Open-source Online Platform Research Articles

Hypoxic indices for obstructive sleep apnoea severity and cardiovascular disease risk prediction: A comparison and application in a community population.

The apnoea-hypopnoea index (AHI) and oxygen desaturation index (ODI) encounter challenges in capturing the intricate relationship between obstructive sleep apnoea (OSA) and cardiovascular disease (CVD) risks. Although novel hypoxic indices have been proposed to tackle these limitations, there remains a gap in comprehensive validation and comparisons across a unified dataset. Samples were derived from the Sleep Heart Health Study (SHHS), involving 4485 participants aged over 40 years after data quality screening. The study compared several key indices, including AHI, ODI, the reconstructed hypoxic burden (rHB), the percentage of sleep time with the duration of respiratory events causing desaturation (pRED_3p) and the sleep breathing impairment index (SBII), in relation to CVD mortality and morbidity risks. Adjusted Cox proportional models were employed to calculate hazard ratios (HRs) for each index, and comparisons were performed. SBII and pRED_3p exhibited significant correlations with both CVD mortality and morbidity, with SBII showing the highest adjusted HR (95% confidence interval) for mortality (2.04 [1.25, 3.34]) and pRED_3p for morbidity (1.43 [1.09-1.88]). In contrast, rHB was only significant in predicting CVD mortality (1.63 [1.05-2.53]), while AHI and ODI did not show significant correlations with CVD outcomes. The adjusted models based on SBII and pRED_3p exhibited optimal performance in the CVD mortality and morbidity datasets, respectively. This study identified the optimal indices for OSA-related CVD risks prediction, SBII for mortality and pRED_3p for morbidity. The open-source online platform provides the computation of the indices.

CPA Methodology: educational technological design proposal to solve problems

Technology Enhanced Learning (TEL) is a teaching and learning approach that utilizes various forms of technology to enhance the learning experience (Cochrane, Redmond, and Corrin, 2018). One example of TEL is the use of digital fabrication, where we can find various open-source prototyping platforms, in educational settings. ADAPTA is an assistive technology educational event, in which elder people and students came together in meaningful and authentic domestic problem-solving educational activity, implementing the CPA methodology, based on a process of observation and prototyping, attempts to open a space for the design of assistive parts/devices for the elderly to improve your quality of life, through creation, testing, fabrication and usage of the pieces. A relevant aspect of the CPA methodology is that it allows the cultivation of 21st Century skills, through challenging projects that encourage the four Ps – Project, Passion, Peers and Play (Resnick, 2017).
 
 The CPA in Spanish, (APC in English for Autonomous Project Cell) methodology is a four-step process that can be used in conjunction with digital design processes and digital fabrication to support TEL. The four steps of the CPA methodology are: (a) Observation: This step involves students observing and documenting their surroundings or a specific problem they want to solve. This can involve collecting data, taking photos, or sketching out ideas in a real space (b) Documentation: In this step, students document their observations and ideas in a clear and organized manner. This can involve creating a written report, creating a visual representation of their ideas, using an open source online platform (c) Make/reflect: In this step, students used analog drawing and sketches, digital drawing, and digital fabrication. Later, the Arduino platform to create a prototype of their solution. This can involve programming the Arduino using a variety of programming languages, such as C++ or Python, and connecting various electronic components to the Arduino to create their desired outcome. As they work on their prototype, students should also be encouraged to reflect on their progress and make any necessary adjustments (d) Show and Tell: In the final step, students present their prototype to their peers and teachers/guides, demonstrating the functioning of their solution and explaining the process they followed to create it. This step allows students to share their learning with others and receive feedback on their work.
 
 Following the 2018 OECD objectives that aspire to an education that promotes individual, social and planetary well-being, we pay special attention to the domestic problems experienced by older adults. Considering that 16.2% of the country's inhabitants are over 60 years of age and, according to data from the Casen Survey, some 460,000 elderly people live in single-person households in Chile, it is important to pay attention to how aging creates difficulties in mobility and coexistence with the environment of the domestic space, such as the kitchen and bathroom, with the handling of forces and weights, difficult access, unattainable heights, etc. The workshop took place at a municipal space (Hub Providencia) in Santiago Chile, which met every week, showing them a variety of possible solutions to start observing in their homes, how they interacted with space and usability.
 Examples of possible solutions: 3d printed gadgets: open bottle, cary 3 bags, separate a book, open a bottle Nb 2.By following the CPA methodology, students were engaged in authentic problem-solving activities and learn valuable skills, such as critical thinking, communication, and collaboration. The use of digital drawing and fabrication, and Arduino in TEL could help the elder group to start designing from the drawing (pencil and paper). Afterwards this product was taken by the younger group of students, who were in charge of building the physical prototype. By providing hands-on opportunities for both group of students to engage in authentic problem-solving activities, they could converge on physical products that were finally given from the younger students to the elder ones.

Cultivating Bioliteracy, Biodiscovery, Data Literacy, and Ecological Monitoring in Undergraduate Courses with iNaturalist

iNaturalist is a large and growing open-source online platform connecting a global community of users to the natural world and to each other. iNaturalist makes it simple for anyone to share observations, get identifications, and make identifications of any species. We demonstrate how iNaturalist can connect students to nature in undergraduate courses, simultaneously contributing to biodiversity knowledge. Our three case studies use iNaturalist to improve student bioliteracy (awareness and knowledge of biodiversity), to engage them in biodiscovery (discovering undocumented biodiversity occurrences, behaviors, and interactions), to introduce students to systematic ecological sampling (documenting biodiversity patterns and trends), and to improve their data literacy (by depositing and accessing open biodiversity data and by performing analyses). Included are examples from a variety of contexts—a required general science course, introductory courses for science majors, and more specialized electives. We discuss the rationale for iNaturalist inclusion, how iNaturalist inspires students and complements courses, and outputs of our students’ iNaturalist use. Introducing students to tools such as iNaturalist helps build the next generation of bioliterate and biocurious citizens and scientists, fluent in open collaborative research and learning. With wider uptake and coordination, iNaturalist has potential to connect undergraduate ecology courses to form a distributed virtual global biodiversity observatory.

ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics.

Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign.

EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control

In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.

Application of a Model that Combines the YOLOv3 Object Detection Algorithm and Canny Edge Detection Algorithm to Detect Highway Accidents

This study proposed a model for highway accident detection that combines the You Only Look Once v3 (YOLOv3) object detection algorithm and Canny edge detection algorithm. It not only detects whether an accident has occurred in front of a vehicle, but further performs a preliminary classification of the accident to determine its severity. First, this study established a dataset consisting of around 4500 images mainly taken from the angle of view of dashcams from an open-source online platform. The dataset was named the Highway Dashcam Car Accident for Classification System (HDCA-CS) and was developed with the aim of conforming to the setting of this study. The HDCA-CS not only considers weather conditions (rainy days, foggy days, nighttime settings, and other low-visibility conditions), but also various types of accidents, thus increasing the diversity of the dataset. In addition, we proposed two types of accidents—accidents involving damaged cars and accidents involving overturned cars—and developed three different design methods for comparing vehicles involved in accidents involving damaged cars. Canny edge detection algorithm processed single high-resolution images of accidents were also added to compensate for the low volume of accident data, thereby addressing the problem of data imbalance for training purposes. Lastly, the results showed that the proposed model achieved a mean average precision (mAP) of 62.60% when applied to the HDCA-CS testing dataset. When comparing the proposed model with a benchmark model, two abovementioned accident types were combined to allow the proposed model to produce binary classification outputs (i.e., non-occurrence and occurrence of an accident). The HDCA-CS was then applied to the two models, and testing was conducted using single high-resolution images. At 76.42%, the mAP of the proposed model outperformed the benchmark model’s 75.18%; and if we were to apply the proposed model to only test scenarios in which an accident has occurred, its performance would be even better relative to the benchmark. Therefore, our findings demonstrate that our proposed model is superior to other existing models.

Introducing Biomedisa as an open-source online platform for biomedical image segmentation

We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.

Expanded SEIRCQ Model Applied to COVID-19 Epidemic Control Strategy Design and Medical Infrastructure Planning

The rapid spread of COVID-19 has demanded a quick response from governments in terms of planning contingency efforts that include the imposition of social isolation measures and an unprecedented increase in the availability of medical services. Both courses of action have been shown to be critical to the success of epidemic control. Under this scenario, the timely adoption of effective strategies allows the outbreak to be decelerated at early stages. The objective of this study is to present an epidemic model specially tailored for the study of the COVID-19 epidemics, and the model is aimed at allowing the integrated study of epidemic control strategies and dimensioning of the required medical infrastructure. Along with the theoretical model, a case study with three prognostic scenarios is presented for the first wave of the epidemic in the city of Manaus, the capital city of Amazonas state, Brazil. Although the temporary collapse of the medical infrastructure is hardly avoidable in the state-of-affairs at this time (April 2020), the results show that there are feasible control strategies that could substantially reduce the overload within reasonable time. Furthermore, this study delivers and presents an intuitive, straightforward, free, and open-source online platform that allows the direct application of the model. The platform can hopefully provide better response time and clarity to the planning of contingency measures.

Crowdsourced Quantification and Visualization of Urban Mobility Space Inequality

Most cities are car-centric, allocating a privileged amount of urban space to cars at the expense of sustainable mobility like cycling. Simultaneously, privately owned vehicles are vastly underused, wasting valuable opportunities for accommodating more people in a livable urban environment by occupying spacious parking areas. Since a data-driven quantification and visualization of such urban mobility space inequality is lacking, here we explore how crowdsourced data can help to advance its understanding. In particular, we describe how the open-source online platform What the Street!? uses massive user-generated data from OpenStreetMap for the interactive exploration of city-wide mobility spaces. Using polygon packing and graph algorithms, the platform rearranges all parking and mobility spaces of cars, rails, and bicycles of a city to be directly comparable, making mobility space inequality accessible to a broad public. This crowdsourced method confirms a prevalent imbalance between modal share and space allocation in 23 cities worldwide, typically discriminating bicycles. Analyzing the guesses of the platform’s visitors about mobility space distributions, we find that this discrimination is consistently underestimated in the public opinion. Finally, we discuss a visualized scenario in which extensive parking areas are regained through fleets of shared, autonomous vehicles. We outline how such accessible visualization platforms can facilitate urban planners and policy makers to reclaim road and parking space for pushing forward sustainable transport solutions.

The flipped classroom for medical students.

The objectives of this curricular innovation project were to implement a flipped classroom curriculum for the gynaecologic oncology topics of the obstetrics and gynaecology medical student clerkship, and to evaluate student satisfaction with the change. Four short online videos on the topics of endometrial hyperplasia, cervical dysplasia, evaluation of an adnexal mass, and ovarian cancer were created, and students were instructed to view them prior to a class-time active learning session. The Learning Activity Management System (lams) open-source online platform was used to create an active learning class-time activity that consisted of a coached discussion of cases. Student satisfaction with the two aspects of the flipped curriculum was obtained. In addition, lecture assessment for the gynaecologic oncology topics and aggregate student performance on the gynaecological oncology questions of the US National Board of Medical Examiners (NBME) Subject Examination were compared before and after implementation of the curriculum. Eighty-nine students rotated on the clerkship during the pilot period of analysis. Seventy-one students (80%) viewed the videos prior to the class session, and 84 (94%) attended the session. Student satisfaction was very high for both parts of the curriculum. There was no significant difference in aggregate student performance on the gynaecological oncology questions of the NBME Subject Examination. The flipped classroom curriculum demonstrates a promising platform for using technology to make better use of students' time Our implementation of the flipped classroom curriculum for the gynaecologic oncology topics successfully demonstrates a promising platform for using technology to make better use of our students' time, and for increasing their satisfaction with the necessary didactic learning of the clerkship.

Evaluating the use of mobile phone technology to enhance cardiovascular disease screening by community health workers

BackgroundPrimary prevention of cardiovascular disease (CVD),by identifying individuals at risk is a well-established, but costly strategy when based on measurements that depend on laboratory analyses. A non-laboratory, paper-based CVD risk assessment chart tool has previously been developed to make screening more affordable in developing countries. Task shifting to community health workers (CHWs) is being investigated to further scale CVD risk screening. This study aimed to develop a mobile phone CVD risk assessment application and to evaluate its impact on CHW training and the duration of screening for CVD in the community by CHWs. MethodsA feature phone application was developed using the open source online platform, CommCare©. CHWs (n=24) were trained to use both paper-based and mobile phone CVD risk assessment tools. They were randomly allocated to using one of the risk tools to screen 10–20 community members and then crossed over to screen the same number, using the alternate risk tool. The impact on CHW training time, screening time and margin of error in calculating risk scores was recorded. A focus group discussion evaluated experiences of CHWs using the two tools. ResultsThe training time was 12.3h for the paper-based chart tool and 3h for the mobile phone application. 537 people were screened. The mean screening time was 36min (SD=12.6) using the paper-base chart tool and 21min (SD=8.71) using the mobile phone application, p=<0.0001. Incorrect calculations (4.3% of average systolic BP measurements, 10.4% of BMI and 3.8% of CVD risk score) were found when using the paper-based chart tool while all the mobile phone calculations were correct. Qualitative findings from the focus group discussion corresponded with the findings of the pilot study. ConclusionThe reduction in CHW training time, CVD risk screening time, lack of errors in calculation of a CVD risk score and end user satisfaction when using a mobile phone application, has implications in terms of adoption and sustainability of this primary prevention strategy to identify people with high CVD risk who can be referred for appropriate diagnoses and treatment.

A PUBLIC PLATFORM FOR GEOSPATIAL DATA SHARING FOR DISASTER RISK MANAGEMENT

Abstract. Several studies have been conducted in Africa to assist local governments in addressing the risk situation related to natural hazards. Geospatial data containing information on vulnerability, impacts, climate change, disaster risk reduction is usually part of the output of such studies and is valuable to national and international organizations to reduce the risks and mitigate the impacts of disasters. Nevertheless this data isn't efficiently widely distributed and often resides in remote storage solutions hardly reachable. Spatial Data Infrastructures are technical solutions capable to solve this issue, by storing geospatial data and making them widely available through the internet. Among these solutions, GeoNode, an open source online platform for geospatial data sharing, has been developed in recent years. GeoNode is a platform for the management and publication of geospatial data. It brings together mature and stable open-source software projects under a consistent and easy-to-use interface allowing users, with little training, to quickly and easily share data and create interactive maps. GeoNode data management tools allow for integrated creation of data, metadata, and map visualizations. Each dataset in the system can be shared publicly or restricted to allow access to only specific users. Social features like user profiles and commenting and rating systems allow for the development of communities around each platform to facilitate the use, management, and quality control of the data the GeoNode instance contains (http://geonode.org/). This paper presents a case study scenario of setting up a Web platform based on GeoNode. It is a public platform called MASDAP and promoted by the Government of Malawi in order to support development of the country and build resilience against natural disasters. A substantial amount of geospatial data has already been collected about hydrogeological risk, as well as several other-disasters related information. Moreover this platform will help to ensure that the data created by a number of past or ongoing projects is maintained and that this information remains accessible and useful. An Integrated Flood Risk Management Plan for a river basin has already been included in the platform and other data from future disaster risk management projects will be added as well.

MMWeb--an online platform for employing multiple ecological niche modeling algorithms.

BackgroundPredicting the ecological niche and potential habitat distribution of a given organism is one of the central domains of ecological and biogeographical research. A wide variety of modeling techniques have been developed for this purpose. In order to implement these models, the users must prepare a specific runtime environment for each model, learn how to use multiple model platforms, and prepare data in a different format each time. Additionally, often model results are difficult to interpret, and a standardized method for comparing model results across platforms does not exist. We developed a free and open source online platform, the multi-models web-based (mMWeb) platform, to address each of these problems, providing a novel environment in which the user can implement and compare multiple ecological niche model (ENM) algorithms.MethodologymMWeb combines 18 existing ENMs and their corresponding algorithms and provides a uniform procedure for modeling the potential habitat niche of a species via a common web browser. mMWeb uses Java Native Interface (JNI), Java R Interface to combine the different ENMs and executes multiple tasks in parallel on a super computer. The cross-platform, user-friendly interface of mMWeb simplifies the process of building ENMs, providing an accessible and efficient environment from which to explore and compare different model algorithms.