ZAF, the first open source fully automated feeder for aquatic facilities.
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ABSTRACT:
SUBMITTER: Lange M
PROVIDER: S-EPMC8776251 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81-0.93 for whole heart and cardiac chambers, 0.43-0.76 for great vessels and conduction nodes, and 0.22-0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7-19.7 mm, and median volume ratio was close to one (0.95-1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets.
| S-EPMC10030448 | biostudies-literature
Project description:ObjectiveResearch in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject's MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool-realistic volumetric-approach to simulate transcranial electric simulation (ROAST)-is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement.ApproachROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline.Main resultsThe electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS.SignificanceWe hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.
| S-EPMC7328433 | biostudies-literature
Project description:T2 Lesion Volume (T2LV) has been an important biomarker for multiple sclerosis (MS). Current methods available to quantify lesions from MR images generally require manual adjustments or multiple images with different contrasts. Further, implementations are often not easily or openly accessible.We created a fully unsupervised, single T2 FLAIR image T2LV quantification package based on the popular open-source imaging toolkit FSL.By scripting various processing tools in FSL, we developed an image processing pipeline that distinguishes normal brain tissue from CSF and lesions. We validated our method by hierarchical multiple regression (HMR) with a preliminary study to see if our T2LVs correlate with clinical disability measures in MS when controlled for other variables.Pearson correlations between T2LV and Expanded Disability Status Scale (EDSS: r = 0.344, P = 0.013), Six-Minute Walk (6MW: r = -0.513, P = 0.000), Timed 25-Foot Walk (T25FW: r = -0.438, P = .000), and Symbol Digit Modalities Test (SDMT: r = -0.499, P = 0.000) were all significant. Partial correlations controlling for age were significant between T2LV and 6MW (r = -0.433, P = 0.002), T25FW (r = -0.392, P = 0.004), and SDMT (r = -0.450, P = 0.001). In HMR, T2LV explained significant additional variance in 6MW (R(2) change = 0.082, P = 0.020), after controlling for confounding variables such as age, white matter volume (WMV), and gray matter volume (GMV).Our T2LV quantification software produces T2LVs from a single FLAIR image that correlate with physical disability in MS and is freely available as open-source software.
| S-EPMC4731385 | biostudies-other
Project description:Scientists have begun using self-replicating rapid prototyper (RepRap) 3-D printers to manufacture open source digital designs of scientific equipment. This approach is refined here to develop a novel instrument capable of performing automated large-area four-point probe measurements. The designs for conversion of a RepRap 3-D printer to a 2-D open source four-point probe (OS4PP) measurement device are detailed for the mechanical and electrical systems. Free and open source software and firmware are developed to operate the tool. The OS4PP was validated against a wide range of discrete resistors and indium tin oxide (ITO) samples of different thicknesses both pre- and post-annealing. The OS4PP was then compared to two commercial proprietary systems. Results of resistors from 10 to 1 MΩ show errors of less than 1% for the OS4PP. The 3-D mapping of sheet resistance of ITO samples successfully demonstrated the automated capability to measure non-uniformities in large-area samples. The results indicate that all measured values are within the same order of magnitude when compared to two proprietary measurement systems. In conclusion, the OS4PP system, which costs less than 70% of manual proprietary systems, is comparable electrically while offering automated 100 micron positional accuracy for measuring sheet resistance over larger areas.
| S-EPMC5459207 | biostudies-other
Project description:BackgroundThe study of malaria transmission requires the experimental infection of mosquitoes with Plasmodium gametocytes. In the laboratory, this is achieved using artificial membrane feeding apparatus that simulate body temperature and skin of the host, and so permit mosquito feeding on reconstituted gametocyte-containing blood. Membrane feeders either use electric heating elements or complex glass chambers to warm the infected blood; both of which are expensive to purchase and can only be sourced from a handful of specialized companies. Presented and tested here is a membrane feeder that can be inexpensively printed using 3D-printing technology.ResultsUsing the Plasmodium falciparum laboratory strain NF54, three independent standard membrane feeding assays (SMFAs) were performed comparing the 3D-printed feeder against a commercial glass feeder. Exflagellation rates did not differ between the two feeders. Furthermore, no statistically significant difference was found in the oocyst load nor oocyst intensity of Anopheles stephensi mosquitoes (mean oocyst range 1.3-6.2 per mosquito; infection prevalence range 41-79%).ConclusionsOpen source provision of the design files of the 3D-printed feeder will facilitate a wider range of laboratories to perform SMFAs in laboratory and field settings, and enable them to freely customize the design to their own requirements.
| S-EPMC6076392 | biostudies-literature
Project description:Quick, efficient, fully automated open-source programs to segment muscle and adipose tissues from computed tomography (CT) images would be a great contribution to body composition research. This study examined the concordance of cross-sectional areas (CSA) and densities for muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) from CT images at the third lumbar (L3) between an automated neural network (test method) and a semi-automatic human-based program (reference method). Concordance was further evaluated by disease status, sex, race/ethnicity, BMI categories. Agreement statistics applied included Lin's Concordance (CCC), Spearman correlation coefficient (SCC), Sorensen dice-similarity coefficient (DSC), and Bland-Altman plots with limits of agreement (LOA) within 1.96 standard deviation. A total of 420 images from a diverse cohort of patients (60.35 ± 10.92 years; body mass index (BMI) of 28.77 ± 7.04 kg/m2; 55% female; 53% Black) were included in this study. About 30% of patients were healthy (i.e., received a CT scan for acute illness or pre-surgical donor work-up), while another 30% had a diagnosis of colorectal cancer. The CCC, SCC, and DSC estimates for muscle, VAT, SAT were all greater than 0.80 (>0.80 indicates good performance). Agreement analysis by diagnosis showed good performance for the test method except for critical illness (DSC 0.65-0.87). Bland-Altman plots revealed narrow LOA suggestive of good agreement despite minimal proportional bias around the zero-bias line for muscle, SAT, and IMAT CSA. The test method shows good performance and almost perfect concordance for L3 muscle, VAT, SAT, and IMAT per DSC estimates, and Bland-Altman plots even after stratification by sex, race/ethnicity, and BMI categories. Care must be taken to assess the density of the CT images from critically ill patients before applying the automated neural network (test method).
| S-EPMC9101564 | biostudies-literature
Project description:We describe the "FishCam", a low-cost ( < 500 USD) autonomous camera package to record videos and images underwater. The system is composed of easily accessible components and can be programmed to turn ON and OFF on customizable schedules. Its 8-megapixel camera module is capable of taking 3280 × 2464-pixel images and videos. An optional buzzer circuit inside the pressure housing allows synchronization of the video data from the FishCam with passive acoustic recorders. Ten FishCam deployments were performed along the east coast of Vancouver Island, British Columbia, Canada, from January to December 2019. Field tests demonstrate that the proposed system can record up to 212 h of video data over a period of at least 14 days. The FishCam data collected allowed us to identify fish species and observe species interactions and behaviors. The FishCam is an operational, easily-reproduced and inexpensive camera system that can help expand both the temporal and spatial coverage of underwater observations in ecological research. With its low cost and simple design, it has the potential to be integrated into educational and citizen science projects, and to facilitate learning the basics of electronics and programming.
| S-EPMC9041213 | biostudies-literature
Project description:We present the open source distributed software package Poisson-Boltzmann Analytical Method (PB-AM), a fully analytical solution to the linearized PB equation, for molecules represented as non-overlapping spherical cavities. The PB-AM software package includes the generation of outputs files appropriate for visualization using visual molecular dynamics, a Brownian dynamics scheme that uses periodic boundary conditions to simulate dynamics, the ability to specify docking criteria, and offers two different kinetics schemes to evaluate biomolecular association rate constants. Given that PB-AM defines mutual polarization completely and accurately, it can be refactored as a many-body expansion to explore 2- and 3-body polarization. Additionally, the software has been integrated into the Adaptive Poisson-Boltzmann Solver (APBS) software package to make it more accessible to a larger group of scientists, educators, and students that are more familiar with the APBS framework. © 2016 Wiley Periodicals, Inc.
| S-EPMC5403608 | biostudies-literature
Project description:Background and objectiveEver since its discovery, calcium imaging has proven its worth in discovering new insights into the mechanisms of cellular communication. Yet, the analysis of the data generated by calcium imaging experiments demands a large amount of time from researchers. Tools enabling automated and semi-automated analysis are available, but often they allow automating only a part of the data analysis process. Therefore, we developed CALIMA (https://aethelraed.nl/calima), a free and open-source standalone software tool that provides an opportunity to quickly detect cells, to obtain the calcium spikes, and to determine the underlying network structure of neuronal cell cultures.MethodsOwing to the difference of Gaussians algorithm applied for the cell detection, CALIMA is able to detect regions of interest (ROIs) quickly. The z-scoring algorithm provides a means to set the requirements for spike detection, and the neuronal connections can be reconstructed by analyzing the cross-correlation between the cellular activity. We evaluated CALIMA's reliability, speed, and functionality with a special focus on neuronal cell detection and network reconstruction. The evaluation was performed by using real-life data such as a known example dataset (cultured primary rat cortical neurons, University of Pennsylvania) and by analyzing video graphic footage of in vitro brain cell samples (SH-SY5Y neuroblastoma cultures, one sample with synchronous neuron firing). The obtained results were compared to the corresponding outcomes observed on same datasets for other similar software solutions. Moreover, we compared the results of segmentation and peak detection analysis, the ones obtained using CALIMA and those acquired manually.ResultsCALIMA was able to detect the cells in the cultures within seconds. The average sensitivity was 82% across the datasets checked, comparing favorably with the alternative software solutions. Using the correct parameters, CALIMA's Ca-spikes detection sensitivity reached 96%. Lastly, neuronal networks were reconstructed by combining the data on the ROI's activity and the cell's positions, finding the most likely inter-cell connections.ConclusionsWe found that CALIMA proved to be a robust and fast tool to analyze the data of experiments for the digital reconstruction of the neuronal cellular network while being able to process the analysis steps with minimal user input required and in a time efficient manner.
| S-EPMC6718774 | biostudies-literature
Project description:As deep learning algorithms drive the progress in protein structure prediction, a lot remains to be studied at this merging superhighway of deep learning and protein structure prediction. Recent findings show that inter-residue distance prediction, a more granular version of the well-known contact prediction problem, is a key to predicting accurate models. However, deep learning methods that predict these distances are still in the early stages of their development. To advance these methods and develop other novel methods, a need exists for a small and representative dataset packaged for faster development and testing. In this work, we introduce protein distance net (PDNET), a framework that consists of one such representative dataset along with the scripts for training and testing deep learning methods. The framework also includes all the scripts that were used to curate the dataset, and generate the input features and distance maps. Deep learning models can also be trained and tested in a web browser using free platforms such as Google Colab. We discuss how PDNET can be used to predict contacts, distance intervals, and real-valued distances.
| S-EPMC7414848 | biostudies-literature