Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human-Machine Interfaces.

Recent advances in flexible wearable devices have boosted the remarkable development of devices for human-machine interfaces, which are of great value to emerging cybernetics, robotics, and Metaverse systems. However, the effectiveness of existing approaches is limited by the quality of sensor data and classification models with high computational costs. Here, a novel gesture recognition system with triboelectric smart wristbands and an adaptive accelerated learning (AAL) model is proposed. The sensor array is well deployed according to the wrist anatomy and retrieves hand motions from a distance, exhibiting highly sensitive and high-quality sensing capabilities beyond existing methods. Importantly, the anatomical design leads to the close correspondence between the actions of dominant muscle/tendon groups and gestures, and the resulting distinctive features in sensor signals are very valuable for differentiating gestures with data from 7 sensors. The AAL model realizes a 97.56% identification accuracy in training 21 classes with only one-third operands of the original neural network. The applications of the system are further exploited in real-time somatosensory teleoperations with a low latency of <1 s, revealing a new possibility for endowing cyber-human interactions with disruptive innovation and immersive experience.

Expression of COVID-19 virus N gene in insect cells

In order to obtain COVID-19 nucleocapsid protein with similar function and activity to natural protein and apply it to practical detection. Firstly, according to Bac-to-bac insect expression system and synthetic COVID-19 nucleocapsid protein(N protein)sequence, BamH I and Xba I on pFastBacTMHTB vector were added to upstream and downstream primers respectively. The N gene was amplified by PCR technology, and T-Vector pMD19(simple)vector and pFastBacTMHTB vector were connected successively and recombinant plasmids pMD19-T(simple)-COV19-N and pFastBacTMHTB-COV19-N, and finally construct recombinant bacmid DH10 Bac-pFastBacTMHTB-COV19-N in DH10 Bac cells was expressed in insect cell Sf9. The recombinant protein was obtained and analyzed by SDS-PAGE and WB. The recombinant plasmid pMD19-T(simple)-COV19-N was identified by PCR and double enzyme digestion. The recombinant bacmid DH10 Bac-pFastBacTMHTB-COV19-N was constructed in DH10 Bac cells was identified by PCR and the expected two bands were 2 430,3 690 bp, respectively, which proved that the recombinant bacmid was successfully obtained. The recombinant bacmid was transfected into Sf9 insect cells. At the same time, the recombinant GFP protein control group was established. After 120 h of transfection, the recombinant N protein and recombinant GFP protein were collected and samples were prepared;SDS-PAGE and WB analysis were carried out respectively. HRP-His labeled antibody was used to verify that the transfection was successful, and both recombinant N protein and recombinant GFP protein were successfully expressed in Sf9 cells. The experimental results were consistent with the expectation, and the size of recombinant N protein band was about 46 ku. The eukaryotic expression vector of respiratory coronavirus N gene was successfully constructed and successfully expressed in insect cells, which provides an experimental basis for the establishment of ELISA detection methods and other related research.

New Insights Into the Social Rumor Characteristics During the COVID-19 Pandemic in China.

Background: In the early stage of the COVID-19 outbreak in China, several social rumors in the form of false news, conspiracy theories, and magical cures had ever been shared and spread among the general public at an alarming rate, causing public panic and increasing the complexity and difficulty of social management. Therefore, this study aims to reveal the characteristics and the driving factors of the social rumors during the COVID-19 pandemic. Methods: Based on a sample of 1,537 rumors collected from Sina Weibo's debunking account, this paper first divided the sample into four categories and calculated the risk level of all kinds of rumors. Then, time evolution analysis and correlation analysis were adopted to study the time evolution characteristics and the spatial and temporal correlation characteristics of the rumors, and the four stages of development were also divided according to the number of rumors. Besides, to extract the key driving factors from 15 rumor-driving factors, the social network analysis method was used to investigate the driver-driver 1-mode network characteristics, the generation driver-rumor 2-mode network characteristics, and the spreading driver-rumor 2-mode characteristics. Results: Research findings showed that the number of rumors related to COVID-19 were gradually decreased as the outbreak was brought under control, which proved the importance of epidemic prevention and control to maintain social stability. Combining the number and risk perception levels of the four types of rumors, it could be concluded that the Creating Panic-type rumors were the most harmful to society. The results of rumor drivers indicated that panic psychology and the lag in releasing government information played an essential role in driving the generation and spread of rumors. The public's low scientific literacy and difficulty in discerning highly confusing rumors encouraged them to participate in spreading rumors. Conclusion: The study revealed the mechanism of rumors. In addition, studies involving rumors on different emergencies and social platforms are warranted to enrich the findings.

Nightmares in People with COVID-19: Did Coronavirus Infect Our Dreams?

INTRODUCTION: A growing number of studies have demonstrated that the coronavirus disease-19 (COVID-19) pandemic has severely affected sleep and dream activity in healthy people. To date, no investigation has examined dream activity specifically in COVID-19 patients. METHODS: As part of the International COVID-19 Sleep Study (ICOSS), we compared 544 COVID-19 participants with 544 matched-controls. A within-subjects comparison between pre-pandemic and pandemic periods computed separately for controls and COVID-19 participants were performed on dream recall and nightmare frequency (DRF; NF). Also, non-parametric comparisons between controls and COVID-19 participants were carried out. Further, we compared psychological measures between the groups collected during pandemic. Ordinal logistic regression to detect the best predictors of NF was performed. RESULTS: We found that people reported greater dream activity during the pandemic. Comparisons between controls and COVID-19 participants revealed a) no difference between groups concerning DRF in the pre-pandemic period and during the pandemic; b) no difference between groups concerning nightmare frequency in the pre-pandemic period; and c) COVID-19 participants reported significantly higher NF than controls during pandemic (p = 0.003). Additionally, we showed that a) anxiety, depression, post-traumatic stress-disorder (PTSD) symptom scores were higher in COVID-19 participants than controls; and b) quality of life and health as well as wellbeing (WHO-5) scores were significantly higher in controls than COVID-19 participants. Finally, ordinal logistic regression indicates that DRF (p < 0.001), PTSD (p < 0.001), anxiety (p = 0.018), insomnia (p = 0.039), COVID-19 severity (p = 0.014), sleep duration (p = 0.003) and age (p = 0.001) predicted NF. DISCUSSION: Our work shows strong associations between increased nightmares in those reporting having had COVID-19. This suggests that the more that people were affected by COVID-19, the greater the impact upon dream activity and quality of life.

Microestimates of wealth for all low- and middle-income countries.

Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development.

Pharmaceutical services and monitoring strategies for patients with new type of Coronavirus pneumonia complicated by diabetes

New coronavirus pneumonia is menacing, and patients with new coronavirus pneumonia combined with other underlying diseases are more at risk. Glycemic control level directly affects the body's immune response and body state. Its low immune status is extremely likely to increase the risk of illness. Infected patients are more likely to aggravate the infection and further cause cytokine storms. Therefore, patients with new type of coronavirus infection and type 2 diabetes need better blood glucose control and management while treating new type of coronavirus infection. This article combines the research on the rational use of diabetes and analyzes the characteristics of the existing clinical data of COVID-19 to explore the pharmacological practice mode and medication monitoring strategy of this special patient. It is hoped to provide COVID-19 patients with diabetes with a more optimized and reasonable medication regimen and improve the clinical medication level.

Biomimetic Design and Mass Production of Sustainable Multiscale Cellulose Fibers‐Based Hierarchical Filter Materials for Protective Clothing

As a core part of personal protective equipment (PPE), filter materials play a key role in individual protection, especially in the fight against the COVID‐19. Here, a high‐performance multiscale cellulose fibers‐based filter material is introduced for protective clothing, which overcomes the limitation of mutual exclusion of filtration and permeability in cellulose‐based filter materials. With the hierarchical biomimetic structure design and the active surface of multiscale cellulose fibers, high PM2.5 removal efficiency of ≈92% is achieved with the high moisture transmission rate of 8 kg m−2 d−1. Through a simple and effective dip‐coating and roll‐to‐roll process, the hierarchical filter materials can be made on a large scale and further fabricated into high‐quality protective clothing by industrial production equipment. [ABSTRACT FROM AUTHOR] Copyright of Advanced Materials Technologies is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)