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Accurate simulation of the quasi‐biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end‐to‐end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge‐informed, compressed representation of high‐dimensional spatio‐temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross‐correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate‐based inference at a fraction of the computational cost of full‐scale simulations. Finally, we analyze the inverse problem using a multi‐objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi‐criteria trade‐off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models. Plain Language Summary Simulating the quasi‐biennial oscillation (QBO), a regular pattern of alternating winds high in the atmosphere, remains a major challenge for climate models. We developed an end‐to‐end workflow to calibrate gravity wave processes in the Energy Exascale Earth System Model, leading to more realistic simulations. We began by compressing complex spatio‐temporal data into a few key, physically meaningful quantities, such as the oscillation's amplitude and period. This data reduction allowed us to isolate the QBO signal from noise and other atmospheric phenomena. Next, we built a fast statistical model that predicts QBO behavior based on critical physics parameters. This surrogate efficiently captures relationships among various atmospheric features, reducing the need for computationally expensive full‐scale simulations. Our analysis revealed a trade‐off between QBO amplitude and period, meaning that improving one aspect often worsened the other. Rather than finding a single perfect solution, we identified a range of balanced settings that offer the best compromise. This integrated approach not only leads to more realistic QBO simulation but also provides a practical framework for tuning other complex atmospheric phenomena. Key Points We developed an end‐to‐end workflow that calibrates gravity wave generation in E3SMv3, improving quasi‐biennial oscillation (QBO) realism The fundamental frequency model compressed wind field data into physically interpretable quantities, isolated the QBO signal, and reduced dimensionality while retaining key QBO variability Our workflow reveals no single optimal configuration for QBO realism, but a frontier of best‐compromise solutions

This paper presents findings for the tonal contrast and phonation differences between words with and without stød in Livonian spontaneous speech. Livonian differentiates between two contrastive phonological tones: the broken tone or stød and the plain tone. Stød is similar to the Danish stød in some respects and is said to be part of the tone systems of the phonologies of languages in the Baltic region. The findings show that the tonal contrast between words with and without stød tends to be neutralised in Livonian spontaneous speech, but there are individual differences between speakers and also differences between men and women. The most common non-modal phonation period categories in words with stød are creaky and tense. The results also indicate that stød disappears when the word has no prominence.

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne. [Display omitted] •The MNE software provides a complete pipeline for MEG and EEG data analysis.•MNE covers preprocessing, forward modeling, inverse methods, and visualization.•MNE supports advanced analysis: time-frequency, statistics, and connectivity.•MNE-Python enables fast and memory-efficient processing of large data sets.•MNE-Python is an open-source software supporting a collaborative development effort.

The frequency distribution of words has been a key object of study in statistical linguistics for the past 70 years. This distribution approximately follows a simple mathematical form known as Zipf’s law . This article first shows that human language has a highly complex, reliable structure in the frequency distribution over and above this classic law, although prior data visualization methods have obscured this fact. A number of empirical phenomena related to word frequencies are then reviewed. These facts are chosen to be informative about the mechanisms giving rise to Zipf’s law and are then used to evaluate many of the theoretical explanations of Zipf’s law in language. No prior account straightforwardly explains all the basic facts or is supported with independent evaluation of its underlying assumptions. To make progress at understanding why language obeys Zipf’s law, studies must seek evidence beyond the law itself, testing assumptions and evaluating novel predictions with new, independent data.

Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.

Peter Visscher and colleagues report an analysis of the heritability explained by common variants identified through genome-wide association studies. They find that 45% of the variance for height can be explained by using a linear model to simultaneously consider the combined effect of common SNPs. SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability? We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data. We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent significance tests. We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.

Coherent oscillations in the theta-to-gamma frequency range have been proposed as a mechanism that coordinates neural activity in large-scale cortical networks in sensory, motor, and cognitive tasks. Whether this mechanism also involves coherent oscillations at delta frequencies (1–4 Hz) is not known. Rather, delta oscillations have been associated with slow-wave sleep. Here, we show coherent oscillations in the delta frequency band between parietal and frontal cortices during the decision-making component of a somatosensory discrimination task. Importantly, the magnitude of this delta-band coherence is modulated by the different decision alternatives. Furthermore, during control conditions not requiring decision making, delta-band coherences are typically much reduced. Our work indicates an important role for synchronous activity in the delta frequency band when large-scale, distant cortical networks coordinate their neural activity during decision making.

An important mechanism for large-scale interactions between cortical areas involves coupling between the phase and the amplitude of different brain rhythms. Could basal ganglia disease disrupt this mechanism? We answered this question by analysis of local field potentials recorded from the primary motor cortex (M1) arm area in patients undergoing neurosurgery. In Parkinson disease, coupling between β-phase (13–30 Hz) and γ-amplitude (50–200 Hz) in M1 is exaggerated compared with patients with craniocervical dystonia and humans without a movement disorder. Excessive coupling may be reduced by therapeutic subthalamic nucleus stimulation. Peaks in M1 γ-amplitude are coupled to, and precede, the subthalamic nucleus β-trough. The results prompt a model of the basal ganglia–cortical circuit in Parkinson disease incorporating phase–amplitude interactions and abnormal corticosubthalamic feedback and suggest that M1 local field potentials could be used as a control signal for automated programming of basal ganglia stimulators.

Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease—causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.

Human populations have experienced recent explosive growth, expanding by at least three orders of magnitude over the past 400 generations. This departure from equilibrium skews patterns of genetic variation and distorts basic principles of population genetics. We characterized the empirical signatures of explosive growth on the site frequency spectrum and found that the discrepancy in rare variant abundance across demographic modeling studies is mostly due to differences in sample size. Rapid recent growth increases the load of rare variants and is likely to play a role in the individual genetic burden of complex disease risk. Hence, the extreme recent human population growth needs to be taken into consideration in studying the genetics of complex diseases and traits.