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Introduction: Electronic influenza surveillance systems aid in health surveillance and clinical decisionmaking within the emergency department (ED). While major advances have been made in integrating clinical decision-making tools within the electronic health record (EHR), tools for sharing surveillance data are often piecemeal, with the need for data downloads and manual uploads to shared servers, delaying time from data acquisition to end-user. Real-time surveillance can help both clinicians and public health professionals recognize circulating influenza earlier in the season and provide ongoing situational awareness.Methods: We created a prototype, cloud-based, real-time reporting system in two large, academically affiliated EDs that streamed continuous data on a web-based dashboard within hours of specimen collection during the influenza season. Data included influenza test results (positive or negative) coupled with test date, test instrument geolocation, and basic patient demographics. The system provided immediate reporting to frontline clinicians and to local, state, and federal health department partners.Results: We describe the process, infrastructure requirements, and challenges of developing and implementing the prototype system. Key process-related requirements for system development included merging data from the molecular test (GeneXpert) with the hospitals’ EHRs, securing data, authorizing/ authenticating users, and providing permissions for data access refining visualizations for end-users.Conclusion: In this case study, we effectively integrated multiple data systems at four distinct hospital EDs, relaying data in near real time to hospital-based staff and local and national public health entities, to provide laboratory-confirmed influenza test results during the 2014-2015 influenza season. Future innovations need to focus on integrating the dashboard within the EHR and clinical decision tools.

Importance Understanding the susceptibility and infectiousness of children and adolescents in comparison to adults is important to appreciate their role in the COVID-19 pandemic. Objective To determine SARS-CoV-2 susceptibility and infectiousness of children and adolescents with adults as comparator for three variants (wild-type, Alpha, Delta) in the household setting. We aimed to identify the effects independent of vaccination. Data Sources We searched EMBASE, PubMed and medRxiv up to January 2022. Additional studies were identified through contacting subject experts. Study Selection Two reviewers independently identified studies providing secondary attack rates (SAR) for SARS-CoV-2 infection in children (0-9 years), adolescents (10-19 years) or both compared with adults (20 years and older) derived from household data. Data Extraction and Synthesis Two reviewers independently performed data extraction. We assessed risk of bias of included studies using a critical appraisal checklist and a random-effects meta-analysis model to pool association estimates. Main Outcomes and Measures Odds ratio (OR) for SARS-CoV-2 infection comparing children and adolescents with adults stratified by wild-type, Alpha, and Delta variant, respectively. Susceptibility was defined as the secondary attack rate (SAR) among susceptible household contacts irrespective of the age of the index case. Infectiousness was defined as the SAR irrespective of the age of household contacts when children/adolescents/adults were the index case. Results Twenty-eight studies (308,857 contacts) were included in the susceptibility analysis, for Delta only one (large) study was available. Compared to adults children and adolescents were less susceptible to the wild-type and Delta variant, but equally susceptible to the Alpha variant. In the infectiousness analysis, 21 studies (201,199 index cases) were included. Compared to adults, children and adolescents

Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots. Recent deep reinforcement learning approaches show promising results to solve the problem, but it is not solved yet, as they typically assume no robot kinodynamic restrictions, holonomic movement or perfect environment knowledge. Moreover, most algorithms fail in the real world due to the inability to generate real-world training data for the huge variability of possible scenarios. In this work, we present a novel planner, DQN-DOVS, that uses deep reinforcement learning on a descriptive robocentric velocity space model to navigate in highly dynamic environments. It is trained using a smart curriculum learning approach on a simulator that faithfully reproduces the real world, reducing the gap between the reality and simulation. We test the resulting algorithm in scenarios with different number of obstacles and compare it with many state-of-the-art approaches, obtaining a better performance. Finally, we try the algorithm in a ground robot, using the same setup as in the simulation experiments., Comment: Perception and Navigation for Autonomous Robotics in Unstructured and Dynamic Environments (PNARUDE) workshop in IROS 2022

Aim of study: To define an early selection strategy based on tests applied to full-sibling progenies of Pinus caribaea var. hondurensis grown in the Cerrado Biome. Area of study: Prata region (MG), Brazil. Material and methods: Progeny tests were cultivated in 2006; the study followed a completely randomized design, with 79 families of full-siblings and 15 repetitions, with one plant per plot. Thinning was carried out at the age of 6 and 8 years; 615 individuals and 44 families were included in the test. The following quantitative variables were used in the statistical analysis of data on the remaining individuals: diameter at breast height (DBH) in cm, total height (H) in m, and volume in dm³ at the age of 3, 4, 5, 6, 7, 8 and 11 years. BLUP multi-trait multivariate model, with non-structured covariance structure matrix, was adopted for calculations. Main results: There were strong additive genetic correlations (above 90%) between variables DBH and H, in all analyzed ages. Strong volume correlations were estimated based on the age group over four years; volume selection efficiency reached its peak at the age of five years. Selection based on volume at the age of 5 years leads to genetic gains in this variable; selection intensity values can range from 7.8% to 6.4% and 5.4%, and from 10% to 20% and 30%. Research highlights: The best strategy lies on carrying out the selections at the age of five years, based on 30% selection intensity.