Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread

Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread

Stimulus Pulse-Based Distributed Control for the Locomotion of a UBot Modular Robot

A distributed control algorithm based on a stimulus pulse signal is proposed in this paper for the locomotion of a Modular Self-reconfigurable Robot (MSRR). This approach can adapt effectively to the dynamic changes in the MSRR's topological configuration: the functional role of the configuration can be recognized through local topology detection, dynamic ID address allocation and local topology matching, such that the features of the entire configuration can be identified and thereby the corresponding stimulus signals can be chosen to control the whole system for coordinated locomotion. This approach has advantages over centralized control in terms of flexibility and robustness, and communication efficiency is not limited by the module number, which can realize coordinated locomotion control conveniently (especially for configurations made up of massive modules and characterized by a chain style or a quadruped style)

Medial Habenula-Interpeduncular Nucleus Circuit Contributes to Anhedonia-Like Behavior in a Rat Model of Depression

The habenula is a nuclear complex composed of the lateral habenula (LHb) and medial habenula (MHb), two distinct structures. Much progress has been made to emphasize the role of the LHb in the pathogenesis of depression. In contrast, relatively less research has focused on the MHb. However, in recent years, the role of the MHb has begun to gain increasing attention. The MHb connects to the interpeduncular nucleus (IPN) both morphologically and functionally. The MHb-IPN pathway plays an important role in regulating higher brain functions, including cognition, reward, and decision making. It indicates a role of the MHb in the pathogenesis of depression. Thus, we investigated the role of the MHb-IPN pathway in depression. MHb metabolic activity was increased in the chronic unpredictable mild stress (CUMS)-exposed rat model of depression. MHb lesions in the CUMS-exposed rats reversed anhedonia-like behavior, as observed in the sucrose preference test, and significantly downregulated the elevated metabolic activity of the IPN. Substance P (SP)-containing neurons of the MHb were found to innervate the IPN and to be the main source of SP in the IPN. SP content of IPN tissue of the CUMS-exposed rats was increased and MHb lesions reversed this change. In the in vitro experiment, firing rate recordings showed that SP perfusion increased the activity of IPN neurons. Our results suggest that hyperactivity of the MHb-IPN circuit is involved in the anhedonia-like behavior of depression, and that SP mediates the effect of the MHb on IPN neurons

A Synthetic Algorithm for Tracking a Moving Object in a Multiple-Dynamic Obstacles Environment Based on Kinematically Planar Redundant Manipulators

This paper presents a synthetic algorithm for tracking a moving object in a multiple-dynamic obstacles environment based on kinematically planar manipulators. By observing the motions of the object and obstacles, Spline filter associated with polynomial fitting is utilized to predict their moving paths for a period of time in the future. Several feasible paths for the manipulator in Cartesian space can be planned according to the predicted moving paths and the defined feasibility criterion. The shortest one among these feasible paths is selected as the optimized path. Then the real-time path along the optimized path is planned for the manipulator to track the moving object in real-time. To improve the convergence rate of tracking, a virtual controller based on PD controller is designed to adaptively adjust the real-time path. In the process of tracking, the null space of inverse kinematic and the local rotation coordinate method (LRCM) are utilized for the arms and the end-effector to avoid obstacles, respectively. Finally, the moving object in a multiple-dynamic obstacles environment is thus tracked via real-time updating the joint angles of manipulator according to the iterative method. Simulation results show that the proposed algorithm is feasible to track a moving object in a multiple-dynamic obstacles environment

Pathologic gene network rewiring implicates PPP1R3A as a central regulator in pressure overload heart failure

Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvest 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtain genome-wide genotyping and gene expression measurements for a subset of 313. We build failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs). PPP1R3A emerges as a regulator whose network connectivity changes significantly between health and disease. RNA sequencing after PPP1R3A knockdown validates network-based predictions, and highlights metabolic pathway regulation associated with increased cardiomyocyte size and perturbed respiratory metabolism. Mice lacking PPP1R3A are protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify previously unreported cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a central regulator in heart failure

Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals’ microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. “Optimal” MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for