A Personalized Avatar-Based Web Application to Help People Understand How Social Distancing Can Reduce the Spread of COVID-19: Cross-sectional, Observational, Pre-Post Study.

BACKGROUND: To reduce the transmission of SARS-CoV-2 and the associated spread of COVID-19, many jurisdictions around the world imposed mandatory or recommended social or physical distancing. As a result, at the beginning of the pandemic, various communication materials appeared online to promote distancing. Explanations of the science underlying these mandates or recommendations were either highly technical or highly simplified. OBJECTIVE: This study aimed to understand the effects of a dynamic visualization on distancing. Our overall aim was to help people understand the dynamics of the spread of COVID-19 in their community and the implications of their own behavior for themselves, those around them, the health care system, and society. METHODS: Using Scrum, which is an agile framework; JavaScript (Vue.js framework); and code already developed for risk communication in another context of infectious disease transmission, we rapidly developed a new personalized web application. In our application, people make avatars that represent themselves and the people around them. These avatars are integrated into a 3-minute animation illustrating an epidemiological model for COVID-19 transmission, showing the differences in transmission with and without distancing. During the animation, the narration explains the science of how distancing reduces the transmission of COVID-19 in plain language in English or French. The application offers full captions to complement the narration and a descriptive transcript for people using screen readers. We used Google Analytics to collect standard usage statistics. A brief, anonymous, optional survey also collected self-reported distancing behaviors and intentions in the previous and coming weeks, respectively. We launched and disseminated the application on Twitter and Facebook on April 8, 2020, and April 9, 2020. RESULTS: After 26 days, the application received 3588 unique hits from 82 countries. The optional survey at the end of the application collected 182 responses. Among this small subsample of users, survey respondents were nearly (170/177, 96%) already practicing distancing and indicated that they intended to practice distancing in the coming week (172/177, 97.2%). Among the small minority of people (n=7) who indicated that they had not been previously practicing distancing, 2 (29%) reported that they would practice distancing in the week to come. CONCLUSIONS: We developed a web application to help people understand the relationship between individual-level behavior and population-level effects in the context of an infectious disease spread. This study also demonstrates how agile development can be used to quickly create personalized risk messages for public health issues like a pandemic. The nonrandomized design of this rapid study prevents us from concluding the application's effectiveness; however, results thus far suggest that avatar-based visualizations may help people understand their role in infectious disease transmission.

Extracranial hypoglossal neurofibroma with a variant ansa cervicalis: a case report.

PURPOSE: This case report aims to explore a rare combination of findings in a cadaver donor: variant ansa cervicalis, vagus (CN X) and hypoglossal (CN XII) nerve fusion, and extracranial hypoglossal neurofibroma. BACKGROUND: The type of ansa cervicalis variation presented in this report has been documented in less than 1% of described cases. The CN X-CN XII fusion has been reported in one prior study. Additionally, hypoglossal neurofibromas are benign neoplasms of the peripheral nerve sheath. There are only two known cases of extracranial hypoglossal neurofibroma described in the literature. CASE REPORT: The study investigated a swelling of the right CN XII in a 90-year-old female cadaver donor. Detailed dissection, examination of the region, and histopathological analysis of the mass followed. The entire course of CN XII and other cranial nerves were examined to exclude concurrent pathology. A fusiform enlargement of the right CN XII was observed in the submandibular region, measuring ~ 1.27 × 1.27 cm. The superior portion of the right CN XII was fused to the right CN X, exiting the jugular foramen. The superior root of ansa cervicalis, normally a branch of CN XII, was found to arise from CN X on the right side. The left CN XII and CN X were unremarkable. Histopathological examination revealed benign neurofibroma. CONCLUSION: The anatomical variation and rare location of the tumor necessitate further investigation to better understand pathogenesis, clinical correlation, and surgical implications. This study furthers knowledge of this condition and contributes to the currently limited body of research.

Correction: Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.

[This corrects the article DOI: 10.1371/journal.pone.0233242.].

Toward Food Sovereignty for Coastal Communities of Eastern Québec: Co-designing A Website to Support Consumption of Edible Resources from the St. Lawrence River, Estuary, and Gulf.

Background. Despite the abundance and proximity of edible marine resources, coastal communities along the St. Lawrence in Eastern Québec rarely consume these resources. Within a community-based food sovereignty project, Manger notre Saint-Laurent ("Sustenance from our St. Lawrence"), members of participating communities (3 non-Indigenous, 1 Indigenous) identified a need for a web-based decision tool to help make informed consumption choices. Methods. We thus aimed to co-design a prototype website that facilitates informed choices about consuming local edible marine resources based on seasonal and regional availability, food safety, nutrition, and sustainability, with community members, regional stakeholders, and experts in user experience design and web development. We conducted 48 interviews with a variety of people over 3 iterative cycles, assessing the prototype's ease of use with a validated measure, the System Usability Scale. Results. Community members, regional stakeholders, and other experts identified problematic elements in initial versions of the website (e.g., confusing symbols). We resolved issues and added features people identified as useful. Usability scores reached "best imaginable" for both the second and the third versions and did not differ significantly between sociodemographic groups. The final prototype includes a tool to explore each species and index cards to regroup accurate evidence relevant to each species. Conclusions. Engaging co-designers with different sociodemographic characteristics brought together a variety of perspectives. Several components would not have been included without co-designers' input; other components were greatly improved thanks to their feedback. Co-design approaches in research and intervention development are preferable to foster the inclusion of a variety of people. Once the prototype is programmed and available online, we hope to evaluate the website to determine its effects on food choices.

Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.

Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84-0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81-0.93) or raw percentages (R2 = 0.76-0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49-0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69-0.92) than 4X (R2 = 0.87-0.95). The MIR (R2 = 0.45-0.80) performed better than NIR (R2 = 0.40-0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86-0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.

Current and next-year cranberry yields predicted from local features and carryover effects.

Wisconsin and Quebec are the world leading cranberry-producing regions. Cranberries are grown in acidic, naturally low-fertility sandy beds. Cranberry fertilization is guided by general soil and tissue nutrient tests in addition to yield target and vegetative biomass. However, other factors such as cultivar, location, and carbon and nutrient storage impact cranberry nutrition and yield. The objective of this study was to customize nutrient diagnosis and fertilizer recommendation at local scale and for next-year cranberry production after accounting for local factors and carbon and nutrient carryover effects. We collected 1768 observations from on-farm surveys and fertilizer trials in Quebec and Wisconsin to elaborate a machine learning model using minimum datasets. We tested carryover effects in a 5-year Quebec fertilizer experiment established on permanent plots. Micronutrients contributed more than macronutrients to variation in tissue compositions. Random Forest model related accurately current-year berry yield to location, cultivars, climatic indices, fertilization, and tissue and soil tests as features (classification accuracy of 0.83). Comparing compositions of defective and successful tissue compositions in the Euclidean space of tissue compositions, the general across-factor diagnosis differed from the local factor-specific diagnosis. Nutrient standards elaborated in one region could hardly be transposed to another and, within the same region, from one bed to another due to site-specific characteristics. Next-year yield and nutrient adjustment could be predicted accurately from current-year yield and tissue composition and other features, with R2 value of 0.73 in regression mode and classification accuracy of 0.85. Compositional and machine learning methods proved to be effective to customize nutrient diagnosis and predict site-specific measures for nutrient management of cranberry stands. This study emphasized the need to acquire large experimental and observational datasets to capture the numerous factor combinations impacting current and next-year cranberry yields at local scale.

A Web Application About Herd Immunity Using Personalized Avatars: Development Study.

BACKGROUND: Herd immunity or community immunity refers to the reduced risk of infection among susceptible individuals in a population through the presence and proximity of immune individuals. Recent studies suggest that improving the understanding of community immunity may increase intentions to get vaccinated. OBJECTIVE: This study aims to design a web application about community immunity and optimize it based on users' cognitive and emotional responses. METHODS: Our multidisciplinary team developed a web application about community immunity to communicate epidemiological evidence in a personalized way. In our application, people build their own community by creating an avatar representing themselves and 8 other avatars representing people around them, for example, their family or coworkers. The application integrates these avatars in a 2-min visualization showing how different parameters (eg, vaccine coverage, and contact within communities) influence community immunity. We predefined communication goals, created prototype visualizations, and tested four iterative versions of our visualization in a university-based human-computer interaction laboratory and community-based settings (a cafeteria, two shopping malls, and a public library). Data included psychophysiological measures (eye tracking, galvanic skin response, facial emotion recognition, and electroencephalogram) to assess participants' cognitive and affective responses to the visualization and verbal feedback to assess their interpretations of the visualization's content and messaging. RESULTS: Among 110 participants across all four cycles, 68 (61.8%) were women and 38 (34.5%) were men (4/110, 3.6%; not reported), with a mean age of 38 (SD 17) years. More than half (65/110, 59.0%) of participants reported having a university-level education. Iterative changes across the cycles included adding the ability for users to create their own avatars, specific signals about who was represented by the different avatars, using color and movement to indicate protection or lack of protection from infectious disease, and changes to terminology to ensure clarity for people with varying educational backgrounds. Overall, we observed 3 generalizable findings. First, visualization does indeed appear to be a promising medium for conveying what community immunity is and how it works. Second, by involving multiple users in an iterative design process, it is possible to create a short and simple visualization that clearly conveys a complex topic. Finally, evaluating users' emotional responses during the design process, in addition to their cognitive responses, offers insights that help inform the final design of an intervention. CONCLUSIONS: Visualization with personalized avatars may help people understand their individual roles in population health. Our app showed promise as a method of communicating the relationship between individual behavior and community health. The next steps will include assessing the effects of the application on risk perception, knowledge, and vaccination intentions in a randomized controlled trial. This study offers a potential road map for designing health communication materials for complex topics such as community immunity.