The infinity vaccine war: linguistic regularities and audience engagement of vaccine debate on Twitter

Purpose: As public health professionals strive to promote vaccines for inoculation efforts, fervent anti-vaccination movements are marshaling against it. This study is motived by a need to better understand the online discussion around vaccination. The authors identified the sentiments, emotions and topics of pro- and anti-vaxxers' tweets, investigated their change since the pandemic started and further examined the associations between these content features and audiences' engagement. Design/methodology/approach: Utilizing a snowball sampling method, data were collected from the Twitter accounts of 100 pro-vaxxers (266,680 tweets) and 100 anti-vaxxers (248,425 tweets). The authors are adopting a zero-shot machine learning algorithm with a pre-trained transformer-based model for sentiment analysis and structural topic modeling to extract the topics. And the authors use the hurdle negative binomial model to test the relationships among sentiment/emotion, topics and engagement. Findings: In general, pro-vaxxers used more positive tones and more emotions of joy in their tweets, while anti-vaxxers utilized more negative terms. The cues of sadness predominantly encourage retweets across the pro- and anti-vaccine corpus, while tweets amplifying the emotion of surprise are more attention-grabbing and getting more likes. Topic modeling of tweets yields the top 15 topics for pro- and anti-vaxxers separately. Among the pro-vaxxers' tweets, the topics of "Child protection” and "COVID-19 situation” are positively predicting audiences' engagement. For anti-vaxxers, the topics of "Supporting Trump,” "Injured children,” "COVID-19 situation,” "Media propaganda” and "Community building” are more appealing to audiences. Originality/value: This study utilizes social media data and a state-of-art machine learning algorithm to generate insights into the development of emotionally appealing content and effective vaccine promotion strategies while combating coronavirus disease 2019 and moving toward a global recovery. Peer review: The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-03-2022-0186 © 2023, Emerald Publishing Limited.

Uniform consistency for local fitting of time series non-parametric regression allowing for discrete-valued response

Local linear kernel fitting is a popular nonparametric technique for modelling nonlinear time series data. Investigations into it, although extensively made for continuousvalued case, are still rare for the time series that are discrete-valued. In this paper, we propose and develop the uniform consistency of local linear maximum likelihood (LLML) fitting for time series regression allowing response to be discrete-valued under β-mixing dependence condition. Specifically, the uniform consistency of LLML estimators is established under time series conditional exponential family distributions with aid of a beta-mixing empirical process through local estimating equations. The rate of convergence is also provided under mild conditions. Performances of the proposed method are demonstrated by a Monte-Carlo simulation study and an application to COVID-19 data. There is a huge potential for the developed theory contributing to further development of discrete-valued response semiparametric time series models © 2022 American Psychological Association

The design and implementation of the all-in-one machine for killing and sorting in the small and medium-sized logistics center

To stop the COVID-19 spread, artificial intelligence and new technologies are also actively participating in the battle. For fighting against the virus, disinfection is one of the effective ways to block the spread of the virus. According to the preliminary market research, only in the large distribution centers is the operator-controlled machine disinfection, at present most of the small and medium-sized express logistics stations are through human resources to carry out disinfection, and sorting of express packages, so the dependence on human costs, medical resources can be imagined. To this end, we designed ultrasonic atomization disinfection, sorting, and whole load notification integrated machine based on deep learning [1] and Internet of Things [2] technology to cope with the trend of normalization and recurrence of the epidemic. After testing logistics with different labels, the experimental results show that the system can effectively distinguish different labels and carry out a series of operations such as disinfection, sorting, and notification of full load, which can be put into production and contribute to epidemic prevention work. © 2023 IEEE.

A Remote Visiting and Monitoring System for Neonatal Intensive Care Units via Internet of Things

To meet the visiting needs of families of children in neonatal intensive care unit and reduce the burden of hospital management during the COVID-19 epidemic, we developed a remote visiting and monitoring system using the internet of things. The Raspberry Pi is used as the core hardware platform. The real-time signal of the bedside monitor is converted into a virtual camera, and is connected to the Raspberry Pi which has a real camera with CMOS Serial Interface (CSI). The frames of the two cameras are collected via FFmpeg technology, and then are pushed to the cloud server through Real-Time Messaging Protocol (RTMP). The video streams are then transferred and distributed via a Nginx server running RTMP protocol, and finally are displayed on the web page via the Flask framework. When tested, the system ran stably, and the real-time pictures from the camera and the bedside monitor screen in the hospital were clearly shown on a personal computer or a mobile phone in a remote distance out of the hospital, just by click the link of the associated web page. We think this system is helpful for families to remotely visit the babies anywhere any time, and it is also helpful for hospitals to reduce the human workload and the financial expenditure. © 2022 ACM.

Rates of New Peanut Allergy and Discontinuation Following Introduction in High-Risk Infants

Rationale: Current guidelines recommend peanut introduction to high-risk infants. However, compliance and rates of new peanut allergy (PA) require further study. Methods: Participants aged 4-11 months with no prior peanut exposure and (i) diagnosis of non-peanut food allergy, (ii) moderate-severe atopic dermatitis, or (iii) first degree relative with PA were enrolled. PA status was determined by skin testing and food challenge. Participants without PA were advised to consume 2 grams of peanut protein three times/week. Monthly questionnaires were administered, with follow-up visits at 18 and 30 months. Results: At baseline, 35/326 (11%) participants were peanut allergic. Of 291 without PA, 78 (27%) discontinued peanut at least temporarily 115 times during follow-up because of suspected participant reaction (40%), fear of reaction (3%), reaction or fear of reaction in a family member (21%), participant refusal (9%), peanut introduction was too much work (3%), or other reasons (23%), including the COVID-19 pandemic. Six of 291 participants (2.1%) who consumed peanut developed PA (2 consistent with FPIES). Among 291 participants without PA at baseline, none of the 17 participants with initial skin prick test of at least 4 mm and <10 mm developed PA. Conclusions: New PA after early introduction recommendations was rare, confirming the LEAP study findings. Transient discontinuation was common, mostly due to suspected participant reaction. High-risk children may require substantial support to keep peanut in their diet.

AN ELECTROCHEMICAL BIOSENSING PLATFORM FOR RAPID DETECTION OF SARS-COV-2 ANTIBODIES

A portable and low-cost electrochemical immunosensor platform is developed for rapid (13 min) and accurate quantification of SARS-CoV-2 serum antibodies (10.1 ng/mL − 60 µg/mL for IgG and 1.64 ng/mL − 50 µg/mL for IgM). No obvious cross-reactivity with other interference proteins was observed. Stable performance of the immunosensor within 24-week storage at room temperature was achieved. The practical use of the immunosensor was demonstrated using real patient samples. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Work-in-Progress - Post-COVID: Adapting Education to a Changing Educational Environment through Immersive Technology

In a Post-COVID world, Hybrid classes remain essential as students face challenges in attending them in person. This creates additional engagement issues. The creation of an easy-to-use platform to enable this mixed-mode classroom is critical for teachers that are frustrated by the difficult management of hybrid classrooms. An aspect is student engagement, which today has no established analytical mechanism to evaluate during classroom sessions. This work-in-progress paper describes a technology that has the ability to obtain real-time analytic data to determine the engagement of all students at all times and helps teachers to know which students are lagging early on. © 2022 Immersive Learning Research Network.

New Insights from Chemical Biology: Molecular Basis of Transmission, Diagnosis, and Therapy of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is caused by a novel strain of coronavirus, designated as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has caused a global pandemic rapidly sweeping across all countries, bringing social and economic hardship to millions. Most countries have implemented early warning measures to detect, isolate, and treat patients infected with SARS-CoV-2. This minireview summarizes some of those steps, in particular, testing methods and drug development in the context of chemical biology, and discusses the molecular basis of COVID-19's virulent transmissibility.

Photocatalytic Chemical Crosslinking for Profiling RNA–Protein Interactions in Living Cells

The dynamic interactions between RNAs and proteins play crucial roles in regulating diverse cellular processes. Proteome‐wide characterization of these interactions in their native cellular context remains desirable but challenging. Herein, we developed a photocatalytic crosslinking (PhotoCAX) strategy coupled with mass spectrometry (PhotoCAX‐MS) and RNA sequencing (PhotoCAX‐seq) for the study of the composition and dynamics of protein‐RNA interactions. By integrating the blue light‐triggered photocatalyst with a dual‐functional RNA–protein crosslinker (RP‐linker) and the phase separation‐based enrichment strategy, PhotoCAX‐MS revealed a total of 2044 RBPs in human HEK293 cells. We further employed PhotoCAX to investigate the dynamic change of RBPome in macrophage cells upon LPS‐stimulation, as well as the identification of RBPs interacting directly with the 5′ untranslated regions of SARS‐CoV‐2 RNA. [ FROM AUTHOR] Copyright of Angewandte Chemie International Edition 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 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 . (Copyright applies to all s.)

Covalently Engineered Protein Minibinders with Enhanced Neutralization Efficacy against Escaping SARS-CoV-2 Variants.

The rapid emergence and spread of escaping mutations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has significantly challenged our efforts in fighting against the COVID-19 pandemic. A broadly neutralizing reagent against these concerning variants is thus highly desirable for the prophylactic and therapeutic treatments of SARS-CoV-2 infection. We herein report a covalent engineering strategy on protein minibinders for potent neutralization of the escaping variants such as B.1.617.2 (Delta), B.1.617.1 (Kappa), and B.1.1.529 (Omicron) through in situ cross-linking with the spike receptor binding domain (RBD). The resulting covalent minibinder (GlueBinder) exhibited enhanced blockage of RBD-human angiotensin-converting enzyme 2 (huACE2) interaction and more potent neutralization effect against the Delta variant than its noncovalent counterpart as demonstrated on authentic virus. By leveraging the covalent chemistry against escaping mutations, our strategy may be generally applicable for restoring and enhancing the potency of neutralizing antibodies to SARS-CoV-2 and other rapidly evolving viral targets.

Severe Acute Respiratory Syndrome Coronavirus-2 Spike Protein Nanogel as a Pro-Antigen Strategy with Enhanced Protective Immune Responses.

Prevention and intervention methods are urgently needed to curb the global pandemic of coronavirus disease-19 caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Herein, a general pro-antigen strategy for subunit vaccine development based on the reversibly formulated receptor binding domain of SARS-CoV-2 spike protein (S-RBD) is reported. Since the poor lymph node targeting and uptake of S-RBD by antigen-presenting cells prevent effective immune responses, S-RBD protein is formulated into a reversible nanogel (S-RBD-NG), which serves as a pro-antigen with enhanced lymph node targeting and dendritic cell and macrophage accumulation. Synchronized release of S-RBD monomers from the internalized S-RBD-NG pro-antigen triggers more potent immune responses in vivo. In addition, by optimizing the adjuvant used, the potency of S-RBD-NG is further improved, which may provide a generally applicable, safer, and more effective strategy for subunit vaccine development against SARS-CoV-2 as well as other viruses.