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opencc-by-4.0Jul 2017View details →

Experimental data for "Spot-On: robust model-based analysis of single-particle tracking experiments"

Overview of experimental spaSPT data To comprehensively test Spot-On over many different conditions, we conducted 1064 spaSPT experiments. The raw data is freely available and the purpose of this ReadMe file is to describe the organization, acquisition parameters and format of the data. The data is for 4 different cell lines imaged over 15 different conditions yielding a total of 60 different conditions. The four cell lines were: <ul> <li> U2OS C32 Halo-CTCF </li> <li> U2OS H2B-Halo-SNAP </li> <li> U2OS Halo-3xNLS </li> <li> mESC (JM8.N4) C3 Halo-Sox2 </li> </ul> The cell lines were constructed in different ways. U2OS C32 Halo-CTCF was made by homozygous endogenous N-terminal tagging of CTCF in human osteosarcoma U2OS cells using CRISPR/Cas9-mediated genome-editing as described (C32 refers to clone number 32)1. We note the CTCF is an essential gene and that N-terminal tagging did not appear to affect CTCF function or expression level according to a series of control experiments1. Moreover, C32 Halo-CTCF has been authenticated using Short Tandem Repeat (STR) profiling (performed by Dr. Alison N. Killilea at the UC Berkeley Cell Culture Facility) against the following loci: THO1, D5S818, D13S317, D7S820, D16S539, CSF1PO, AMEL, vWA and TPOX. The C32 Halo-CTCF cell line showed a 100% match with U2OS. U2OS H2B-Halo-SNAP was made through random integration of a H2B-HaloTag-SNAP-Tag transgene expressed using the EF1a promoter with an IRES-NeoR gene for drug selection. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously1. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS. U2OS Halo-3xNLS was made through random integration of a FLAG-Halo-3xNLS (3x SV40 NLS: PKKKRKV) transgene expressed using the EF1a promoter. NeoR for drug selection was separately expressed using an SV40 promoter. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously1. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS. mESC C3 Halo-Sox2 was made through homozygous N-terminal tagging of Sox2 in JM8.N42 mouse embryonic stem cells using CRISPR/Cas9-mediated genome editing as previously described (C3 refers to clone number 3)3. The functionality of the C3 Halo-Sox2 knock-in was validated through control experiments and pluripotency through teratoma assays as described previously3. Each file contains single-molecule trajectories from a single cell imaged over 30,000 frames. Localization and tracking was performed using a custom-written Matlab implementation of the MTT-algorithm4 and the following settings: Localization error: 10-6.25; deflation loops: 0; Blinking (frames): 1; max competitors: 3; max D (m2/s): 20. The same 15 conditions were used for each of the 4 cell lines. ExpA PA-JF549 The purpose of this experiment was to test the effect of “motion-blurring” on the Spot-On estimated DFREE and FBOUND. 5 different experimental conditions were considered. Full details are given in the Methods section. Briefly, cells were grown overnight on plasma-cleaned 25 mm circular coverslips either directly (U2OS) and MatriGel coated as described1. Cell were labeled with 5-50 nM PA-JF5495 for around 15-30 min, washed twice and medium exchanged to phenol-red free medium. 30,000 frames were collected at a camera exposure time (Andor iXon Ultra 897; frame-transfer mode; vertical shift speed: 0.9 μs; -70C) of 9.5 ms which together with a ~447 μs camera integration time gave a frame rate of ~100 Hz. PA-JF549 dyes were photo-activated during the ~447 μs camera integration time using 405 nm pulses and the 405 nm pulse intensity optimized to achieve a mean density of 1 molecule per frame per nucleus. The JF549 dye was excited using a 561 nm laser and the total number of excitation photons kept constant but either delivered during a 1 ms pulse, a 2 ms pulse, a 4 ms pulse, a 7 ms pulse or with constant illumination. For each cell line and condition, 4 replicates were performed. We count a replicate as an independent experiment performed on a different day. For each replicate around 5 cells were imaged. Occasionally, fewer than 5 cells are available. To avoid tracking errors, we removed cells with too high a localization density from the analysis. All of this information is available in the file name. For example, “U2OS_C32_Halo-CTCF_PA-JF549_1ms-561nm_100Hz_rep2_cell03” refers to the third cell imaged in the second replicate of U2OS C32 Halo-CTCF using a 1 ms excitation pulse of 561 nm laser at a frame rate of 100 Hz. Similarly, “U2OS_C32_Halo-CTCF_PA-JF549_cont-561nm_100Hz_rep4_cell01” refers to the first cell imaged in the fourth replicate of U2OS C32 Halo-CTCF using constant 561 nm laser at a frame rate of 100 Hz. The five ExpA_PAJF549 conditions are separated by cell line such that each cell line is provided in a separate directory. E.g. the directory “U2OS_H2B_ExpA_PAJF549” contains all data for the U2OS H2B-Halo-SNAP cell line. ExpA PA-JF646 This experiment was exactly identical to the “ExpA_PA-JF549” experiment except cell were labeled with PA-JF6465 and excited using a 633 nm laser. The file names and data organization was otherwise the same and the same five excitation conditions were considered. ExpB PA-JF646 The purpose of this experiment was to test if the Spot-On estimated DFREE and FBOUND values would depend on the frame rate. In particular, all four proteins exhibit some levels of apparent anomalous diffusion, which could cause a dependence on the frame rate. Cells were labeled with PA-JF646 and grown and imaged as described above. Photo-activation took place during the ~447 μs camera integration time and JF646 dyes were excited using 1 ms stroboscopic 633 nm excitation pulses. To change the frame rate, the camera exposure time was set to 4.5 ms (~201 Hz), 5.5 ms (~167 Hz), 7 ms (~134 Hz), 13 ms (~74 Hz) and 19.5 ms (~50 Hz) when also counting the ~447 μs camera integration time. All of this information is available in the file name. For example, “U2OS_Halo-3xNLS_PA-JF646_1ms-633nm_74Hz_rep2_cell04” refers to the fourth cell imaged in the second replicate of U2OS Halo-3xNLS using a 1 ms excitation pulse of 633 nm laser at a frame rate of 74 Hz. Similarly, “mESC_C3_Halo-Sox2_PA-JF646_1ms-633nm_201Hz_rep1_cell03” refers to the third cell imaged in the first replicate of mESC Halo-Sox2 using a 1 ms excitation pulse of 633 nm laser at a frame rate of 201 Hz. Data format All data is available in two different formats: CSV-files and Matlab MAT-files. Both file formats are readable by the web-version of Spot-On. The Matlab version of Spot-On is only able to read the MAT-files. The CSV format consists of comma-separated values and contains headers. If opened with Microsoft Excel, it should appear as shown: Here the “frame” column contains the frame number in which the molecule was detected. The “t” column contains the timestamp. The “trajectory” column contains the trajectory number. For example, trajectory number 1 was only detected in frame 13 after which it disappeared. In contrast, trajectory number 4 was detected in frames 20, 21 22, 23 and 24. Finally, the “x” and “y” columns contain the x,y coordinates of the localization in units of micrometers (μm). The MAT-files contain a structure array named “trackedPar”. trackedPar contains three variables: <ul> <li> trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of localizations in that trajectory. The first column is the x-coordinate and the second column is the y-coordinate. The units are micrometers (μm). </li> <li> trackedPar.Frame: “Frame” is a column vector where each element is the frame where the particle was localized. </li> <li> trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint where the particle was localized. </li> </ul> Each element in the structure array “trackedPar” correspond to a different trajectory.

opencc-by-4.0Jul 2017View details →

Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments"

Generation of simulated data To systematically evaluate the performance of Spot-On as well as other common analysis tools such as MSDi and vbSPT, we considered a comprehensive set of 3480 realistic SPT simulations spanning the range of plausible dynamics. The simulations were performed using simSPT, which is freely available at GitLab: https://gitlab.com/tjian-darzacq-lab/simSPT. The simulation methods are described in detail at GitLab. A full description of the parameters which allows exact reproduction of the simulations is available together with the data (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, centered at the middle of the HiLo beam. Molecules are assumed to have a half-life 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 100000 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. This allows a quantitative assessment of which method works the best. Content of the archives: <ol> <li>170718_simSPT_simulations.zip the code and instructions to reproduce the simulations</li> <li>4um.tar.bz2 simulated data inside a 4 µm nucleus</li> <li>20um.tar.bz2 simulated data inside a 20 µm nucleus, in which virtually no confinement occurs.</li> <li>subsampled.tar.bz2 is a set of subsampled datasets, containing either 99999, 30000, 10000, 3000, 1000, 300, 100 or 30 trajectories. Each subsampling was done 50 times, yielding 50 files per subsmpling.</li> </ol> Formats: The data is provided both in CSV and .mat formats. .mat files are provided in the following dataset: 10.5281/zenodo.835541

opencc-by-4.0Jan 2022View details →

Model-based analysis of tuberculosis genotype clusters in the United States reveals high degree of heterogeneity in transmission, and state-level differences across California, Florida, New York, and Texas.

Data and codes for the publication

opencc-by-4.0Sep 2024View details →

Data and codes for "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"

Data and codes for the manuscript entitled: "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"   Please see Readme.txt for details

FIG. 64 in Model-based analysis of postcranial osteology of marsupials from the Palaeocene of Itaboraí (Brazil) and the phylogenetics and biogeography of Metatheria

FIG. 64. — Same as Fig. 61 but with a different body weight surrogate.

FIG. 62 in Model-based analysis of postcranial osteology of marsupials from the Palaeocene of Itaboraí (Brazil) and the phylogenetics and biogeography of Metatheria

FIG. 62. — Same as Fig. 61 but with a different body weight surrogate.

FIG. 44 in Model-based analysis of postcranial osteology of marsupials from the Palaeocene of Itaboraí (Brazil) and the phylogenetics and biogeography of Metatheria

FIG. 44. — Same as Fig. 43 but with a different index on the y-axis.

FIG. 63 in Model-based analysis of postcranial osteology of marsupials from the Palaeocene of Itaboraí (Brazil) and the phylogenetics and biogeography of Metatheria

FIG. 63. — Same as Fig. 61 but with a different body weight surrogate.

opencc-by-4.0Jun 2023View details →

Quantifying the potential epidemiological impact of a two-year active case finding for tuberculosis in rural Nepal: A model-based analysis

Includes data and codes for the manuscript entitled: Quantifying the potential epidemiological impact of a two-year active case finding for tuberculosis in rural Nepal: A model-based analysis <div> <div> <div> doi:10.1136/ bmjopen-2022-062123 </div> </div> </div>

opencc-by-4.0Mar 2018View details →

Model-based analysis of postprandial glycemic response dynamics for different types of food

Background & aims Knowledge of postprandial glycemic response (PPGR) dynamics is important in nutrition management and diabetes research, care and (self)management. In daily life, food intake is the most important factor influencing the occurrence of hyperglycemia. However, the large variability in PPGR dynamics to different types of food is inadequately predicted by existing glycemic measures. The objective of this study was therefore to quantitatively describe PPGR dynamics using a systems approach. Methods Postprandial glucose and insulin data were collected from literature for many different food products and mixed meals. The predictive value of existing measures, such as the Glycemic Index, was evaluated. A physiology-based dynamic model was used to reconstruct the full postprandial response profiles of both glucose and insulin simultaneously. Results We collected a large range of postprandial glucose and insulin dynamics for 53 common food products and mixed meals. Currently available glycemic measures were found to be inadequate to describe the heterogeneity in postprandial dynamics. By estimating model parameters from glucose and insulin data, the physiology-based dynamic model accurately describes the measured data whilst adhering to physiological constraints. Conclusions The physiology-based dynamic model provides a systematic framework to analyze postprandial glucose and insulin profiles. By changing parameter values the model can be adjusted to simulate impaired glucose tolerance and insulin resistance.

dryad32/100

Data from: Model-based analysis supports interglacial refugia over long-dispersal events in the diversification of two South American cactus species

Pilosocereus machrisii and P. aurisetus are cactus species within the P. aurisetus complex, a group of eight cacti that are restricted to rocky habitats within the Neotropical savannas of eastern South America. Previous studies have suggested that diversification within this complex was driven by distributional fragmentation, isolation leading to allopatric differentiation, and secondary contact among divergent lineages. These events have been associated with Quaternary climatic cycles, leading to the hypothesis that the xerophytic vegetation patches which presently harbor these populations operate as refugia during the current interglacial. However, owing to limitations of the standard phylogeographic approaches used in these studies, this hypothesis was not explicitly tested. Here we use Approximate Bayesian Computation to refine the previous inferences and test the role of different events in the diversification of two species within P. aurisetus group. We used molecular data from chloroplast DNA and simple sequence repeats loci of P. machrisii and P. aurisetus, the two species with broadest distribution in the complex, in order to test if the diversification in each species was driven mostly by vicariance or by long-dispersal events. We found that both species were affected primarily by vicariance, with a refuge model as the most likely scenario for P. aurisetus and a soft vicariance scenario most probable for P. machrisii. These results emphasize the importance of distributional fragmentation in these species, and add support to the hypothesis of long-term isolation in interglacial refugia previously proposed for the P. aurisetus species complex diversification.

opencc-zeroDec 2015View details →

opencc-by-4.0Jul 2017View details →

Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments" (MATLAB format)

See 10.5281/zenodo.834787 for a more complete description.

opencc-by-4.0Jul 2024View details →

ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis - Data Set

Data set of the Paper "ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://abunai.dev

opengpl-3.0-or-laterAug 2024View details →

zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation

Data and code used to generate the analyses and figures in  paper "zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation" are provided here.

dryad32/100

Data from: Model-based analysis supports interglacial refugia over long-dispersal events in the diversification of two South American cactus species

Open the record for dataset details and reuse information.

publicFeb 2016View details →

dryad28/100

Data from: Achieving a step change in the tuberculosis epidemic through comprehensive community-wide intervention: A model-based analysis

Background: Global progress towards reducing tuberculosis (TB) incidence and mortality has consistently lagged behind World Health Organization targets leading to a perception that large reductions in TB burden cannot be achieved. However, several recent and historical trials suggest that intervention efforts that are comprehensive and focused can have substantial epidemiological impact. We aimed to quantify the potential epidemiological impact of an intensive but realistic, community-wide campaign utilizing existing tools, and designed to achieve a "step change" in TB burden. Methods: We developed a compartmental model of tuberculosis transmission in a mid-sized city in India, the country with the greatest absolute burden of TB worldwide. We modeled the impact of a campaign comprising one-time community-wide screening with treatment for TB disease and preventive therapy for latent TB infection (LTBI). This one-time intervention was followed by strengthening of tuberculosis-related health system achieved by leveraging the one-time campaign. We estimated the tuberculosis cases and deaths that could be averted over 10 years using this comprehensive approach and assessed the contributions of individual components of the intervention. Results: A campaign that successfully screened 70% of the adult population for active and latent tuberculosis and subsequently reduced diagnostic and treatment delays and unsuccessful treatment outcomes by 50% was projected to avert 7,800 (95% range: 5,450 – 10,200) cases and 1,710 (1,290 – 2,180) tuberculosis-related deaths per 1 million population over 10 years. Of the total averted deaths, 33.5% (28.2 – 38.3) were attributable to inclusion of preventive therapy and 52.9% (48.4 - 56.9) to health system strengthening. Conclusions: A one-time, community-wide mass campaign, comprehensively designed to detect, treat, and prevent tuberculosis with currently existing tools can have meaningful and long-lasting epidemiological impact. Successful treatment of LTBI is critical to achieving this result. Health system strengthening is essential to any effort to transform the TB response.

opencc-zeroJun 2021View details →

zenodo28/100

FIG. 24 in Model-based analysis of postcranial osteology of marsupials from the Palaeocene of Itaboraí (Brazil) and the phylogenetics and biogeography of Metatheria

FIG. 24. — Details of Pucadelphys (Marshall & Muizon, 1988); A-D, left humerus; A, anterior view; B, posterior view; C, proximal view; D, distal view; E-G, ulna; E, anterior view; F, medial view; G, lateral view. For numbered designations of specific characters see text. Prefixes before the numbers: h, humeral; u, ulnar. Scale bars: 2 mm.