dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this blind study, we compared eleven kinetic analysis tools for the inference of quantitative kinetic rate constants based on singlemolecule FRET trajectories. We explicitly considered the major (kinetic) challenges that the single-molecule experimentalists are typically confronted with: determining the best model to describe the data, especially with multiple FRET states, a varying signal-to-noise ratio, directional non-equilibrium steady-state flow, and kinetic heterogeneity (i. e. , states with indistinguishable FRET efficiency but distinct kinetics). We assessed the inferred FRET efficiencies, rate constants, and the reported uncertainties, based on three simulated datasets and four experimental datasets from two biological systems measured using two different setups in different laboratories.
The equilibrium constants and steady-state flow were more accurately inferred than individual rate constants due to the cancellation of systematic errors (Supplementary Figs. 1  and 2d , e, Fig. 3 ). Caution is advised with the uncertainties of rate constants since different uncertainty measures are reported by different approaches. Even for small models (Figs. 2  and 3 ), we found that some uncertainty estimates were smaller than the uncertainties caused by the finite dataset size, while interestingly, more plausible uncertainties were reported for the more complex model in Fig. 4  (Supplementary Fig. 4 ). In general, the comparison of uncertainties is complicated by the fact that no common standard exists and the mathematical interpretation of the reported uncertainty intervals differs from tool to tool.
(i) As a general consistency test, the inferred kinetic model (connectivity and rate constants) can be simulated and the output of the simulation compared to the original input data. For example, the simulator used herein is publicly available as a simple and powerful (MATLAB) tool to test whether the proposed model can generate data analogous to the original input, e. g. regarding FRET histogram, smFRET traces, etc. (ii) Potential biases in the analysis (e. g. regarding model size, state occupation, etc. ) can be revealed by subjecting the re-simulated data (with known ground truth) to the same analysis approach as the experimental data. (iii) Where possible, kinetic models with a specific number and connectivity of states are preferred over mean residence times, since the latter leave the individual transition rate constants undetermined for more than 2 states.
(iv) Uncertainty measures are necessary indicators of significance, and a unified standard would greatly improve their comparability. The 95% confidence interval was the most frequently used uncertainty measure in this study, and we encourage its use as a common standard for the future. (v) Benchmarking new analysis tools using datasets of varied complexityincluding models with more than 2 statescan reveal systematic errors, e. g. regarding the weighting of multiple rate constants that depopulate a given state, an issue encountered in this study. (vi) Benchmarking new software with established test data helps the potential users to judge the added benefits of newly introduced analysis tools. The diverse datasets used herein are publicly available and can serve to assess a tool's performance under varied experimental conditions.
(vii) Supporting broadly accepted file formats for newly developed analysis tools facilitates fast dissemination in the field. We offer the simple format described in Supplementary Note 1, which proved to be very useful for this study. a In the course of this study, several difficulties with the analysis of kinetic data have become apparent. Out of this experience, we have compiled a list of recommendations for those developingand using kinetic analysis tool. envisioned for a model-free kinetic analysis, which bears the potential to improve model selection significantly  18, 50. Demonstrating such new tools using public training datasets and supporting the simple file format introduced in this study, will accelerate the dissemination of the newest theoretical developments within the community of singlemolecule experimentalists.
Procedure of this benchmark study The need for a comparison of analysis tools for smFRET trajectories has grown with the increasing number of smFRET users and published tools. This was addressed at the Biophysical Society Meeting 2019 (Baltimore, US) by initiating a kinetic software challenge, short kin-SoftChallenge. In line with more efforts to assess, promote, and potentially standardize experimental and analytical smFRET procedures (Refs. 2,3, 51 in preparation), the kinSoftChallenge represents an important step aimed to improve the reliability and accuracy of kinetic inference from smFRET trajectories. In a first round of the study (July 2019 to November 2019), the participants received three simulated datasets (shown in Figs. 2, 3, a n d4 ). In the second round (December 2019 to February 2020), the participants analyzed the experimental dataset shown in Fig.
Simulation of smFRET trajectories In short, simulated smFRET datasets were generated to mimic fluorescence traces obtained by TIRF-based experiments. State trajectories were modeled with a continuous-time approach and later discretized. Similar to experiments, this allows state transitions to occur during the integration time window (time bin of the detector). Noise was added to the fluorescence intensity traces using experiment-derived parameters to generate realistic data. In more detail, for each molecule a continuous-time state trajectory was simulated based on the kinetic model, as specified by a transition rate matrix. A summary of the specific simulation parameters is given in the Supplementary Table  5  and all configuration files with all parameters are provided as Supplementary Datafiles.
First, for each molecule, individual intensity levels for each state were chosen. To do so, the intensity level was drawn from an empirically determined state-specific Gaussian distribution (with mean μ I and standard deviation 5* ffiffiffiffi ffi μ I p ). Second, for each molecule, an individual brightness factor was determined by 1:20 r where r was randomly chosen from the interval [-1, 1]. Thus, this factor is distributed in the interval [0. 83, 1. 20] and all channels were multiplied by the same factor. For the simulated data shown in Fig. 4 , independent blinking of the donor and acceptor dye was modeled by a simple 2-state system ("bright", "dark"). In the case of an acceptor dark state, the FRET efficiency was set to zero. Details are given in Supplementary Table  5. Five hundred additional datasets from the same parameter set were created and compared, to validate that the dwell time distribution of the dataset used in this study shows the expected behaviour (see Supplementary Fig.
3 ). Configuration files with all simulation parameters (including the ground truth for the kinetic models) for the synthetic data in Figs. 2, 3 ,an d4 ca nb efo undi ntheSu ppl em en tar y Datafiles. The MATLAB scripts used for the simulation are publicly available at: www. kinSoftChallenge. com and https://doi. org/10. 5281/ zenodo. 5701310. A Supplementary  The SNR was obtained from the FRET efficiency histogram using Gaussian fits and SNR = μ 1 À μ 2 = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi σ 1 2 + σ 2 2 p. parameters and a Supplementary Note on the file format used herein are provided in the Supplementary Information file.
Estimated minimal uncertainty of rate constants inferred from simulations Because of the finite number of traces per datasets, only a limited random sample of dwell times is observed for each given transition, resulting in a variation of the rate constants inferred from different datasets with identical ground truth. In order to estimate this lower bound of the uncertainty for the inference of rate constants from a finite dataset, we randomly drew the same number of dwell times as provided in the simulated challenge dataset from an exponential distribution with time constant τ =1=k. Themaximum likelihood estimator (MLE) for the rate constant that produced this set of dwell times Δt is given by 1=Δt. This calculation of the MLE was repeated one million times. The standard deviation of these 1 million MLEs is a function of the number of dwell times present in the challenge data setthe more dwell times are observed, the narrower the MLE distributionand hence, it depends on the transition rate constants and the total observation time.
Simulation of cumulative dwell-time distributions from inferred kinetic models In order to compare submissions with the same number of FRET states but different underlying kinetic models (i. e. , number of hidden states and connectivity), we simulated dwell times from the submitted kinetic models for the three datasets shown in Figs. 4  and 5. This yields cumulative dwell-time distributions that are characteristic for the kinetic model. Dwell times were accumulated from simulations of continuous time state trajectories (Supplementary Note 1) that included roughly 200x (Fig. 4d ) or 400x (Fig. 5c , f, i) more time points than the original datasets.
Origin of the experimental datasets The experimental data shown in Fig. 2  and Supplementary Figs. 1, 2 was kindly provided by Benjamin Schuler. It shows the interaction between the nuclear-coactivator binding domain of CBP/ p300 (NCBD) and the intrinsically disordered activation domain of the steroid receptor coactivator 3 (ACTR), measured using confocal single-photon detection  5. The experimental data shown in Fig. 5  and Supplementary Fig. 4  was kindly provided by Michael Schlierf. It shows binding of single-strand binding proteins (SSB) to a fluorescently labelled DNA hairpin, measured in prism-type total-internal reflection fluorescence (TIRF) mode using camerabased detection (EMCCD)  4.
Code availability The simulation code and parameters to generate the simulated datasets are available at https://doi. org/10. 5281/zenodo. 5701310. All software tools are available: Pomegranate v0. 0. 1 at https://github. com/ hatzakislab/DeepFRET-GUI; Tracy v4. 4. 8 upon request as it is being replaced by a new program for multi-color analysis (contact: Don C. Lamb [d. lamb@lmu. de], requests will be addressed as soon as possible, typically within 1 week); FRETboard v0. 0. 3 at https://github. com/ cvdelannoy/FRETboard; Hidden-Markury v0. 0. 1 at https://github. com/ ChristianGebhardt/Hidden-Markury;S M A C K Sv 1. 4a thttps://github. com/sciSonja/SMACKS;C o r r e l a t i o nv 0. 1 ba thttps://doi. org/10. 5281/ zenodo. 5512005;Edgefinding (CK and k-means) v0. 0. 1 at https://www. physics.
ncsu. edu/weninger/KinSoft. html;S t e pfinding v0. 0. 2 at https://github. com/SMB-Lab/PyStepFinder; StaSI v0. 0. 1 at https:// github. com/LandesLab/StaSI; MASH-FRET v. 1. 3. 2 (bootstrap and probabilistic) at https://github. com/RNA-FRETools/MASH-FRET;post-FRET v4. 0 at https://github. com/nkchenjx/postFRET.
For IgA analysis, 12 samples were pooled to build a reference for the standard curve construction. Oxidative stress status of the rabbits was assessed on serums by quantification of the derivatives of reactive oxygen metabolites (hydroperoxydes primarily) following the procedure of the d-ROM test (Diacron International, Grosseto, Italy). Evaluation of the cecal metabolome. Ammonia was measured quantitatively with a colorimetric method using a continuous flow analyzer (SAN11, Skalar, Norcross, GA, USA) as previously described  (67). Results were expressed according to the liquid phase of the cecal content after determination of its dry matter at 103°C for 24h. Cecal content metabolome was analyzed by using 1 H nuclear magnetic resonance (NMR)-based metabolomics, following the experimental procedure and the data processing previously described  (46).
ACKNOWLEDGMENTS We gratefully acknowledge  M. Moulis  and  J. -M. Bonnemere  for their assistance at the rabbit experimental unit, as well as  M. Segura , who contributed to sample preparation, and  C. Bannelier , who performed the food analysis. We thank the people of the research teams  NED  (https://genphyse. toulouse. inra. fr/groups/ned)and SYSED (https://genphyse. toulouse. inra. fr/groups/sysed)for their assistance during data collection. We especially thank  C. Knudsen  for her careful proofreading. We acknowledge  L. Gress  and  S. Fourre , who performed the quantitative Fluidigm assay. The authors are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  and the  Sigenae group  for providing computing and storage resources thanks to Galaxy instance http://sigenae-workbench.
A positive (C amb < C sat ) or negative (C amb > C sat ) sign depicted the cluster's saturation state, i. e. , whether the system was in a sub-or a super-saturated state. Bulk energy of the system was extracted from the data by subtracting the surface energy from free energy, and the curve was observed to be linear with a negative slope, representing a supersaturated system (Fig. 1D ). P(n) represents the modeled relative frequency distribution of cluster size (n), and n c is the critical cluster size attaining a maximum value of ΔG(n c ), which is the nucleation barrier (Fig. 1E ). The data allowed us to extract the nucleation barrier ΔG(n c ) from the polynomial fit, which was normalized (ΔG(n c ) = 1. 00 ± 0. 08). The critical radius of cluster (R c; n c ∝ (R c )  3  ) of untreated control dataset was calculated and normalized (R c = 0.
Nanoscale condensation of endogenous SAP97/hDLG is fine tuned by Ca 2+ -dependent first-order phase transition. Though the phase transitions point towards an altered free energy profile, we identified the nanoscale fingerprints of these transitions using different stimulation paradigms. For this, we analyzed the variability of the different morpho-functional characteristics such as the average intensity and area of nanoclusters of SAP97/hDLG. The incubation of Neuro-2a cells with Tg (1 µM) (10 min), w7 (25 µM) (30 min) and Bapta (50 µM) (20 min) resulted in an enhancement of both average intensity (Fig. 3A-C ) and area (Supplementary Fig. 2A-C ) of SAP97/hDLG clusters. For peforming the rank-order analysis, all datasets from each treated condition were sampled to 80% of the corresponding set, and was sorted in ascending order.
The resultant datasets were named as scaled-treated data. To determine the actual scaling factor, the control and the scaled-treated datasets were tested for significance using Kolmogorov-Smirnov test (K-S test)  48. The actual scaling factor should give highest pvalue, as the difference in the datasets would be non-significant. The method was repeated 100 times with each dataset. The results provided us with 100 p-values for each condition, and they were plotted against their corresponding scaling factors (Fig. 3G-I ). The scaling factor corresponding to the highest p-value was chosen as the actual scaling factor. The resultant values of actual scaling factors for Tg, w7, and Bapta incubations were 1. 66, 3. 10, and 3. 76, respectively (Fig. 3G-I ). Thus, the scaling factors showed an upscaling of the average intensity of SAP97 in these clusters.
The cumulative frequency distribution (CFD) of the average intensity of control, treated, and scaled-treated datasets were plotted against the average intensity (Fig. 3J-L ). When the treated datasets were downscaled by the actual scaling factor, the scaled-treated datasets replicated the control data. This scaling was confirmed by the plot of the CFD curve of average intensity (Fig. 3J-L ). In parallel, we studied the effect of Ca 2+ perturbation on the area of SAP97/hDLG clusters. The frequency distribution histograms were plotted for the treated and control conditions (Supplementary Fig. 2A-C ). A total of 1000 cluster area datapoints were chosen randomly from each dataset and rank-   1 ). Scalebar = 4 µm. Source data are provided as Source Data file. ordered. The rank-ordered data of cluster area was plotted for treated vs.
On application of CaM blocker w7, endogeneous Fig. 3  Scaling of endogenous SAP97 clusters using rank-order analysis. A-C The histogram was plotted for the average intensity of SAP97/hDLG clusters between control and all treated conditions. A Thapsigargin (1 μM), B w7 (25 μM), and C Bapta (50 μM). N = 10 cells for control and treated datasets. D-F A total of 1000 cluster datapoints of average intensity were chosen randomly from each dataset, rank-ordered, and plotted to provide a linear regression for each condition. The slope of regression depicts an arbitrary scaling factor. G-I The analysis provided a scaling factor with a maximum p-value between control and scaled-treated datasets. The p-values for Tg (D), w7 (E), and Bapta (F) were p = 0. 44, p = 0. 09, and p = 0. 08, respectively. A total of 800-1000 random average intensity datapoints were chosen from each dataset, rank-ordered, and plotted to provide a linear equation for each treatment.
J-L A cumulative frequency distribution was plotted for control, treated, and scaled-treated conditions of SAP97/hDLG in Neuro-2a cells. The datasets were analyzed for a significant change in mean values by two-tailed K-S test, *p < 0. 05, **p < 0. 01, ****p < 0. 0001 (Supplementary Table  2 ). SAP97/hDLG aggregated as clusters potentially via a CaM independent mechanism. This occured with a slight yet significant increment in the area of clusters by a factor of 1. 22 (Supplementary Fig. 2H ), but with substantial scaling of the average intensity of clusters by a factor of 3. 10 (Fig. 3H ). The inhibition of basal CaM activity led to a significant increase in the number of molecules inside the clusters as well as the monomer concentration outside clusters, resulting in an increase in the nucleation barrier and critical radius (Fig.
Methods Animals. All the procedures in this study were performed according to the rules and guidelines declared in the Compendium of CPCSEA 2018 by the Committee for the Purpose of Control and Supervision of Experimental Animals (CPCSEA), Ministry of Fisheries, Animal Husbandry and Dairying, India. The research protocol (CAF/Ethics/659 and CAF/Ethics/790) was approved by the Institutional Animal Ethics Committee (IAEC) of the Indian Institute of Science. Plasmid constructs. The hDLG constructs tagged with enhanced green fluorescent protein (eGFP) at the N-terminus of the protein sequence were ectopically expressed in Neuro-2a cells for the FRAP experiments. The sequences can be found in the public domain of GenBank data base (accession code: U13896 and U13897 [https://doi. org/10. 1073/pnas.
The samples were loaded on a 96 well plate and real-time Q-PCR was performed in Quant Studio 7Flex real-time Q-PCR system (Applied Biosystems). The cDNA was tested by PCR (Applied Biosystems, USA) and separated by electrophoresis (Bio-Rad, USA), labeled using Sybr safe (Invitrogen, USA) and visualized on a Gel documentation system (Bio-Rad, USA). The expression of β-2-microglobulin was used as the endogenous control. The analysis was performed with comparative Ct method and the graphs were plotted using Graph pad Prism 6 software. The values were obtained using the equation N = 2 -ΔΔCt (N = normalized fold-change, ΔCt 1 = difference between the cycle threshold (Ct) of endogenous control and the sample, ΔΔCt = Difference between ΔCt sample1 -ΔCt sample2. Here, sample1 was taken to be the cortex).
The Ludin chamber loaded with the sample was mounted on an Axio observer Z1 inverted microscope (Zeiss, Germany) equipped with an Apotome 2 module. The image acquisition was performed using a planapochromat 63x/1. 4 NA oil immersion objective and excitation/emission filters of GFP and DsRed (Zeiss, Germany). Zen 2 blue edition was used for image acquisition (Zeiss, Germany) software. The images were obtained using an Axiocam 506 mono 6 Mp CCD camera with a frame size of 2752p x 2208p (as czi or tif files) at 8-bit depth. The image processing was done using Fiji (NIH, Bethesda, USA) and MetaMorph (Molecular Devices, San Jose, USA). Confocal microscopy. Neuro-2a cells over-expressing SAP97/hDLG isoforms were imaged on a Zeiss LSM 780 (Carl Zeiss, Thornwood, USA) confocal inverted motorized microscope equipped with a 63x oil objective of refractive index1.
5 and NA1. 4. The cells were imaged at a sampling of ≈88 nm/pixel. 3D Confocal Z-stacks of total volume (≈2000 μm 3 ) were acquired with single optical sections of thickness 100 nm. ZEN 2011 LSM imaging software was used for acquisition and the image analysis was performed using Metamorph. Fluorescence recovery after photobleaching (FRAP). FRAP experiments were performed on Neuro-2a cells to study the ectopic expression of alternative splice variants of hDLG fused with eGFP. The cells were cultured on 18 mm coverslips and were loaded on a Ludin Chamber (Life imaging services, Switzerland) supplemented with an extracellular solution suitable for live cell imaging (HEPES buffer-7. 5 pH: 10 mM Glucose, 120 mM NaCl, 3 mM KCl, 1 mM MgCl2, 2 mM CaCl 2 , 10 mM HEPES). The Ludin chamber was mounted on a motorized inverted Olympus microscope (IX83) and was imaged at 37 °C (OKO lab, Italy).
GFP excitation was performed using a 488 nm excitation laser in total internal reflection fluorescence (TIRF) mode. Image acquisition was done using a 100x objective (U Apo N 100x Oil immersion TIRF; NA: 1. 49) and an EMCCD (Evolve, Photometric, USA). A pulsed 488 nm laser of 100 mW average power was used for bleaching within a region of interest (ROI) of 3 μm diameter at an exposure of 20 ms. After acquiring ten pre-bleach images, the ROI was bleached, and 400 post-bleach images were acquired. The image acquisition and data collection were performed using Metamorph software. Normalized fluorescence intensity was calculated using the equation, Single-molecule localization microscopy. Cells were plated on 18 mm round coverslips and imaged at 37 °C (OKO lab, Italy) in a closed chamber (Ludin Chamber; Life Imaging Services, Switzerland).
Stochastic Optical Reconstruction Microscopy (dSTORM) and Photoactivation Localization Microscopy (PALM) was performed at 37 °C in an open chamber (Ludin chamber, Life Imaging Services) mounted on an inverted motorized microscope (IX83 Olympus) equipped with a 100× TIRF, 1. 49 NA objective allowing long acquisition by HILO (highly inclined and laminated optical sheet) illumination  40, 41, 77, 82. MetaMorph software was used to steer the illumination and acquisition. Beads of 100 nm diameter were mounted on coverslips for 10 min (Tetraspeck; Thermo Fisher Scientific, USA) and was used as fiducial markers for lateral drift correction. Immunolabeled cells were imaged in dSTORM buffer, an enzymatic oxygen scavenging solution (catalase, TCEP, glycerin, glucose and glucose oxidase dissolved in Tris-HCl buffer), to induce stochastic activation of sparse subsets of molecules  40, 41, 82.
Simultaneously, ΔG bulk = ± bn defines the bulk energy term that depends on the SAP97/hDLG ambient monomer concentration (C amb ) and the saturation concentration (C sat ) at equilibrium with the clustered phase. For a cluster of n molecules, ΔG bulk = Δμn and Δμ = k B T Log(C amb /C sat ). We then extracted the number of detected SAP97 molecules per nanoscale aggregates, resembling nanodomains. ΔG(n) was determined from the distribution of cluster sizes using the equation ΔG = -k B T Log(P(n) + Log A [for n < nc]  34  (Fig. 1C ), where k B is the Boltzmann constant and T, the temperature (Kelvin). A positive (C amb < C sat ) or negative (C amb > C sat ) sign depicts the cluster's saturation state, i. e. , whether the system is in a sub-saturated or super-saturated state. The term "bn" was extracted from the data by subtracting "an 2/3 " from ΔG, and the curve was observed to be linear with a negative slope, representing a super-saturated system.