Epidemic patterns of the different influenza virus types and subtypes/lineages for 10 years in Chongqing, China, 2010-2019.

To optimize seasonal influenza control and prevention programs in regions with potentially complicated seasonal patterns. Descriptive epidemiology was used to analyze the etiology of influenza, and chi-square tests were used to compare the epidemic patterns among different influenza virus types and subtypes/lineages. From January 2010 to December 2019, a total of 63,626 ILI cases were reported in Chongqing and 14,136 (22.22%) were laboratory-confirmed influenza cases. The proportions of specimens positive for influenza A and influenza B were 13.32% (8,478/63,626) and 8.86% (5,639/63,626), respectively. The proportion of positive specimens for influenza A reached the highest in winter (23.33%), while the proportion of positive specimens for influenza B reached the highest in spring (11.88%). Children aged 5-14 years old had the highest proportion of positive specimens for influenza. The influenza virus types/subtypes positive was significantly different by seasons and age groups (P<.001), but not by gender (p = .436). The vaccine strains were matched to the circulating influenza virus strains in all other years except for 2018 (vaccine strain was B/Colorado/06/2017; circulating strain was B/Yamagata). The study showed significant variations in epidemic patterns, including seasonal epidemic period and age distributions, among different influenza types, subtypes/lineages in Chongqing. Influenza vaccines matched to the circulating influenza virus strain in nine of the ten years. To prevent and mitigate the influenza outbreaks in this area, high risk population, especially children aged 5-14 years, are encouraged to get vaccinated against influenza before the epidemic seasons.

DiffDomain enables identification of structurally reorganized topologically associating domains.

Topologically associating domains (TADs) are critical structural units in three-dimensional genome organization of mammalian genome. Dynamic reorganizations of TADs between health and disease states are associated with essential genome functions. However, computational methods for identifying reorganized TADs are still in the early stages of development. Here, we present DiffDomain, an algorithm leveraging high-dimensional random matrix theory to identify structurally reorganized TADs using high-throughput chromosome conformation capture (Hi-C) contact maps. Method comparison using multiple real Hi-C datasets reveals that DiffDomain outperforms alternative methods for false positive rates, true positive rates, and identifying a new subtype of reorganized TADs. Applying DiffDomain to Hi-C data from different cell types and disease states demonstrates its biological relevance. Identified reorganized TADs are associated with structural variations and epigenomic changes such as changes in CTCF binding sites. By applying to a single-cell Hi-C data from mouse neuronal development, DiffDomain can identify reorganized TADs between cell types with reasonable reproducibility using pseudo-bulk Hi-C data from as few as 100 cells per condition. Moreover, DiffDomain reveals differential cell-to-population variability and heterogeneous cell-to-cell variability in TADs. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi-C and single-cell Hi-C data.

Global pattern and determinant for interaction of seasonal influenza viruses.

BACKGROUND: The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored. METHODS: Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM). RESULTS: A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1. CONCLUSION: The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.