The recovery of European freshwater biodiversity has come to a halt.

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss1. Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity2. Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of very large-scale computational discovery carried out through experimental validation remain scarce, especially for materials with product applicability. Here, we demonstrate how this vision became reality by combining state-of-the-art machine learning (ML) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the NaxLi3-xYCl6 (0≤ x≤ 3) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. The showcased screening of millions of materials candidates highlights the transformative potential of advanced ML and HPC methodologies, propelling materials discovery into a new era of efficiency and innovation.

Workplace violence, work-related exhaustion, and workplace cognitive failure among nurses: A cross-sectional study.

AIM: To examine the relationships between nurses' exposure to workplace violence and self-reports of workplace cognitive failure. DESIGN: A cross-sectional study. METHODS: An online questionnaire was administered in April 2023 to nurses in Michigan, US. Structural equation modelling was used to examine effects of physical and non-physical workplace violence (occupational stressors) and work efficiency and competence development (occupational protective factors) on workplace cognitive failure. RESULTS: Physical violence was a significant predictor of the action subscale of cognitive failure. There were no direct effects of non-physical violence, workplace efficiency, or competence development on any of the workplace cognitive failure dimensions. Both types of violence and efficiency had significant indirect effects on workplace cognitive failure via work-related exhaustion. Work-related exhaustion predicted significantly higher scores for workplace cognitive failure. CONCLUSION: Workplace violence and work efficiency exhibited primarily indirect effects on workplace cognitive failure among nurses via work-related exhaustion. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses experiencing workplace violence may be at increased risk for workplace cognitive failure, especially if they are also experiencing work-related exhaustion. Workplaces that nurses perceive as more efficient can help to mitigate the effects of violence on nurses' cognitive failure. IMPACT: This study addressed the possible effects of workplace violence as well as work efficiency and competence development on nurses' cognitive failure at work. Analyses revealed primarily indirect effects of workplace violence, and indirect protective effects of work efficiency, on nurses' cognitive failure via work-related exhaustion. This research has implications for healthcare organizations and suggests that efforts made by healthcare workplaces to prevent violence and work-related exhaustion, and to enhance work efficiency, may help to mitigate workplace cognitive failure among nurses. REPORTING METHOD: We have followed the STROBE checklist in reporting this study. PATIENT OR PUBLIC CONTRIBUTION: No Patient or public contribution.

Data-driven parameterization of the generalized Langevin equation.

We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method.

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness.

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these difficulties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measures and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such difficulties, we developed a new data-driven approach to construct orthonormal polynomials for arbitrary mutually dependent randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The effectiveness of our method is demonstrated in challenging problems such as partial differential equations and realistic molecular systems within high-dimensional (O(10)) conformational spaces where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Spermine Condenses DNA, but Not RNA Duplexes.

Interactions between the polyamine spermine and nucleic acids drive important cellular processes. Spermine condenses DNA and some RNAs, such as poly(rA):poly(rU). A large fraction of the spermine present in cells is bound to RNA but apparently does not condense it. Here, we study the effect of spermine binding to short duplex RNA and DNA, and compare our findings with predictions of molecular-dynamics simulations. When small numbers of spermine are introduced, RNA with a designed sequence containing a mixture of 14 GC pairs and 11 AU pairs resists condensation relative to DNA of an equivalent sequence or to 25 bp poly(rA):poly(rU) RNA. A comparison of wide-angle x-ray scattering profiles with simulation results suggests that spermine is sequestered deep within the major groove of mixed-sequence RNA. This prevents condensation by limiting opportunities to bridge to other molecules and stabilizes the RNA by locking it into a particular conformation. In contrast, for DNA, simulations suggest that spermine binds externally to the duplex, offering opportunities for intermolecular interaction. The goal of this study is to explain how RNA can remain soluble and available for interaction with other molecules in the cell despite the presence of spermine at concentrations high enough to precipitate DNA.

PB-AM: An open-source, fully analytical linear poisson-boltzmann solver.

We present the open source distributed software package Poisson-Boltzmann Analytical Method (PB-AM), a fully analytical solution to the linearized PB equation, for molecules represented as non-overlapping spherical cavities. The PB-AM software package includes the generation of outputs files appropriate for visualization using visual molecular dynamics, a Brownian dynamics scheme that uses periodic boundary conditions to simulate dynamics, the ability to specify docking criteria, and offers two different kinetics schemes to evaluate biomolecular association rate constants. Given that PB-AM defines mutual polarization completely and accurately, it can be refactored as a many-body expansion to explore 2- and 3-body polarization. Additionally, the software has been integrated into the Adaptive Poisson-Boltzmann Solver (APBS) software package to make it more accessible to a larger group of scientists, educators, and students that are more familiar with the APBS framework. © 2016 Wiley Periodicals, Inc.

The Role of Correlation and Solvation in Ion Interactions with B-DNA.

The ionic atmospheres around nucleic acids play important roles in biological function. Large-scale explicit solvent simulations coupled to experimental assays such as anomalous small-angle x-ray scattering can provide important insights into the structure and energetics of such atmospheres but are time- and resource intensive. In this article, we use classical density functional theory to explore the balance among ion-DNA, ion-water, and ion-ion interactions in ionic atmospheres of RbCl, SrCl2, and CoHexCl3 (cobalt hexamine chloride) around a B-form DNA molecule. The accuracy of the classical density functional theory calculations was assessed by comparison between simulated and experimental anomalous small-angle x-ray scattering curves, demonstrating that an accurate model should take into account ion-ion correlation and ion hydration forces, DNA topology, and the discrete distribution of charges on the DNA backbone. As expected, these calculations revealed significant differences among monovalent, divalent, and trivalent cation distributions around DNA. Approximately half of the DNA-bound Rb(+) ions penetrate into the minor groove of the DNA and half adsorb on the DNA backbone. The fraction of cations in the minor groove decreases for the larger Sr(2+) ions and becomes zero for CoHex(3+) ions, which all adsorb on the DNA backbone. The distribution of CoHex(3+) ions is mainly determined by Coulomb and steric interactions, while ion-correlation forces play a central role in the monovalent Rb(+) distribution and a combination of ion-correlation and hydration forces affect the Sr(2+) distribution around DNA. This does not imply that correlations in CoHex solutions are weaker or stronger than for other ions. Steric inaccessibility of the grooves to large CoHex ions leads to their binding at the DNA surface. In this binding mode, first-order electrostatic interactions (Coulomb) dominate the overall binding energy as evidenced by low sensitivity of ionic distribution to the presence or absence of second-order electrostatic correlation interactions.

Opposing Effects of Multivalent Ions on the Flexibility of DNA and RNA.

Increasing the concentration of counterions (salt) is known to reduce the bending persistence length of DNA. Here we use atomistic molecular dynamics simulations to predict that multivalent counterions have the opposite effect on double-stranded RNA, increasing its bending rigidity by at least 30%. This counterintuitive effect is observed for various tri- and tetravalent ions alike, and is robust to methodological details and the RNA sequence. In contrast to DNA, multivalent counterions bind inside the RNA major groove, causing significant contraction of the molecule along its helical axis-as a result, its further deformation due to bending becomes energetically more expensive compared to bending without bound multivalent ions. Thus, the relationship between mechanical properties of a charged polymer and its ionic atmosphere may be richer than previously thought.

Understanding nucleic acid structural changes by comparing wide-angle x-ray scattering (WAXS) experiments to molecular dynamics simulations.

Wide-angle x-ray scattering (WAXS) is emerging as a powerful tool for increasing the resolution of solution structure measurements of biomolecules. Compared to its better known complement, small angle x-ray scattering (SAXS), WAXS targets higher scattering angles and can enhance structural studies of molecules by accessing finer details of solution structures. Although the extension from SAXS to WAXS is easy to implement experimentally, the computational tools required to fully harness the power of WAXS are still under development. Currently, WAXS is employed to study structural changes and ligand binding in proteins; however, the methods are not as fully developed for nucleic acids. Here, we show how WAXS can qualitatively characterize nucleic acid structures as well as the small but significant structural changes driven by the addition of multivalent ions. We show the potential of WAXS to test all-atom molecular dynamics (MD) simulations and to provide insight into understanding how the trivalent ion cobalt(III) hexammine (CoHex) affects the structure of RNA and DNA helices. We find that MD simulations capture the RNA structural change that occurs due to addition of CoHex.

Multi-shell model of ion-induced nucleic acid condensation.

We present a semi-quantitative model of condensation of short nucleic acid (NA) duplexes induced by trivalent cobalt(iii) hexammine (CoHex) ions. The model is based on partitioning of bound counterion distribution around single NA duplex into "external" and "internal" ion binding shells distinguished by the proximity to duplex helical axis. In the aggregated phase the shells overlap, which leads to significantly increased attraction of CoHex ions in these overlaps with the neighboring duplexes. The duplex aggregationfree energy is decomposed into attractive and repulsive components in such a way that they can be represented by simple analytical expressions with parameters derived from molecular dynamic simulations and numerical solutions of Poisson equation. The attractive term depends on the fractions of bound ions in the overlapping shells and affinity of CoHex to the "external" shell of nearly neutralized duplex. The repulsive components of the free energy are duplex configurational entropy loss upon the aggregation and the electrostatic repulsion of the duplexes that remains after neutralization by bound CoHex ions. The estimates of the aggregationfree energy are consistent with the experimental range of NA duplex condensation propensities, including the unusually poor condensation of RNA structures and subtle sequence effects upon DNAcondensation. The model predicts that, in contrast to DNA, RNA duplexes may condense into tighter packed aggregates with a higher degree of duplex neutralization. An appreciable CoHex mediated RNA-RNA attraction requires closer inter-duplex separation to engage CoHex ions (bound mostly in the "internal" shell of RNA) into short-range attractive interactions. The model also predicts that longer NA fragments will condense more readily than shorter ones. The ability of this model to explain experimentally observed trends in NAcondensation lends support to proposed NAcondensation picture based on the multivalent "ion binding shells."

Why double-stranded RNA resists condensation.

The addition of small amounts of multivalent cations to solutions containing double-stranded DNA leads to inter-DNA attraction and eventual condensation. Surprisingly, the condensation is suppressed in double-stranded RNA, which carries the same negative charge as DNA, but assumes a different double helical form. Here, we combine experiment and atomistic simulations to propose a mechanism that explains the variations in condensation of short (25 base-pairs) nucleic acid (NA) duplexes, from B-like form of homopolymeric DNA, to mixed sequence DNA, to DNA:RNA hybrid, to A-like RNA. Circular dichroism measurements suggest that duplex helical geometry is not the fundamental property that ultimately determines the observed differences in condensation. Instead, these differences are governed by the spatial variation of cobalt hexammine (CoHex) binding to NA. There are two major NA-CoHex binding modes--internal and external--distinguished by the proximity of bound CoHex to the helical axis. We find a significant difference, up to 5-fold, in the fraction of ions bound to the external surfaces of the different NA constructs studied. NA condensation propensity is determined by the fraction of CoHex ions in the external binding mode.

Biomolecular electrostatics and solvation: a computational perspective.

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

Side-chain oxysterols modulate cholesterol accessibility through membrane remodeling.

Side-chain oxysterols, such as 25-hydroxycholesterol (25-HC), are key regulators of cholesterol homeostasis. New evidence suggests that the alteration of membrane structure by 25-HC contributes to its regulatory effects. We have examined the role of oxysterol membrane effects on cholesterol accessibility within the membrane using perfringolysin O (PFO), a cholesterol-dependent cytolysin that selectively binds accessible cholesterol, as a sensor of membrane cholesterol accessibility. We show that 25-HC increases cholesterol accessibility in a manner dependent on the membrane lipid composition. Structural analysis of molecular dynamics simulations reveals that increased cholesterol accessibility is associated with membrane thinning, and that the effects of 25-HC on cholesterol accessibility are driven by these changes in membrane thickness. Further, we find that the 25-HC antagonist LY295427 (agisterol) abrogates the membrane effects of 25-HC in a nonenantioselective manner, suggesting that agisterol antagonizes the cholesterol-homeostatic effects of 25-HC indirectly through its membrane interactions. These studies demonstrate that oxysterols regulate cholesterol accessibility, and thus the availability of cholesterol to be sensed and transported throughout the cell, by modulating the membrane environment. This work provides new insights into how alterations in membrane structure can be used to relay cholesterol regulatory signals.

Bayesian model aggregation for ensemble-based estimates of protein pKa values.

This article investigates an ensemble-based technique called Bayesian Model Averaging (BMA) to improve the performance of protein amino acid pKa predictions. Structure-based pKa calculations play an important role in the mechanistic interpretation of protein structure and are also used to determine a wide range of protein properties. A diverse set of methods currently exist for pKa prediction, ranging from empirical statistical models to ab initio quantum mechanical approaches. However, each of these methods are based on a set of conceptual assumptions that can effect a model's accuracy and generalizability for pKa prediction in complicated biomolecular systems. We use BMA to combine eleven diverse prediction methods that each estimate pKa values of amino acids in staphylococcal nuclease. These methods are based on work conducted for the pKa Cooperative and the pKa measurements are based on experimental work conducted by the García-Moreno lab. Our cross-validation study demonstrates that the aggregated estimate obtained from BMA outperforms all individual prediction methods with improvements ranging from 45 to 73% over other method classes. This study also compares BMA's predictive performance to other ensemble-based techniques and demonstrates that BMA can outperform these approaches with improvements ranging from 27 to 60%. This work illustrates a new possible mechanism for improving the accuracy of pKa prediction and lays the foundation for future work on aggregate models that balance computational cost with prediction accuracy.

Application of a hemolysis assay for analysis of complement activation by perfluorocarbon nanoparticles.

Nanoparticles offer new options for medical diagnosis and therapeutics with their capacity to specifically target cells and tissues with imaging agents and/or drug payloads. The unique physical aspects of nanoparticles present new challenges for this promising technology. Studies indicate that nanoparticles often elicit moderate to severe complement activation. Using human in vitro assays that corroborated the mouse in vivo results we previously presented mechanistic studies that define the pathway and key components involved in modulating complement interactions with several gadolinium-functionalized perfluorocarbon nanoparticles (PFOB). Here we employ a modified in vitro hemolysis-based assay developed in conjunction with the mouse in vivo model to broaden our analysis to include PFOBs of varying size, charge and surface chemistry and examine the variations in nanoparticle-mediated complement activity between individuals. This approach may provide the tools for an in-depth structure-activity relationship study that will guide the eventual development of biocompatible nanoparticles. FROM THE CLINICAL EDITOR: Unique physical aspects of nanoparticles may lead to moderate to severe complement activation in vivo, which represents a challenge to clinical applicability. In order to guide the eventual development of biocompatible nanoparticles, this team of authors report a modified in vitro hemolysis-based assay developed in conjunction with their previously presented mouse model to enable in-depth structure-activity relationship studies.

Cryptic species complex shows population-dependent, rather than lineage-dependent tolerance to a neonicotinoid.

Cryptic species are rarely considered in ecotoxicology, resulting in misleading outcomes when using a single morphospecies that encompasses multiple cryptic species. This oversight contributes to the lack of reproducibility in ecotoxicological experiments and promotes unreliable extrapolations. The important question of ecological differentiation and the sensitivity of cryptic species is rarely tackled, leaving a substantial knowledge gap regarding the vulnerability of individual cryptic species within species complexes. In times of agricultural intensification and the frequent use of pesticides, there is an urgent need for a better understanding of the vulnerability of species complexes and possible differences in adaptive processes. We used the cryptic species complex of the aquatic amphipod Gammarus roeselii, which comprises at least 13 genetic mtDNA lineages and spans from small-scale endemic lineages in Greece to a large-scale widely distributed lineage in central Europe. We exposed eleven populations belonging to four lineages to the neonicotinoid thiacloprid in an acute toxicity assay. We recorded various environmental variables in each habitat to assess the potential pre-exposure of the populations to contaminants. Our results showed that the populations differed up to 4-fold in their tolerances. The lineage identity had a rather minor influence, suggesting that the cryptic species complex G. roeselii does not differ significantly in tolerance to the neonicotinoid thiacloprid. However, the observed population differentiation implies that recent pre-exposure to thiacloprid (or similar substances) or general habitat contamination has triggered adaptive processes. Though, the extent to which these mechanisms are equally triggered in all lineages needs to be addressed in the future. Our study provides two key findings: Firstly, it shows that observed phylogenetic differences within the G. roeselii species complex did not reveal differences in thiacloprid tolerance. Second, it confirms that differentiation occurs at the population level, highlighting that susceptibility to toxicants is population-dependent. The population-specific differences were within the range of accepted intraspecific variability from a regulatory standpoint. From an evolutionary-ecological perspective, it remains intriguing to observe how persistent stresses will continue to influence tolerance and whether different populations are on distinct pathways of adaptation. Given that the potential selection process has only lasted a relatively short number of generations, it is crucial to monitor these populations in the future, as even brief exposure periods significantly impact evolutionary responses.

Multi-decadal improvements in the ecological quality of European rivers are not consistently reflected in biodiversity metrics.

Humans impact terrestrial, marine and freshwater ecosystems, yet many broad-scale studies have found no systematic, negative biodiversity changes (for example, decreasing abundance or taxon richness). Here we show that mixed biodiversity responses may arise because community metrics show variable responses to anthropogenic impacts across broad spatial scales. We first quantified temporal trends in anthropogenic impacts for 1,365 riverine invertebrate communities from 23 European countries, based on similarity to least-impacted reference communities. Reference comparisons provide necessary, but often missing, baselines for evaluating whether communities are negatively impacted or have improved (less or more similar, respectively). We then determined whether changing impacts were consistently reflected in metrics of community abundance, taxon richness, evenness and composition. Invertebrate communities improved, that is, became more similar to reference conditions, from 1992 until the 2010s, after which improvements plateaued. Improvements were generally reflected by higher taxon richness, providing evidence that certain community metrics can broadly indicate anthropogenic impacts. However, richness responses were highly variable among sites, and we found no consistent responses in community abundance, evenness or composition. These findings suggest that, without sufficient data and careful metric selection, many common community metrics cannot reliably reflect anthropogenic impacts, helping explain the prevalence of mixed biodiversity trends.

Time series of freshwater macroinvertebrate abundances and site characteristics of European streams and rivers.

Freshwater macroinvertebrates are a diverse group and play key ecological roles, including accelerating nutrient cycling, filtering water, controlling primary producers, and providing food for predators. Their differences in tolerances and short generation times manifest in rapid community responses to change. Macroinvertebrate community composition is an indicator of water quality. In Europe, efforts to improve water quality following environmental legislation, primarily starting in the 1980s, may have driven a recovery of macroinvertebrate communities. Towards understanding temporal and spatial variation of these organisms, we compiled the TREAM dataset (Time seRies of European freshwAter Macroinvertebrates), consisting of macroinvertebrate community time series from 1,816 river and stream sites (mean length of 19.2 years and 14.9 sampling years) of 22 European countries sampled between 1968 and 2020. In total, the data include >93 million sampled individuals of 2,648 taxa from 959 genera and 212 families. These data can be used to test questions ranging from identifying drivers of the population dynamics of specific taxa to assessing the success of legislative and management restoration efforts.

The structural basis of cholesterol accessibility in membranes.

Although the majority of free cellular cholesterol is present in the plasma membrane, cholesterol homeostasis is principally regulated through sterol-sensing proteins that reside in the cholesterol-poor endoplasmic reticulum (ER). In response to acute cholesterol loading or depletion, there is rapid equilibration between the ER and plasma membrane cholesterol pools, suggesting a biophysical model in which the availability of plasma membrane cholesterol for trafficking to internal membranes modulates ER membrane behavior. Previous studies have predominantly examined cholesterol availability in terms of binding to extramembrane acceptors, but have provided limited insight into the structural changes underlying cholesterol activation. In this study, we use both molecular dynamics simulations and experimental membrane systems to examine the behavior of cholesterol in membrane bilayers. We find that cholesterol depth within the bilayer provides a reasonable structural metric for cholesterol availability and that this is correlated with cholesterol-acceptor binding. Further, the distribution of cholesterol availability in our simulations is continuous rather than divided into distinct available and unavailable pools. This data provide support for a revised cholesterol activation model in which activation is driven not by saturation of membrane-cholesterol interactions but rather by bulk membrane remodeling that reduces membrane-cholesterol affinity.