The impact of sample processing on the rapid antigen detection test for SARS-CoV-2: Virus inactivation, VTM selection, and sample preservation

Many factors have been identified as having the ability to affect the sensitivity of rapid antigen detection(RAD)tests for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This study aimed to identify the impact of sample processing on the sensitivity of the RAD tests.We explored the effect of different inactivation methods,viral transport media(VTM)solutions,and sample preservation on the sensitivity of four RAD kits based on two SARS-CoV-2 strains.Compared with non-inactivation,heat inactivation significantly impacted the sensitivity of most RAD kits;however,β-propiolactone inactivation only had a minor effect.Some of the VTM solutions(VTM2,MANTACC)had a significant influence on the sensitivity of the RAD kits,especially for low viral-loads samples.The detection value of RAD kits was slightly decreased,while most of them were still in the detection range with the extension of preservation time and the increase of freeze–thaw cycles.Our results showed that selecting the appropriate inactivation methods and VTM solutions is necessary during reagent development,performance evaluation,and clinical application。

A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.

Dimension Properties of Sample Paths of Self-Similar Processes

The Hansdorff dimensions of the image and the graph of random fields are given under general conditions.The results can be used to a wider class of self-similar random fields and processes,including Brownian motion,Brownian sheet,fractional Brownian motion,processes with stable or(α,β)-fractional stable components.

THE DESIGN AND DATA PROCESSING OF THE SAMPLING SURVEY OF CHILDREN'S SITUATION IN CHINA, 1987

In July of 1987, the Sampling Survey of Children's Situation was conducted in 9 provincesautonomous regions of China. A stratified two--stage cluster sampling plan was designed for thesurvey. The paper presents the methods of stratification, selecting n=2 PSU's (cities/counties) withunequal probabilities without replacement in each stratum and selecting residents/village committeein each sampled city/county. All formulae of estimating population characteristics (especiallypopulation totals and the ratios of two totals), and estimating variances of those estimators aregiven. Finally, we analyse the precision of the survey preliminarily from the result of dataprocessing.

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

As one of the most fundamental topics in reinforcement learning(RL),sample efficiency is essential to the deployment of deep RL algorithms.Unlike most existing exploration methods that sample an action from different types of posterior distributions,we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the environment.Specifically,we first employ clustering methods in the policy sampling process to generate an action candidate set.Then we introduce a clustering buffer for modeling the internal hierarchy,which consists of on-policy data,off-policy data,and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage.In this way,our approach is able to take advantage of the supervision information in the expert demonstration data.Experiments on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective sampling.In particular,on the LGSVL task,our method can reduce the number of convergence steps by 46.7%and the convergence time by 28.5%.Furthermore,our code is open-source for reproducibility.The code is available at https://github.com/Shihwin/SelectiveSampling.