Online Interactive Platform for COVID-19 Literature Visual Analytics: Platform Development Study.

BACKGROUND: Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. OBJECTIVE: We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. METHODS: We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher's query. RESULTS: PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. CONCLUSIONS: The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.

A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information.

Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf's ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.

In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.

Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.

The complex, unpredictable nature of pathogen occurrence has required substantial efforts to accurately predict infectious diseases (IDs). With rising popularity of Machine Learning (ML) and Deep Learning (DL) techniques combined with their unique ability to uncover connections between large amounts of diverse data, we conducted a PRISMA systematic review to investigate advances in ID prediction for human and animal diseases using ML and DL. This review included the type of IDs modeled, ML and DL techniques utilized, geographical distribution, prediction tasks performed, input features utilized, spatial and temporal scales, error metrics used, computational efficiency, uncertainty quantification, and missing data handling methods. Among 237 relevant articles published between January 2001 and May 2021, highly contagious diseases in humans were most often represented, including COVID-19 (37.1%), influenza/influenza-like illnesses (9.3%), dengue (8.9%), and malaria (5.1%). Out of 37 diseases identified, 51.4% were zoonotic, 37.8% were human-only, and 8.1% were animal-only, with only 1.6% economically significant, non-zoonotic livestock diseases. Despite the number of zoonoses, 86.5% of articles modeled humans whereas only a few articles (5.1%) contained more than one host species. Eastern Asia (32.5%), North America (17.7%), and Southern Asia (13.1%) were the most represented locations. Frequent approaches included tree-based ML (38.4%) and feed-forward neural networks (26.6%). Articles predicted temporal incidence (66.7%), disease risk (38.0%), and/or spatial movement (31.2%). Less than 10% of studies addressed uncertainty quantification, computational efficiency, and missing data, which are essential to operational use and deployment. This study highlights trends and gaps in ML and DL for ID prediction, providing guidelines for future works to better support biopreparedness and response. To fully utilize ML and DL for improved ID forecasting, models should include the full disease ecology in a One-Health context, important food and agricultural diseases, underrepresented hotspots, and important metrics required for operational deployment.

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time.

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder-decoder model). The disease models were trained on data from seven different countries at the region-level between 2009-2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

Digital Biosurveillance for Zoonotic Disease Detection in Kenya.

Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01-4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00-0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.

Characterization of Root-Knot Nematode Resistance in Medicago truncatula.

Root knot (Meloidogyne spp.) and cyst (Heterodera and Globodera spp.) nematodes infect all important crop species, and the annual economic loss due to these pathogens exceeds $90 billion. We screened the worldwide accession collection with the root-knot nematodes Meloidogyne incognita, M. arenaria and M. hapla, soybean cyst nematode (SCN-Heterodera glycines), sugar beet cyst nematode (SBCN-Heterodera schachtii) and clover cyst nematode (CLCN-Heterodera trifolii), revealing resistant and susceptible accessions. In the over 100 accessions evaluated, we observed a range of responses to the root-knot nematode species, and a non-host response was observed for SCN and SBCN infection. However, variation was observed with respect to infection by CLCN. While many cultivars including Jemalong A17 were resistant to H. trifolii, cultivar Paraggio was highly susceptible. Identification of M. truncatula as a host for root-knot nematodes and H. trifolii and the differential host response to both RKN and CLCN provide the opportunity to genetically and molecularly characterize genes involved in plant-nematode interaction. Accession DZA045, obtained from an Algerian population, was resistant to all three root-knot nematode species and was used for further studies. The mechanism of resistance in DZA045 appears different from Mi-mediated root-knot nematode resistance in tomato. Temporal analysis of nematode infection showed that there is no difference in nematode penetration between the resistant and susceptible accessions, and no hypersensitive response was observed in the resistant accession even several days after infection. However, less than 5% of the nematode population completed the life cycle as females in the resistant accession. The remainder emigrated from the roots, developed as males, or died inside the roots as undeveloped larvae. Genetic analyses carried out by crossing DZA045 with a susceptible French accession, F83005, suggest that one gene controls resistance in DZA045.

Child-Adult Differences in Using Dual-Task Paradigms to Measure Listening Effort.

PURPOSE: The purpose of the project was to investigate the effects modifying the secondary task in a dual-task paradigm to measure objective listening effort. To be specific, the complexity and depth of processing were increased relative to a simple secondary task. METHOD: Three dual-task paradigms were developed for school-age children. The primary task was word recognition. The secondary task was a physical response to a visual probe (simple task), a physical response to a complex probe (increased complexity), or word categorization (increased depth of processing). Sixteen adults (22-32 years, M = 25.4) and 22 children (9-17 years, M = 13.2) were tested using the 3 paradigms in quiet and noise. RESULTS: For both groups, manipulations of the secondary task did not affect word recognition performance. For adults, increasing depth of processing increased the calculated effect of noise; however, for children, results with the deep secondary task were the least stable. CONCLUSIONS: Manipulations of the secondary task differentially affected adults and children. Consistent with previous findings, increased depth of processing enhanced paradigm sensitivity for adults. However, younger participants were more likely to demonstrate the expected effects of noise on listening effort using a secondary task that did not require deep processing.

Vowel perception in listeners with normal hearing and in listeners with hearing loss: a preliminary study.

OBJECTIVES: To determine the influence of hearing loss on perception of vowel slices. METHODS: Fourteen listeners aged 20-27 participated; ten (6 males) had hearing within normal limits and four (3 males) had moderate-severe sensorineural hearing loss (SNHL). Stimuli were six naturally-produced words consisting of the vowels /i a u æ ɛ ʌ/ in a /b V b/ context. Each word was presented as a whole and in eight slices: the initial transition, one half and one fourth of initial transition, full central vowel, one-half central vowel, ending transition, one half and one fourth of ending transition. Each of the 54 stimuli was presented 10 times at 70 dB SPL (sound press level); listeners were asked to identify the word. Stimuli were shaped using signal processing software for the listeners with SNHL to mimic gain provided by an appropriately-fitting hearing aid. RESULTS: Listeners with SNHL had a steeper rate of decreasing vowel identification with decreasing slice duration as compared to listeners with normal hearing, and the listeners with SNHL showed different patterns of vowel identification across vowels when compared to listeners with normal hearing. CONCLUSION: Abnormal temporal integration is likely affecting vowel identification for listeners with SNHL, which in turn affects vowel internal representation at different levels of the auditory system.

Phylogenetic analysis of Pasteuria penetrans by use of multiple genetic loci.

Pasteuria penetrans is a gram-positive, endospore-forming eubacterium that apparently is a member of the Bacillus-Clostridium clade. It is an obligate parasite of root knot nematodes (Meloidogyne spp.) and preferentially grows on the developing ovaries, inhibiting reproduction. Root knot nematodes are devastating root pests of economically important crop plants and are difficult to control. Consequently, P. penetrans has long been recognized as a potential biocontrol agent for root knot nematodes, but the fastidious life cycle and the obligate nature of parasitism have inhibited progress on mass culture and deployment. We are currently sequencing the genome of the Pasteuria bacterium and have performed amino acid level analyses of 33 bacterial species (including P. penetrans) using concatenation of 40 housekeeping genes, with and without insertions/deletions (indels) removed, and using each gene individually. By application of maximum-likelihood, maximum-parsimony, and Bayesian methods to the resulting data sets, P. penetrans was found to cluster tightly, with a high level of confidence, in the Bacillus class of the gram-positive, low-G+C-content eubacteria. Strikingly, our analyses identified P. penetrans as ancestral to Bacillus spp. Additionally, all analyses revealed that P. penetrans is surprisingly more closely related to the saprophytic extremophile Bacillus haladurans and Bacillus subtilis than to the pathogenic species Bacillus anthracis and Bacillus cereus. Collectively, these findings strongly imply that P. penetrans is an ancient member of the Bacillus group. We suggest that P. penetrans may have evolved from an ancient symbiotic bacterial associate of nematodes, possibly as the root knot nematode evolved to be a highly specialized parasite of plants.