Cell maturation: Hallmarks, triggers, and manipulation.

How cells become specialized, or "mature," is important for cell and developmental biology. While maturity is usually deemed a terminal fate, it may be more helpful to consider maturation not as a switch but as a dynamic continuum of adaptive phenotypic states set by genetic and environment programing. The hallmarks of maturity comprise changes in anatomy (form, gene circuitry, and interconnectivity) and physiology (function, rhythms, and proliferation) that confer adaptive behavior. We discuss efforts to harness their chemical (nutrients, oxygen, and growth factors) and physical (mechanical, spatial, and electrical) triggers in vitro and in vivo and how maturation strategies may support disease research and regenerative medicine.

Competing Roles of Slow Oscillations and Delta Waves in Memory Consolidation versus Forgetting.

Sleep has been implicated in both memory consolidation and forgetting of experiences. However, it is unclear what governs the balance between consolidation and forgetting. Here, we tested how activity-dependent processing during sleep might differentially regulate these two processes. We specifically examined how neural reactivations during non-rapid eye movement (NREM) sleep were causally linked to consolidation versus weakening of the neural correlates of neuroprosthetic skill. Strikingly, we found that slow oscillations (SOs) and delta (δ) waves have dissociable and competing roles in consolidation versus forgetting. By modulating cortical spiking linked to SOs or δ waves using closed-loop optogenetic methods, we could, respectively, weaken or strengthen consolidation and thereby bidirectionally modulate sleep-dependent performance gains. We further found that changes in the temporal coupling of spindles to SOs relative to δ waves could account for such effects. Thus, our results indicate that neural activity driven by SOs and δ waves have competing roles in sleep-dependent memory consolidation.

The science and engineering behind sensitized brain-controlled bionic hands.

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

Programmable 2D materials through shape-controlled capillary forces.

In recent years, self-assembly has emerged as a powerful tool for fabricating functional materials. Since self-assembly is fundamentally determined by the particle interactions in the system, if we can gain full control over these interactions, it would open the door for creating functional materials by design. In this paper, we exploit capillary interactions between colloidal particles at liquid interfaces to create two-dimensional (2D) materials where particle interactions and self-assembly can be fully programmed using particle shape alone. Specifically, we consider colloidal particles which are polygonal plates with homogeneous surface chemistry and undulating edges as this particle geometry gives us precise and independent control over both short-range hard-core repulsions and longer-range capillary interactions. To illustrate the immense potential provided by our system for programming self-assembly, we use minimum energy calculations and Monte Carlo simulations to show that polygonal plates with different in-plane shapes (hexagons, truncated triangles, triangles, squares) and edge undulations of different multipolar order (hexapolar, octopolar, dodecapolar) can be used to create a rich variety of 2D structures, including hexagonal close-packed, honeycomb, Kagome, and quasicrystal lattices. Since the required particle shapes can be readily fabricated experimentally, we can use our colloidal system to control the entire process chain for materials design, from initial design and fabrication of the building blocks, to final assembly of the emergent 2D material.

Synergistic material-microbe interface toward deeper anaerobic defluorination.

Per- and polyfluoroalkyl substances (PFAS), particularly the perfluorinated ones, are recalcitrant to biodegradation. By integrating an enrichment culture of reductive defluorination with biocompatible electrodes for the electrochemical process, a deeper defluorination of a C6-perfluorinated unsaturated PFAS was achieved compared to the biological or electrochemical system alone. Two synergies in the bioelectrochemical system were identified: i) The in-series microbial-electrochemical defluorination and ii) the electrochemically enabled microbial defluorination of intermediates. These synergies at the material-microbe interfaces surpassed the limitation of microbial defluorination and further turned the biotransformation end products into less fluorinated products, which could be less toxic and more biodegradable in the environment. This material-microbe hybrid system brings opportunities in the bioremediation of PFAS driven by renewable electricity and warrants future research on mechanistic understanding of defluorinating and electroactive microorganisms at the material-microbe interface for system optimizations.

The role of the water contact layer on hydration and transport at solid/liquid interfaces.

Understanding the structure in the nanoscopic region of water that is in direct contact with solid surfaces, so-called contact layer, is key to quantifying macroscopic properties that are of interest to e.g. catalysis, ice nucleation, nanofluidics, gas adsorption, and sensing. We explore the structure of the water contact layer on various technologically relevant solid surfaces, namely graphene, MoS[Formula: see text], Au(111), Au(100), Pt(111), and Pt(100), which have been previously hampered by time and length scale limitations of ab initio approaches or force field inaccuracies, by means of molecular dynamics simulations based on ab initio machine learning potentials built using an active learning scheme. Our results reveal that the in-plane intermolecular correlations of the water contact layer vary greatly among different systems: Whereas the contact layer on graphene and on Au(111) is predominantly homogeneous and isotropic, it is inhomogeneous and anisotropic on MoS[Formula: see text], on Au(100), and on the Pt surfaces, where it additionally forms two distinct sublayers. We apply hydrodynamics and the theory of the hydrophobic effect, to relate the energy corrugation and the characteristic length-scales of the contact layer with wetting, slippage, the hydration of small hydrophobic solutes and diffusio-osmotic transport. Thus, this work provides a microscopic picture of the water contact layer and links it to macroscopic properties of liquid/solid interfaces that are measured experimentally and that are relevant to wetting, hydrophobic solvation, nanofluidics, and osmotic transport.

Development of broad modulus profile upon polymer-polymer interface formation between immiscible glassy-rubbery domains.

Interfaces of glassy materials such as thin films, blends, and composites create strong unidirectional gradients to the local heterogeneous dynamics that can be used to elucidate the length scales and mechanisms associated with the dynamic heterogeneity of glasses. We focus on bilayer films of two different polymers with very different glass transition temperatures ([Formula: see text]) where previous work has demonstrated a long-range (∼200 nm) profile in local [Formula: see text] is established between immiscible glassy and rubbery polymer domains when the polymer-polymer interface is formed to equilibrium. Here, we demonstrate that an equally long-ranged gradient in local modulus [Formula: see text] is established when the polymer-polymer interface ([Formula: see text]5 nm) is formed between domains of glassy polystyrene (PS) and rubbery poly(butadiene) (PB), consistent with previous reports of a broad [Formula: see text] profile in this system. A continuum physics model for the shear wave propagation caused by a quartz crystal microbalance across a PB/PS bilayer film is used to measure the viscoelastic properties of the bilayer during the evolution of the PB/PS interface showing the development of a broad gradient in local modulus [Formula: see text] spanning [Formula: see text]180 nm between the glassy and rubbery domains of PS and PB. We suggest these broad profiles in [Formula: see text] and [Formula: see text] arise from a coupling of the spectrum of vibrational modes across the polymer-polymer interface as a result of acoustic impedance matching of sound waves with [Formula: see text] nm during interface broadening that can then trigger density fluctuations in the neighboring domain.

Cation valency in water-in-salt electrolytes alters the short- and long-range structure of the electrical double layer.

Highly concentrated aqueous electrolytes (termed water-in-salt electrolytes, WiSEs) at solid-liquid interfaces are ubiquitous in myriad applications including biological signaling, electrosynthesis, and energy storage. This interface, known as the electrical double layer (EDL), has a different structure in WiSEs than in dilute electrolytes. Here, we investigate how divalent salts [zinc bis(trifluoromethylsulfonyl)imide, Zn(TFSI)2], as well as mixtures of mono- and divalent salts [lithium bis(trifluoromethylsulfonyl)imide (LiTFSI) mixed with Zn(TFSI)2], affect the short- and long-range structure of the EDL under confinement using a multimodal combination of scattering, spectroscopy, and surface forces measurements. Raman spectroscopy of bulk electrolytes suggests that the cation is closely associated with the anion regardless of valency. Wide-angle X-ray scattering reveals that all bulk electrolytes form ion clusters; however, the clusters are suppressed with increasing concentration of the divalent ion. To probe the EDL under confinement, we use a Surface Forces Apparatus and demonstrate that the thickness of the adsorbed layer of ions at the interface grows with increasing divalent ion concentration. Multiple interfacial layers form following this adlayer; their thicknesses appear dependent on anion size, rather than cation. Importantly, all electrolytes exhibit very long electrostatic decay lengths that are insensitive to valency. It is likely that in the WiSE regime, electrostatic screening is mediated by the formation of ion clusters rather than individual well-solvated ions. This work contributes to understanding the structure and charge-neutralization mechanism in this class of electrolytes and the interfacial behavior of mixed-electrolyte systems encountered in electrochemistry and biology.

Metastable precipitation and ion-extractant transport in liquid-liquid separations of trivalent elements.

The extractant-assisted transport of metal ions from aqueous to organic environments by liquid-liquid extraction has been widely used to separate and recover critical elements on an industrial scale. While current efforts focus on designing better extractants and optimizing process conditions, the mechanism that underlies ionic transport remains poorly understood. Here, we report a nonequilibrium process in the bulk aqueous phase that influences interfacial ion transport: the formation of metastable ion-extractant precipitates away from the liquid-liquid interface, separated from it by a depletion region without precipitates. Although the precipitate is soluble in the organic phase, the depletion region separates the two and ions are sequestered in a long-lived metastable state. Since precipitation removes extractants from the aqueous phase, even extractants that are sparingly soluble in water will continue to be withdrawn from the organic phase to feed the aqueous precipitation process. Solute concentrations in both phases and the aqueous pH influence the temporal evolution of the process and ionic partitioning between the precipitate and organic phase. Aqueous ion-extractant precipitation during liquid-liquid extraction provides a reaction path that can influence the extraction kinetics, which plays an important role in designing advanced processes to separate rare earths and other minerals.

Water dissociation at the water-rutile TiO2(110) interface from ab initio-based deep neural network simulations.

The interaction of water with TiO2 surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO2-water interface has a critical role in the surface chemistry of TiO2, but is difficult to determine both experimentally and computationally. Among TiO2 surfaces, rutile TiO2(110) is of special interest as the most abundant surface of TiO2's stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO2(110) with gas-phase water, much less is known about the TiO2(110)-water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO2(110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO2(110) interactions over a wide range of water coverages. Simulations on TiO2(110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns. These quantities are both much larger than corresponding estimates for the aqueous anatase TiO2(101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase.

Doping-induced assembly interface for noninvasive in vivo local and systemic immunomodulation.

Peripheral neural interfaces, potent in modulating local and systemic immune responses for disease treatment, face significant challenges due to the peripheral nerves' broad distribution in tissues like the fascia, periosteum, and skin. The incongruity between static electronic components and the dynamic, complex organization of the peripheral nervous system often leads to interface failure, stalling circuit research and clinical applications. To overcome these, we developed a self-assembling, tissue-adaptive electrode composed of a single-component cocktail nanosheet colloid, including dopants, conducting polymers, stabilizers, and an MXene catalyst. Delivered via a jet injector to designated nerve terminals, this assembly utilizes reactive oxygen species to catalytically dope poly (3,4-ethylenedioxythiophene), enhancing π-π interactions between nanosheets, and yielding a conductive, biodegradable interface. This interface effectively regulates local immune activity and promotes sensory and motor nerve functional restoration in nerve-injured mice, while engaging the vagal-adrenal axis in freely moving mice, eliciting catecholamine neurotransmitter release, and suppressing systemic cytokine storms. This innovative strategy specifically targets nerve substructures, bolstering local and systemic immune modulation, and paving the way for the development of self-adaptive dynamic neural interfaces.

Uncoordinated chemistry enables highly conductive and stable electrolyte/filler interfaces for solid-state lithium-sulfur batteries.

Composite-polymer-electrolytes (CPEs) embedded with advanced filler materials offer great promise for fast and preferential Li+ conduction. The filler surface chemistry determines the interaction with electrolyte molecules and thus critically regulates the Li+ behaviors at the interfaces. Herein, we probe into the role of electrolyte/filler interfaces (EFI) in CPEs and promote Li+ conduction by introducing an unsaturated coordination Prussian blue analog (UCPBA) filler. Combining scanning transmission X-ray microscope stack imaging studies and first-principle calculations, fast Li+ conduction is revealed only achievable at a chemically stable EFI, which can be established by the unsaturated Co-O coordination in UCPBA to circumvent the side reactions. Moreover, the as-exposed Lewis-acid metal centers in UCPBA efficiently attract the Lewis-base anions of Li salts, which facilitates the Li+ disassociation and enhances its transference number (tLi+). Attributed to these superiorities, the obtained CPEs realize high room-temperature ionic conductivity up to 0.36 mS cm-1 and tLi+ of 0.6, enabling an excellent cyclability of lithium metal electrodes over 4,000 h as well as remarkable capacity retention of 97.6% over 180 cycles at 0.5 C for solid-state lithium-sulfur batteries. This work highlights the crucial role of EFI chemistry in developing highly conductive CPEs and high-performance solid-state batteries.

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

Integrating Upper-Limb Prostheses with the Human Body: Technology Advances, Readiness, and Roles in Human-Prosthesis Interaction.

Significant advances in bionic prosthetics have occurred in the past two decades. The field's rapid expansion has yielded many exciting technologies that can enhance the physical, functional, and cognitive integration of a prosthetic limb with a human. We review advances in the engineering of prosthetic devices and their interfaces with the human nervous system, as well as various surgical techniques for altering human neuromusculoskeletal systems for seamless human-prosthesis integration. We discuss significant advancements in research and clinical translation, focusing on upper limbprosthetics since they heavily rely on user intent for daily operation, although many discussed technologies have been extended to lower limb prostheses as well. In addition, our review emphasizes the roles of advanced prosthetics technologies in complex interactions with humans and the technology readiness levels (TRLs) of individual research advances. Finally, we discuss current gaps and controversies in the field and point out future research directions, guided by TRLs.

Molecular Insights into Chemical Reactions at Aqueous Aerosol Interfaces.

Atmospheric aerosols facilitate reactions between ambient gases and dissolved species. Here, we review our efforts to interrogate the uptake of these gases and the mechanisms of their reactions both theoretically and experimentally. We highlight the fascinating behavior of N2O5 in solutions ranging from pure water to complex mixtures, chosen because its aerosol-mediated reactions significantly impact global ozone, hydroxyl, and methane concentrations. As a hydrophobic, weakly soluble, and highly reactive species, N2O5 is a sensitive probe of the chemical and physical properties of aerosol interfaces. We employ contemporary theory to disentangle the fate of N2O5 as it approaches pure and salty water, starting with adsorption and ending with hydrolysis to HNO3, chlorination to ClNO2, or evaporation. Flow reactor and gas-liquid scattering experiments probe even greater complexity as added ions, organic molecules, and surfactants alter the interfacial composition and reaction rates. Together, we reveal a new perspective on multiphase chemistry in the atmosphere.

Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model.

Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses a specific movement without physically executing it. Recently, MI-based brain-computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding of neural mechanisms still face huge challenges. These seriously hinder the clinical application and development of BCI systems based on MI. Thus, it is very necessary to develop new methods to decode MI tasks. In this work, we propose a multi-branch convolutional neural network (MBCNN) with a temporal convolutional network (TCN), an end-to-end deep learning framework to decode multi-class MI tasks. We first used MBCNN to capture the MI electroencephalography signals information on temporal and spectral domains through different convolutional kernels. Then, we introduce TCN to extract more discriminative features. The within-subject cross-session strategy is used to validate the classification performance on the dataset of BCI Competition IV-2a. The results showed that we achieved 75.08% average accuracy for 4-class MI task classification, outperforming several state-of-the-art approaches. The proposed MBCNN-TCN-Net framework successfully captures discriminative features and decodes MI tasks effectively, improving the performance of MI-BCIs. Our findings could provide significant potential for improving the clinical application and development of MI-based BCI systems.

Nonequilibrium statistical thermodynamics of multicomponent interfaces.

Nonequilibrium interfacial thermodynamics has important implications for crucial biological, physical, and industrial-scale transport processes. Here, we discuss a theory of local equilibrium for multiphase multicomponent interfaces that builds upon the "sharp" interface concept first introduced by Gibbs, allowing for a description of nonequilibrium interfacial processes such as those arising in evaporation, condensation, adsorption, etc. By requiring that the thermodynamics be insensitive to the precise location of the dividing surface, one can identify conditions for local equilibrium and develop methods for measuring the values of intensive variables at the interface. We then use extensive, high-precision nonequilibrium molecular dynamics (NEMD) simulations to verify the theory and establish the validity of the local equilibrium hypothesis. In particular, we demonstrate that equilibrium equations of state are also valid out of equilibrium, and can be used to determine interfacial temperature and chemical potential(s) that are consistent with nonequilibrium generalizations of the Clapeyron and Gibbs adsorption equations. We also show, for example, that, far from equilibrium, temperature or chemical potential differences need not be uniform across an interface and may instead exhibit pronounced discontinuities. However, even in these circumstances, we demonstrate that the local equilibrium hypothesis and its implications remain valid. These results provide a thermodynamic foundation and computational tools for studying or revisiting a wide variety of interfacial transport phenomena.

On the formation of ordered protein assemblies in cell-cell interfaces.

Crystal structures of many cell-cell adhesion receptors reveal the formation of linear "molecular zippers" comprising an ordered one-dimensional array of proteins that form both intercellular (trans) and intracellular (cis) interactions. The clustered protocadherins (cPcdhs) provide an exemplar of this phenomenon and use it as a basis of barcoding of vertebrate neurons. Here, we report both Metropolis and kinetic Monte Carlo simulations of cPcdh zipper formation using simplified models of cPcdhs that nevertheless capture essential features of their three-dimensional structure. The simulations reveal that the formation of long zippers is an implicit feature of cPcdh structure and is driven by their cis and trans interactions that have been quantitatively characterized in previous work. Moreover, in agreement with cryo-electron tomography studies, the zippers are found to organize into two-dimensional arrays even in the absence of attractive interactions between individual zippers. Our results suggest that the formation of ordered two-dimensional arrays of linear zippers of adhesion proteins is a common feature of cell-cell interfaces. From the perspective of simulations, they demonstrate the importance of a realistic depiction of adhesion protein structure and interactions if important biological phenomena are to be properly captured.

Prevention of the foreign body response to implantable medical devices by inflammasome inhibition.

SignificanceImplantable electronic medical devices (IEMDs) are used for some clinical applications, representing an exciting prospect for the transformative treatment of intractable conditions such Parkinson's disease, deafness, and paralysis. The use of IEMDs is limited at the moment because, over time, a foreign body reaction (FBR) develops at the device-neural interface such that ultimately the IEMD fails and needs to be removed. Here, we show that macrophage nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3) inflammasome activity drives the FBR in a nerve injury model yet integration of an NLRP3 inhibitor into the device prevents FBR while allowing full healing of damaged neural tissue to occur.

Injectable Fluorescent Neural Interfaces for Cell-Specific Stimulating and Imaging.

Building on current explorations in chronic optical neural interfaces, it is essential to address the risk of photothermal damage in traditional optogenetics. By focusing on calcium fluorescence for imaging rather than stimulation, injectable fluorescent neural interfaces significantly minimize photothermal damage and improve the accuracy of neuronal imaging. Key advancements including the use of injectable microelectronics for targeted electrical stimulation and their integration with cell-specific genetically encoded calcium indicators have been discussed. These injectable electronics that allow for post-treatment retrieval offer a minimally invasive solution, enhancing both usability and reliability. Furthermore, the integration of genetically encoded fluorescent calcium indicators with injectable bioelectronics enables precise neuronal recording and imaging of individual neurons. This shift not only minimizes risks such as photothermal conversion but also boosts safety, specificity, and effectiveness of neural imaging. Embracing these advancements represents a significant leap forward in biomedical engineering and neuroscience, paving the way for advanced brain-machine interfaces.