Chuharper7118

Wild birds harbour a large number of adenoviruses that remain uncharacterised with respect to their genomic organisation, diversity, and evolution within complex ecosystems. Here, we present the first complete genome sequence of an atadenovirus from a passerine bird that is tentatively named Passerine adenovirus 1 (PaAdV-1). The PaAdV-1 genome is 39,664 bp in length, which was the longest atadenovirus to be sequenced, to the best of our knowledge, and contained 42 putative genes. Its genome organisation was characteristic of the members of genus Atadenovirus; however, the novel PaAdV-1 genome was highly divergent and showed the highest sequence similarity with psittacine adenovirus-3 (55.58%). Importantly, PaAdV-1 complete genome was deemed to contain 17 predicted novel genes that were not present in any other adenoviruses sequenced to date, with several of these predicted novel genes encoding proteins that harbour transmembrane helices. Subsequent analysis of the novel PaAdV-1 genome positioned phylogenetically to a distinct sub-clade with all others sequenced atadenoviruses and did not show any obvious close evolutionary relationship. This study concluded that the PaAdV-1 complete genome described here is not closely related to any other adenovirus isolated from avian or other natural host species and that it should be considered a separate species.Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients' dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients' unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. DNA Methyltransferase inhibitor The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model's accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.This work aims to investigate the feasibility of employing multi-frequency bioimpedance analysis for hemodynamic assessment. Towards this, we aim to explore one of its implementations, electrical impedance spectroscopy (EIS), for estimating changes in radial artery diameter due to blood flow. Following from our previous investigations, here, we use a commercial device-the Quadra® Impedance Spectroscopy device-for impedance measurements of the forearm of three subjects under normal conditions and occluding the artery with a cuff. This was performed simultaneously with ultrasound measurements as a reference. The impedance spectra were measured over time, yielding waveforms reflecting changes due to blood flow. Contributions from the fat/muscle domains were accounted for using the occluded impedance response, resulting in arterial impedance. A modified relationship was approximated to calculate the diameter from the arterial impedance, which showed a similarity with ultrasound measurements. Comparison with the ultrasound measurements revealed differences in phase and amplitude, primarily due to the approximated relationship between impedance and diameter and neglecting the impedance phase analysis.