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The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.

Analyses of somatic hypermutation (SHM) patterns in B cell immunoglobulin (Ig) sequences contribute to our basic understanding of adaptive immunity, and have broad applications not only for understanding the immune response to pathogens, but also to determining the role of SHM in autoimmunity and B cell cancers. Although stochastic, SHM displays intrinsic biases that can confound statistical analysis, especially when combined with the particular codon usage and base composition in Ig sequences. Analysis of B cell clonal expansion, diversification, and selection processes thus critically depends on an accurate background model for SHM micro-sequence targeting (i.e., hot/cold-spots) and nucleotide substitution. Existing models are based on small numbers of sequences/mutations, in part because they depend on data from non-coding regions or non-functional sequences to remove the confounding influences of selection. Here, we combine high-throughput Ig sequencing with new computational analysis methods to produce improved models of SHM targeting and substitution that are based only on synonymous mutations, and are thus independent of selection. The resulting \"S5F\" models are based on 806,860 Synonymous mutations in 5-mer motifs from 1,145,182 Functional sequences and account for dependencies on the adjacent four nucleotides (two bases upstream and downstream of the mutation). The estimated profiles can explain almost half of the variance in observed mutation patterns, and clearly show that both mutation targeting and substitution are significantly influenced by neighboring bases. While mutability and substitution profiles were highly conserved across individuals, the variability across motifs was found to be much larger than previously estimated. The model and method source code are made available at http://clip.med.yale.edu/SHM.

De novo mutations are central for evolution, since they provide the raw material for natural selection by regenerating genetic variation. However, studying de novo mutations is challenging and is generally restricted to model species, so we have a limited understanding of the evolution of the mutation rate and spectrum between closely related species. Here, we present a mutation accumulation (MA) experiment to study de novo mutation in the unicellular green alga Chlamydomonas incerta and perform comparative analyses with its closest known relative, Chlamydomonas reinhardtii. Using whole-genome sequencing data, we estimate that the median single nucleotide mutation (SNM) rate in C. incerta is μ = 7.6 × 10−10, and is highly variable between MA lines, ranging from μ = 0.35 × 10−10 to μ = 131.7 × 10−10. The SNM rate is strongly positively correlated with the mutation rate for insertions and deletions between lines (r > 0.97). We infer that the genomic factors associated with variation in the mutation rate are similar to those in C. reinhardtii, allowing for cross-prediction between species. Among these genomic factors, sequence context and complexity are more important than GC content. With the exception of a remarkably high C→T bias, the SNM spectrum differs markedly between the two Chlamydomonas species. Our results suggest that similar genomic and biological characteristics may result in a similar mutation rate in the two species, whereas the SNM spectrum has more freedom to diverge.

The mutational process varies at many levels, from within genomes to among taxa. Many mechanisms have been linked to variation in mutation, but understanding of the evolution of the mutational process is rudimentary. Physiological condition is often implicated as a source of variation in microbial mutation rate and may contribute to mutation rate variation in multicellular organisms. Deleterious mutations are an ubiquitous source of variation in condition. We test the hypothesis that the mutational process depends on the underlying mutation load in two groups of Caenorhabditis elegans mutation accumulation (MA) lines that differ in their starting mutation loads. “First-order MA” (O1MA) lines maintained under minimal selection for ∼250 generations were divided into high-fitness and low-fitness groups and sets of “second-order MA” (O2MA) lines derived from each O1MA line were maintained for ∼150 additional generations. Genomes of 48 O2MA lines and their progenitors were sequenced. There is significant variation among O2MA lines in base-substitution rate (µbs), but no effect of initial fitness; the indel rate is greater in high-fitness O2MA lines. Overall, µbs is positively correlated with recombination and proximity to short tandem repeats and negatively correlated with 10 bp and 1 kb GC content. However, probability of mutation is sufficiently predicted by the three-nucleotide motif alone. Approximately 90% of the variance in standing nucleotide variation is explained by mutability. Total mutation rate increased in the O2MA lines, as predicted by the “drift barrier” model of mutation rate evolution. These data, combined with experimental estimates of fitness, suggest that epistasis is synergistic.

We used trait activation theory to investigate the relationship between narcissism and knowledge hiding based on a two-dimensional concept and a process model of narcissism. We collected data from 363 members of graduate and undergraduate research teams and found that narcissistic admiration had a significant negative impact on knowledge hiding, whereas narcissistic rivalry had a significant positive impact. The status-attaining motivation of prestige acted as a mediator between narcissistic admiration and knowledge hiding, while the motivation of dominance acted as a mediator between narcissistic rivalry and knowledge hiding. Status mutability served as a first-stage moderator, affecting the relationship between narcissistic admiration and prestige motivation, and the mediating effect of the motivation of prestige between narcissistic admiration and knowledge hiding. Status mutability moderated the relationship between narcissistic rivalry and dominance motivation, and the mediating effect of the motivation of dominance between narcissistic rivalry and knowledge hiding. This study expands understanding of knowledge-hiding antecedents and provides a valuable framework for reducing these behaviors in organizations.

Workload prediction is the key technology for elastic resource management in cloud platforms, and the prediction accuracy affects the efficiency of elastic management. However, a multitude of workload sequences have the characteristics of short-term mutation and nonlinearity in the cloud. A widely used strategy for predicting workloads is based on machine learning. The loss function of the existing workload prediction model cannot capture the nonlinear features well in the sequence. Moreover, it is sensitive to outliers and has low robustness, which affects the prediction accuracy. To address this issue, an integrated workload prediction method (TimesLap) is proposed based on Laplacian Kernel improved loss function(Laplacian Kernel MSE). Firstly, the workload sequence is decomposed into a high-frequency fluctuation sequence and a low-frequency fluctuation sequence. Then, the ARIMA model and LSTM-GRU model are applied to predict the high and low volatility sequences respectively. Laplacian Kernel MSE loss function is used to quantify the complex variability of high-frequency sequences, with the final prediction results are obtained by aggregating the prediction results of the models. Finally, the real trace of Google cloud and Microsoft Azure cloud are used for experiments. The experimental results show that TimesLap can effectively improve the generalization of model prediction. Compared with the state-of-the-art prediction methods based on Mean-Square Error, the Visualization Mean Absolute Error of prediction is reduced by 44%, and the R2 score is increased by 58%.

Variable regions of Ig chains provide the antigen recognition portion of B-cell receptors and derivative antibodies. Ig heavy-chain variable region exons are assembled developmentally from V, D, J gene segments. Each variable region contains three antigen-contacting complementarity-determining regions (CDRs), with CDR1 and CDR2 encoded by the V segment and CDR3 encoded by the V(D)J junction region. Antigen-stimulated germinal center (GC) B cells undergo somatic hypermutation (SHM) of V(D)J exons followed by selection for SHMs that increase antigen-binding affinity. Some HIV-1–infected human subjects develop broadly neutralizing antibodies (bnAbs), such as the potent VRC01-class bnAbs, that neutralize diverse HIV-1 strains. Mature VRC01-class bnAbs, including VRC-PG04, accumulate very high SHM levels, a property that hinders development of vaccine strategies to elicit them. Because many VRC01-class bnAb SHMs are not required for broad neutralization, high overall SHM may be required to achieve certain functional SHMs. To elucidate such requirements, we used a V(D)J passenger allele system to assay, in mouse GC B cells, sequence-intrinsic SHM-targeting rates of nucleotides across substrates representing maturation stages of human VRC-PG04. We identify rate-limiting SHM positions for VRC-PG04 maturation, as well as SHM hotspots and intrinsically frequent deletions associated with SHM. We find that mature VRC-PG04 has low SHM capability due to hotspot saturation but also demonstrate that generation of new SHM hotspots and saturation of existing hotspot regions (e.g., CDR3) does not majorly influence intrinsic SHM in unmutated portions of VRCPG04 progenitor sequences. We discuss implications of our findings for bnAb affinity maturation mechanisms.

Mutability—the ability to change form and substance—is a key feature of glass and metals. This quality, however, has proven frustrating for archaeological and archaeometric research. This article assesses the typological, chemical and theoretical elements of material reuse and recycling, reframing these practices as an opportunity to understand past behaviour, rather than as an obstacle to understanding. Using diverse archaeological data, the authors present case studies to illustrate the potential for documenting mutability in the past, and to demonstrate what this can reveal about the movement, social context and meaning of archaeological material culture. They hope that through such examples archaeologists will consider and integrate mutability as a formative part of chaînes opératoires.

• Background and Aims The actual number of domestications of a crop is one of the key questions in domestication studies. Answers to this question have generally been based on relationships between wild progenitors and domesticated descendants determined with anonymous molecular markers. In this study, this question was investigated by determining the number of instances a domestication phenotype had been selected in a crop species. One of the traits that appeared during domestication of common bean (Phaseolus vulgaris) is determinacy, in which stems end with a terminal inflorescence. It has been shown earlier that a homologue of the arabidopsis TFL1 gene -PvTFL1y -controls determinacy in a naturally occurring variation of common bean. • Methods Sequence variation was analysed for PvTFL1y in a sample of 46 wild and domesticated accessions that included determinate and indeterminate accessions. • Key Results Indeterminate types - wild and domesticated -showed only synonymous nucleotide substitutions. Determinate types - observed only among domesticated accessions - showed, in addition to synonymous substitutions, non-synonymous substitutions, indels, a putative intron-splicing failure, a retrotransposon insertion and a deletion of the entire locus. The retrotransposon insertion was observed in 70 % of determinate cultivars, in the Americas and elsewhere. Other determinate mutants had a more restricted distribution in the Americas only, either in the Andean or in the Mesoamerican gene pool of common bean. • Conclusions Although each of the determinacy haplotypes probably does not represent distinct domestication events, they are consistent with the multiple (seven) domestication pattern in the genus Phaseolus. The predominance of determinacy in the Andean gene pool may reflect domestication of common bean prior to maize introduction in the Andes.

We introduce a novel entropy-related function, non-repeatability, designed to capture dynamical behaviors in complex systems. Its normalized form, mutability, has been previously applied in statistical physics as a dynamical entropy measure associated with any observable stored in a sequential file. We now extend this concept by calculating the sorted mutability for the same data file previously ordered by increasing or decreasing value. To present the scope and advantages of these quantities, we analyze two distinct systems: (a) Monte Carlo simulations of magnetic moments on a square lattice, and (b) seismic time series from the United States Geological Survey catalog. Both systems are well established in the literature, serving as robust benchmarks. Shannon entropy is employed as a reference point to assess the similarities and differences with the proposed measures. A key distinction lies in the sensitivity of non-repeatability and mutability to the temporal ordering of data, which contrasts with traditional entropy definitions. Moreover, sorted mutability reveals additional insights into the critical behavior of the systems under study.