Visualizing and quantifying movement from pre-recorded videos: The spectral time-lapse (STL) algorithm.
Ontology highlight
ABSTRACT:
SUBMITTER: Madan CR
PROVIDER: S-EPMC4038320 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
Visualizing and quantifying movement from pre-recorded videos: The spectral time-lapse (STL) algorithm.
Madan Christopher R CR Spetch Marcia L ML
F1000Research 20140121
When studying animal behaviour within an open environment, movement-related data are often important for behavioural analyses. Therefore, simple and efficient techniques are needed to present and analyze the data of such movements. However, it is challenging to present both spatial and temporal information of movements within a two-dimensional image representation. To address this challenge, we developed the spectral time-lapse (STL) algorithm that re-codes an animal's position at every time poi ...[more]
Similar Datasets
Project description:BACKGROUND: Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods. METHODS: Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios. RESULT: The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected. CONCLUSION: The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.
| S-EPMC2680402 | biostudies-other
Project description:A key challenge in single cell RNA-sequencing (scRNA-seq) data analysis are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in scRNA-seq data from a variety of datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score (\texttt{cms}) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dimensional space), the score is able to detect local batch bias and differentiate between unbalanced batches (i.e., when one cell type is more abundant in a batch) and systematic differences between cells of the same cell type. We implemented the \texttt{cms}, as well as related metrics to detect batch effects or measure structure preservation, in the CellMixS R/Bioconductor package. We systematically compare different metrics that have been proposed to quantify batch effects or bias in scRNA-seq data using real datasets with known batch effects and synthetic data that mimic various real data scenarios. While these metrics target the same question and are used interchangeably, we find differences in inter- and intra-dataset scalability, sensitivity and in a metric's ability to handle batch effects with differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.
2021-01-31 | E-MTAB-9916 | biostudies-arrayexpress
Project description:Understanding the structural adaptation and signaling of adhesion sites in response to mechanical stimuli requires in situ characterization of the dynamic activation of a large number of adhesion components. Here, we review high-resolution live cell imaging approaches to measure forces, assembly, and interaction of adhesion components, and the activation of adhesion-mediated signals. We conclude by outlining computational multiplexing as a framework for the integration of these data into comprehensive models of adhesion signaling pathways.
| S-EPMC2661108 | biostudies-literature
Project description:Capturing microbial growth on a macroscopic scale is of great importance to further our understanding of microbial life. However, methods for imaging microbial life on a scale of millimeters to centimeters are often limited by designs that have poor environmental control, resulting in dehydration of the agar plate within just a few days. Here, we created MOCHA (microbial chamber), a simple but effective chamber that allows users to study microbial growth for extended periods (weeks) in a stable environment. Agar hydration is maintained with a double-decker design, in which two glass petri dishes are connected by a wick, allowing the lower plate to keep the upper plate hydrated. This flexible chamber allows the observation of a variety of microbiological phenomena, such as the growth and development of single bacterial and fungal colonies, interspecies interactions, swarming motility, and pellicle formation.IMPORTANCE Detailed study of microbial life on the colony scale of millimeters to centimeters has been lagging considerably behind microscopic inspection of microbes. One major reason for this is the lack of inexpensive instrumentation that can reproducibly capture images in a controlled environment. In this study, we present the design and use of a unique chamber that was used to produce several time-lapse movies that aimed to capture the diversity of microbial colony phenotypes over long periods.
| S-EPMC5738739 | biostudies-literature
Project description:Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.
| S-EPMC10586693 | biostudies-literature
Project description:Background and aimsUpper GI endoscopy is speculated to be an aerosol-generating procedure (AGP). Robust evidence exists for aerosol transmission of severe acute respiratory syndrome coronavirus 2. The quality of data available confirming aerosol generation during GI endoscopy is limited. We aimed to objectively demonstrate that GI endoscopy is an AGP and illustrate the mechanism by which the greatest risk for aerosolization of droplets during endoscopy may occur.MethodsAerosolized droplets generated during insertion and withdrawal of an endoscope and with passage of various tools through the endoscopic working channel using 2 experimental apparatuses modeling an upper GI tract (ie, a fluid-filled tube and a lamb esophagus) were qualitatively assessed by laser light scattering.ResultsInsertion and withdrawal of the upper endoscope into the upper GI tract models generated numerous aerosolized particles. A large number of brightly scattering particles were observed at the site of insertion and withdrawal of the endoscope. Passage of a cytology brush, biopsy forceps, and hemostatic clip through the working endoscope channel also generated aerosolized particles but in fewer numbers. There was no significant variation in quantity or brightness of droplets generated on testing different biopsy valve cap models or when suctioning fluid with an open versus closed biopsy valve cap. These results were reproducible over several trials.ConclusionsWe illustrate in an objective manner that upper GI endoscopy is an AGP. These findings may have implications for transmission of infectious airborne pathogens outside of severe acute respiratory syndrome coronavirus 2 and can help to inform guidance on appropriate personal protective equipment use and other measures for transmission risk mitigation during GI endoscopy.
| S-EPMC9596173 | biostudies-literature
Project description:Optical colonoscopy (OC), the most prevalent colon cancer screening tool, has a high miss rate due to a number of factors, including the geometry of the colon (haustral fold and sharp bends occlusions), endoscopist inexperience or fatigue, endoscope field of view. We present a framework to visualize the missed regions per-frame during OC, and provides a workable clinical solution. Specifically, we make use of 3D reconstructed virtual colonoscopy (VC) data and the insight that VC and OC share the same underlying geometry but differ in color, texture and specular reflections, embedded in the OC. A lossy unpaired image-to-image translation model is introduced with enforced shared latent space for OC and VC. This shared space captures the geometric information while deferring the color, texture, and specular information creation to additional Gaussian noise input. The latter can be utilized to generate one-to-many mappings from VC to OC and OC to OC. The code, data and trained models will be released via our Computational Endoscopy Platform at https://github.com/nadeemlab/CEP.
| S-EPMC8504422 | biostudies-literature
Project description:Although the human visual system is remarkable at perceiving and interpreting motions, it has limited sensitivity, and we cannot see motions that are smaller than some threshold. Although difficult to visualize, tiny motions below this threshold are important and can reveal physical mechanisms, or be precursors to large motions in the case of mechanical failure. Here, we present a "motion microscope," a computational tool that quantifies tiny motions in videos and then visualizes them by producing a new video in which the motions are made large enough to see. Three scientific visualizations are shown, spanning macroscopic to nanoscopic length scales. They are the resonant vibrations of a bridge demonstrating simultaneous spatial and temporal modal analysis, micrometer vibrations of a metamaterial demonstrating wave propagation through an elastic matrix with embedded resonating units, and nanometer motions of an extracellular tissue found in the inner ear demonstrating a mechanism of frequency separation in hearing. In these instances, the motion microscope uncovers hidden dynamics over a variety of length scales, leading to the discovery of previously unknown phenomena.
| S-EPMC5676878 | biostudies-literature
Project description:Finding new sustainable means of diagnosing and treating diseases is one of the most pressing issues of our time. In recent years, several endogenous peptides have been found to be both excellent biomarkers for many diseases and to possess important physiological roles which may be utilized in treatments. The detection of peptides has been facilitated by the rapid development of biological mass spectrometry and now the combination of fast and sensitive high resolution MS instruments and stable nano HP-LC equipment sequences thousands of peptides in one single experiment. In most research conducted with these advanced systems, proteolytically cleaved proteins are analyzed and the specific peptides are identified by software dedicated for protein quantification using different proteomics workflows. Analysis of endogenous peptides with peptidomics workflows also benefit from the novel sensitive and advanced instrumentation, however, the generated peptidomic data is vast and subsequently laborious to visualize and examine, creating a bottleneck in the analysis. Therefore, we have created Peptimetric, an application designed to allow researchers to investigate and discover differences between peptidomic samples. Peptimetric allows the user to dynamically and interactively investigate the proteins, peptides, and some general characteristics of multiple samples, and is available as a web application at https://peptimetric.herokuapp.com. To illustrate the utility of Peptimetric, we've applied it to a peptidomic dataset of 15 urine samples from diabetic patients and corresponding data from healthy subjects.
| S-EPMC9581017 | biostudies-literature
Project description:BackgroundOne of the keys to running a successful clinical trial is the selection of high quality clinical sites, i.e., sites that are able to enroll patients quickly, engage them on an ongoing basis to prevent drop-out, and execute the trial in strict accordance to the clinical protocol. Intuitively, the historical track record of a site is one of the strongest predictors of its future performance; however, issues such as data availability and wide differences in protocol complexity can complicate interpretation. Here, we demonstrate how operational data derived from central laboratory services can provide key insights into the performance of clinical sites and help guide operational planning and site selection for new clinical trials.MethodsOur methodology uses the metadata associated with laboratory kit shipments to clinical sites (such as trial and anonymized patient identifiers, investigator names and addresses, sample collection and shipment dates, etc.) to reconstruct the complete schedule of patient visits and derive insights about the operational performance of those sites, including screening, enrollment, and drop-out rates and other quality indicators. This information can be displayed in its raw form or normalized to enable direct comparison of site performance across studies of varied design and complexity.ResultsLeveraging Covance's market leadership in central laboratory services, we have assembled a database of operational metrics that spans more than 14,000 protocols, 1400 indications, 230,000 unique investigators, and 23 million patient visits and represents a significant fraction of all clinical trials run globally in the last few years. By analyzing this historical data, we are able to assess and compare the performance of clinical investigators across a wide range of therapeutic areas and study designs. This information can be aggregated across trials and geographies to gain further insights into country and regional trends, sometimes with surprising results.ConclusionsThe use of operational data from Covance Central Laboratories provides a unique perspective into the performance of clinical sites with respect to many important metrics such as patient enrollment and retention. These metrics can, in turn, be used to guide operational planning and site selection for new clinical trials, thereby accelerating recruitment, improving quality, and reducing cost.
| S-EPMC5898563 | biostudies-literature