COVID-19 vaccination and concerns regarding vaccine hesitancy after the termination of the zero-COVID policy in China: A nationwide cross-sectional study.

COVID-19 vaccine hesitancy remains prevalent globally. However, national data on this issue in the general population after the termination of the zero-COVID policy in China are limited. In March 2023, we conducted a nationwide cross-sectional survey among Chinese adults using a self-administered questionnaire. Descriptive statistics and multivariate logistic regressions were employed. Among 4,966 participants, 43.8% reported COVID-19 vaccine hesitancy following the end of the zero-COVID policy in China. Higher rates of vaccine hesitancy were associated with being married (married: OR 1.36, 95%CI 1.17-1.57; other marital status: OR 1.86, 95%CI 1.36-2.55), working in healthcare (OR 1.64, 95%CI 1.38-1.96), having both minors and older adults in the household (OR 1.45, 95%CI 1.20-1.75), having no minors and older adults in the household (OR 1.44, 95%CI 1.17-1.77), having chronic diseases (OR 1.42, 95%CI 1.23-1.64), experiencing adverse events post-vaccination (OR 1.39, 95%CI 1.19-1.61), and uncertainty about previous COVID-19 infection (OR 1.45, 95%CI 1.13-1.86). Conversely, participants who had received the influenza vaccine in the past three years (OR 0.62, 95%CI 0.54-0.72), had previously taken the COVID-19 vaccine (OR 0.44, 95%CI 0.32-0.59), and had higher confidence in vaccines (OR 0.63, 95%CI 0.60-0.67) were less likely to exhibit hesitancy. Our findings indicate a significant level of vaccine hesitancy, underscoring the urgent need for tailored public health strategies to address vaccine hesitancy and improve uptake post-zero-COVID policy in China. A comprehensive understanding of public concerns and related factors is essential for developing effective vaccine communication strategies.

Expression profile analysis of cotton fiber secondary cell wall thickening stage.

To determine the genes associated with the fiber strength trait in cotton, three different cotton cultivars were selected: Sea Island cotton (Xinhai 32, with hyper-long fibers labeled as HL), and upland cotton (17-24, with long fibers labeled as L, and 62-33, with short fibers labeled as S). These cultivars were chosen to assess fiber samples with varying qualities. RNA-seq technology was used to analyze the expression profiles of cotton fibers at the secondary cell wall (SCW) thickening stage (20, 25, and 30 days post-anthesis (DPA)). The results showed that a large number of differentially expressed genes (DEGs) were obtained from the three assessed cotton cultivars at different stages of SCW development. For instance, at 20 DPA, Sea Island cotton (HL) had 6,215 and 5,364 DEGs compared to upland cotton 17-24 (L) and 62-33 (S), respectively. Meanwhile, there were 1,236 DEGs between two upland cotton cultivars, 17-24 (L) and 62-33 (S). Gene Ontology (GO) term enrichment identified 42 functions, including 20 biological processes, 11 cellular components, and 11 molecular functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis identified several pathways involved in SCW synthesis and thickening, such as glycolysis/gluconeogenesis, galactose metabolism, propanoate metabolism, biosynthesis of unsaturated fatty acids pathway, valine, leucine and isoleucine degradation, fatty acid elongation pathways, and plant hormone signal transduction. Through the identification of shared DEGs, 46 DEGs were found to exhibit considerable expressional differences at different fiber stages from the three cotton cultivars. These shared DEGs have functions including REDOX enzymes, binding proteins, hydrolases (such as GDSL thioesterase), transferases, metalloproteins (cytochromatin-like genes), kinases, carbohydrates, and transcription factors (MYB and WRKY). Therefore, RT-qPCR was performed to verify the expression levels of nine of the 46 identified DEGs, an approach which demonstrated the reliability of RNA-seq data. Our results provided valuable molecular resources for clarifying the cell biology of SCW biosynthesis during fiber development in cotton.

TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.

The role of apparent diffusion coefficient in the grading of adult isocitrate dehydrogenase-mutant astrocytomas: relationship with the Ki-67 proliferation index.

BACKGROUND: The grading of adult isocitrate dehydrogenase (IDH)-mutant astrocytomas is a crucial prognostic factor. PURPOSE: To investigate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) in the grading of adult IDH-mutant astrocytomas, and to analyze the correlation between ADC and the Ki-67 proliferation index. MATERIAL AND METHODS: The clinical and MRI data of 82 patients with adult IDH-mutant astrocytoma who underwent surgical resection and molecular genetic testing with IDH and 1p/19q were retrospectively analyzed. The conventional MRI features, ADCmin, ADCmean, and nADC of the tumors were compared using the Kruskal-Wallis single factor ANOVA and chi-square tests. Receiver operating characteristic (ROC) curves were drawn to evaluate conventional MRI and ADC accuracy in differentiating tumor grades. Pearson correlation analysis was performed to determine the correlation between ADC and the Ki-67 proliferation index. RESULTS: The difference in enhancement, ADCmin, ADCmean, and nADC among WHO grade 2, 3, and 4 tumors was statistically significant (all P <0.05). ADCmin showed the preferable diagnostic accuracy for grading WHO grade 2 and 3 tumors (AUC=0.724, sensitivity=63.4%, specificity=80%, positive predictive value (PPV)=62.0%; negative predictive value (NPV)=82.5%), and distinguishing grade 3 from grade 4 tumors (AUC=0.764, sensitivity=70%, specificity=76.2%, PPV=75.0%, NPV=71.4%). Enhancement + ADC model showed an optimal predictive accuracy (grade 2 vs. 3: AUC = 0.759; grade 3 vs. 4: AUC = 0.799). The Ki-67 proliferation index was negatively correlated with ADCmin, ADCmean, and nADC (all P <0.05), and positively correlated with tumor grade. CONCLUSION: Conventional MRI features and ADC are valuable to predict pathological grading of adult IDH-mutant astrocytomas.

Data-driven prediction of colonization outcomes for complex microbial communities.

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Perceptions about respiratory syncytial virus (RSV) and attitudes toward the RSV vaccine among the general public in China: A cross-sectional survey.

Our study aims to assess the public's perceptions of respiratory syncytial virus (RSV) and attitudes toward the RSV vaccine and to identify associated factors in China. A nationwide cross-sectional survey conducted using an online platform between August 16 and September 14, 2023. Questions related to socio-demographics, awareness, knowledge, perceptions of susceptibility and severity of RSV, and attitudes toward the RSV vaccine were included in the questionnaire. We used the chi-square test and logistic regression model to explore the associated factors. Overall, 2133 individuals were included in this study. Nearly a quarter of participants (24.3%) indicated that they had never heard of RSV. The proportion of individuals aged over 50 years reporting never having heard of RSV (36.5%) and having a low knowledge level of RSV (55.3%) was significantly higher that of other younger age groups. More than half of individuals (55.7%) exhibited low level of perceptions of susceptibility concerning RSV infection. A total of 68.4% of the participants expressed willingness to receive the RSV vaccine. Younger age was positively associated with a higher willingness to be vaccinated. The most frequent reason for declining the vaccine was "Concern about vaccine's safety or side effects." About 60% of individuals considered a price of RSV vaccine below 200 CNY (28 USD) as acceptable. The awareness and perceived susceptibility to RSV infection were limited to the Chinese public. It is necessary to take measures to address the low awareness and knowledge of RSV and acceptability of the RSV vaccine among older adults.

Influence of Femtosecond Laser Surface Modification on Tensile Properties of Titanium Alloy.

Titanium alloy components often experience damage from impact loads during usage, which makes improving the mechanical properties of TC4 titanium alloys crucial. This paper investigates the influence of laser scanning irradiation on the tensile properties of thin titanium alloy sheets. Results indicate that the tensile strength of thin titanium alloy sheets exhibits a trend of initial increase followed by a decrease. Different levels of enhancement are observed in the elongation at break of a cross-section. Optimal improvement in the elongation at break is achieved when the laser fluence is around 8 J/cm2, while the maximum increase in tensile strength occurs at approximately 10 J/cm2. Using femtosecond laser surface irradiation, this study compares the maximum enhancement in the tensile strength of titanium alloy base materials, which is approximately 8.54%, and the maximum increase in elongation at break, which reaches 25.61%. In addition, the results verify that cracks in tensile fractures of TC4 start from the middle, while laser-induced fracture cracks occur from both ends.

Ecological dynamics imposes fundamental challenges in community-based microbial source tracking.

Quantifying the contributions of possible environmental sources ("sources") to a specific microbial community ("sink") is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities were formed, but also have far-reaching applications in pollution control, public health, and forensics. MST methods generally fall into two categories: target-based methods (focusing on the detection of source-specific indicator species or chemicals); and community-based methods (using community structure to measure similarity between sink samples and potential source environments). As next-generation sequencing becomes a standard community-assessment method in microbiology, numerous community-based computational methods, referred to as MST solvers hereafter have been developed and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider microbial interactions and priority effects in microbial communities. Here, we revisit the performance of several representative MST solvers. We show compelling evidence that solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. In particular, we clearly demonstrate that the presence of either microbial interactions or priority effects will render the MST problem mathematically unsolvable for MST solvers. We further analyze data from fecal microbiota transplantation studies, finding that the state-of-the-art MST solvers fail to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state-of-the-art MST solvers fail to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in MST. Interpretation of results of existing MST solvers should be done cautiously.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations.

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.