Shannon Entropy Research Articles

Exploring other clustering methods and the role of Shannon Entropy in an unsupervised setting

In the ever-expanding landscape of digital technologies, the exponential growth of data in information science and health informatics presents both challenges and opportunities, demanding innovative approaches to data curation. This study focuses on evaluating various feasible clustering methods within the Data Washing Machine (DWM), a novel tool designed to streamline unsupervised data curation processes. The DWM integrates Shannon Entropy into its clustering process, allowing for adaptive refinement of clustering strategies based on entropy levels observed within data clusters. Rigorous testing of the DWM prototype on various annotated test samples revealed promising outcomes, particularly in scenarios with high-quality data. However, challenges arose when dealing with poor data quality, emphasizing the importance of data quality assessment and improvement for successful data curation. To enhance the DWM’s clustering capabilities, this study explored alternative unsupervised clustering methods, including spectral clustering, autoencoders, and density-based clustering like DBSCAN. The integration of these alternative methods aimed to augment the DWM’s ability to handle diverse data scenarios effectively. The findings demonstrated the practicability of constructing an unsupervised entity resolution engine with the DWM, highlighting the critical role of Shannon Entropy in enhancing unsupervised clustering methods for effective data curation. This study underscores the necessity of innovative clustering strategies and robust data quality assessments in navigating the complexities of modern data landscapes. This content is structured by the following sections: Introduction, Methodology, Results, Discussion, and Conclusion.

Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution

We formally prove the equivalence between Assembly Theory (AT) and Shannon Entropy via a method based upon the principles of statistical compression that belongs to the LZ family of popular compression algorithms. Such popular lossless compression algorithms behind file formats such as ZIP and PNG have been shown to empirically reproduce the results that AT considers its cornerstone. The same results have also been reported before AT in successful application of other complexity measures in the areas covered by AT such as separating organic from non-organic molecules and in the context of the study of selection and evolution. We demonstrate that the assembly index is equivalent to the size of a minimal context-free grammar. The statistical compressibility of such a method is bounded by Shannon Entropy and other equivalent traditional LZ compression schemes, such as LZ77 and LZW. We also demonstrate that AT, and the algorithms supporting its pathway complexity, assembly index, and assembly number, define compression schemes and methods that are subsumed into algorithmic information theory. We conclude that the assembly index and the assembly number do not lead to an explanation or quantification of biases in generative (physical or biological) processes, including those brought about by (abiotic or biotic) selection and evolution, that could not have been arrived at using Shannon Entropy, or that have not been already reported before using classical information theory or algorithmic complexity.

Gastrointestinal-inert prebiotic micro-composites improve the growth and community diversity of mucosal-associated bacteria.

The process of microencapsulation and the development of microparticle-based drug formulations have gained increased pharmaceutical interest, particularly for drug delivery and bacterial-encapsulation purposes for probiotic delivery. Existing studies have examined microcomposite (MC) responses to gastrointestinal (GI) conditions with the aim of controlling disintegration, and thus release, across the small and large bowel. However, the delivery of MCs which remain intact, without degrading, could act as bacterial growth scaffolds or materials providing a prebiotic support, conferring potentially beneficial GI health properties. This present study employs prilling as a method to produce a portfolio of MCs using a variety of biopolymers (alginate, chitosan, pectin and gellan gum) with a range of MC diameters and density compositions. Fluorescent probes are co-encapsulated within each MC to enable flow-cytometry directed release profile assessments following exposure to chemical simulated gastric and intestinal digestion conditions. We observe that MC size, gel-strength, density, and biopolymer material all influence response to gastric and intestinal conditions. Gellan gum (GG) MCs demonstrated complete resistance to disintegration throughout GI-simulation in the stomach and small intestine. Considering these MCs could reach the colon intact, we then examined how such MCs, doped with prebiotic growth supporting carboxymethyl cellulose (CMC) polymers, could impact microbial communities using a bioreactor model of the colonic microbiome. Following supplementation with GGCMC MCs, mucosal bacterial diversity (using 16 s rRNA sequencing and Shannon entropy and observed feature diversity metrics) and taxonomic composition changes were observed. Concentrations of short chain fatty acid (SCFA) metabolites were also found to be altered. This is the first study to comprehensivelyexamine how MC physicochemistry can be manipulated to tailor MCs to have the desired GI release performance and subsequently, how GI-resistant MCs could have influential microbial altering properties and be adopted in novel prebiotic strategies.

Multiscale groundwater level forecasts with multi-model ensemble approaches: Combining machine learning models using decision theories and bayesian model averaging

Creating precise groundwater level (GWL) prediction models is of crucial significance for the productive use, extended planning, and controlling of limited sub-surface water supplies. In this research, the accuracy of GWL forecasts in Bangladesh was enhanced for three weeks by utilizing ensembles of Machine Learning (ML) models. Six advanced ML-based models were developed and assessed using eight performance indices, and an Overall Ranking (OR) was provided by combining the rankings produced by Grey Relational Analysis (GRA), Variation Coefficient (COV), and Shannon's Entropy (SE). The standalone forecasting models demonstrated excellent performance across the three forecasting horizons, with accuracy values ranging from 0.986 to 0.997 for one-step, 0.971 to 0.999 for two-step, and 0.960 to 0.997 for three-step forecasts at GT3330001. Results also revealed that three ranking techniques (SE, COV, and GRA), as well as their combined ranking (OR), produced different best-performing models at different prediction horizons for different observation wells. Weighted average ensembles of the prediction models were developed by calculating individual model weights using four ensemble modelling techniques: SE, COV, GRA, and Bayesian Model Averaging (BMA). The BMA-based ensemble technique outperformed three benchmark ensemble approaches, achieving R = 0.947, KGE = 0.925, IOA = 0.972, MAE = 0.062 m, and RMSE = 0.123 m for one-step-ahead forecasts at GT3330001. The findings exhibit a consistent trend across other forecasting horizons and observation wells. Finally, the Dempster-Shafer evidence theory was employed to rank the single and composite models. The ranking results demonstrated that the BMA-based ensemble consistently secured the top position (with the weight values of 0.997, 0.991, and 0.987 for one-week, two-weeks, and three-weeks forward forecasts at GT3330001) for all forecasting horizons and observation wells. This study shows that the BMA-based composite model can produce more accurate GWL projections at Bangladesh study location, with potential for application in other regions worldwide.

Bitcoin, Fintech, Energy Consumption, and Environmental Pollution Nexus: Chaotic Dynamics with Threshold Effects in Tail Dependence, Contagion, and Causality

The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCH–copula causality methods for daily Bitcoin, Fintech, energy consumption, and CO2 emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, Rényi, and Tsallis entropy measures; Kolmogorov–Sinai complexity; Hurst–Mandelbrot and Lo’s R/S tests; and Phillips’ and Geweke and Porter-Hudak’s fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCH–copula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO2 emissions as well as causality from energy consumption to CO2 emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech.

On complexity of colloid cellular automata

The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from them. We analyze the space-time complexity of Boolean circuits derived from the electrical responses of colloids-specifically ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and their combinations in response to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata-arrays of binary state machines that update their states synchronously according to the same rule-creating the colloid cellular automata. We then analyze the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

LINGPASS: An approach for multilingual passphrase generation by integrating English and Tamil

AbstractIn today's world of ever‐increasing cyber threats, safeguarding digital accounts is paramount. Traditional passwords are often vulnerable to brute force attacks and can be easily guessed, leading to a shift towards passphrases. However, using monolingual passphrases can pose several challenges, such as lack of diversity, predictability, memorability, and accessibility. To address this issue, this paper presents a model called LINGPASS, short for Language INtegrated Graph‐based PASSphrase, which generates multilingual passphrases by combining English and Tamil languages. The proposed approach is implemented as a web application for user convenience, and the entropy of the passphrase is evaluated using Shannon entropy. The LINGPASS model generates higher entropy passphrases than existing multilingual passphrase generators, increasing the security of digital accounts. By integrating two languages, LINGPASS increases the entropy of the passphrase by approximately 72%, making it less vulnerable to dictionary attacks. Additionally, the copy/paste functionality reduces typing errors, making it more user‐friendly.

Complexity measures of electric screening effect in interstellar medium

Using the screening potential, we explore probability density properties in interstellar mediums (ISM) in information theory. With the eigenfunctions obtained for the screening potential using the Schrodinger equation, we studied the probability distribution for Shannon entropy, Fisher information, and their complexity measures for the values of n and l. From our numerical result, for Shannon entropy, we observed a more localized probability distribution in position and a decreasing localization in the momentum space as the values of n and l increases. For Fisher information, localization increases in the position space and, consequently, delocalization in their momentum spaces. The uncertainty relations of Shannon entropy and Fisher information were computed and satisfied the relations. For interstellar mediums, our findings align with Heinsberg’s uncertainty principle. Complexity measure was also studied for the interstellar mediums and our findings showed increasing disorder in the position space for increasing values of n and l. Furthermore, we extended the Debye-Hückel length to other regimes particularly, Magnetosphere (D=102) and Intergalactic Mediums (D=105)

Relative entropy based uncertainty principles for graph signals

In physical quantum mechanics, the uncertainty principle in presence of quantum memory [Berta M, Christandl M, Colbeck R,et al., Nature Physics] can reach much lower bound, which has resulted in a huge breakthrough in quantum mechanics. Inspired by this idea, this paper would propose some novel uncertainty relations in terms of relative entropy for signal representation and time-frequency resolution analysis. On one hand, the relative entropy measures the distinguishability between the known (priori) basis and the client basis, which implies that we have partial “memory” of the client basis so that the uncertainty bounds become sharper in some cases. On the other hand, in some cases, if the reference basis along with nearly the same energy distribution could be given, then the uncertainty bound would tend to zero, as shows that there is no uncertainty any longer. These novel uncertainty relationships with sharper bounds would give us the potential advantages over the classical counterpart. In addition, the detailed comparison with classical Shannon entropy based uncertainty principle has been addressed as well via combined uncertainty relations. Finally, the theoretical analysis and numerical experiments on certain application over graph signals have been demonstrated to show the efficiency of these proposed relations.

Phlebotomine sand flies (Diptera: Psychodidae) of Bosnia and Herzegovina: distribution, ecology and environmental preferences

Sand flies (Diptera: Phlebotominae) are the principal vectors for the protozoan parasites Leishmania spp. and for phleboviruses. The sand fly fauna on the Balkan Peninsula, including Bosnia and Herzegovina (BIH), is diverse and the circulation of Leishmania infantum as well as phleboviruses has been proven. However, recent data on the sand fly fauna in BIH are scarce. In this study, we surveyed understudied regions in central and northeastern BIH to update the sand fly distribution and gain insights into the ecological and environmental factors shaping their appearance. CDC light trapping was conducted in 2022 and 2023 and a combination of morphological and molecular methods (cytochrome oxidase I barcoding) was performed for species identifications. We mapped the currently known distribution, modelled climatic suitability patterns and performed environmental analyses by applying machine learning methods. In addition, we analyzed blood meals by host gene sequencing and MALDI-TOF peptide mass mapping and screened for Leishmania spp. DNA and Phlebovirus RNA.Altogether, 591 sand flies of four species were trapped, predominantly Phlebotomus neglectus (97 %), but also Ph. balcanicus, Ph. mascittii, and Ph. papatasi. Records of seven sand fly species known to be endemic were plotted onto distribution maps based on 101 datapoints, identifying Ph. neglectus as the overall predominant species. The environmental analyses of sand fly species indicated variation in altitudinal, thermal, and precipitation conditions across the sand fly-positive sites. Phlebotomus simici, Phlebotomus tobbi, and Sergentomyia minuta are typically found exclusively in Mediterranean and subtropical climate zones, whereas other species typically inhabit continental regions. The Inverse Distance Weighted (IDW) interpolation of sand fly species numbers and Shannon entropy values suggested the southeastern coastal region of BIH as a primary focus for sand fly occurrence. This finding was corroborated by modeled average climatic suitability patterns for sand flies, depicting four distinct meso-regions for sand fly occurrence. The results of the ensemble method highlight the importance of annual precipitation to distinguish between positive and negative sand fly trapping sites in BIH. In total, 55 blood meals of two sand fly species, Ph. neglectus and Ph. balcanicus, were analyzed and five host species identified.Our comprehensive assessment of ecological and environmental preferences of sand flies in BIH may support further entomological surveys and help to better understand and evaluate potential hot spots of disease transmission in the country.

Multidimensional Analysis of Physiological Entropy during Self-Paced Marathon Running.

The pacing of a marathon is arguably the most challenging aspect for runners, particularly in avoiding a sudden decline in speed, or what is colloquially termed a "wall", occurring at approximately the 30 km mark. To gain further insight into the potential for optimizing self-paced marathon performance through the coding of comprehensive physiological data, this study investigates the complex physiological responses and pacing strategies during a marathon, with a focus on the application of Shannon entropy and principal component analysis (PCA) to quantify the variability and unpredictability of key cardiorespiratory measures. Nine recreational marathon runners were monitored throughout the marathon race, with continuous measurements of oxygen uptake (V˙O2), carbon dioxide output (V˙CO2), tidal volume (Vt), heart rate, respiratory frequency (Rf), and running speed. The PCA revealed that the entropy variance of V˙O2, V˙CO2, and Vt were captured along the F1 axis, while cadence and heart rate variances were primarily captured along the F2 axis. Notably, when distance and physiological responses were projected simultaneously on the PCA correlation circle, the first 26 km of the race were positioned on the same side of the F1 axis as the metabolic responses, whereas the final kilometers were distributed on the opposite side, indicating a shift in physiological state as fatigue set in. The separation of heart rate and cadence entropy variances from the metabolic parameters suggests that these responses are independent of distance, contrasting with the linear increase in heart rate and decrease in cadence typically observed. Additionally, Agglomerative Hierarchical Clustering further categorized runners' physiological responses, revealing distinct clusters of entropy profiles. The analysis identified two to four classes of responses, representing different phases of the marathon for individual runners, with some clusters clearly distinguishing the beginning, middle, and end of the race. This variability emphasizes the personalized nature of physiological responses and pacing strategies, reinforcing the need for individualized approaches. These findings offer practical applications for optimizing pacing strategies, suggesting that real-time monitoring of entropy could enhance marathon performance by providing insights into a runner's physiological state and helping to prevent the onset of hitting the wall.

Prioritizing industrial wastes and technologies for bioenergy production: Case study

Energy supply stability in the industrial sector is crucial to maintain operational efficiency and avoiding costly disruptions. In the face of pressing environmental challenges, transitioning to sustainable, efficient, and eco-friendly energy sources is imperative. This study aims to assess the potential of industrial waste for bioenergy production in Khorasan Province, Iran, addressing the research gap of developing a comprehensive framework of criteria that was lacking in previous studies. Employing a combined technology and material assessment methodology, the research began with the collection and analysis of questionnaires to identify and weight critical criteria using Shannon Entropy and expert insights. The Additive Ratio Assessment method was then used to rank various types of industrial waste and technologies according to established value criteria. The Findings reveal that anaerobic digestion of organic waste emerges as the most viable bioenergy solution with an 85 % desirability score, followed by anaerobic digestion of sewage sludge and gasification of plastic waste, scoring 77.31 % and 69.41 %, respectively. The innovative aspect of this study is the development and implementation of five novel evaluation criteria, including process temperature, technology lifetime, production cost, waste collection cost, and waste separation cost, which have not been previously applied to the assessment of industrial waste for renewable energy production, especially in developing countries.

The opposite aging effect to single cell transcriptome profile among cell subsets.

Comparing transcriptome profiling between younger and older samples reveals genes related to aging and provides insight into the biological functions affected by aging. Recent research has identified sex, tissue, and cell type-specific age-related changes in gene expression. This study reports the overall picture of the opposite aging effect, in which aging increases gene expression in one cell subset and decreases it in another cell subset. Using the Tabula Muris Senis dataset, a large public single-cell RNA sequencing dataset from mice, we compared the effects of aging in different cell subsets. As a result, the opposite aging effect was observed widely in the genes, particularly enriched in genes related to ribosomal function and translation. The opposite aging effect was observed in the known aging-related genes. Furthermore, the opposite aging effect was observed in the transcriptome diversity quantified by the number of expressed genes and the Shannon entropy. This study highlights the importance of considering the cell subset when intervening with aging-related genes.

The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels' long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels' correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Comprehensive linear and nonlinear heart rate variability normative data in children.

The autonomic nervous system (ANS) is critical in regulating involuntary bodily functions, including heart rate. Heart rate variability (HRV) reflects the complex interplay between the ANS and humoral factors, making it a valuable noninvasive tool for assessing autonomic function. While HRV has been extensively studied in adults, normative data for HRV in children, primarily based on long-term rhythm recordings, are limited. This study aimed to establish comprehensive normative data for HRV in children. In this retrospective study, we examined 24-h Holter monitors of children aged 1day to 18years, divided into six age groups, at Nemours Children's Health in Orlando, Florida, spanning the years 2013-2023. HRV analysis encompassed time-domain, frequency-domain, and nonlinear indices. Holter data for a total of 247 patients in six age groups were included. An age-related uptrend was observed in all time- and frequency-domain variables except the normalized unit of low-frequency power. Entropy analysis revealed contradictory results among different entropy techniques. Sample and approximate entropy analyses were consistent and showed less complexity and more predictability of HRV with decreasing heart rate, while Shannon entropy analysis showed the opposite. Fractal detrended fluctuation analysis exhibited significant decreases across the age groups, suggestive of diminishing self-similarity of HRV patterns. Control of heart rate and HRV is a highly complex process and requires further study for a better understanding. It seems that no single parameter can fully elucidate the entire process. A combination of time-domain, frequency-domain, and nonlinear indices may be necessary to explain HRV behavior in the growing body.

Jet injection and spout formation in a fluidized bed

A single jet was studied in a rectangular fluidized bed by analyzing the pressure fluctuations data to characterize the jet hydrodynamics. The bed contained with glass beads of different mean sizes (450, 650, and 920 µm) as well as pharmaceutical pellets (920 µm). The analysis of the pressure fluctuation data was performed using the power spectral density function (PSDF) and discrete wavelet transforms technique. This study investigated how the flow regime changed from a jet in a fluidized bed to a spouted regime as the gas injection velocity was increased. Increasing the mean particle size led to an increase in the minimum spouting velocity. The minimum spouting velocity for 450, 650, and 920 μm glass bead particles and for 920 μm pharmaceutical pellets were determined to be 32.47 m s-1, 38.66 m s-1, 44.78 m s-1, and 44.47 m s-1, respectively. The study also examined how the dominant frequency of the various particles and the energy percentage of scales changed with increasing injection velocities. The ratio of energy percentages of meso-scale changes as the injection velocity increases and these changes were used to estimate the minimum spouting velocity. Wavelet sub-signals analysis revealed that the Daubechies 2 (DB2) wavelet was the most effective at capturing the characteristics of the jet. Based on the Shannon entropy of the approximate coefficients, the wavelet analysis showed that 11 levels of decomposition were required. By combining the wavelet analysis and PSDF, a more detailed analysis of the meso-scale was achieved. This study provided valuable insights into the behavior of jets and spouts in fluidized beds, which can greatly contribute to the optimization of a jet in fluidized beds and spout-fluidized beds by enhancing our understanding of jet behavior in such environments.

Shannon entropy in quasiparticle states of quantum chains

Abstract We investigate the Shannon entropy of the total system and its subsystems, as well as the subsystem Shannon mutual information, in quasiparticle excited states of free bosonic and fermionic chains and the ferromagnetic phase of the spin-1/2 XXX chain. For single-particle and double-particle states, we derive various analytical formulas for free bosonic and fermionic chains in the scaling limit. These formulas are also applicable to certain magnon excited states in the XXX chain in the scaling limit. We also calculate numerically the Shannon entropy and mutual information for triple-particle and quadruple-particle states in bosonic, fermionic, and XXX chains. We discover that Shannon entropy, unlike entanglement entropy, typically does not separate for quasiparticles with large momentum differences. Moreover, in the limit of large momentum difference, we obtain universal quantum bosonic and fermionic results that are generally distinct and cannot be explained by a semiclassical picture.

A novel histopathological feature of spatial tumor-stroma distribution predicts lung squamous cell carcinoma prognosis.

We used a mathematical approach to investigate the quantitative spatial profile of cancer cells and stromain lung squamous cell carcinoma tissues and its clinical relevance. The study enrolled 132 patients with 3-5 cm peripheral lung squamous cell carcinoma, resected at the National Cancer Center Hospital East. We utilized machine learning to segment cancer cells and stroma on cytokeratin AE1/3 immunohistochemistry images. Subsequently, a spatial form of Shannon's entropy was employed to precisely quantify the spatial distribution of cancer cells and stroma. This quantification index was defined as the spatial tumor-stroma distribution index (STSDI). The patients were classified as STSDI-low and -high groups for clinicopathological comparison. The STSDI showed no significant association with baseline clinicopathological features, including sex, age, pathological stage, and lymphovascular invasion. However, the STSDI-low group had significantly shorter recurrence-free survival (5-years RFS: 49.5% vs. 76.2%, p < 0.001) and disease-specific survival (5-years DSS: 53.6% vs. 81.5%, p < 0.001) than the STSDI-high group. In contrast, the application of Shannon's entropy without spatial consideration showed no correlation with patient outcomes. Moreover, low STSDI was an independent unfavorable predictor of tumor recurrence and disease-specific death (RFS; HR = 2.668, p < 0.005; DSS; HR = 3.057, p < 0.005), alongside the pathological stage. Further analysis showed a correlation between low STSDI and destructive growth patterns of cancer cells within tumors, potentially explaining the aggressive nature of STSDI-low tumors. In this study, we presented a novel approach for histological analysis of cancer tissues that revealed the prognostic significance of spatial tumor-stroma distribution in lung squamous cell carcinoma.

Examining intra-host genetic variation of RSV by short read high-throughput sequencing.

Every viral infection entails an evolving population of viral genomes. High-throughput sequencing technologies can be used to characterize such populations, but to date there are few published examples of such work. In addition, mixed sequencing data are sometimes used to infer properties of infecting genomes without discriminating between genome-derived reads and reads from the much more abundant, in the case of a typical active viral infection, transcripts. Here we apply capture probe-based short read high-throughput sequencing to nasal wash samples taken from a previously described group of adult hematopoietic cell transplant (HCT) recipients naturally infected with respiratory syncytial virus (RSV). We separately analyzed reads from genomes and transcripts for the levels and distribution of genetic variation by calculating per position Shannon entropies. Our analysis reveals a low level of genetic variation within the RSV infections analyzed here, but with interesting differences between genomes and transcripts in 1) average per sample Shannon entropies; 2) the genomic distribution of variation 'hotspots'; and 3) the genomic distribution of hotspots encoding alternative amino acids. In all, our results suggest the importance of separately analyzing reads from genomes and transcripts when interpreting high-throughput sequencing data for insight into intra-host viral genome replication, expression, and evolution.

The evolutionary features and roles of single nucleotide variants and charged amino acid mutations in influenza outbreaks during NPI period

The epidemic and outbreaks of influenza B Victoria lineage (Bv) during 2019–2022 led to an analysis of genetic, epitopes, charged amino acids and Bv outbreaks. Based on the National Influenza Surveillance Network (NISN), the Bv 72 strains isolated during 2019–2022 were selected by spatio-temporal sampling, then were sequenced. Using the Compare Means, Correlate and Cluster, the outbreak data were analyzed, including the single nucleotide variant (SNV), amino acid (AA), epitope, evolutionary rate (ER), Shannon entropy value (SV), charged amino acid and outbreak. With the emergence of COVID-19, the non-pharmaceutical interventions (NPIs) made Less distant transmission and only Bv outbreak. The 2021–2022 strains in the HA genes were located in the same subset, but were distinct from the 2019–2020 strains (P < 0.001). The codon G → A transition in nucleotide was in the highest ratio but the transversion of C → A and T → A made the most significant contribution to the outbreaks, while the increase in amino acid mutations characterized by polar, acidic and basic signatures played a key role in the Bv epidemic in 2021–2022. Both ER and SV were positively correlated in HA genes (R = 0.690) and NA genes (R = 0.711), respectively, however, the number of mutations in the HA genes was 1.59 times higher than that of the NA gene (2.15/1.36) from the beginning of 2020 to 2022. The positively selective sites 174, 199, 214 and 563 in HA genes and the sites 73 and 384 in NA genes were evolutionarily selected in the 2021–2022 influenza outbreaks. Overall, the prevalent factors related to 2021–2022 influenza outbreaks included epidemic timing, Tv, Ts, Tv/Ts, P137 (B → P), P148 (B → P), P199 (P → A), P212 (P → A), P214 (H → P) and P563 (B → P). The preference of amino acid mutations for charge/pH could influence the epidemic/outbreak trends of infectious diseases. Here was a good model of the evolution of infectious disease pathogens. This study, on account of further exploration of virology, genetics, bioinformatics and outbreak information, might facilitate further understanding of their deep interaction mechanisms in the spread of infectious diseases.