MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules.
Ontology highlight
ABSTRACT:
SUBMITTER: Kapoor V
PROVIDER: S-EPMC6405942 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10?000?s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.
| S-EPMC4892463 | biostudies-literature
Project description:Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception. The merged LiDAR data is treated with an efficient MODT framework, considering the limitations of the vehicle-embedded computing environment. The ground classification is obtained through a grid-based method while considering a non-planar ground. Furthermore, unlike prior works, 3D grid-based clustering technique is developed to detect objects under elevated structures. The centroid measurements obtained from the object detection are tracked using Interactive Multiple Model-Unscented Kalman Filter-Joint Probabilistic Data Association Filter (IMM-UKF-JPDAF). IMM captures different motion patterns, UKF handles the nonlinearities of motion models, and JPDAF associates the measurements in the presence of clutter. The proposed algorithm is implemented on two slightly dissimilar platforms, giving real-time performance on embedded computers. The performance evaluation metrics by MOT16 and ground truths provided by KITTI Datasets are used for evaluations and comparison with the state-of-the-art. The experimentation on platforms and comparisons with state-of-the-art techniques suggest that the proposed framework is a feasible solution for MODT tasks.
| S-EPMC6470994 | biostudies-literature
Project description:Recent advances in imaging technology and fluorescent probes have made it possible to gain information about the dynamics of subcellular processes at unprecedented spatiotemporal scales. Unfortunately, a lack of automated tools to efficiently process the resulting imaging data encoding fine details of the biological processes remains a major bottleneck in utilizing the full potential of these powerful experimental techniques. Here we present a computational tool, called PunctaSpecks, that can characterize fluorescence signals arising from a wide range of biological molecules under normal and pathological conditions. Among other things, the program can calculate the number, areas, life-times, and amplitudes of fluorescence signals arising from multiple sources, track diffusing fluorescence sources like moving mitochondria, and determine the overlap probability of two processes or organelles imaged using indicator dyes of different colors. We have tested PunctaSpecks on synthetic time-lapse movies containing mobile fluorescence objects of various sizes, mimicking the activity of biomolecules. The robustness of the software is tested by varying the level of noise along with random but known pattern of appearing, disappearing, and movement of these objects. Next, we use PunctaSpecks to characterize protein-protein interaction involved in store-operated Ca2+ entry through the formation and activation of plasma membrane-bound ORAI1 channel and endoplasmic reticulum membrane-bound stromal interaction molecule (STIM), the evolution of inositol 1,4,5-trisphosphate (IP3)-induced Ca2+ signals from sub-micrometer size local events into global waves in human cortical neurons, and the activity of Alzheimer's disease-associated β amyloid pores in the plasma membrane. The tool can also be used to study other dynamical processes imaged through fluorescence molecules. The open source algorithm allows for extending the program to analyze more than two types of biomolecules visualized using markers of different colors.
| S-EPMC7343294 | biostudies-literature
Project description:BACKGROUND:The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG while at the same time preserving interpretability for medical decision-making. METHODS AND RESULTS:We identified 36?186 ECGs from the University of California, San Francisco database that would enable training of models for estimation of cardiac structure or function or detection of disease. We segmented the ECG into standard component waveforms and intervals using a novel combination of convolutional neural networks and hidden Markov models and evaluated this segmentation by comparing resulting electrical intervals against 141?864 measurements produced during the clinical workflow. We then built a patient-level ECG profile, a 725-element feature vector and used this profile to train and interpret machine learning models for examples of cardiac structure (left ventricular mass, left atrial volume, and mitral annulus e-prime) and disease (pulmonary arterial hypertension, hypertrophic cardiomyopathy, cardiac amyloid, and mitral valve prolapse). ECG measurements derived from the convolutional neural network-hidden Markov model segmentation agreed with clinical estimates, with median absolute deviations as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Models trained using patient-level ECG profiles enabled surprising quantitative estimates of left ventricular mass and mitral annulus e' velocity (median absolute deviation of 16% and 19%, respectively) with good discrimination for left ventricular hypertrophy and diastolic dysfunction as binary traits. Model performance using our approach for disease detection demonstrated areas under the receiver operating characteristic curve of 0.94 for pulmonary arterial hypertension, 0.91 for hypertrophic cardiomyopathy, 0.86 for cardiac amyloid, and 0.77 for mitral valve prolapse. CONCLUSIONS:Modern machine learning methods can extend the 12-lead ECG to quantitative applications well beyond its current uses while preserving the transparency that is so fundamental to clinical care.
| S-EPMC6951431 | biostudies-literature
Project description:The dynamics of cargo movement in axons encodes crucial information about the underlying regulatory mechanisms of the axonal transport process in neurons, a central problem in understanding many neurodegenerative diseases. Quantitative analysis of cargo dynamics in axons usually includes three steps: (1) acquiring time-lapse image series, (2) localizing individual cargos at each time step, and (3) constructing dynamic trajectories for kinetic analysis. Currently, the later two steps are usually carried out with substantial human intervention. This article presents a method of automatic image analysis aiming for constructing cargo trajectories with higher data processing throughput, better spatial resolution, and minimal human intervention. The method is based on novel applications of several algorithms including 2D kymograph construction, seed points detection, trajectory curve tracing, back-projection to extract spatial information, and position refining using a 2D Gaussian fitting. This method is sufficiently robust for usage on images with low signal-to-noise ratio, such as those from single molecule experiments. The method was experimentally validated by tracking the axonal transport of quantum dot and DiI fluorophore-labeled vesicles in dorsal root ganglia neurons.
| S-EPMC3022967 | biostudies-literature
Project description:When cells with a mesenchymal type of motility come into contact with an adhesive substrate they adhere and start spreading by the formation of lamellipodia. Using a label-free approach and virtual synchronization approach we analyzed spreading in fibroblasts and cancer cells. In all cell lines spreading is a non-linear process undergoing isotropic or anisotropic modes with first fast (5-20?min) and then slow (30-120?min) phases. In the first 10 min cell area increases 2-4 times, while the absolute rate of initial spreading decreases 2-8 times. Fast spreading depends on actin polymerization and dynamic microtubules. Inhibition of microtubule growth was sufficient for a slowdown of initial spreading. Inhibition of myosin II in the presence of stable microtubules restored fast spreading. Inhibition of actin polymerization or complete depolymerization of microtubules slowed down fast spreading. However, in these cases inhibition of myosin II only partially restored spreading kinetics. We conclude that rapid growth of microtubules towards cell margins at the first stage of cell spreading temporarily inhibits phosphorylation of myosin II and is essential for the fast isotropic spreading. Comparison of the fibroblasts with cancer cells shows that fast spreading in different cell types shares similar kinetics and mechanisms, and strongly depends on dynamic microtubules.
| S-EPMC6310885 | biostudies-other
Project description:In this paper, we report a one-step tumor cell detection approach based on the dynamic morphological behavior tracking of cancer cells on a ligand modified surface. Every cell on the surface was tracked in real time for several minutes immediately after seeding until these were finally attached. Cancer cells were found to be very active in the aptamer microenvironment, changing their shapes rapidly from spherical to semi-elliptical, with much flatter spread and extending pseudopods at regular intervals. When incubated on a functionalized surface, the balancing forces between cell surface molecules and the surface-bound aptamers, together with the flexibility of the membranes, caused cells to show these distinct dynamic activities and variations in their morphologies. On the other hand, healthy cells remained distinguishingly inactive on the surface over the same period. The quantitative image analysis of cell morphologies provided feature vectors that were statistically distinct between normal and cancer cells.
| S-EPMC4703374 | biostudies-literature
Project description:BackgroundA laboratory-free test for assessing recovery from pulmonary tuberculosis (TB) would be extremely beneficial in regions of the world where laboratory facilities are lacking. Our hypothesis is that analysis of cough sound recordings may provide such a test. In the current paper, we present validation of a cough analysis tool.Methodology/principal findingsCough data was collected from a cohort of TB patients in Lima, Peru and 25.5 hours of recordings were manually annotated by clinical staff. Analysis software was developed and validated by comparison to manual scoring. Because many patients cough in bursts, coughing was characterized in terms of cough epochs. Our software correctly detects 75.5% of cough episodes with a specificity of 99.6% (comparable to past results using the same definition) and a median false positive rate of 4 false positives/hour, due to the noisy, real-world nature of our dataset. We then manually review detected coughs to eliminate false positives, in effect using the algorithm as a pre-screening tool that reduces reviewing time to roughly 5% of the recording length. This cough analysis approach provides a foundation to support larger-scale studies of coughing rates over time for TB patients undergoing treatment.
| S-EPMC3468608 | biostudies-literature
Project description:BACKGROUND:Mitochondria form a dynamic tubular network within the cell. Proper mitochondria movement and distribution are critical for their localized function in cell metabolism, growth, and survival. In mammalian cells, mechanisms of mitochondria positioning appear dependent on the microtubule cytoskeleton, with kinesin or dynein motors carrying mitochondria as cargos and distributing them throughout the microtubule network. Interestingly, the timescale of microtubule dynamics occurs in seconds, and the timescale of mitochondria distribution occurs in minutes. How does the cell couple these two time constants? RESULTS:Fission yeast also relies on microtubules for mitochondria distribution. We report here a new microtubule-dependent but motor-independent mechanism for proper mitochondria positioning in fission yeast. We identify the protein mmb1p, which binds to mitochondria and microtubules. mmb1p attaches the tubular mitochondria to the microtubule lattice at multiple discrete interaction sites. mmb1 deletion causes mitochondria to aggregate, with the long-term consequence of defective mitochondria distribution and cell death. mmb1p decreases microtubule dynamicity. CONCLUSIONS:mmb1p is a new microtubule-mitochondria binding protein. We propose that mmb1p acts to couple long-term mitochondria distribution to short-term microtubule dynamics by attenuating microtubule dynamics, thus enhancing the mitochondria-microtubule interaction time.
| S-EPMC4720976 | biostudies-literature
Project description:This paper presents in detail the methodology for the detection and tracking of dynamic objects from the article in press (A new methodology applied to dynamic object detection and tracking systems for visually impaired people [1]). In order to validate this methodology, four different architectures have been designed in this paper. These architectures have implemented the techniques of pattern recognition, optical flow, background subtraction and color tracking to enable comparison and to see which is the most appropriate in a given environment. In this paper we also present a method created to quantify the effectiveness of each architecture implemented.
| S-EPMC6728795 | biostudies-literature