CACTI: free, open-source software for the sequential coding of behavioral interactions.
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ABSTRACT:
SUBMITTER: Glynn LH
PROVIDER: S-EPMC3397966 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Visual field impairment affects more than 100 million people globally. However, due to the lack of the access to appropriate ophthalmic healthcare in undeveloped regions as a result of associated costs and expertise this number may be an underestimate. Improved access to affordable diagnostic software designed for visual field examination could slow the progression of diseases, such as glaucoma, allowing for early diagnosis and intervention. We have developed Specvis, a free and open-source application written in Java programming language that can run on any personal computer to meet this requirement (http://www.specvis.pl/). Specvis was tested on glaucomatous, retinitis pigmentosa and stroke patients and the results were compared to results using the Medmont M700 Automated Static Perimeter. The application was also tested for inter-test intrapersonal variability. The results from both validation studies indicated low inter-test intrapersonal variability, and suitable reliability for a fast and simple assessment of visual field impairment. Specvis easily identifies visual field areas of zero sensitivity and allows for evaluation of its levels throughout the visual field. Thus, Specvis is a new, reliable application that can be successfully used for visual field examination and can fill the gap between confrontation and perimetry tests. The main advantages of Specvis over existing methods are its availability (free), affordability (runs on any personal computer), and reliability (comparable to high-cost solutions).
| S-EPMC5640235 | biostudies-literature
Project description:ObjectiveTo assess the adequacy of image agreement regarding uveitis based on color fundus and fluorescein angiography images alone, and to use free and open source applications to conduct an image agreement study.DesignCross-sectional agreement study.ParticipantsBaseline fundus and fluorescein images of patients with panuveitis, posterior, or intermediate uveitis enrolled in the Multi-center Uveitis Steroid Treatment (MUST) trial.MethodsThree fellowship-trained specialists in uveitis independently reviewed patient images using ClearCanvas™ and responded using Epi Info™. The diagnoses of the 3 reviewers were compared with the MUST clinician as a gold standard. A rank transformation adjusted for the possible variation in number of responses per patient. Chance-corrected interobserver agreement among the 3 reviewers was estimated with the ι coefficient. Confidence interval (CI) and SE were bootstrapped.ResultsAgreement between the diagnoses of the respondents and the baseline MUST clinician's diagnosis was poor across all diagnostic categories, ι = 0.09 (95% CI, 0.07-0.11). The agreement among respondents alone also was poor, ι = 0.11 ± 0.02 (95% CI, 0.08-0.13). The specialists requested more patient historical and clinical information to make a diagnosis on all patients.ConclusionsThe role in distinguishing the multiple conditions in uveitis appears to be limited when based on fundus imaging alone. Future studies should investigate different categories of clinical data to supplement image data. Freely available applications have excellent utility in ophthalmic imaging agreement studies.
| S-EPMC3741676 | biostudies-literature
Project description:Minimization is a valuable method for allocating participants between the control and experimental arms of clinical studies. The use of minimization reduces differences that might arise by chance between the study arms in the distribution of patient characteristics such as gender, ethnicity and age. However, unlike randomization, minimization requires real time assessment of each new participant with respect to the preceding distribution of relevant participant characteristics within the different arms of the study. For multi-site studies, this necessitates centralized computational analysis that is shared between all study locations. Unfortunately, there is no suitable freely available open source or free software that can be used for this purpose. OxMaR was developed to enable researchers in any location to use minimization for patient allocation and to access the minimization algorithm using any device that can connect to the internet such as a desktop computer, tablet or mobile phone. The software is complete in itself and requires no special packages or libraries to be installed. It is simple to set up and run over the internet using online facilities which are very low cost or even free to the user. Importantly, it provides real time information on allocation to the study lead or administrator and generates real time distributed backups with each allocation. OxMaR can readily be modified and customised and can also be used for standard randomization. It has been extensively tested and has been used successfully in a low budget multi-centre study. Hitherto, the logistical difficulties involved in minimization have precluded its use in many small studies and this software should allow more widespread use of minimization which should lead to studies with better matched control and experimental arms. OxMaR should be particularly valuable in low resource settings.
| S-EPMC4213009 | biostudies-literature
Project description:Multiplexed sequential and combinatorial imaging enables the simultaneous detection of multiple biological molecules, e.g. proteins, DNA, or RNA, enabling single-cell spatial multi-omics measurements at sub-cellular resolution. Recently, we designed a multiplexed imaging approach (Hi-M) to study the spatial organization of chromatin in single cells. In order to enable Hi-M sequential imaging on custom microscope setups, we developed Qudi-HiM, a modular software package written in Python 3. Qudi-HiM contains modules to automate the robust acquisition of thousands of three-dimensional multicolor microscopy images, the handling of microfluidics devices, and the remote monitoring of ongoing acquisitions and real-time analysis. In addition, Qudi-HiM can be used as a stand-alone tool for other imaging modalities.
| S-EPMC10445908 | biostudies-literature
Project description:Open source software is becoming crucial in the design and testing of quantum algorithms. Many of the tools are backed by major commercial vendors with the goal to make it easier to develop quantum software: this mirrors how well-funded open machine learning frameworks enabled the development of complex models and their execution on equally complex hardware. We review a wide range of open source software for quantum computing, covering all stages of the quantum toolchain from quantum hardware interfaces through quantum compilers to implementations of quantum algorithms, as well as all quantum computing paradigms, including quantum annealing, and discrete and continuous-variable gate-model quantum computing. The evaluation of each project covers characteristics such as documentation, licence, the choice of programming language, compliance with norms of software engineering, and the culture of the project. We find that while the diversity of projects is mesmerizing, only a few attract external developers and even many commercially backed frameworks have shortcomings in software engineering. Based on these observations, we highlight the best practices that could foster a more active community around quantum computing software that welcomes newcomers to the field, but also ensures high-quality, well-documented code.
| S-EPMC6301779 | biostudies-literature
Project description:13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package.
| S-EPMC5933345 | biostudies-literature
Project description:Bioreactors are designed to support highly controlled environments for growth of tissues, cell cultures or microbial cultures. A variety of bioreactors are commercially available, often including sophisticated software to enhance the functionality of the bioreactor. However, experiments that the bioreactor hardware can support, but that were not envisioned during the software design cannot be performed without developing custom software. In addition, support for third party or custom designed auxiliary hardware is often sparse or absent. This work presents flexible open source freeware for the control of bioreactors of the Bioflo product family. The functionality of the software includes setpoint control, data logging, and protocol execution. Auxiliary hardware can be easily integrated and controlled through an integrated plugin interface without altering existing software. Simple experimental protocols can be entered as a CSV scripting file, and a Python-based protocol execution model is included for more demanding conditional experimental control. The software was designed to be a more flexible and free open source alternative to the commercially available solution. The source code and various auxiliary hardware plugins are publicly available for download from https://github.com/LibourelLab/BiofloSoftware. In addition to the source code, the software was compiled and packaged as a self-installing file for 32 and 64 bit windows operating systems. The compiled software will be able to control a Bioflo system, and will not require the installation of LabVIEW.
| S-EPMC3965399 | biostudies-literature
Project description:As Free and Open Source Software (FOSS) increases in importance and use by global corporations, understanding the dynamics of its communities becomes critical. This paper measures up to 21 years of activities in 1314 individual projects and 1.4 billion lines of code managed. After analyzing the FOSS activities on the projects and organizations level, such as commits frequency, source code lines, and code comments, we find that there is less activity now than there was a decade ago. Moreover, our results suggest a greater decrease in the activities in large and well-established FOSS organizations. Our findings indicate that as technologies and business strategies related to FOSS mature, the role of large formal FOSS organizations serving as intermediary between developers diminishes.
| S-EPMC8460050 | biostudies-literature
Project description:BackgroundObject detection, size determination, and colour detection of images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While methods exist for determining these important phenotypes, they often require proprietary software or require coding knowledge to adapt existing code.ResultsWe provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart. Further scripts identify objects, use an object of known size to calibrate for size, and extract the average colour of objects in RGB, Lab, and YUV colour spaces. We use two examples to demonstrate the use of these scripts. We show the consistency of these scripts by imaging in four different lighting conditions, and then we use two examples to show how the scripts can be used. In the first example, we estimate the lycopene content in tomatoes (Solanum lycopersicum) var. Tiny Tim using fruit images and an exponential model to predict lycopene content. We demonstrate that three different cameras (a DSLR camera and two separate mobile phones) are all able to model lycopene content. The models that predict lycopene or chlorophyll need to be adjusted depending on the camera used. In the second example, we estimate the chlorophyll content of basil (Ocimum basilicum) using leaf images and an exponential model to predict chlorophyll content.ConclusionA fast, cheap, non-destructive, and inexpensive method is provided for the determination of the size and colour of plant materials using a rig consisting of a lightbox, camera, and colour checker card and using free and open-source scripts that run in Python 3.8. This method accurately predicted the lycopene content in tomato fruit and the chlorophyll content in basil leaves.
| S-EPMC10647133 | biostudies-literature
Project description:Fluorescence Recovery After Photobleaching (FRAP) and inverse FRAP (iFRAP) assays can be used to assess the mobility of fluorescent molecules. These assays measure diffusion by monitoring the return of fluorescence in bleached regions (FRAP), or the dissipation of fluorescence from photoconverted regions (iFRAP). However, current FRAP/iFRAP analysis methods suffer from simplified assumptions about sample geometry, bleaching/photoconversion inhomogeneities, and the underlying reaction-diffusion kinetics. To address these shortcomings, we developed the software PyFRAP, which fits numerical simulations of three-dimensional models to FRAP/iFRAP data and accounts for bleaching/photoconversion inhomogeneities. Using PyFRAP we determined the diffusivities of fluorescent molecules spanning two orders of magnitude in molecular weight. We measured the tortuous effects that cell-like obstacles exert on effective diffusivity and show that reaction kinetics can be accounted for by model selection. These applications demonstrate the utility of PyFRAP, which can be widely adapted as a new extensible standard for FRAP analysis.
| S-EPMC5910415 | biostudies-other