Project description:This paper describes an interactive web-based near real-time (NRT) forest monitoring system using four levels of geographic information services: 1) the acquisition of continuous data streams from satellite and community-based monitoring using mobile devices, 2) NRT forest disturbance detection based on satellite time-series, 3) presentation of forest disturbance data through a web-based application and social media and 4) interaction of the satellite based disturbance alerts with the end-user communities to enhance the collection of ground data. The system is developed using open source technologies and has been implemented together with local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. The results show that the system is able to provide easy access to information on forest change and considerably improves the collection and storage of ground observation by local experts. Social media leads to higher levels of user interaction and noticeably improves communication among stakeholders. Finally, an evaluation of the system confirms the usability of the system in Ethiopia. The implemented system can provide a foundation for an operational forest monitoring system at the national level for REDD+ MRV applications.
Project description:Large events and gatherings, particularly those taking place indoors, have been linked to multi-transmission events that have accelerated the pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To provide real-time, geo-localized risk information, we developed an interactive online dashboard that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States. The website combines documented case reports at the county level with ascertainment bias information obtained via population-wide serological surveys to estimate real time circulating, per-capita infection rates. These rates are updated daily as a means to visualize the risk associated with gatherings, including county maps and state-level plots. The website provides data-driven information to help individuals and policy-makers make prudent decisions (e.g., increasing mask wearing compliance and avoiding larger gatherings) that could help control the spread of SARS-CoV-2, particularly in hard-hit regions.
Project description:The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system's ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/.
Project description:Mobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy. In this paper we propose a real-time monitoring framework for tracking the economic consequences of various forms of mobility reductions involving European countries. We adopt a granular representation of mobility patterns during both the first and second waves of SARS-COV-2 in Italy, Germany, France and Spain to provide an analytical characterization of the rate of losses of industrial production by means of a nowcasting methodology. Our approach exploits the information encoded in massive datasets of human mobility provided by Facebook and Google, which are published at higher frequencies than the target economic variables, in order to obtain an early estimate before the official data becomes available. Our results show, in first place, the ability of mobility-related policies to induce a contraction of mobility patterns across jurisdictions. Besides this contraction, we observe a substitution effect which increases mobility within jurisdictions. Secondly, we show how industrial production strictly follows the dynamics of population commuting patterns and of human mobility trends, which thus provide information on the day-by-day variations in countries' economic activities. Our work, besides shedding light on how policy interventions targeted to induce a mobility contraction impact the real economy, constitutes a practical toolbox for helping governments to design appropriate and balanced policy actions aimed at containing the SARS-COV-2 spread, while mitigating the detrimental effect on the economy. Our study reveals how complex mobility patterns can have unequal consequences to economic losses across countries and call for a more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic activities.
Project description:UnlabelledCytoscape Web is a web-based network visualization tool-modeled after Cytoscape-which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF.Availability and implementationCytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/.
Project description:Fiber tracking is a technique that, based on a diffusion tensor magnetic resonance imaging dataset, locates the fiber bundles in the human brain. Because it is a computationally expensive process, the interactivity of current fiber tracking tools is limited. We propose a new approach, which we termed real-time interactive fiber tracking, which aims at providing a rich and intuitive environment for the neuroradiologist. In this approach, fiber tracking is executed automatically every time the user acts upon the application. Particularly, when the volume of interest from which fiber trajectories are calculated is moved on the screen, fiber tracking is executed, even while it is being moved. We present our fiber tracking tool, which implements the real-time fiber tracking concept by using the video card's graphics processing units to execute the fiber tracking algorithm. Results show that real-time interactive fiber tracking is feasible on computers equipped with common, low-cost video cards.
Project description:Airborne transmission of aerosols contributes to a large portion of the SARS-CoV-2 spread indoors. This study develops a real-time interactive web-based platform for the public to compare various strategies to curb indoor airborne transmission of COVID-19 in different archetype buildings at a city scale. Although many countries have started vaccination and a gradual re-opening, because of emerging new variants of the virus and the possibility of future pandemics, a lively updated tool for monitoring and mitigation of infection risk is essential. As a demonstration, we evaluated the impacts of six mitigation measures on the infection risks in various building types in a city. It shows that the same strategy could perform quite differently, depending on building types and properties. All strategies are shown to reduce the infection risk but wearing a mask and reducing exposure time are the most effective strategies in many buildings, with around 60% reduction. Doubling the minimum required outdoor air ventilation rate is not as effective as other strategies to reduce the risk. It also causes considerable penalties on energy consumption. Therefore, new building ventilation standards, control actions, and design criteria should be considered to mitigate the infection risk and save energy.
Project description:MotivationDNA copy number profiles characterize regions of chromosome gains, losses and breakpoints in tumor genomes. Although many models have been proposed to detect these alterations, it is not clear which model is appropriate before visual inspection the signal, noise and models for a particular profile.ResultsWe propose SegAnnDB, a Web-based computer vision system for genomic segmentation: first, visually inspect the profiles and manually annotate altered regions, then SegAnnDB determines the precise alteration locations using a mathematical model of the data and annotations. SegAnnDB facilitates collaboration between biologists and bioinformaticians, and uses the University of California, Santa Cruz genome browser to visualize copy number alterations alongside known genes.Availability and implementationThe breakpoints project on INRIA GForge hosts the source code, an Amazon Machine Image can be launched and a demonstration Web site is http://bioviz.rocq.inria.fr.
Project description:COVID-19-related disruptions of people and goods' circulation can affect drug markets, especially for new psychoactive substances (NPSs). Drug shortages could cause a change in available NPS, with the introduction of new, unknown, substances. The aims of the current research were to use a web crawler, NPSfinder®, to identify and categorize emerging NPS discussed on a range of drug enthusiasts/psychonauts' websites/fora at the time of the pandemic; social media for these identified NPS were screened as well. The NPSfinder® was used here to automatically scan 24/7 a list of psychonaut websites and NPS online resources. The NPSs identified in the time frame between January and August 2020 were searched in both the European Monitoring Center for Drugs and Drug Addictions (EMCDDA)/United Nations Office on Drugs and Crime (UNODC) databases and on social media (Facebook, Twitter, Instagram, Pinterest, and YouTube) as well, with a content qualitative analysis having been carried out on reddit.com. Of a total of 229 NPSs being discussed at the time of the pandemic, some 18 NPSs were identified for the first time by the NPSfinder®. These included six cathinones, six opioids, two synthetic cannabinoid receptor agonists (SCRAs), two phenylcyclohexylpiperidine (PCP)-like molecules, and two psychedelics. Of these NPSs, 10 were found to be previously unreported to either the UNODC or the EMCDDA. Of these 18 NPSs, opioids and cathinones were the most discussed on social media/reddit, with the highest number of threads associated. Current findings may support the use of both automated web crawlers and social listening approaches to identify emerging NPSs; the pandemic-related imposed restrictions may somehow influence the demand for specific NPS classes.