was supported by  NC3Rs  grant  NC/L000601/1  and  Royal Society  grants  UF120026 ,  RG130034  and  CH160018. S. C. W. was supported by  NIH/NIAID  award  U19AI089676-01S1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH.
Acknowledgements. References. Acknowledgements This work has been supported by grants  B15MS-001 ,  B15RM-060 ,  M15CO-041  from the  Belarusian Republican Foundation for Fundamental Research ; from  CTQ2011-23245 , CTQ-2014-54004-P(MINECO), and  Consortium NANODENDMED  ref  S2011/BMD-2351  (CAM) to  University of Alcalá. CIBER-BBN  is an initiative funded by the  VI National R&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program ,  CIBER Actions  and financed by the  Instituto de Salud Carlos III  with assistance from the  European Regional Development Fund. This work was supported by a  Marie Curie International Research Staff Exchange Scheme Fellowship  within the  7th European Community Framework Programme , project No. PIRSES-GA-2012-316730 NANOGENE.
TIFA and TRAF6 form co-localizing oligomers in infected and bystander cells In order to better characterize the role of TIFA in S. flexneri infection of epithelial cells, we monitored its subcellular localization. For this, cells were transfected with a TIFA cDNA construct and TIFA was visualized after infection by immunofluorescence using a TIFA-specific antibody. In the absence of infection, the protein was uniformly distributed in the cytoplasm and the nucleus (Fig  4A ). Following infection with S. flexneri, punctate structures, likely corresponding to large TIFA protein oligomers  [27] , were formed. These structures were still visible and binding to TRAF6 are both required to induce IL-8 expression after S. flexneri infection. These results further suggested that, in line with published data on TNFα signaling  [27] , TIFA also induces the oligomerization of TRAF6 and the subsequent activation of NF-κB following S.
Notably, when ALPK1-depleted cells were transfected with full-length YFP-ALPK1, TIFA oligomerization was restored in a large fraction of infected and bystander cells. This result excluded the possible contribution of RNAi off target effects and unambiguously established the role of ALPK1 in S. flexneri-induced TIFA oligomerization. Interestingly, transfection of a siRNA-resistant cDNA construct deleted for the kinase domain of ALPK1 (YFP-ALPK1-ΔK) failed to rescue TIFA oligomerization (Fig 6I  and 6J ), showing that the kinase domain of ALPK1 was necessary for the induction of TIFA oligomerization after S. flexneri infection. Finally, the role of ALPK1 on the TIFA-TRAF6 interaction was investigated by co-immunoprecipitation experiments. Data showed that the TIFA-TRAF6 interaction induced upon S.
Microscopy was performed with Molecular Devices ImageXpress microscopes. MetaXpress plate acquisition wizard with no gain, 12 bit dynamic range, 9 sites per well in a 3×3 grid with no spacing and no overlap and laser-based focusing was used. Robotic plate handling was used to load and unload plates (Thermo Scientific). A 10X S Fluor objective with 0. 45NA was used. Data analysis was performed using the computational infrastructure described in  [17]. Cell counts, rates of infection and IL-8 positive cells were quantified as described in  [17]. In brief, intensity and texture features were extracted from bacterial and IL-8 images. Based on these features, cells were scored for infection and IL-8 expression using CellClassifier and supervised machine learning using a Support Vector Machine based binary classifier  [49].
Automated microscopy and image analysis Images were automatically acquired with an ImageXpress Micro (Molecular devices, Sunnyvale, USA). Image analysis was performed using the custom module editor (CME) of MetaXpress. Briefly, cell nuclei were identified by the "autofind blobs" function of the CME. Nuclei were then extended by 6 pixels to define the cellular mask of each cell that was used to measure bacteria and IL-8 signals. Bacteria and IL-8 signals were both detected with the "keep marked object" function of the CME based on minimal/maximal size requirements and intensity threshold. Cells showing IL-8 signals above threshold were defined as IL-8 positive. Quantification of NF-κB activation was performed with the "translocation enhanced" module of MetaXpress (Molecular Devices, USA) that automatically identifies the nuclei and cytoplasmic compartments from a Hoechst image.
Statistical analysis Data are expressed as mean ± standard deviation of triplicates samples as indicated. p values were calculated with a two-tailed two-sample equal variance t-test. ELISA assay showing that S. flexneri-induced IL-8 expression is ALPK1-dependent. HeLa cells were transfected with control or ALPK1 siRNA, and infected, or not, with S. flexneri for 6 hours. IL-8 secretion was measured in the supernatant of infected cells by ELISA. Data correspond to the mean +/-SD of three independent experiments, p Ã <0. 05. B) HeLa cells were transfected with control or ALPK1 siRNA, and infected or not with S. flexneri for 6 hours. Cytokine secretion was measured in the supernatant of infected cells by a multiplex cytokine assay. Data correspond to the mean +/-SD of triplicates, p ÃÃ <0. 005, p ÃÃÃ <0.
The fraction of infected cells showing TIFA punctuates was manually evaluated. Data correspond to the mean +/-SD 3 independent experiments. (PDF) S6 Fig. The production of cytokines induced by S. flexneri infection is largely HBP-dependent. A) HeLa cells were infected or not with S. flexneri for 6 hours with wt (MOI 10), ΔhldE (MOI 0. 1) and ΔwaaC (MOI 0. 1) S. flexneri. Cytokine secretion was measured in the supernatant of infected cells by a multiplex cytokine assay. Data correspond to the mean +/-SD of triplicates, p ÃÃ <0. 005, p ÃÃÃ <0. 0005. # indicates not detected. B) Caco-2 cells were infected or not with S. flexneri for 6 hours with wt (MOI 10), ΔhldE (MOI 0. 1) and ΔwaaC (MOI 0. 1) S. flexneri. Cytokine secretion was measured in the supernatant of infected cells by a multiplex cytokine assay.
Acknowledgments We thank  Prof. X. Nassif  ( INEM, Paris, France ) for the generous gift of Neisseria meningitidis bacteria (wt and ΔhldA),  Prof. P. Cossart  ( Institut Pasteur, Paris, France ) for Listeria monocytogenes expressing GFP,  Prof. M. D. Tsai  ( Institute of Biological Chemistry, Academia Sinica, Taiwan ) for the TIFA cDNA constructs and  Prof. R. Jacob  for the YFP-ALPK1 cDNA. We also thank  K. Bailly  from the  Cochin Cytometry and Immunobiology Facility  for her technical help.
This work was supported by the  Swiss National Science Foundation  (http://www. snf. ch/fr/Pages/default. aspx) with the grant  310030_138377  to CA and by the  Agence Nationale de la Recherche  (http://www. agence-nationale-recherche. fr/) with the Project  ANR-14-ACHN-HostTarget  to CA. The funders had no role in study design, data collection.
Although limited in time and in the number of CSF specimens analyzed, this study nevertheless illustrated the significant inter-individual operator variability of CSF cell counting using standard optical microscopy. Based on the observation of inter-operator variability in optical microscopy counting of CSF cells, we aimed to progressively adapt the previous versions of lens-free microscopy and algorithms  16, 17  for use in the detection of leukocytes and erythrocytes in CSF. In this second step of our study, we analyzed a dataset of 215 CSF specimens and, step-by-step, we modified the lens-free microscopy protocol and its associated algorithms. This dataset of 215 CSF clinical specimens included 15 cases of microbiology-confirmed infectious meningitis and 48 cases of non-infectious meningitis, including 11 cases of carcinomatous meningitis, 24 cases of hemorrhages, and 13 cases of autoimmune diseases.