Sex contextualism in laboratory research: Enhancing rigor and precision in the study of sex-related variables.

Understanding sex-related variation in health and illness requires rigorous and precise approaches to revealing underlying mechanisms. A first step is to recognize that sex is not in and of itself a causal mechanism; rather, it is a classification system comprising a set of categories, usually assigned according to a range of varying traits. Moving beyond sex as a system of classification to working with concrete and measurable sex-related variables is necessary for precision. Whether and how these sex-related variables matter-and what patterns of difference they contribute to-will vary in context-specific ways. Second, when researchers incorporate these sex-related variables into research designs, rigorous analytical methods are needed to allow strongly supported conclusions. Third, the interpretation and reporting of sex-related variation require care to ensure that basic and preclinical research advance health equity for all.

Quality control and analytic best practices for testing genetic models of sex differences in large populations.

Phenotypic sex-based differences exist for many complex traits. In other cases, phenotypes may be similar, but underlying biology may vary. Thus, sex-aware genetic analyses are becoming increasingly important for understanding the mechanisms driving these differences. To this end, we provide a guide outlining the current best practices for testing various models of sex-dependent genetic effects in complex traits and disease conditions, noting that this is an evolving field. Insights from sex-aware analyses will not only teach us about the biology of complex traits but also aid in achieving the goals of precision medicine and health equity for all.

Cellular Electron Cryotomography: Toward Structural Biology In Situ.

Electron cryotomography (ECT) provides three-dimensional views of macromolecular complexes inside cells in a native frozen-hydrated state. Over the last two decades, ECT has revealed the ultrastructure of cells in unprecedented detail. It has also allowed us to visualize the structures of macromolecular machines in their native context inside intact cells. In many cases, such machines cannot be purified intact for in vitro study. In other cases, the function of a structure is lost outside the cell, so that the mechanism can be understood only by observation in situ. In this review, we describe the technique and its history and provide examples of its power when applied to cell biology. We also discuss the integration of ECT with other techniques, including lower-resolution fluorescence imaging and higher-resolution atomic structure determination, to cover the full scale of cellular processes.

Cell-Type-Specific Optical Recording of Membrane Voltage Dynamics in Freely Moving Mice.

Electrophysiological field potential dynamics are of fundamental interest in basic and clinical neuroscience, but how specific cell types shape these dynamics in the live brain is poorly understood. To empower mechanistic studies, we created an optical technique, TEMPO, that records the aggregate trans-membrane voltage dynamics of genetically specified neurons in freely behaving mice. TEMPO has >10-fold greater sensitivity than prior fiber-optic techniques and attains the noise minimum set by quantum mechanical photon shot noise. After validating TEMPO's capacity to track established oscillations in the delta, theta, and gamma frequency bands, we compared the D1- and D2-dopamine-receptor-expressing striatal medium spiny neurons (MSNs), which are interspersed and electrically indistinguishable. Unexpectedly, MSN population dynamics exhibited two distinct coherent states that were commonly indiscernible in electrical recordings and involved synchronized hyperpolarizations across both MSN subtypes. Overall, TEMPO allows the deconstruction of normal and pathologic neurophysiological states into trans-membrane voltage activity patterns of specific cell types.

An overview of real-world data sources for oncology and considerations for research.

Generating evidence on the use, effectiveness, and safety of new cancer therapies is a priority for researchers, health care providers, payers, and regulators given the rapid pace of change in cancer diagnosis and treatments. The use of real-world data (RWD) is integral to understanding the utilization patterns and outcomes of these new treatments among patients with cancer who are treated in clinical practice and community settings. An initial step in the use of RWD is careful study design to assess the suitability of an RWD source. This pivotal process can be guided by using a conceptual model that encourages predesign conceptualization. The primary types of RWD included are electronic health records, administrative claims data, cancer registries, and specialty data providers and networks. Careful consideration of each data type is necessary because they are collected for a specific purpose, capturing a set of data elements within a certain population for that purpose, and they vary by population coverage and longitudinality. In this review, the authors provide a high-level assessment of the strengths and limitations of each data category to inform data source selection appropriate to the study question. Overall, the development and accessibility of RWD sources for cancer research are rapidly increasing, and the use of these data requires careful consideration of composition and utility to assess important questions in understanding the use and effectiveness of new therapies.

Bacterial lifestyle shapes pangenomes.

Pangenomes vary across bacteria. Some species have fluid pangenomes, with a high proportion of genes varying between individual genomes. Other species have less fluid pangenomes, with different genomes tending to contain the same genes. Two main hypotheses have been suggested to explain this variation: differences in species' bacterial lifestyle and effective population size. However, previous studies have not been able to test between these hypotheses because the different features of lifestyle and effective population size are highly correlated with each other, and phylogenetically conserved, making it hard to disentangle their relative importance. We used phylogeny-based analyses, across 126 bacterial species, to tease apart the causal role of different factors. We found that pangenome fluidity was lower in i) host-associated compared with free-living species and ii) host-associated species that are obligately dependent on a host, live inside cells, and are more pathogenic and less motile. In contrast, we found no support for the competing hypothesis that larger effective population sizes lead to more fluid pangenomes. Effective population size appears to correlate with pangenome variation because it is also driven by bacterial lifestyle, rather than because of a causal relationship.

The metabolic domestication syndrome of budding yeast.

Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.

The origins of unpredictability in life outcome prediction tasks.

Why are some life outcomes difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multidecade longitudinal study. Our sampling and interviewing process was informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a mathematical decomposition of prediction error led us to create a conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life outcome prediction tasks, even in the presence of complex algorithms and large datasets. Our work provides a foundation for future empirical and theoretical work on unpredictability in human lives.

Do we still need animals? Surveying the role of animal-free models in Alzheimer's and Parkinson's disease research.

The use of animals in neuroscience and biomedical research remains controversial. Policy is built around the "3R" principle of "Refining, Reducing and Replacing" animal experiments, and across the globe, different initiatives stimulate the use of animal-free methods. Based on an extensive literature screen to map the development and adoption of animal-free methods in Alzheimer's and Parkinson's disease research, we find that at least two in three examined studies rely on animals or on animal-derived models. Among the animal-free studies, the relative contribution of innovative models that may replace animal experiments is limited. We argue that the distinction between animal research and alternative models presents a false dichotomy, as the role and scientific value of both animal and animal-free approaches are intertwined. Calls to halt all animal experiments appear premature, as insufficient non-animal-based alternatives are available and their development lags behind. In light of this, we highlight the need for objective, unprejudiced monitoring, and more robust performance indicators of animal-free approaches.

Making human immune systems more interpretable through systems immunology.

The human immune system is a distributed system of specialized cell populations with unique functions that collectively give rise to immune responses to infections and during immune-mediated diseases. Cell composition, plasma proteins, and functional responses vary among individuals, making the system difficult to interpret, but this variation is nonrandom. With careful analyses using novel experimental and computational tools, human immune system composition and function carry interpretable information. Here, we propose that systems-level analyses offer an opportunity to make human immune responses more interpretable in the future, and we discuss herein important considerations and lessons learned to this end. Predictable human immunology holds implications for better diagnostic and curative precision in patients with infectious and immune-associated diseases.

Two-step devitrification of ultrastable glasses.

The discovery of ultrastable glasses raises novel challenges about glassy systems. Recent experiments studied the macroscopic devitrification of ultrastable glasses into liquids upon heating but lacked microscopic resolution. We use molecular dynamics simulations to analyze the kinetics of this transformation. In the most stable systems, devitrification occurs after a very large time, but the liquid emerges in two steps. At short times, we observe the rare nucleation and slow growth of isolated droplets containing a liquid maintained under pressure by the rigidity of the surrounding glass. At large times, pressure is released after the droplets coalesce into large domains, which accelerates devitrification. This two-step process produces pronounced deviations from the classical Avrami kinetics and explains the emergence of a giant lengthscale characterizing the devitrification of bulk ultrastable glasses. Our study elucidates the nonequilibrium kinetics of glasses following a large temperature jump, which differs from both equilibrium relaxation and aging dynamics, and will guide future experimental studies.

When, how, and why the path of an air bubble rising in pure water becomes unstable.

Recently, [Herrada, M. A. and Eggers, J. G., Proc. Natl. Acad. Sci. U.S.A. 120, e2216830120 (2023)] reported predictions for the onset of the path instability of an air bubble rising in water and put forward a physical scenario to explain this intriguing phenomenon. In this Brief Report, we review a series of previously established results, some of which were overlooked or misinterpreted by the authors. We show that this set of findings provides an accurate prediction and a consistent explanation of the phenomenon that invalidates the suggested scenario. The instability mechanism actually at play results from the hydrodynamic fluid-body coupling made possible by the unconstrained motion of the bubble which behaves essentially, in the relevant size range, as a rigid, nearly spheroidal body on the surface of which water slips freely.

Multidimensional visualization of the dynamic evolution of Li metal via in situ/operando methods.

The growing demands for high-energy density electrical energy storage devices stimulate the coupling of conversion-type cathodes and lithium (Li) metal anodes. While promising, the use of these "Li-free" cathodes brings new challenges to the Li anode interface, as Li needs to be dissolved first during cell operation. In this study, we have achieved a direct visualization and comprehensive analysis of the dynamic evolution of the Li interface. The critical metrics of the interfacial resistance, Li growth, and solid electrolyte interface (SEI) distribution during the initial dissolution/deposition processes were systematically investigated by employing multidimensional analysis methods. They include three-electrode impedance tests, in situ atomic force microscopy, scanning electrochemical microscopy, and cryogenic scanning transmission electron microscopy. The high-resolution imaging and real-time observations show that a loose, diffuse, and unevenly distributed SEI is formed during the initial dissolution process. This leads to the dramatically fast growth of Li during the subsequent deposition, deviating from Fick's law, which exacerbates the interfacial impedance. The compactness of the interfacial structure and enrichment of electrolyte species at the surface during the initial deposition play critical roles in the long-term stability of Li anodes, as revealed by operando confocal Raman spectroscopic mapping. Our observations relate to ion transfer, morphological and structural evolution, and Li (de)solvation at Li interfaces, revealing the underlying pathways influenced by the initial dissolution process, which promotes a reconsideration of anode investigations and effective protection strategies.

Consistently estimating network statistics using aggregated relational data.

Collecting complete network data is expensive, time-consuming, and often infeasible. Aggregated Relational Data (ARD), which ask respondents questions of the form "How many people with trait X do you know?" provide a low-cost option when collecting complete network data is not possible. Rather than asking about connections between each pair of individuals directly, ARD collect the number of contacts the respondent knows with a given trait. Despite widespread use and a growing literature on ARD methodology, there is still no systematic understanding of when and why ARD should accurately recover features of the unobserved network. This paper provides such a characterization by deriving conditions under which statistics about the unobserved network (or functions of these statistics like regression coefficients) can be consistently estimated using ARD. We first provide consistent estimates of network model parameters for three commonly used probabilistic models: the beta-model with node-specific unobserved effects, the stochastic block model with unobserved community structure, and latent geometric space models with unobserved latent locations. A key observation is that cross-group link probabilities for a collection of (possibly unobserved) groups identify the model parameters, meaning ARD are sufficient for parameter estimation. With these estimated parameters, it is possible to simulate graphs from the fitted distribution and analyze the distribution of network statistics. We can then characterize conditions under which the simulated networks based on ARD will allow for consistent estimation of the unobserved network statistics, such as eigenvector centrality, or response functions by or of the unobserved network, such as regression coefficients.

A formally certified end-to-end implementation of Shor's factorization algorithm.

Quantum computing technology may soon deliver revolutionary improvements in algorithmic performance, but it is useful only if computed answers are correct. While hardware-level decoherence errors have garnered significant attention, a less recognized obstacle to correctness is that of human programming errors-"bugs." Techniques familiar to most programmers from the classical domain for avoiding, discovering, and diagnosing bugs do not easily transfer, at scale, to the quantum domain because of its unique characteristics. To address this problem, we have been working to adapt formal methods to quantum programming. With such methods, a programmer writes a mathematical specification alongside the program and semiautomatically proves the program correct with respect to it. The proof's validity is automatically confirmed-certified-by a "proof assistant." Formal methods have successfully yielded high-assurance classical software artifacts, and the underlying technology has produced certified proofs of major mathematical theorems. As a demonstration of the feasibility of applying formal methods to quantum programming, we present a formally certified end-to-end implementation of Shor's prime factorization algorithm, developed as part of a framework for applying the certified approach to general applications. By leveraging our framework, one can significantly reduce the effects of human errors and obtain a high-assurance implementation of large-scale quantum applications in a principled way.

Diamagnetic response and phase stiffness for interacting isolated narrow bands.

Superconductivity is a macroscopic manifestation of a quantum phenomenon where pairs of electrons delocalize and develop phase coherence over a long distance. A long-standing quest has been to address the underlying microscopic mechanisms that fundamentally limit the superconducting transition temperature, Tc. A platform which serves as an ideal playground for realizing "high"-temperature superconductors are materials where the electrons' kinetic energy is quenched and interactions provide the only energy scale in the problem. However, when the noninteracting bandwidth for a set of isolated bands is small compared to the interactions, the problem is inherently nonperturbative. In two spatial dimensions, Tc is controlled by superconducting phase stiffness. Here, we present a theoretical framework for computing the electromagnetic response for generic model Hamiltonians, which controls the maximum possible superconducting phase stiffness and thereby Tc, without resorting to any mean-field approximation. Our explicit computations demonstrate that the contribution to the phase stiffness arises from i) "integrating out" the remote bands that couple to the microscopic current operator and ii) the density-density interactions projected on to the isolated narrow bands. Our framework can be used to obtain an upper bound on the phase stiffness and relatedly Tc for a range of physically inspired models involving both topological and nontopological narrow bands with density-density interactions. We discuss a number of salient aspects of this formalism by applying it to a specific model of interacting flat bands and compare the upper bound against the known Tc from independent numerically exact computations.

Path instability of an air bubble rising in water.

It has been documented since the Renaissance that an air bubble rising in water will deviate from its straight, steady path to perform a periodic zigzag or spiral motion once the bubble is above a critical size. Yet, unsteady bubble rise has resisted quantitative description, and the physical mechanism remains in dispute. Using a numerical mapping technique, we for the first time find quantitative agreement with high-precision measurements of the instability. Our linear stability analysis shows that the straight path of an air bubble in water becomes unstable to a periodic perturbation (a Hopf bifurcation) above a critical spherical radius of R = 0.926 mm, within 2% of the experimental value. While it was previously believed that the bubble's wake becomes unstable, we now demonstrate a new mechanism, based on the interplay between flow and bubble deformation.

Discordance in chromosomal and self-reported sex in the UK Biobank: Implications for transgender- and intersex-inclusive data collection.

There is growing need to distinguish between sex and gender. While sex is assigned at birth, gender is socially constructed and may not correspond to one's assigned sex. However, in most research studies, sex or gender is assessed in isolation or the terms are used interchangeably, which has implications for research accuracy and inclusivity. We used data from the UK Biobank to quantify the prevalence of disagreement between chromosomal and self-reported sex and identify potential reasons for discordance. Among approximately 200 individuals with sex discordance, 71% of discordances were potentially explained by the presence of intersex traits or transgender identity. The findings indicate that when describing sex- and/or gender-specific differences in health, researchers may be limited in their ability to draw conclusions regarding specific sex and/or gender health information.

Full Bayesian identification of linear dynamic systems using stable kernels.

System identification learns mathematical models of dynamic systems starting from input-output data. Despite its long history, such research area is still extremely active. New challenges are posed by identification of complex physical processes given by the interconnection of dynamic systems. Examples arise in biology and industry, e.g., in the study of brain dynamics or sensor networks. In the last years, regularized kernel-based identification, with inspiration from machine learning, has emerged as an interesting alternative to the classical approach commonly adopted in the literature. In the linear setting, it uses the class of stable kernels to include fundamental features of physical dynamical systems, e.g., smooth exponential decay of impulse responses. Such class includes also unknown continuous parameters, called hyperparameters, which play a similar role as the model discrete order in controlling complexity. In this paper, we develop a linear system identification procedure by casting stable kernels in a full Bayesian framework. Our models incorporate hyperparameters uncertainty and consist of a mixture of dynamic systems over a continuum spectrum of dimensions. They are obtained by overcoming drawbacks related to classical Markov chain Monte Carlo schemes that, when applied to stable kernels, are proved to become nearly reducible (i.e., unable to reconstruct posteriors of interest in reasonable time). Numerical experiments show that full Bayes frequently outperforms the state-of-the-art results on typical benchmark problems. Two real applications related to brain dynamics (neural activity) and sensor networks are also included.

Phylogenomic comparative methods: Accurate evolutionary inferences in the presence of gene tree discordance.

Phylogenetic comparative methods have long been a mainstay of evolutionary biology, allowing for the study of trait evolution across species while accounting for their common ancestry. These analyses typically assume a single, bifurcating phylogenetic tree describing the shared history among species. However, modern phylogenomic analyses have shown that genomes are often composed of mosaic histories that can disagree both with the species tree and with each other-so-called discordant gene trees. These gene trees describe shared histories that are not captured by the species tree, and therefore that are unaccounted for in classic comparative approaches. The application of standard comparative methods to species histories containing discordance leads to incorrect inferences about the timing, direction, and rate of evolution. Here, we develop two approaches for incorporating gene tree histories into comparative methods: one that constructs an updated phylogenetic variance-covariance matrix from gene trees, and another that applies Felsenstein's pruning algorithm over a set of gene trees to calculate trait histories and likelihoods. Using simulation, we demonstrate that our approaches generate much more accurate estimates of tree-wide rates of trait evolution than standard methods. We apply our methods to two clades of the wild tomato genus Solanum with varying rates of discordance, demonstrating the contribution of gene tree discordance to variation in a set of floral traits. Our approaches have the potential to be applied to a broad range of classic inference problems in phylogenetics, including ancestral state reconstruction and the inference of lineage-specific rate shifts.