Activity

As many countries fear and even experience the emergence of a second wave of COVID-19, reminding health care workers (HCWs) and other hospital employees of the critical role they play in preventing SARS-CoV-2 transmission is more important than ever. Building and strengthening the intrinsic motivation of HCWs to apply infection prevention and control (IPC) guidelines to avoid contaminating their colleagues, patients, friends, and relatives is a goal that must be energetically pursued. A high rate of nosocomial infections during the first COVID-19 wave was detected by IPC specialists and further cemented their belief in the need for an engaging intervention that could improve compliance with COVID-19 safe behaviors.

Our aim was to develop a serious game that would promote IPC practices with a specific focus on COVID-19 among HCWs and other hospital employees.

The first 3 stages of the SERES framework were used to develop this serious game. A brainswarming session between developers and IPC specialists waement systems.This paper is concerned with the problem of finite-time H∞ state estimation for genetic regulatory networks with randomly occurring uncertainties. The persistent dwell-time switching, as a more versatile class of switching signal, is considered in this paper. Besides, several random variables that obey the Bernoulli distribution are used to represent randomly occurring uncertainties. The overriding purpose of this paper is to design an estimator to ensure that the estimation error system is stochastically finite-time bounded and satisfies the H∞ performance. The sufficient conditions for the explicit form of the estimator gains can be obtained by the Lyapunov method. Finally, a numerical example is given to verify the correctness and feasibility of the proposed method.The firing rate of some biological neurons such as neocortical pyramidal neurons is consistent with fractional order derivative, and the fractional-order neuron models depict the firing rate of neurons more accurately than other integer order neuron models do. For this reason, first, the dynamical characteristics of fractional order Hindmarsh Rose (HR) neuron are investigated, here and then a two coupled neuronal system based on Hindmarsh Rose neuron is presented. The results show several differences in the dynamical cha.racteristics of integer order and fractional order Hindmarsh Rose neuron model. The integer order model shows only one type of firing characteristics when the parameter of the model remained the same. The fractional-order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied with the same parameter values. The firing frequency increases as the order of the fractional operator decreases.Tree-based phylogenetic networks, which may be roughly defined as leaf-labeled networks built by adding arcs only between the original tree edges, have elegant properties for modeling evolutionary histories. We answer an open question of Francis, Semple, and Steel about the complexity of determining how far a phylogenetic network is from being tree-based, including non-binary phylogenetic networks. We show that finding a phylogenetic tree covering the maximum number of nodes in a phylogenetic network can be computed in polynomial time via an encoding into a minimum-cost flow problem.Among all the PTMs, the protein phosphorylation is pivotal for various pathological and physiological processes. About 30% of eukaryotic proteins undergo the phosphorylation modification, leading to various changes in conformation, function, stability, localization, and so forth. In eukaryotic proteins, phosphorylation occurs on serine (S), Threonine (T) and Tyrosine (Y) residues. Among these all, serine phosphorylation has its own importance as it is associated with various important biological processes, including energy metabolism, signal transduction pathways, cell cycling, and apoptosis. Mevastatin mouse Thus, its identification is important, however, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need of an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Herein, we propose a novel predictor for identification of Phosphoserine sites (PhosS) in proteins, by integrating the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep features. We used well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications. Among different DNNs, the best score is shown by Convolutional Neural Network-based model which renders CNN based prediction model the best for Phosphoserine prediction.This article is the second in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. In this work, we track the hand of healthy individuals as they perform a variety of activities of daily living (ADLs) in three ways decoupled from hand orientation end-point locations of the hand trajectory, whole path trajectories of the hand, and straight-line paths generated using start and end points of the hand. These data are examined by a clustering procedure to reduce the wide range of hand use to a smaller representative set. Hand orientations are subsequently analyzed for the end-point location clustering results and subsets of orientations are identified in three reference frames global, torso, and forearm. Data driven methods that are used include dynamic time warping (DTW), DTW barycenter averaging (DBA), and agglomerative hierarchical clustering with Ward’s linkage. Analysis of the end-point locations, path trajectory, and straight-line path trajectory identified 5, 5, and 7 ADL task categories, respectively, while hand orientation analysis identified up to 4 subsets of orientations for each task location, discretized and classified to the facets of a rhombicuboctahedron. Together these provide insight into our hand usage in daily life and inform an implementation in prosthetic or robotic devices using sequential control.