Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

Introduction: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health.

Materials And Methods: This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting.

[Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype HN].

Introduction: The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data.

Complete genome sequence of and proposal of Thermofilum uzonense sp. nov. a novel hyperthermophilic crenarchaeon and emended description of the genus Thermofilum.

A strain of a hyperthermophilic filamentous archaeon was isolated from a sample of Kamchatka hot spring sediment. Isolate 1807-2 grew optimally at 85 °C, pH 6.0-6.