Re-evaluation of the evolution of influenza H1 viruses using direct PCA

Predictive analysis for pathogenicity classification of H5Nx avian influenza strains using machine learning techniques

Over the past decades, avian influenza (AI) outbreaks have been reported across different parts of the globe, resulting in large-scale economic and livestock loss and, in some cases raising concerns about their zoonotic potential. The virulence and pathogenicity of H5Nx (e.g., H5N1, H5N2) AI strains for poultry could be inferred through various approaches, and it has been frequently performed by detecting certain pathogenicity markers in their haemagglutinin (HA) gene. The utilization of predictive modeling methods represents a possible approach to exploring this genotypic-phenotypic relationship for assisting experts in determining the pathogenicity of circulating AI viruses. Therefore, the main objective of this study was to evaluate the predictive performance of different machine learning (ML) techniques for in-silico prediction of pathogenicity of H5Nx viruses in poultry, using complete genetic sequences of the HA gene. We annotated 2137 H5Nx HA gene sequences based on the presence of the polybasic HA cleavage site (HACS) with 46.33% and 53.67% of sequences previously identified as highly pathogenic (HP) and low pathogenic (LP), respectively. We compared the performance of different ML classifiers (e.g., logistic regression (LR) with the lasso and ridge regularization, random forest (RF), K-nearest neighbor (KNN), Naïve Bayes (NB), support vector machine (SVM), and convolutional neural network (CNN)) for pathogenicity classification of raw H5Nx nucleotide and protein sequences using a 10-fold cross-validation technique. We found that different ML techniques can be successfully used for the pathogenicity classification of H5 sequences with ∼99% classification accuracy. Our results indicate that for pathogenicity classification of (1) aligned deoxyribonucleic acid (DNA) and protein sequences, with NB classifier had the lowest accuracies of 98.41% (+/-0.89) and 98.31% (+/-1.06), respectively; (2) aligned DNA and protein sequences, with LR (L1/L2), KNN, SVM (radial basis function (RBF)) and CNN classifiers had the highest accuracies of 99.20% (+/-0.54) and 99.20% (+/-0.38), respectively; (3) unaligned DNA and protein sequences, with CNN's achieved accuracies of 98.54% (+/-0.68) and 99.20% (+/-0.50), respectively. ML methods show potential for regular classification of H5Nx virus pathogenicity for poultry species, particularly when sequences containing regular markers were frequently present in the training dataset.

Mutations in SARS-CoV-2 are on the increase against the acquired immunity

Monovalent vaccines using mRNA or adenoviruses have provided substantial protection against the COVID-19 pandemic in many countries. However, viral mutations have hampered the efficacy of this approach. The Omicron variant, which appeared in Dec 2021, has caused a pandemic that has exerted pressure on the healthcare system worldwide. The COVID-19 vaccines are not very effective against this variant, resulting in an increased rate of infection and mortality. Owing to the rapidly increasing number of patients, few countries, such as Australia, New Zealand, and Taiwan, which aimed at zero-COVID cases, have discontinued their attempts to contain the spread of infection by imposing strict lockdowns, for example. Therefore, the administration of booster vaccinations has been initiated; however, there are concerns about their effectiveness, sustainability, and possible dangers. There is also the question of how a variant with such isolated mutations originated and whether this is likely to continue in the future. Here, we compare the mutations in the Omicron variant with others by direct PCA to consider questions pertaining to their evolution and characterisation. The Omicron variant, like the other variants, has mutated in humans. The accumulated mutations overwhelmed the acquired immunity and caused a pandemic. Similar mutations are likely to occur in the future. Additionally, the variants infecting animals were investigated; they rapidly mutated in animals and varied from the human strains. These animal-adapted strains are probably not highly infectious or pathogenic to humans. Hence, the possibility of using these strains as vaccines will be discussed.

PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography

Empty space in germanium (ESG) or germanium-on-nothing (GON) are unique self-assembled germanium structures with multiscale cavities of various morphologies. Due to their simple fabrication process and high-quality crystallinity after self-assembly, they can be applied in various fields including micro-/nanoelectronics, optoelectronics, and precision sensors, to name a few. In contrast to their simple fabrication, inspection is intrinsically difficult due to buried structures. Today, ultrasonic atomic force microscopy and interferometry are some prevalent non-destructive 3-D imaging methods that are used to inspect the underlying ESG structures. However, these non-destructive characterization methods suffer from low throughput due to slow measurement speed and limited measurable thickness. To overcome these limitations, this work proposes a new methodology to construct a principal-component-analysis based database that correlates surface images with empirically determined sub-surface structures. Then, from this database, the morphology of buried sub-surface structure is determined only using surface topography. Since the acquisition rate of a single nanoscale surface micrograph is up to a few orders faster than a thorough 3-D sub-surface analysis, the proposed methodology benefits from improved throughput compared to current inspection methods. Also, an empirical destructive test essentially resolves the measurable thickness limitation. We also demonstrate the practicality of the proposed methodology by applying it to GON devices to selectively detect and quantitatively analyze surface defects. Compared to state-of-the-art deep learning-based defect detection schemes, our method is much effortlessly finetunable for specific applications. In terms of sub-surface analysis, this work proposes a fast, robust, and high-resolution methodology which could potentially replace the conventional exhaustive sub-surface inspection schemes.

Continuous mutation of SARS-CoV-2 during migration via three routes at the beginning of the pandemic

Background

It remains unclear how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection started, spread worldwide, and mutated to result in the present variants. This difficulty can be attributed to the limitations associated with the analytical methodology for presenting the differences among genomic sequences. In this study, we critically analysed the early data to explain the start and spread of the pandemic.

Methods

Objective analyses of the RNA sequences of earlier variants of SARS-CoV-2 (up to September 1, 2020, available in DDBJ and GISAID) were performed using Principal Component Analysis (PCA). The results were compared with information on the collection dates and location. The PCA was also conducted for 12 variants of interest to the WHO as of September 2021, and compared with earlier data.

Results

The pandemic began in Wuhan, China. This strain was suspected to be related to other reported animal viruses; however, they had a minimal similarity. The strain then spreads via three routes while accumulating mutations. Several viral subgroups were identified along the routes, each with a large number of patients reported, indicating high infectivity to humans. These routes were only confirmed by the early data analysis, because newer variants would have more mutations, and would be preferentially be examined by PCA if they were included. On the original axes found in the early variants, the newer variants revealed that they retained previously acquired mutations, which helped to reveal the viral ancestors of the newer variants. The rate of mutation was found to be comparable to that of the influenza H1N1 virus, which causes recurrent seasonal epidemics. Another threat imposed by SARS-CoV-2 is that if the pandemic cannot be contained, new variants may emerge annually, preventing herd immunity.

Principal component analysis of coronaviruses reveals their diversity and seasonal and pandemic potential

Coronaviruses and influenza viruses have similarities and differences. In order to comprehensively compare them, their genome sequencing data were examined by principal component analysis. Coronaviruses had fewer variations than a subclass of influenza viruses. In addition, differences among coronaviruses that infect a variety of hosts were also small. These characteristics may have facilitated the infection of different hosts. Although many of the coronaviruses were conservative, those repeatedly found among humans showed annual changes. If SARS-CoV-2 changes its genome like the Influenza H type, it will repeatedly spread every few years. In addition, the coronavirus family has many other candidates for new pandemics.