Electroencephalogram-Based Metastability in Mild Cognitive Impairment Alzheimer's Disease

Background: In this study, we analyze metastability, a feature of brain dynamics in subjects experiencing mild cognitive impairment Alzheimer's disease (MCI-AD) under eyes open (EO) and eyes closed (EC) conditions. Alzheimer's disease (AD) is a critically prolonged brain disorder that interrupts neural synchronization and desynchronization. Thus, studying metastability under EO and EC conditions would help in understanding the cortical dynamics and its impact in early-stage AD. Methods: Metastability is investigated using three methods namely frequency variance analysis, Kuramoto order parameter, and through meta-state activation patterns. Frequency variance estimated from 21 electroencephalogram (EEG) channels was clustered into three regions namely anterior, central, and posterior to study the regional metastability analysis. Global metastability was assessed from Kuramoto order parameter and meta-state activation patterns by collating the 21 EEG channels. Results: Reduction in metastability was observed in central regions of MCI-AD subjects through the study of frequency variance analysis. There was a marked reduction in global metastability in the patient group under the resting EO condition. Reduction in meta-state activation properties such as temporal activation sequence complexity, modularity, and leap size in MCI-AD condition under the EO condition indicates an overall reduction in brain flexibility. Conclusion: Taken together, the study infers an underlying structural change in neuronal dynamics influencing the reduction of metastability under the MCI-AD condition. The study further revealed that this reduction in metastability is more pronounced in the EO condition.

Whole-Brain Dynamics Disruptions in the Progression of Alzheimer's Disease: Understanding the Influence of Amyloid-Beta and Tau

INTRODUCTION

Alzheimer's disease (AD) affects brain structure and function along its evolution, but brain network dynamic changes remain largely unknown.

METHODS

To understand how AD shapes brain activity, we investigated the spatiotemporal dynamics and resting state functional networks using the intrinsic ignition framework, which characterizes how an area transmits neuronal activity to others, resulting in different degrees of integration. Healthy participants, MCI, and AD patients were scanned using resting state fMRI. Mixed effects models were used to assess the impact of ABeta and tau, at the regional and whole-brain levels.

RESULTS

Dynamic complexity is progressively reduced, with Healthy participants showing higher metastability (i.e., a more complex dynamical regime over time) than observed in the other stages, while AD subjects showed the lowest.

DISCUSSION

Our study provides further insight into how AD modulates brain network dynamics along its evolution, progressively disrupting the whole-brain and resting state network dynamics.

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons' intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.

Structure of cytotoxic amyloid oligomers generated during disaggregation

Amyloidosis is characterized by the abnormal accumulation of amyloid proteins. The causative proteins aggregate from monomers to oligomers and fibrils, among which some intermediate oligomers considered as major toxins. Cytotoxic oligomers are generated not only by aggregation but also via fibril disaggregation. However, little is known about the structural characteristics and generation conditions of cytotoxic oligomers produced during disaggregation. Herein, we summarized the structural commonalities of cytotoxic oligomers formed under various disaggregation conditions, including the addition of heat shock proteins or small compounds. In vitro experimental data demonstrated the presence of high-molecular-weight oligomers (protofibrils or protofilaments) that exhibited a fibrous morphology and β-sheet structure. Molecular dynamics simulations indicated that the distorted β-sheet structure contributed to their metastability. The tendency of these cytotoxic oligomers to appear under mild disaggregation conditions, implied formation during the early stages of disaggregation. This review will aid researchers in exploring the characteristics of highly cytotoxic oligomers and developing drugs that target amyloid aggregates.

Metastability indexes global changes in the dynamic working point of the brain following brain stimulation

Several studies have shown that coordination among neural ensembles is a key to understand human cognition. A well charted path is to identify coordination states associated with cognitive functions from spectral changes in the oscillations of EEG or MEG. A growing number of studies suggest that the tendency to switch between coordination states, sculpts the dynamic repertoire of the brain and can be indexed by a measure known as metastability. In this article, we characterize perturbations in the metastability of global brain network dynamics following Transcranial Magnetic Stimulation that could quantify the duration for which information processing is altered. Thus allowing researchers to understand the network effects of brain stimulation, standardize stimulation protocols and design experimental tasks. We demonstrate the effect empirically using publicly available datasets and use a digital twin (a whole brain connectome model) to understand the dynamic principles that generate such observations. We observed a significant reduction in metastability, concurrent with an increase in coherence following single-pulse TMS reflecting the existence of a window where neural coordination is altered. The reduction in complexity was validated by an additional measure based on the Lempel-Ziv complexity of microstate labeled EEG data. Interestingly, higher frequencies in the EEG signal showed faster recovery in metastability than lower frequencies. The digital twin shed light on how the phase resetting introduced by the single-pulse TMS in local cortical networks can propagate globally, giving rise to changes in metastability and coherence.

Atomic Reconstruction of Au Thin Films through Interfacial Strains

Interfaces play a critical thermodynamic role in the existence of multilayer systems. Due to their utility in bridging energetic and compositional differences between distinct species, the formation of interfaces inherently creates internal strain in the bulk due to the reorganization needed to accommodate such a change. We report the effect of scaling interfacial stress by deposition of different adlayers on a host thin metal film. Intrinsic property differences between host and deposited metal atoms result in varying degree of composition and energy gradient within the interface. Interfacial stress can increase defects in the host leading to (i) energy dissipation and reorganization to minimize surface energy, and (ii) increased material strength. We infer that dissipation of interfacial stress induces defect migration, hence bulk and surface atomic reconstruction as captured by the surface roughness and grain size reduction coupled with a concomitant increase in material strength.

An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.

Photo-Activated Growth and Metastable Phase Transition in Metallic Solid Solutions

Laser processing in metals is versatile yet limited by its reliance on phase transformation through heating rather than electronic excitation due to their low absorptivity, attributing from highly ordered structures. Metastable states (i.e., surfaces, glasses, undercooled liquids), however, present a unique platform, both energetically and structurally to enable energy landscape tuning through selective stimuli. Herein, this ansatz is demonstrated by exploiting thin passivating oxides to stabilize an undercooled state, followed by photo-perturbation of the near surface order to induce convective Marangoni flows, edge-coalescence and phase transition into a larger metastable solid bearing asymmetric composition between the near surface and core of the formed structure. The self-terminating nature of the process creates a perfectly contained system which can maintain a high relaxation energy barrier hence deep metastable states for extended periods of time.

Mechanical Alloying as a Way to Produce Metastable Single-Phase High-Entropy Alloys beyond the Stability Criteria

Various stability criteria developed for high-entropy alloys are applied to compositions produced by mechanical alloying. While they agree with the annealed samples, these criteria fail to describe the as-milled metastable systems, highlighting the ability of mechanical alloying to overcome the limitations imposed by these criteria. The criteria are based on atomic size (Ω ≥ 1.1 and δr ≤ 6.6%) and/or electronegativity misfit, as well as on mixing enthalpy (Λ>0.95 J mol-1K-1 and -5 kJ mol-1<∆Hmix<0), or purely thermodynamic (ϕYe>20; ϕKing>1; Teff<500 K). These criteria are applied to several compositions found in the literature and to two metastable fcc solid solutions produced by mechanical alloying with compositions Al0.75CoXFeNi with X = Cr and Mn. Single-phase microstructures are stable up to above 600 K, leading to more stable multiphase systems after annealing above this temperature. Mössbauer spectrometry shows that, whereas the alloy with Cr is paramagnetic in the as-milled and annealed state, the alloy with Mn changes from paramagnetic to ferromagnetic behavior (Curie temperature ~700 K) after annealing. Thermomagnetic experiments on annealed samples show for both compositions some hysteretic events at high temperatures (850 to 1000 K), probably ascribed to reversible ordering phenomena.

Metastable atomic-ordered configurations for Al1/2Ga1/2N predicted by Monte-Carlo method based on first-principles calculations

Metastability of Aln/12Ga1-n/12N (n= 2-10: integer) with the 1-2 monolayer (ML) in-plane configuration towards thec[0001] direction has been demonstrated recently. To theoretically explain the existence of these metastable structures, relatively large calculation cells are needed. However, previous calculations were limited to the use of small calculation cell sizes to estimate the local potential depth (Δσ) of ordered Al1/2Ga1/2N models. In this work, we were able to evaluate large calculation cells based on the interaction energies between proximate Al atoms (δEAl-Al) in AlGaN alloys. To do this,δEAl-Alvalues were estimated by first-principles calculations (FPCs) using a (5a1× 5a2× 5c) cell. Next, a survey of the possible ordered configurations using various large calculation cell models was performed using the estimatedδEAl-Alvalues and the Monte-Carlo method. Then, various Δσvalues were estimated by FPCs and compared with the configurations previously reported by other research groups. We found that the ordered configuration obtained from the (4a1× 2a2× 1c) calculation cell (C42) has the lowest Δσof -9.3 meV/cation and exhibited an in-plane configuration at thec(0001) plane having (-Al-Al-Ga-Ga-) and (-Al-Ga-) sequence arrangements observed along them11-00planes. Hence, we found consistencies between the morphology obtained from experiment and the shape of the primitive cell based on our numerical calculations.

Over-Stoichiometric Metastabilization of Cation-Disordered Rock Salts

Cation-disordered rock salts (DRXs) are well known for their potential to realize the goal of achieving scalable Ni- and Co-free high-energy-density Li-ion batteries. Unlike in most cathode materials, the disordered cation distribution may lead to more factors that control the electrochemistry of DRXs. An important variable that is not emphasized by research community is regarding whether a DRX exists in a more thermodynamically stable form or a more metastable form. Moreover, within the scope of metastable DRXs, over-stoichiometric DRXs, which allow relaxation of the site balance constraint of a rock salt structure, are particularly underexplored. In this work, these findings are reported in locating a generally applicable approach to "metastabilize" thermodynamically stable Mn-based DRXs to metastable ones by introducing Li over-stoichiometry. The over-stoichiometric metastabilization greatly stimulates more redox activities, enables better reversibility of Li deintercalation/intercalation, and changes the energy storage mechanism. The metastabilized DRXs can be transformed back to the thermodynamically stable form, which also reverts the electrochemical properties, further contrasting the two categories of DRXs. This work enriches the structural and compositional space of DRX families and adds new pathways for rationally tuning the properties of DRX cathodes.

Exploiting Benefits of Vaterite Metastability to Design Degradable Systems for Biomedical Applications

The widespread application of calcium carbonate is determined by its high availability in nature and simplicity of synthesis in laboratory conditions. Moreover, calcium carbonate possesses highly attractive physicochemical properties that make it suitable for a wide range of biomedical applications. This review provides a conclusive analysis of the results on using the tunable vaterite metastability in the development of biodegradable drug delivery systems and therapeutic vehicles with a controlled and sustained release of the incorporated cargo. This manuscript highlights the nuances of vaterite recrystallization to non-porous calcite, dissolution at acidic pH, biodegradation at in vivo conditions and control over these processes. This review outlines the main benefits of vaterite instability for the controlled liberation of the encapsulated molecules for the development of biodegradable natural and synthetic polymeric materials for biomedical purposes.

Stable and metastable structures of tin (IV) oxide at high pressure

We have analysed [Formula: see text] with a combination of synchrotron X-ray diffraction and X-ray absorption spectroscopy across a pressure range of [Formula: see text] GPa with thermal annealing by a [Formula: see text] laser allowing access to all of the known high-density polymorphs of [Formula: see text], and here report their crystallographic information. The metastability of the post-rutile [Formula: see text]-[Formula: see text] and [Formula: see text] structures in [Formula: see text] are investigated by experiment and PW-DFT simulations, revealing a complex energetic landscape and suggesting a significant dependence of the observed phases on the pressure-temperature pathway taken in experiment. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 1)'.

X-ray structure of the metastable SEPT14-SEPT7 coiled coil reveals a hendecad region crucial for heterodimerization

Septins are membrane-associated, GTP-binding proteins that are present in most eukaryotes. They polymerize to play important roles as scaffolds and/or diffusion barriers as part of the cytoskeleton. α-Helical coiled-coil domains are believed to contribute to septin assembly, and those observed in both human SEPT6 and SEPT8 form antiparallel homodimers. These are not compatible with their parallel heterodimeric organization expected from the current model for protofilament assembly, but they could explain the interfilament cross-bridges observed by microscopy. Here, the first structure of a heterodimeric septin coiled coil is presented, that between SEPT14 and SEPT7; the former is a SEPT6/SEPT8 homolog. This new structure is parallel, with two long helices that are axially shifted by a full helical turn with reference to their sequence alignment. The structure also has unusual knobs-into-holes packing of side chains. Both standard seven-residue (heptad) and the less common 11-residue (hendecad) repeats are present, creating two distinct regions with opposite supercoiling, which gives rise to an overall straight coiled coil. Part of the hendecad region is required for heterodimerization and therefore may be crucial for selective septin recognition. These unconventional sequences and structural features produce a metastable heterocomplex that nonetheless has enough specificity to promote correct protofilament assembly. For instance, the lack of supercoiling may facilitate unzipping and transitioning to the antiparallel homodimeric state.

Stochastic stability analysis of legged locomotion using unscented transformation

In this manuscript, we present a novel method for estimating the stochastic stability characteristics of metastable legged systems using the unscented transformation. Prior methods for stability analysis in such systems often required high-dimensional state space discretization and a broad set of initial conditions, resulting in significant computational complexity. Our approach aims to alleviate this issue by reducing the dimensionality of the system and utilizing the unscented transformation to estimate the output distribution. This technique allows us to account for multiple sources of uncertainty and high-dimensional system dynamics, while leveraging prior knowledge of noise statistics to inform the selection of initial conditions for experiments. As a result, our method enables the efficient assessment of controller performance and analysis of parametric dependencies with fewer experiments. To demonstrate the efficacy of our proposed method, we apply it to the analysis of a one-dimensional hopper and an underactuated bipedal walking simulation with a hybrid zero dynamics controller.

Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST

Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As a benchmark model, we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 10⁶ neurons (> 3 · 10¹²synapses) on a high-end GPU, and up to 250, 000 neurons (25 · 10⁹ synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.

The Metastable State of Fermi-Pasta-Ulam-Tsingou Models

Classical statistical mechanics has long relied on assumptions such as the equipartition theorem to understand the behavior of the complicated systems of many particles. The successes of this approach are well known, but there are also many well-known issues with classical theories. For some of these, the introduction of quantum mechanics is necessary, e.g., the ultraviolet catastrophe. However, more recently, the validity of assumptions such as the equipartition of energy in classical systems was called into question. For instance, a detailed analysis of a simplified model for blackbody radiation was apparently able to deduce the Stefan-Boltzmann law using purely classical statistical mechanics. This novel approach involved a careful analysis of a "metastable" state which greatly delays the approach to equilibrium. In this paper, we perform a broad analysis of such a metastable state in the classical Fermi-Pasta-Ulam-Tsingou (FPUT) models. We treat both the α-FPUT and β-FPUT models, exploring both quantitative and qualitative behavior. After introducing the models, we validate our methodology by reproducing the well-known FPUT recurrences in both models and confirming earlier results on how the strength of the recurrences depends on a single system parameter. We establish that the metastable state in the FPUT models can be defined by using a single degree-of-freedom measure-the spectral entropy (η)-and show that this measure has the power to quantify the distance from equipartition. For the α-FPUT model, a comparison to the integrable Toda lattice allows us to define rather clearly the lifetime of the metastable state for the standard initial conditions. We next devise a method to measure the lifetime of the metastable state tm in the α-FPUT model that reduces the sensitivity to the exact initial conditions. Our procedure involves averaging over random initial phases in the plane of initial conditions, the P1-Q1 plane. Applying this procedure gives us a power-law scaling for tm, with the important result that the power laws for different system sizes collapse down to the same exponent as Eα2→0. We examine the energy spectrum E(k) over time in the α-FPUT model and again compare the results to those of the Toda model. This analysis tentatively supports a method for an irreversible energy dissipation process suggested by Onorato et al.: four-wave and six-wave resonances as described by the "wave turbulence" theory. We next apply a similar approach to the β-FPUT model. Here, we explore in particular the different behavior for the two different signs of β. Finally, we describe a procedure for calculating tm in the β-FPUT model, a very different task than for the α-FPUT model, because the β-FPUT model is not a truncation of an integrable nonlinear model.

Deterministic and Stochastic Components of Cortical Down States: Dynamics and Modulation

Slow oscillations are an emergent activity of the cerebral cortex network consisting of alternating periods of activity (Up states) and silence (Down states). Up states are periods of persistent cortical activity that share properties with that of underlying wakefulness. However, the occurrence of Down states is almost invariably associated with unconsciousness, both in animal models and clinical studies. Down states have been attributed relevant functions, such as being a resetting mechanism or breaking causal interactions between cortical areas. But what do Down states consist of? Here, we explored in detail the network dynamics (e.g., synchronization and phase) during these silent periods in vivo (male mice), in vitro (ferrets, either sex), and in silico, investigating various experimental conditions that modulate them: anesthesia levels, excitability (electric fields), and excitation/inhibition balance. We identified metastability as two complementary phases composing such quiescence states: a highly synchronized "deterministic" period followed by a low-synchronization "stochastic" period. The balance between these two phases determines the dynamical properties of the resulting rhythm, as well as the responsiveness to incoming inputs or refractoriness. We propose detailed Up and Down state cycle dynamics that bridge cortical properties emerging at the mesoscale with their underlying mechanisms at the microscale, providing a key to understanding unconscious states.SIGNIFICANCE STATEMENT The cerebral cortex expresses slow oscillations consisting of Up (active) and Down (silent) states. Such activity emerges not only in slow wave sleep, but also under anesthesia and in brain lesions. Down states functionally disconnect the network, and are associated with unconsciousness. Based on a large collection of data, novel data analysis approaches and computational modeling, we thoroughly investigate the nature of Down states. We identify two phases: a highly synchronized "deterministic" period, followed by a low-synchronization "stochastic" period. The balance between these two phases determines the dynamic properties of the resulting rhythm and responsiveness to incoming inputs. This finding reconciles different theories of slow rhythm generation and provides clues about how the brain switches from conscious to unconscious brain states.

Self-Healing Ability of Perovskites Observed via Photoluminescence Response on Nanoscale Local Forces and Mechanical Damage

The photoluminescence (PL) of metal halide perovskites can recover after light or current-induced degradation. This self-healing ability is tested by acting mechanically on MAPbI3 polycrystalline microcrystals by an atomic force microscope tip (applying force, scratching, and cutting) while monitoring the PL. Although strain and crystal damage induce strong PL quenching, the initial balance between radiative and nonradiative processes in the microcrystals is restored within a few minutes. The stepwise quenching-recovery cycles induced by the mechanical action is interpreted as a modulation of the PL blinking behavior. This study proposes that the dynamic equilibrium between active and inactive states of the metastable nonradiative recombination centers causing blinking is perturbed by strain. Reversible stochastic transformation of several nonradiative centers per microcrystal under application/release of the local stress can lead to the observed PL quenching and recovery. Fitting the experimental PL trajectories by a phenomenological model based on viscoelasticity provides a characteristic time of strain relaxation in MAPbI3 on the order of 10-100 s. The key role of metastable defect states in nonradiative losses and in the self-healing properties of perovskites is suggested.

Enactive and simondonian reflections on mental disorders

As an alternative to linear and unidimensional perspectives focused mainly on either organic or psychological processes, the enactive approach to life and mind-a branch of 4-E (embodied, embedded, enactive, extended) cognitive theories-offers an integrative framework to study mental disorders that encompasses and articulates organic, sensorimotor, and intersubjective dimensions of embodiment. These three domains are deeply entangled in a non-trivial manner. A question remains on how this systemic and multi-dimensional approach may be applied to our understanding of mental disorders and symptomatic behavior. Drawing on Gilbert Simondon's philosophy of individuation (focusing particularly on the concepts of tension, metastability, and preindividual), we provide some enactive conceptual tools to better understand the dynamic, interactive, and multi-dimensional nature of human bodies in mental disorders and psychopathological symptoms. One of such tools cursiva is sense-making, a key notion that captures the relational process of generating meaning by interacting with the sociomaterial environment. The article analyzes five aspects related to sense-making: temporality, adaptivity, the multiplicity of normativities it involves, the fundamental role of tension, and its participatory character. On this basis, we draw certain implications for our understanding of mental disorders and diverse symptoms, and suggest their interpretation in terms of difficulties to transform tensions and perform individuation processes, which result in a reduction of the field of potentialities for self-individuation and sense-making.

Cast Microstructure of a Complex Concentrated Noble Alloy Ag20Pd20Pt20Cu20Ni20

A complex concentrated noble alloy (CCNA) of equiatomic composition (Ag₂₀Pd₂₀Pt₂₀Cu₂₀Ni₂₀-20 at. %) was studied as a potential high-performance material. The equiatomic composition was used so that this alloy could be classified in the subgroup of high-entropy alloys (HEA). The alloy was prepared by induction melting at atmospheric pressure, using high purity elements. The degree of metastability of the cast state was estimated on the basis of changes in the microstructure during annealing at high temperatures in a protective atmosphere of argon. Characterisation of the metallographically prepared samples was performed using a scanning electron microscope (SEM) equipped with an energy dispersive spectrometer (EDS), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). Observation shows that the microstructure of the CCNA is in a very metastable state and multiphase, consisting of a continuous base of dendritic solidification-a matrix with an interdendritic region without other microstructural components and complex spheres. A model of the probable flow of metastable solidification of the studied alloy was proposed, based on the separation of L-melts into L₁ (rich in Ni) and L₂ (rich in Ag). The phenomenon of liquid phase separation in the considered CCNA is based on the monotectic reaction in the Ag-Ni system.

Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity

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Spatiotemporal patterns of phase-locking tend to be time-invariant.

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Global metastability is representative and stable in a cohort of heathy young adults.

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dFC characteristics are in general unique to any fMRI acquisition.

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Dynamical and informational complexity are interrelated.

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Complexity science contributes to a coherent description of brain dynamics.

Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.

Renaissance of Topotactic Ion-Exchange for Functional Solids with Close Packed Structures

Recently, many new, complex, functional oxides have been discovered with the surprising use of topotactic ion-exchange reactions on close-packed structures, such as found for wurtzite, rutile, perovskite, and other structure types. Despite a lack of apparent cation-diffusion pathways in these structure types, synthetic low-temperature transformations are possible with the interdiffusion and exchange of functional cations possessing ns² stereoactive lone pairs (e. g., Sn(II)) or unpaired nd^x electrons (e. g., Co(II)), targeting new and favorable modulations of their electronic, magnetic, or catalytic properties. This enables a synergistic blending of new functionality to an underlying three-dimensional connectivity, i. e., [-M-O-M-O-]_n , that is maintained during the transformation. In many cases, this tactic represents the only known pathway to prepare thermodynamically unstable solids that otherwise would commonly decompose by phase segregation, such as that recently applied to the discovery of many new small bandgap semiconductors.

May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data

Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence-the 4C's-and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.

Microenvironment-mediated cancer dormancy: Insights from metastability theory

Dormancy is an evolutionarily conserved protective mechanism widely observed in nature. A pathological example is found during cancer metastasis, where cancer cells disseminate from the primary tumor, home to secondary organs, and enter a growth-arrested state, which could last for decades. Recent studies have pointed toward the microenvironment being heavily involved in inducing, preserving, or ceasing this dormant state, with a strong focus on identifying specific molecular mechanisms and signaling pathways. Increasing evidence now suggests the existence of an interplay between intracellular as well as extracellular biochemical and mechanical cues in guiding such processes. Despite the inherent complexities associated with dormancy, proliferation, and growth of cancer cells and tumor tissues, viewing these phenomena from a physical perspective allows for a more global description, independent from many details of the systems. Building on the analogies between tissues and fluids and thermodynamic phase separation concepts, we classify a number of proposed mechanisms in terms of a thermodynamic metastability of the tumor with respect to growth. This can be governed by interaction with the microenvironment in the form of adherence (wetting) to a substrate or by mechanical confinement of the surrounding extracellular matrix. By drawing parallels with clinical and experimental data, we advance the notion that the local energy minima, or metastable states, emerging in the tissue droplet growth kinetics can be associated with a dormant state. Despite its simplicity, the provided framework captures several aspects associated with cancer dormancy and tumor growth.

Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease

Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.

Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics

Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference.

Metastability and dynamic modes in magnetic island chains

The uniform states of a model for one-dimensional chains of thin magnetic islands on a nonmagnetic substrate coupled via dipolar interactions are described here. Magnetic islands oriented with their long axes perpendicular to the chain direction are assumed, whose shape anisotropy imposes a preference for the dipoles to point perpendicular to the chain. The competition between anisotropy and dipolar interactions leads to three types of uniform states of distinctly different symmetries, including metastable transverse or remanent states, transverse antiferromagnetic states, and longitudinal states where all dipoles align with the chain direction. The stability limits and normal modes of oscillation are found for all three types of states, even including infinite range dipole interactions. The normal mode frequencies are shown to be determined from the eigenvalues of the stability problem.

A Metastable p-Type Semiconductor as a Defect-Tolerant Photoelectrode

A p-type Cu3Ta7O19 semiconductor was synthesized using a CuCl flux-based approach and investigated for its crystalline structure and photoelectrochemical properties. The semiconductor was found to be metastable, i.e., thermodynamically unstable, and to slowly oxidize at its surfaces upon heating in air, yielding CuO as nano-sized islands. However, the bulk crystalline structure was maintained, with up to 50% Cu(I)-vacancies and a concomitant oxidation of the Cu(I) to Cu(II) cations within the structure. Thermogravimetric and magnetic susceptibility measurements showed the formation of increasing amounts of Cu(II) cations, according to the following reaction: Cu3Ta7O19 + x/2 O2 → Cu(3-x)Ta7O19 + x CuO (surface) (x = 0 to ~0.8). With minor amounts of surface oxidation, the cathodic photocurrents of the polycrystalline films increase significantly, from <0.1 mA cm-2 up to >0.5 mA cm-2, under visible-light irradiation (pH = 6.3; irradiant powder density of ~500 mW cm-2) at an applied bias of -0.6 V vs. SCE. Electronic structure calculations revealed that its defect tolerance arises from the antibonding nature of its valence band edge, with the formation of defect states in resonance with the valence band, rather than as mid-gap states that function as recombination centers. Thus, the metastable Cu(I)-containing semiconductor was demonstrated to possess a high defect tolerance, which facilitates its high cathodic photocurrents.

Droplet tilings for rapid exploration of spatially constrained many-body systems

Geometry in materials is a key concept which can determine material behavior in ordering, frustration, and fragmentation. More specifically, the behavior of interacting degrees of freedom subject to arbitrary geometric constraints has the potential to be used for engineering materials with exotic phase behavior. While advances in lithography have allowed for an experimental exploration of geometry on ordering that has no precedent in nature, many of these methods are low throughput or the underlying dynamics remain difficult to observe directly. Here, we introduce an experimental system that enables the study of interacting many-body dynamics by exploiting the physics of multidroplet evaporation subject to two-dimensional spatial constraints. We find that a high-energy initial state of this system settles into frustrated, metastable states with relaxation on two timescales. We understand this process using a minimal dynamical model that simulates the overdamped dynamics of motile droplets by identifying the force exerted on a given droplet as being proportional to the two-dimensional vapor gradients established by its neighbors. Finally, we demonstrate the flexibility of this platform by presenting experimental realizations of droplet-lattice systems representing different spin degrees of freedom and lattice geometries. Our platform enables a rapid and low-cost means to directly visualize dynamics associated with complex many-body systems interacting via long-range interactions. More generally, this platform opens up the rich design space between geometry and interactions for rapid exploration with minimal resources.

Dynamic functional connectivity profile of the salience network across the life span

The insular cortex and anterior cingulate cortex together comprise the salience or midcingulo-insular network, involved in detecting salient events and initiating control signals to mediate brain network dynamics. The extent to which functional coupling between the salience network and the rest of the brain undergoes changes due to development and aging is at present largely unexplored. Here, we examine dynamic functional connectivity (dFC) of the salience network in a large life span sample (n = 601; 6-85 years old). A sliding-window analysis and k-means clustering revealed five states of dFC formed with the salience network, characterized by either widespread asynchrony or different patterns of synchrony between the salience network and other brain regions. We determined the frequency, dwell time, total transitions, and specific state-to-state transitions for each state and subject, regressing the metrics with subjects' age to identify life span trends. A dynamic state characterized by low connectivity between the salience network and the rest of the brain had a strong positive quadratic relationship between age and both frequency and dwell time. Additional frequency, dwell time, total transitions, and state-to-state transition trends were observed with other salience network states. Our results highlight the metastable dynamics of the salience network and its role in the maturation of brain regions critical for cognition.

Peptide Triazole Thiol Irreversibly Inactivates Metastable HIV-1 Env by Accessing Conformational Triggers Intrinsic to Virus-Cell Entry

KR13, a peptide triazole thiol previously established to inhibit HIV-1 infection and cause virus lysis, was evaluated by flow cytometry against JRFL Env-presenting cells to characterize induced Env and membrane transformations leading to irreversible inactivation. Transiently transfected HEK293T cells were preloaded with calcein dye, treated with KR13 or its thiol-blocked analogue KR13b, fixed, and stained for gp120 (35O22), MPER (10E8), 6-helix-bundle (NC-1), immunodominant loop (50-69), and fusion peptide (VRC34.01). KR13 induced dose-dependent transformations of Env and membrane characterized by transient poration, MPER exposure, and 6-helix-bundle formation (analogous to native fusion events), but also reduced immunodominant loop and fusion peptide exposure. Using a fusion peptide mutant (V504E), we found that KR13 transformation does not require functional fusion peptide for poration. In contrast, simultaneous treatment with fusion inhibitor T20 alongside KR13 prevented membrane poration and MPER exposure, showing that these events require 6-helix-bundle formation. Based on these results, we formulated a model for PTT-induced Env transformation portraying how, in the absence of CD4/co-receptor signaling, PTT may provide alternate means of perturbing the metastable Env-membrane complex, and inducing fusion-like transformation. In turn, the results show that such transformations are intrinsic to Env and can be diverted for irreversible inactivation of the protein complex.

Attractor competition enriches cortical dynamics during awakening from anesthesia

Slow oscillations (≲ 1 Hz), a hallmark of slow-wave sleep and deep anesthesia across species, arise from spatiotemporal patterns of activity whose complexity increases as wakefulness is approached and cognitive functions emerge. The arousal process constitutes an open window to the unknown mechanisms underlying the emergence of such dynamical richness in awake cortical networks. Here, we investigate the changes in network dynamics as anesthesia fades out in the rat visual cortex. Starting from deep anesthesia, slow oscillations gradually increase their frequency, eventually expressing maximum regularity. This stage is followed by the abrupt onset of an infra-slow (~0.2 Hz) alternation between sleep-like oscillations and activated states. A population rate model reproduces this transition driven by an increased excitability that brings it to periodically cross a critical point. Based on our model, dynamical richness emerges as a competition between two metastable attractor states, a conclusion strongly supported by the data.

The shaping of a molecular linguist: How a career studying DNA energetics revealed the language of molecular communication

My personal and professional journeys have been far from predictable based on my early childhood. Owing to a range of serendipitous influences, I miraculously transitioned from a rebellious, apathetic teenage street urchin who did poorly in school to a highly motivated, disciplined, and ambitious academic honors student. I was the proverbial “late bloomer.” Ultimately, I earned my PhD in biophysical chemistry at Yale, followed by a postdoc fellowship at Berkeley. These two meccas of thermodynamics, coupled with my deep fascination with biology, instilled in me a passion to pursue an academic career focused on mapping the energy landscapes of biological systems. I viewed differential energetics as the language of molecular communication that would dictate and control biological structures, as well as modulate the modes of action associated with biological functions. I wanted to be a “molecular linguist.” For the next 50 years, my group and I used a combination of spectroscopic and calorimetric techniques to characterize the energy profiles of the polymorphic conformational space of DNA molecules, their differential ligand-binding properties, and the energy landscapes associated with mutagenic DNA damage recognition, repair, and replication. As elaborated below, the resultant energy databases have enabled the development of quantitative molecular biology through the rational design of primers, probes, and arrays for diagnostic, therapeutic, and molecular-profiling protocols, which collectively have contributed to a myriad of biomedical assays. Such profiling is further justified by yielding unique energy-based insights that complement and expand elegant, structure-based understandings of biological processes.

Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club

Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50-64 years versus older group (n = 310); age range, 65-91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

Stabilization of Metastable Indomethacin α in Cellulose Nanocrystal Aerogel Scaffolds

Indomethacin (IM) is a small molecule active pharmaceutical ingredient (API) that exhibits polymorphism with the γ-form being the most thermodynamically stable form of the drug. The α-form is metastable, but it exhibits higher solubility, making it a more attractive form for drug delivery. As with other metastable polymorphs, α-IM undergoes interconversion to the stable form when subjected to certain stimuli, such as solvent, heat, pH, or exposure to seed crystals of the stable form. In this study, IM was crystallized into cellulose nanocrystal aerogel scaffolds as a mixture of the two polymorphic forms, α-IM and γ-IM. Differential scanning calorimetry (DSC) and Raman spectroscopy were used to quantitatively determine the amount of each form. Our investigation found that the metastable α-IM could be stabilized within the aerogel without phase transformation, even in the presence of external stimuli, including heat and γ-IM seed crystals. Because interconversion is often a concern during production of metastable forms of APIs, this approach has important implications in being able to produce and stabilize metastable drug forms. While IM was used as a model drug in this study, this approach could be expanded to additional drugs and provide access to other metastable API forms.

Segregated brain state during hypnosis

Can the brain be shifted into a different state using a simple social cue, as tests on highly hypnotizable subjects would suggest? Demonstrating an altered global brain state is difficult. Brain activation varies greatly during wakefulness and can be voluntarily influenced. We measured the complexity of electrophysiological response to transcranial magnetic stimulation in one 'hypnotic virtuoso'. Such a measure produces a response arguably outside the subject's voluntary control and has been proven adequate for discriminating conscious from unconscious brain states. We show that a single-word hypnotic induction robustly shifted global neural connectivity into a state where activity remained sustained but failed to ignite strong, coherent activity in frontoparietal cortices. Changes in perturbational complexity indicate a similar move towards a more segregated state. We interpret these findings to suggest a shift in the underlying state of the brain, likely moderating subsequent hypnotic responding.

Stabilization of Undercooled Metals via Passivating Oxide Layers

Undercooling metals relies on frustration of liquid-solid transition mainly by an increase in activation energy. Passivating oxide layers are a way to isolate the core from heterogenous nucleants (physical barrier) while also raising the activation energy (thermodynamic/kinetic barrier) needed for solidification. The latter is due to composition gradients (speciation) that establishes a sharp chemical potential gradient across the thin (0.7-5 nm) oxide shell, slowing homogeneous nucleation. When this speciation is properly tuned, the oxide layer presents a previously unaccounted for interfacial tension in the overall energy landscape of the relaxing material. We demonstrate that 1) the integrity of the passivation oxide is critical in stabilizing undercooled particle, a key tenet in developing heat-free solders, 2) inductive effects play a critical role in undercooling, and 3) the magnitude of the influence of the passivating oxide can be larger than size effects in undercooling.

Vacancy-Enabled O3 Phase Stabilization for Manganese-Rich Layered Sodium Cathodes

Manganese-rich layered oxide materials hold great potential as low-cost and high-capacity cathodes for Na-ion batteries. However, they usually form a P2 phase and suffer from fast capacity fade. In this work, an O3 phase sodium cathode has been developed out of a Li and Mn-rich layered material by leveraging the creation of transition metal (TM) and oxygen vacancies and the electrochemical exchange of Na and Li. The Mn-rich layered cathode material remains primarily O3 phase during sodiation/desodiation and can have a full sodiation capacity of ca. 220 mAh g^-1 . It delivers ca. 160 mAh g^-1 specific capacity between 2-3.8 V with >86 % retention over 250 cycles. The TM and oxygen vacancies pre-formed in the sodiated material enables a reversible migration of TMs from the TM layer to the tetrahedral sites in the Na layer upon de-sodiation and sodiation. The migration creates metastable states, leading to increased kinetic barrier that prohibits a complete O3-P3 phase transition.

Metastable-solid phase diagrams derived from polymorphic solidification kinetics

Nonequilibrium processes during solidification can lead to kinetic stabilization of metastable crystal phases. A general framework for predicting the solidification conditions that lead to metastable-phase growth is developed and applied to a model face-centered cubic (fcc) metal that undergoes phase transitions to the body-centered cubic (bcc) as well as the hexagonal close-packed phases at high temperatures and pressures. Large-scale molecular dynamics simulations of ultrarapid freezing show that bcc nucleates and grows well outside of the region of its thermodynamic stability. An extensive study of crystal-liquid equilibria confirms that at any given pressure, there is a multitude of metastable solid phases that can coexist with the liquid phase. We define for every crystal phase, a solid cluster in liquid (SCL) basin, which contains all solid clusters of that phase coexisting with the liquid. A rigorous methodology is developed that allows for practical calculations of nucleation rates into arbitrary SCL basins from the undercooled melt. It is demonstrated that at large undercoolings, phase selections made during the nucleation stage can be undone by kinetic instabilities amid the growth stage. On these bases, a solidification-kinetic phase diagram is drawn for the model fcc system that delimits the conditions for macroscopic grains of metastable bcc phase to grow from the melt. We conclude with a study of unconventional interfacial kinetics at special interfaces, which can bring about heterogeneous multiphase crystal growth. A first-order interfacial phase transformation accompanied by a growth-mode transition is examined.

On the Elemental Impact Factor, a Method to Determine an Alloy's Compositional Influences upon Phase Stability and Metallurgical Material Properties

Design-driven materials engineering is gaining wider acceptance with the advancement and refinement of commercially available thermodynamic software as well as enhanced computing power. Computationally designed materials are a significant improvement over the more common and resource-intensive experimental approach to materials design by way of trial and error. While not entirely eliminating experimental methods for alloy design, thermodynamic and kinetic models provide accurate predictions of phases within a given alloy, which enables material properties to be calculated. Accordingly, the present paper introduces a new technique that offers a systematic method of material design by way of utilizing commercial computational software, which has been termed the elemental impact factor. In turn, the present manuscript considers Al 6061 as a proof-of-concept metallic alloy system for elemental impact factor substantiation. Effects of chemical composition on resultant equilibrium and metastable material phases as well as properties can be efficiently assessed with the elemental impact factor framework for metallurgical materials design. Desired phases or properties may be produced by adding elements with a positive elemental impact factor, while deleterious phases or undesired properties may be reduced by adding elements with a negative elemental impact factor. Therefore, the elemental impact factor methodology was presented and then demonstrated herein with examples that showcase the technique’s potential applications and utility for integrated structure-processing-property-performance analysis.

Randomness in appendage coordination facilitates strenuous ground self-righting

Randomness is common in biological and artificial systems, resulting either from stochasticity of the environment or noise in organisms or devices themselves. In locomotor control, randomness is typically considered a nuisance. For example, during dynamic walking, randomness in stochastic terrain leads to metastable dynamics, which must be mitigated to stabilize the system around limit cycles. Here, we studied whether randomness in motion is beneficial for strenuous locomotor tasks. Our study used robotic simulation modeling of strenuous, leg-assisted, winged ground self-righting observed in cockroaches, in which unusually large randomness in wing and leg motions is present. We developed a simplified simulation robot capable of generating similar self-righting behavior and varied the randomness level in wing-leg coordination. During each wing opening attempt, the more randomness added to the time delay between wing opening and leg swinging, the more likely it was for the naive robot (which did not know what coordination is best) to self-right within a finite time. Wing-leg coordination, measured by the phase between wing and leg oscillations, had a crucial impact on self-righting outcome. Without randomness, periodic wing and leg oscillations often limited the system to visit a few bad phases, leading to failure to escape from the metastable state. With randomness, the system explored phases thoroughly and had a better chance of encountering good phases to self-right. Our study complements previous work by demonstrating that randomness helps destabilize locomotor systems from being trapped in undesired metastable states, a situation common in strenuous locomotion.

Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms

Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies towards integration and segregation by operating in a metastable regime of their coordination dynamics. Metastability may confer important behavioural qualities by binding distributed local areas into large-scale neurocognitive networks. We tested this hypothesis by analysing fMRI data in a large cohort of healthy individuals (N = 566) and comparing the metastability of the brain's large-scale resting network architecture at rest and during the performance of several tasks. Metastability was estimated using a well-defined collective variable capturing the level of 'phase-locking' between large-scale networks over time. Task-based reasoning was principally characterised by high metastability in cognitive control networks and low metastability in sensory processing areas. Although metastability between resting state networks increased during task performance, cognitive ability was more closely linked to spontaneous activity. High metastability in the intrinsic connectivity of cognitive control networks was linked to novel problem solving or fluid intelligence, but was less important in tasks relying on previous experience or crystallised intelligence. Crucially, subjects with resting architectures similar or 'pre-configured' to a task-general arrangement demonstrated superior cognitive performance. Taken together, our findings support a key linkage between the spontaneous metastability of large-scale networks in the cerebral cortex and cognition.

Coordination Dynamics: A Foundation for Understanding Social Behavior

Humans' interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people…) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB's evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights.

Mapping the Effects of Genetic Variation on Chromatin State and Gene Expression Reveals Loci That Control Ground State Pluripotency

Mouse embryonic stem cells (mESCs) cultured in the presence of LIF occupy a ground state with highly active pluripotency-associated transcriptional and epigenetic circuitry. However, ground state pluripotency in some inbred strain backgrounds is unstable in the absence of ERK1/2 and GSK3 inhibition. Using an unbiased genetic approach, we dissect the basis of this divergent response to extracellular cues by profiling gene expression and chromatin accessibility in 170 genetically heterogeneous mESCs. We map thousands of loci affecting chromatin accessibility and/or transcript abundance, including 10 QTL hotspots where genetic variation at a single locus coordinates the regulation of genes throughout the genome. For one hotspot, we identify a single enhancer variant ∼10 kb upstream of Lifr associated with chromatin accessibility and mediating a cascade of molecular events affecting pluripotency. We validate causation through reciprocal allele swaps, demonstrating the functional consequences of noncoding variation in gene regulatory networks that stabilize pluripotent states in vitro.

Pressure dependence of room-temperature structural properties of CaAl2Si2

We investigated the pressure dependence of the crystal structure of CaAl2Si2by means ofab initiocalculations and room-temperature synchrotron x-ray powder diffraction.Ab initiocalculations reproduce satisfactorily the experimentally observed pressure-dependent structural evolution up to 3 GPa where the title system remains in the trigonalP3¯m1phase. In the pressure range 3-8 GPa, pressure evolution of the calculated in-plane lattice parameters is steeper than the observed.Ab initiocalculations predict a structural phase transition to a tetragonal phase (P3¯m1to I4/mmm) near 7.5 GPa for zero (or room) temperature. Temperature effects are included through calculation of vibrational properties (phonon spectra). These calculations confirm that both phases are either globally or locally stable (metastable) and allow for the construction of aP-Tphase diagram for this system. However, our experiments show no sign of such a transition up to 12 GPa. Such a discrepancy can be explained if one considers the trigonal (P3¯m1) structure to be metastable above the critical pressure, but is separated from the predicted tetragonal (I4/mmm) structure by a relatively high energy barrier. The applied pressure alone may not be able to surpass the energy-barrier; rather a joint high-pressure and high-temperature (HPHT) treatment may lead to it. However, empirical verification of such a hypothetical transition may be hampered by the chemistry of CaAl2Si2system which shows tendency to decompose peritectically into Ca2Al3Si4and aluminum under HPHT treatment.

Proteome Homeostasis Dysfunction: A Unifying Principle in ALS Pathogenesis

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease but currently has no effective treatment. Growing evidence suggests that proteome homeostasis underlies ALS pathogenesis. Protein production, trafficking, and degradation all shape the proteome. We present a hypothesis that proposes all genetic lesions associated with ALS (including in mRNA-binding proteins) cause widespread imbalance to an already metastable proteome. The impact of such dysfunction is felt across the entire proteome and is not restricted to a small subset of proteins. Proteome imbalance may cause functional defects, such as excitability alterations, and eventually cell death. While this idea is a unifying principle for all of ALS, we propose that stratification will appear that might dictate the efficacy of therapeutics based on the proteostasis network.

Kinetic trapping in protein folding

The founding principles of protein folding introduced by Christian Anfinsen, together with the numerous mechanistic investigations that followed, assume that protein folding is a thermodynamically controlled process. On the other hand, this review underscores the fact that thermodynamic control is far from being the norm in protein folding, as long as one considers an extended chemical-potential landscape encompassing aggregates, in addition to native, unfolded and intermediate states. Here, we highlight the key role of kinetic trapping of the protein native state relative to unfolded, intermediate and, most importantly, aggregated states. We propose that kinetic trapping serves an important role in biology by protecting the bioactive states of a large number of proteins from deleterious aggregation. In the event that undesired aggregates were somehow formed, specialized intracellular disaggregation machines have evolved to convert any aberrant populations back to the native state, thus restoring a fully bioactive and aggregation-protected protein cohort.

Antifragility in Climbing: Determining Optimal Stress Loads for Athletic Performance Training

In the past decades, much research has examined the negative effects of stressors on the performance of athletes. However, according to evolutionary biology, organisms may exhibit growth under stress, a phenomenon called antifragility. For both coaches and their athletes, a key question is how to design training conditions to help athletes develop the kinds of physical, physiological, and behavioral adaptations underlying antifragility. An answer to this important question requires a better understanding of how individual athletes respond to stress or loads in the context of relevant sports tasks. In order to contribute to such understanding, the present study leverages a theoretical and methodological approach to generate individualized load-response profiles in the context of a climbing task. Climbers (n = 37) were asked to complete different bouldering (climbing) routes with increasing loading (i.e. difficulty). We quantified the behavioral responses of each individual athlete by mathematically combining two measures obtained for each route: (a) maximal performance (i.e. the percentage of the route that was completed) and (b) number of attempts required to achieve maximal performance. We mapped this composite response variable as a function of route difficulty. This procedure resulted in load-response curves that captured each athlete's adaptability to stress, termed phenotypic plasticity (PP), specifically operationalized as the area under the generated curves. The results indicate individual load-response profiles (and by extension PP) for athletes who perform at similar maximum levels. We discuss how these profiles might be used by coaches to systematically select stress loads that may be ideally featured in performance training.

Local and global perspectives on diffusion maps in the analysis of molecular systems

Diffusion maps approximate the generator of Langevin dynamics from simulation data. They afford a means of identifying the slowly evolving principal modes of high-dimensional molecular systems. When combined with a biasing mechanism, diffusion maps can accelerate the sampling of the stationary Boltzmann-Gibbs distribution. In this work, we contrast the local and global perspectives on diffusion maps, based on whether or not the data distribution has been fully explored. In the global setting, we use diffusion maps to identify metastable sets and to approximate the corresponding committor functions of transitions between them. We also discuss the use of diffusion maps within the metastable sets, formalizing the locality via the concept of the quasi-stationary distribution and justifying the convergence of diffusion maps within a local equilibrium. This perspective allows us to propose an enhanced sampling algorithm. We demonstrate the practical relevance of these approaches both for simple models and for molecular dynamics problems (alanine dipeptide and deca-alanine).