Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.

Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.

BACKGROUND: Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines. OBJECTIVE: We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination. METHODS: We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. RESULTS: A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization. CONCLUSIONS: The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.

GTZ: a fast compression and cloud transmission tool optimized for FASTQ files.

BACKGROUND: The dramatic development of DNA sequencing technology is generating real big data, craving for more storage and bandwidth. To speed up data sharing and bring data to computing resource faster and cheaper, it is necessary to develop a compression tool than can support efficient compression and transmission of sequencing data onto the cloud storage. RESULTS: This paper presents GTZ, a compression and transmission tool, optimized for FASTQ files. As a reference-free lossless FASTQ compressor, GTZ treats different lines of FASTQ separately, utilizes adaptive context modelling to estimate their characteristic probabilities, and compresses data blocks with arithmetic coding. GTZ can also be used to compress multiple files or directories at once. Furthermore, as a tool to be used in the cloud computing era, it is capable of saving compressed data locally or transmitting data directly into cloud by choice. We evaluated the performance of GTZ on some diverse FASTQ benchmarks. Results show that in most cases, it outperforms many other tools in terms of the compression ratio, speed and stability. CONCLUSIONS: GTZ is a tool that enables efficient lossless FASTQ data compression and simultaneous data transmission onto to cloud. It emerges as a useful tool for NGS data storage and transmission in the cloud environment. GTZ is freely available online at: https://github.com/Genetalks/gtz .

SLAM medical imaging enabled by pre-chirp and gain jointly managed Yb-fiber laser.

We demonstrate a pre-chirp and gain jointly managed Yb-fiber laser that drives simultaneous label-free autofluorescence-multiharmonic (SLAM) medical imaging. We show that a gain managed Yb-fiber amplifier produces high-quality compressed pulses when the seeding pulses exhibit proper negative pre-chirp. The resulting laser source can generate 43-MHz, 34-fs pulses centered at 1110 nm with more than 90-nJ energy. We apply this ultrafast source to SLAM imaging of cellular and extracellular components in various human tissues of intestinal adenocarcinoma, lung adenocarcinoma, and liver.

Caregivers' understanding of childhood influenza vaccination during the epidemic in China. A mixed-methods study.

Introduction: Influenza vaccination uptake among young children has been poor in China, but it is unclear how it changed during the COVID-19. This study aimed to investigate the uptake status and reasons of childhood influenza vaccination during the pandemic in China. Methods: A mixed-methods study combining a questionnaire survey and semi-structured interviews was conducted in Anhui, Shaanxi, and Guangdong provinces between September and November 2021. 2081 caregivers completed the valid questionnaire. 38 caregivers participated in interviews, and data were analyzed thematically, using deductive and inductive coding. Results: A total of 2081 caregivers completed the valid questionnaire, and 38 caregivers participated in interviews. Among the caregivers, a total of 1796 were in the age group for high-risk groups in the 2019-2020 flu season, and 46.10% reported that their children received influenza vaccination in the 2019-2020 flu season; 43.63% said that they vaccinated their children against influenza in the 2020-2021 flu season. Many caregivers indicated that the adoption of nonpharmacologic interventions (NPIs) during COVID-19 reduced the risk of influenza infection for children. Most caregivers consider the severity of influenza to be low, and some confused the common cold with influenza. Meanwhile, some caregivers lack confidence in the vaccine's effectiveness and importance. They thought that vaccines are not effective in preventing the constantly mutating virus. Despite clear perceptions about the severity of influenza and the effectiveness of the vaccine, we found that most caregivers did not receive any relevant medical information, and the communication about vaccines between caregivers and professional information sources, such as healthcare workers, is inadequate. Hence, caregivers have no scientific evidence to back up their perceptions. In terms of access to vaccination service, caregivers reported conflicts between time of vaccination service and their schedule, and the need for vaccine prices to be reduced. Discussion: Targeted interventions are needed to address caregivers' lack of risk perception on influenza during COVID-19 and promote communication between caregivers and professional information sources. Extending vaccination service hours and increasing the number of vaccine clinics close to residential areas and expansion of financing sources for self-paid vaccination could facilitate the access to influenza vaccination service.

Application of Multiphoton Microscopic Imaging in Study of Gastric Cancer.

Multiphoton microscopy (MPM) imaging relies on the nonlinear interaction between ultrashort optical pulses and the samples to achieve image contrast. Featuring larger penetration depth, less phototoxicity, 3-dimensional sectioning capability, no need for labeling, MPM become a powerful medical imaging technique that can identify structural characteristics of tissues at the cellular and subcellular levels. In this review paper, we introduce the working principle of MPM imaging, present the current results of MPM imaging applied to the study of gastric tumors, and discuss the future prospects of this interdisciplinary research field.

Mapping global acceptance and uptake of COVID-19 vaccination: A systematic review and meta-analysis.

Background: The COVID-19 pandemic exit strategies depend on widespread acceptance of COVID-19 vaccines. We aim to estimate the global acceptance and uptake of COVID-19 vaccination, and their variations across populations, countries, time, and sociodemographic subgroups. Methods: We searched four peer-reviewed databases (PubMed, EMBASE, Web of Science, and EBSCO) for papers published in English from December 1, 2019 to February 27, 2022. This review included original survey studies which investigated acceptance or uptake of COVID-19 vaccination, and study quality was assessed using the Appraisal tool for Cross-Sectional Studies. We reported the pooled acceptance or uptake rates and 95% confidence interval (CI) using meta-analysis with a random-effects model. Results: Among 15690 identified studies, 519 articles with 7,990,117 participants are eligible for meta-analysis. The global acceptance and uptake rate of COVID-19 vaccination are 67.8% (95% CI: 67.1-68.6) and 42.3% (95% CI: 38.2-46.5), respectively. Among all population groups, pregnant/breastfeeding women have the lowest acceptance (54.0%, 46.3-61.7) and uptake rates (7.3%, 1.7-12.8). The acceptance rate varies across countries, ranging from 35.9% (34.3-37.5) to 86.9% (81.4-92.5) for adults, and the lowest acceptance is found in Russia, Ghana, Jordan, Lebanon, and Syria (below 50%). The acceptance rate declines globally in 2020, then recovers from December 2020 to June 2021, and further drops in late 2021. Females, those aged < 60 years old, Black individuals, those with lower education or income have the lower acceptance than their counterparts. There are large gaps (around 20%) between acceptance and uptake rates for populations with low education or income. Conclusion: COVID-19 vaccine acceptance needs to be improved globally. Continuous vaccine acceptance monitoring is necessary to inform public health decision making.

Childhood Influenza Vaccination and Its Determinants during 2020-2021 Flu Seasons in China: A Cross-Sectional Survey.

Young children aged 6−59 months are recommended as one of the priority groups for seasonal influenza vaccination in China. This study assessed influenza vaccination coverage and the factors associated with vaccination uptake among children in three Chinese provinces. In September 2021, 2081 caregivers with children <5 years completed self-administered questionnaires as part of a cross-sectional survey. Logistic regression was used to assess determinants of childhood influenza vaccination. A total of 43.63% of respondents reported vaccinating their children against influenza during the 2020−2021 flu season. Caregivers who lived in Anhui province, had a bachelor degree or above, and an annual household income <20,000 RMB were more likely to vaccinate their children against influenza. Confidence in the importance (OR: 2.50; 95%CI: 1.77−3.54), safety (OR: 1.60; 95%CI: 1.29−1.99), and effectiveness (OR: 1.54; 95%CI: 1.23−1.93) of influenza vaccine was significantly associated with childhood vaccine acceptance. Respondents who saw that other caregivers were vaccinating their children had significantly higher odds of vaccinating their own children. Caregivers' receiving positive influence from healthcare workers (OR: 1.33; 95%CI: 1.00−1.77), family members, or friends (OR: 1.30; 95%CI: 1.14−1.49) were also significantly associated with childhood influenza vaccination. Poor access, including conflicts between caregivers' availability and vaccination service schedules and inconvenient transportation to the vaccination site were negatively associated with childhood flu vaccination. To promote childhood influenza vaccination, public health information campaigns need to target wealthier and less educated caregivers to enhance caregivers' confidence in influenza vaccination. Targeted interventions are also needed to optimize access to vaccination services, including extending vaccination service hours and increasing the number of vaccination sites close to residential areas. Interventions are also needed to encourage primary care providers to play a greater role in promoting vaccination. Finally, the dissemination of related information and the public response need to be monitored for the timely understanding of public perceptions.

Erratum: Author Correction: Mapping global acceptance and uptake of COVID-19 vaccination: a systematic review and meta-analysis.

[This corrects the article DOI: 10.1038/s43856-022-00177-6.].

OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data.

High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called "the curse of dimensionality" in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.

[Determination of bis (p-fluorobenzyl) trisulfide and bis (p-fluoro-benzyl) disulfide in lungs of rat by HPLC].

OBJECTIVE: To establish an HPLC method for analysis of bis(p-fluorobenzyl) trisulfide(BFTS) and bis(p-fluorobenzyl)disulfide(BFDS) in the lungs of rat. METHODS: 5.0 ml extract solvent (n-hexane: isopropyl alcohol=95:5, v/v) and 20 microl of 11.50 microg/ml dibenzyl disulfide (internal standard) were added to 0.2 g lung sample followed by homogenization. After centrifugation, 4.0 ml of supernatant was separated and vaporized to dryness, and the residue was reconstituted in mobile phase for HPLC analysis. The HPLC analysis was performed on an SB C18 column using acetonitrile and water (65:35, v/v) as mobile phase with a flow rate of 1.0 ml/min with UV detection at 220 nm. RESULT: The calibration curves for BFTS and BFDS in sample were linear over the concentration ranges of 0.04712-14.78 microg/g(r=0.999) and 0.04831-23.96 microg/g(r=0.999), respectively. The limits of quantification were 0.04712 microg/g and 0.04831 microg/g for BFTS and BFDS, respectively. The assay recoveries for BFTS and BFDS ranged from 95.71%-107.2% and 90.00%-110.5%, respectively. The precisions were obtained with RSD of <10%. The developed method was successfully applied to study the content of BFTS and BFDS in the lungs of rats after intravenous injection of 12.5 mg/kg BFTS. CONCLUSION: The method developed is simple, selective, repeatable and accurate, which can be applied to study the tissue distribution of BFTS and BFDS.