CytoPy: An autonomous cytometry analysis framework.
Ontology highlight
ABSTRACT:
SUBMITTER: Burton RJ
PROVIDER: S-EPMC8213167 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We propose a log analysis framework for test driving of autonomous vehicles. The log of a vehicle is a fundamental source to detect and analyze events during driving. A set of dumped logs are, however, usually mixed and fragmented since they are generated concurrently by a number of modules such as sensors, actuators and programs. This makes it hard to analyze them to discover latent errors that could occur due to complex chain reactions among those modules. Our framework provides a logging architecture based on formal specifications, which hierarchically organizes them to find out a priori relationships between them. Then, algorithmic or implementation errors can be detected by examining a posteriori relationships. However, a test in a situation of certain parameters, so called an oracle test, does not necessarily trigger latent violations of the relationships. In our framework, this is remedied by adopting metamorphic testing to quantitatively verify the formal specification. As a working proof, we define three metamorphic relations critical for testing autonomous vehicles and verify them in a quantitative manner based on our logging system.
| S-EPMC7085529 | biostudies-literature
Project description:Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry.
| S-EPMC8129071 | biostudies-literature
Project description:Automated Driving Systems (ADSs) require robust and scalable control systems in order to achieve a safe, efficient and comfortable driving experience. Most global planners for autonomous vehicles provide as output a sequence of waypoints to be followed. This paper proposes a modular and scalable waypoint tracking controller for Robot Operating System (ROS)-based autonomous guided vehicles. The proposed controller performs a smooth interpolation of the waypoints and uses optimal control techniques to ensure robust trajectory tracking even at high speeds in urban environments (up to 50 km/h). The delays in the localization system and actuators are compensated in the control loop to stabilize the system. Forward velocity is adapted to path characteristics using a velocity profiler. The controller has been implemented as an ROS package providing scalability and exportability to the system in order to be used with a wide variety of simulators and real vehicles. We show the results of this controller using the novel and hyper realistic CARLA Simulator and carrying out a comparison with other standard and state-of-art trajectory tracking controllers.
| S-EPMC7412167 | biostudies-literature
Project description:Cell-cell communication is essential for multicellular development and, consequently, evolution has brought about an array of distinct mechanisms serving this purpose. Consistently, induction and maintenance of stem cell fate by noncell autonomous signals is a feature shared by many organisms and may depend on secreted factors, direct cell-cell contact, matrix interactions, or a combination of these mechanisms. Although many basic cellular processes are well conserved between animals and plants, cell-to-cell signaling is one function where substantial diversity has arisen between the two kingdoms of life. One of the most striking differences is the presence of cytoplasmic bridges, called plasmodesmata, which facilitate the exchange of molecules between neighboring plant cells and provide a unique route for cell-cell communication in the plant lineage. Here, we provide evidence that the stem cell inducing transcription factor WUSCHEL (WUS), expressed in the niche, moves to the stem cells via plasmodesmata in a highly regulated fashion and that this movement is required for WUS function and, thus, stem cell activity in Arabidopsis thaliana. We show that cell context-independent mobility is encoded in the WUS protein sequence and mediated by multiple domains. Finally, we demonstrate that parts of the protein that restrict movement are required for WUS homodimerization, suggesting that formation of WUS dimers might contribute to the regulation of apical stem cell activity.
| S-EPMC4210042 | biostudies-literature
Project description:Assistive robotic arms (ARAs) that provide care to the elderly and people with disabilities, are a significant part of Human-Robot Interaction (HRI). Presently available ARAs provide non-intuitive interfaces such as joysticks for control and thus, lacks the autonomy to perform daily activities. This study proposes that, for inducing autonomous behavior in ARAs, visual sensors integration is vital, and visual servoing in the direct Cartesian control mode is the preferred method. Generally, ARAs are designed in a configuration where its end-effector's position is defined in the fixed base frame while orientation is expressed in the end-effector frame. We denoted this configuration as 'mixed frame robotic arms'. Consequently, conventional visual servo controllers which operate in a single frame of reference are incompatible with mixed frame ARAs. Therefore, we propose a mixed-frame visual servo control framework for ARAs. Moreover, we enlightened the task space kinematics of a mixed frame ARAs, which led us to the development of a novel "mixed frame Jacobian matrix". The proposed framework was validated on a mixed frame JACO-2 7 DoF ARA using an adaptive proportional derivative controller for achieving image-based visual servoing (IBVS), which showed a significant increase of 31% in the convergence rate, outperforming conventional IBVS joint controllers, especially in the outstretched arm positions and near the base frame. Our Results determine the need for the mixed frame controller for deploying visual servo control on modern ARAs, that can inherently cater to the robotic arm's joint limits, singularities, and self-collision problems.
| S-EPMC8778787 | biostudies-literature
Project description:The computer vision systems driving autonomous vehicles are judged by their ability to detect objects and obstacles in the vicinity of the vehicle in diverse environments. Enhancing this ability of a self-driving car to distinguish between the elements of its environment under adverse conditions is an important challenge in computer vision. For example, poor weather conditions like fog and rain lead to image corruption which can cause a drastic drop in object detection (OD) performance. The primary navigation of autonomous vehicles depends on the effectiveness of the image processing techniques applied to the data collected from various visual sensors. Therefore, it is essential to develop the capability to detect objects like vehicles and pedestrians under challenging conditions such as like unpleasant weather. Ensembling multiple baseline deep learning models under different voting strategies for object detection and utilizing data augmentation to boost the models' performance is proposed to solve this problem. The data augmentation technique is particularly useful and works with limited training data for OD applications. Furthermore, using the baseline models significantly speeds up the OD process as compared to the custom models due to transfer learning. Therefore, the ensembling approach can be highly effective in resource-constrained devices deployed for autonomous vehicles in uncertain weather conditions. The applied techniques demonstrated an increase in accuracy over the baseline models and were able to identify objects from the images captured in the adverse foggy and rainy weather conditions. The applied techniques demonstrated an increase in accuracy over the baseline models and reached 32.75% mean average precision (mAP) and 52.56% average precision (AP) in detecting cars in the adverse fog and rain weather conditions present in the dataset. The effectiveness of multiple voting strategies for bounding box predictions on the dataset is also demonstrated. These strategies help increase the explainability of object detection in autonomous systems and improve the performance of the ensemble techniques over the baseline models.
| S-EPMC8516532 | biostudies-literature
Project description:BackgroundWith the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations.ResultTo address this issue, we compared three classes of performance measures, "precision" as external evaluation, "coherence" as internal evaluation, and stability, of nine methods based on six independent benchmark datasets. Seven unsupervised methods (Accense, Xshift, PhenoGraph, FlowSOM, flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cytometry datasets. We compute and compare all defined performance measures against random subsampling, varying sample sizes, and the number of clusters for each method. LDA reproduces the manual labels most precisely but does not rank top in internal evaluation. PhenoGraph and FlowSOM perform better than other unsupervised tools in precision, coherence, and stability. PhenoGraph and Xshift are more robust when detecting refined sub-clusters, whereas DEPECHE and FlowSOM tend to group similar clusters into meta-clusters. The performances of PhenoGraph, Xshift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample size increases.ConclusionAll the evaluations including precision, coherence, stability, and clustering resolution should be taken into synthetic consideration when choosing an appropriate tool for cytometry data analysis. Thus, we provide decision guidelines based on these characteristics for the general reader to more easily choose the most suitable clustering tools.
| S-EPMC6929440 | biostudies-literature
Project description:A DNA-sensing platform is developed by exploiting the easy surface functionalization of metal-organic framework (MOF) particles and their highly parallelized fluorescence detection by flow cytometry. Two strategies were employed to functionalize the surface of MIL-88A, using either covalent or non-covalent interactions, resulting in alkyne-modified and biotin-modified MIL-88A, respectively. Covalent surface coupling of an azide-dye and the alkyne-MIL-88A was achieved by means of a click reaction. Non-covalent streptavidin-biotin interactions were employed to link biotin-PNA to biotin-MIL-88A particles mediated by streptavidin. Characterization by confocal imaging and flow cytometry demonstrated that DNA can be bound selectively to the MOF surface. Flow cytometry provided quantitative data of the interaction with DNA. Making use of the large numbers of particles that can be simultaneously processed by flow cytometry, this MOF platform was able to discriminate between fully complementary, single-base mismatched, and randomized DNA targets.
| S-EPMC5396136 | biostudies-literature
Project description:Extracellular vesicles (EVs) are small, heterogeneous and difficult to measure. Flow cytometry (FC) is a key technology for the measurement of individual particles, but its application to the analysis of EVs and other submicron particles has presented many challenges and has produced a number of controversial results, in part due to limitations of instrument detection, lack of robust methods and ambiguities in how data should be interpreted. These complications are exacerbated by the field's lack of a robust reporting framework, and many EV-FC manuscripts include incomplete descriptions of methods and results, contain artefacts stemming from an insufficient instrument sensitivity and inappropriate experimental design and lack appropriate calibration and standardization. To address these issues, a working group (WG) of EV-FC researchers from ISEV, ISAC and ISTH, worked together as an EV-FC WG and developed a consensus framework for the minimum information that should be provided regarding EV-FC. This framework incorporates the existing Minimum Information for Studies of EVs (MISEV) guidelines and Minimum Information about a FC experiment (MIFlowCyt) standard in an EV-FC-specific reporting framework (MIFlowCyt-EV) that supports reporting of critical information related to sample staining, EV detection and measurement and experimental design in manuscripts that report EV-FC data. MIFlowCyt-EV provides a structure for sharing EV-FC results, but it does not prescribe specific protocols, as there will continue to be rapid evolution of instruments and methods for the foreseeable future. MIFlowCyt-EV accommodates this evolution, while providing information needed to evaluate and compare different approaches. Because MIFlowCyt-EV will ensure consistency in the manner of reporting of EV-FC studies, over time we expect that adoption of MIFlowCyt-EV as a standard for reporting EV- FC studies will improve the ability to quantitatively compare results from different laboratories and to support the development of new instruments and assays for improved measurement of EVs.
| S-EPMC7034442 | biostudies-literature
Project description:BACKGROUND:B cells play a critical role in the development and maintenance of food allergy by producing allergen-specific IgE. Despite the importance of B cells in IgE-mediated food allergy, the identity of sIgE-producing human B cells and how IgE is regulated are poorly understood. OBJECTIVE:To identify the immunophenotypes of circulating B cells associated with the production of galactose-alpha-1,3-galactose-specific IgE production in patients with red meat allergy. METHODS:B cells in PBMC samples obtained from 19 adults with physician-diagnosed red meat allergy and 20 non-meat allergic healthy controls were assessed by mass cytometry along with a bioinformatics analysis pipeline to identify discrete B cell phenotypes that associated with serum sIgE. Fluorescent flow cytometry was then applied to sort purify discrete B cell subsets, and B cells were functionally evaluated on an individual cell level for the production of sIgE by ELISPOT. RESULTS:Discrete B cell phenotypes abundant in meat allergic subjects compared to non-meat allergic controls were found in peripheral blood that do not share typical characteristics of classical isotype-switched memory B cells that express high levels of CD27. These B cell subsets shared higher IgD and lower IgM expression levels coupled with CXCR4, CCR6 and CD25 expression. In vitro polyclonal stimulation of purified B cell subsets from meat allergic subjects demonstrated that these subsets were enriched for cells induced to secrete sIgE. CONCLUSIONS AND CLINICAL RELEVANCE:Circulating B cells display increased abundance of discrete B cell subsets in meat allergic subjects. This observation, coupled with the capacity of individual B cell subsets to produce sIgE following activation, implicates these novel B cell phenotypes in promoting IgE in meat allergy.
| S-EPMC6488422 | biostudies-literature