Liao ning virus in China

<p>Abstract</p> <p>Background</p> <p>Liao ning virus is in the genus Seadornavirus within the family Reoviridae and has a genome composed of 12 segments of double-stranded RNA (dsRNA). It is transmitted by mosquitoes and only isolated in China to date and it is the only species within the genus Seadornavirus which was reported to have been propagated in mammalian cell lines. In the study, we report 41 new isolates from northern and southern Xinjiang Uygur autonomous region in China and describe the phylogenetic relationships among all 46 Chinese LNV isolates.</p> <p>Findings</p> <p>The phylogenetic analysis indicated that all the isolates evaluated in this study can be divided into 3 different groups that appear to be related to geographic origin based on partial nucleotide sequence of the 10th segment which is predicted to encode outer coat proteins of LNV. Bayesian coalescent analysis estimated the date of the most recent common ancestor for the current Chinese LNV isolates to be 318 (with a 95% confidence interval of 30-719) and the estimated evolutionary rates is 1.993 × 10^-3substitutions per site per year.</p> <p>Conclusions</p> <p>The results indicated that LNV may be an emerging virus at a stage that evaluated rapidly and has been widely distributed in the north part of China.</p

Liao ning virus in China

<p>Abstract</p> <p>Background</p> <p>Liao ning virus is in the genus Seadornavirus within the family Reoviridae and has a genome composed of 12 segments of double-stranded RNA (dsRNA). It is transmitted by mosquitoes and only isolated in China to date and it is the only species within the genus Seadornavirus which was reported to have been propagated in mammalian cell lines. In the study, we report 41 new isolates from northern and southern Xinjiang Uygur autonomous region in China and describe the phylogenetic relationships among all 46 Chinese LNV isolates.</p> <p>Findings</p> <p>The phylogenetic analysis indicated that all the isolates evaluated in this study can be divided into 3 different groups that appear to be related to geographic origin based on partial nucleotide sequence of the 10th segment which is predicted to encode outer coat proteins of LNV. Bayesian coalescent analysis estimated the date of the most recent common ancestor for the current Chinese LNV isolates to be 318 (with a 95% confidence interval of 30-719) and the estimated evolutionary rates is 1.993 × 10^-3substitutions per site per year.</p> <p>Conclusions</p> <p>The results indicated that LNV may be an emerging virus at a stage that evaluated rapidly and has been widely distributed in the north part of China.</p

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Finite element model updating through smooth nonconvex optimization

During the past few decades, great efforts have been devoted towards finite element (FE) modeling of structures, in order to simulate the structural behavior under various loading conditions. Due to the complexity of large-scale civil structures, the simulation results generated by an FE model are usually different from these of the as-built structure. To reduce the difference, selected structural parameters can be updated by utilizing the data collected from the actual structure. This process is known as FE model updating. This research explores FE model updating utilizing the measured frequency-domain modal properties, i.e. resonance frequencies and mode shapes. Naturally, frequency-domain FE model updating is formulated as optimization problem aiming to minimize the difference between simulated and experimentally-measured modal properties. This research focuses on three frequency-domain model updating formulations, i.e. MAC value, eigenvector difference and modal dynamic residual formulations. Local search optimization algorithms are first studied for comparison. The performance of model updating formulations and local search optimization algorithms are validated through numerical simulations, laboratory and field experiments. To collect structural vibration data from the actual structure, a new wireless sensing node, named Martlet, is developed. To overcome the limitation of local search optimization algorithms, this research also investigates two global optimization algorithms, i.e. branch-and-bound and primal-relaxed dual algorithms to solve the optimization problems in FE model updating. Again, the performance of the two global optimization algorithms are validated through both numerical simulation and laboratory experiment.Ph.D

Fault Diagnosis of Reciprocating Compressor Using Component Estimating Empirical Mode Decomposition and De-Dimension Template with Double-Loop Correction Algorithm

This paper presents an approach to implement multi-parameter (i.e., pressure, temperature, vibration, current, and liquid level) signals for fault diagnosis of the reciprocating compressor (RC). Due to the complexity of structure and motion of such compressor, the acquired signals involve transient impacts and noises. This causes the useful information to be corrupted and makes it difficult to diagnose the fault patterns accurately. A component estimating empirical mode decomposition (CEEMD) method is proposed to remove the random noise and improve data quality. Furthermore, a new template matching algorithm called de-dimension template with double-loop correction (DDT-DLC) is applied to diagnose the fault pattern contained in the time series signals. The DDT employs a judging criterion for key characterization parameters extraction and a multicellular parameter fusion method to reduce the dimension of the matching template, and then, the DLC supplies a double-loop correction algorithm to build a parameter state array computing model of the time series data by adjusting the dynamic factors. The proposed approach is validated with three fault patterns and the healthy pattern in a two-stage reciprocating air compressor. To confirm the superiority of the proposed method, its performance is compared with that of the traditional methods. The results have indicated that the proposed approach is of highly diagnostic accuracy and shortly computing time in the fault diagnosis

Hitchhiker: A Quadrotor Aggressively Perching on a Moving Inclined Surface Using Compliant Suction Cup Gripper

Perching on surfaces of moving objects, like vehicles, could extend the flight times and range of quadrotors. For surfaces attachment, suction cups are usually adopted due to their durability and large adhesion force. To seal on surfaces, suction cups are required to be aligned with surfaces and match to the frictions on end faces induced by relative tangential velocity. However, when the object surface is moving and inclined such that an aggressive maneuver is required, the attitude and relative velocity errors of quadrotors would become significant, which poses challenges to perch. To address the problem, we proposed a real-time trajectory planning algorithm to alleviate the velocity errors of quadrotors relative to moving surfaces. Multimodal search in dynamic time-domain is developed in the algorithm and thus the time-optimal aggressive trajectories can be efficiently generated. To further adapt to the residual attitude and relative velocity errors, we design a compliant gripper using self-sealing cups. Multiple cups in different directions are integrated into a wheel-like mechanism to increase the tolerance to attitude errors. The wheel mechanism in the gripper also eliminates the requirement of matching the attitude and tangential velocity and thus increases the adaptability to tangential velocity. Extensive tests are conducted, including comparison experiments, to perch on static and moving surfaces at various inclinations. Results demonstrate that our proposed system enables a quadrotor to reliably perch on static and moving inclined (up to 1.18$m/s$ and 90$^\circ$) surfaces with a success rate of 70\% or higher. The trajectory planner is valid and efficient. Compared to conventional suction cup grippers in moving surface perching, our gripper has larger adaptability to attitude errors and tangential velocities, and the success rate increases by 45\%.Comment: This paper has been submitted to IEEE Transactions on Automation Science and Engineering at 22-Januray-202

An integrated approach to developing self-adaptive software in open environments

One of the main challenges of developing self-adaptive systems in open environment comes from uncertain self-adaptation requirements due to the unpredictability of environment changes and its co-existence with well-defined self-adaptation requirements in self-adaptive systems. This paper presents an integrated approach that combines off-line and on-line self-adaptation together in a unified technical framework to support the development and running of such systems. We consider self-adaptive system as a multi-agent organization and propose a novel dynamic binding self-adaptation mechanism inspired from organization metaphors to specify and analyze self-adaptation. A description language, SADL, is designed to program well-defined self-adaptation logic at design-time and implement off-line self-adaptation. In order to deal with uncertain self-adaptation, a reinforcement learning method is incorporated with the dynamic binding mechanism, which enables software agents to make decisions on self-adaptation at run-time and implement on-line self-adaptation. Our approach provides a unified frame-work to accommodate off-line and on-line approaches and a general-purpose methodology to develop complex self-adaptive systems in a systematic way. A supported platform called SADE+ is developed and a case is studied to illustrate the proposed approach