Dual-Atom Nanozyme Eye Drops Attenuate Inflammation and Break the Vicious Cycle in Dry Eye Disease.

Dry eye disease (DED) is a major ocular pathology worldwide, causing serious ocular discomfort and even visual impairment. The incidence of DED is gradually increasing with the high-frequency use of electronic products. Although inflammation is core cause of the DED vicious cycle, reactive oxygen species (ROS) play a pivotal role in the vicious cycle by regulating inflammation from upstream. Therefore, current therapies merely targeting inflammation show the failure of DED treatment. Here, a novel dual-atom nanozymes (DAN)-based eye drops are developed. The antioxidative DAN is successfully prepared by embedding Fe and Mn bimetallic single-atoms in N-doped carbon material and modifying it with a hydrophilic polymer. The in vitro and in vivo results demonstrate the DAN is endowed with superior biological activity in scavenging excessive ROS, inhibiting NLRP3 inflammasome activation, decreasing proinflammatory cytokines expression, and suppressing cell apoptosis. Consequently, the DAN effectively alleviate ocular inflammation, promote corneal epithelial repair, recover goblet cell density and tear secretion, thus breaking the DED vicious cycle. Our findings open an avenue to make the DAN as an intervention form to DED and ROS-mediated inflammatory diseases.

Surface Active Salivary Metabolites Indicate Oxidative Stress and Inflammation in Obstructive Sleep Apnea.

PURPOSE: Obstructive sleep apnea (OSA), a highly prevalent and potentially serious sleep disorder, requires effective screening tools. Saliva is a useful biological fluid with various metabolites that might also influence upper airway patency by affecting surface tension in the upper airway. However, little is known about the composition and role of salivary metabolites in OSA. Therefore, we investigated the metabolomics signature in saliva from the OSA patients and evaluated the associations between identified metabolites and salivary surface tension. METHODS: We studied 68 subjects who visited sleep clinic due to the symptoms of OSA. All underwent full-night in-lab polysomnography. Patients with apnea-hypopnea index (AHI) < 10 were classified to the control, and those with AHI ≥ 10 were the OSA groups. Saliva samples were collected before and after sleep. The centrifuged saliva samples were analyzed by liquid chromatography with high-resolution mass spectrometry (ultra-performance liquid chromatography-tandem mass spectrometry; UPLC-MS/MS). Differentially expressed salivary metabolites were identified using open source software (XCMS) and Compound Discoverer 2.1. Metabolite set enrichment analysis (MSEA) was performed using MetaboAnalyst 5.0. The surface tension of the saliva samples was determined by the pendant drop method. RESULTS: Three human-derived metabolites (1-palmitoyl-2-[5-hydroxyl-8-oxo-6-octenoyl]-sn-glycerol-3-phosphatidylcholine [PHOOA-PC], 1-palmitoyl-2-[5-keto-8-oxo-6-octenoyl]-sn-glycerol-3-phosphatidylcholine [KPOO-PC], and 9-nitrooleate) were significantly upregulated in the after-sleep salivary samples from the OSA patients compared to the control group samples. Among the candidate metabolites, only PHOOA-PC was correlated with the AHI. In OSA samples, salivary surface tension decreased after sleep. The differences in surface tension were negatively correlated with PHOOA-PC and 9-nitrooleate concentrations. Furthermore, MSEA revealed that arachidonic acid-related metabolism pathways were upregulated in the after-sleep samples from the OSA group. CONCLUSIONS: This study revealed that salivary PHOOA-PC was correlated positively with the AHI and negatively with salivary surface tension in the OSA group. Salivary metabolomic analysis may improve our understanding of upper airway dynamics and provide new insights into novel biomarkers and therapeutic targets in OSA.

Evidence of Two-Source King Plot Nonlinearity in Spectroscopic Search for New Boson.

Optical precision spectroscopy of isotope shifts can be used to test for new forces beyond the standard model, and to determine basic properties of atomic nuclei. We measure isotope shifts on the highly forbidden ^{2}S_{1/2}→^{2}F_{7/2} octupole transition of trapped ^{168,170,172,174,176}Yb ions. When combined with previous measurements in Yb^{+} and very recent measurements in Yb, the data reveal a King plot nonlinearity of up to 240σ. The trends exhibited by experimental data are explained by nuclear density functional theory calculations with the Fayans functional. We also find, with 4.3σ confidence, that there is a second distinct source of nonlinearity, and discuss its possible origin.

Sorting Gold and Sand (Silica) Using Atomic Force Microscope-Based Dielectrophoresis.

Additive manufacturing-also known as 3D printing-has attracted much attention in recent years as a powerful method for the simple and versatile fabrication of complicated three-dimensional structures. However, the current technology still exhibits a limitation in realizing the selective deposition and sorting of various materials contained in the same reservoir, which can contribute significantly to additive printing or manufacturing by enabling simultaneous sorting and deposition of different substances through a single nozzle. Here, we propose a dielectrophoresis (DEP)-based material-selective deposition and sorting technique using a pipette-based quartz tuning fork (QTF)-atomic force microscope (AFM) platform DEPQA and demonstrate multi-material sorting through a single nozzle in ambient conditions. We used Au and silica nanoparticles for sorting and obtained 95% accuracy for spatial separation, which confirmed the surface-enhanced Raman spectroscopy (SERS). To validate the scheme, we also performed a simulation for the system and found qualitative agreement with the experimental results. The method that combines DEP, pipette-based AFM, and SERS may widely expand the unique capabilities of 3D printing and nano-micro patterning for multi-material patterning, materials sorting, and diverse advanced applications.

A semiautomated measurement of muscle fiber size using the Imaris software.

The size and shape of skeletal muscle fibers are affected by various physiological and pathological conditions, such as muscle atrophy, hypertrophy, regeneration, and dystrophies. Hence, muscle fiber cross-sectional area (CSA) is an important determinant of muscle health and plasticity. We adapted the Imaris software to automatically segment muscle fibers based on fluorescent labeling of the plasma membrane and measure muscle fiber CSA. Analysis of muscle cross sections by the Imaris semiautomated and manual approaches demonstrated a similar decrease in CSA of atrophying muscles from fasted mice compared with fed controls. In addition, we previously demonstrated that downregulation of the Ca2+-specific protease calpain-1 attenuates muscle atrophy. Accordingly, both the Imaris semiautomated and manual approaches showed a similar increase in CSA of fibers expressing calpain-1 shRNA compared with adjacent nontransfected fibers in the same muscle cross section. Although both approaches seem valid for measurements of muscle fiber size, the manual marking method is less preferable because it is highly time-consuming, subjective, and limits the number of cells that can be analyzed. The Imaris semiautomated approach is user-friendly, requires little training or optimization, and can be used to efficiently and accurately mark thousands of fibers in a short period. As a novel addition to the commonly used statistics, we also describe statistical tests that quantify the strength of an effect on fiber size, enabling detection of significant differences between skewed distributions that would otherwise not be detected using typical methods.

Probing the shear viscoelasticity of a nanoscale ionic liquid meniscus.

Ionic liquids (ILs) are emerging as novel solvents that exhibit peculiar mechanical properties in the form of thin films on metal surfaces under normal pressure. However, the mechanical properties of ILs in the form of nano-meniscus have not been analyzed yet. Here, we investigate the shear viscoelasticity of a single IL meniscus at the nanoscale. To characterize the shear rheological properties of ILs, we employ a quartz tuning fork-based atomic force microscope, conduct dynamic force spectroscopy, and analyse shear properties using the non-Newtonian-Maxwell model. The elastic response of the IL nanomeniscus is found to be about 25 times higher than that of the bulk IL bridge, whereas the viscous responses are similar. In addition, by conducting shear velocity-dependent measurements, we find that the IL meniscus shows nonlinear rheological behaviours. Interestingly, we observe that the relaxation time of the IL increases at a tip-substrate distance of about 60 nm.

Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction.

MOTIVATION: Characterizing drug-protein interactions (DPIs) is crucial to the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict DPIs without human trial and error. However, because data labeling requires significant resources, the available protein data size is relatively small, which consequently decreases model performance. Here, we propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset. RESULTS: At first, we use transfer learning in encoding protein sequences with a pretrained model, which trains general sequence representations in an unsupervised manner. Second, we use a Bayesian neural network to make a robust model by estimating the data uncertainty. Our resulting model performs better than the previous baselines at predicting interactions between molecules and proteins. We also show that the quantified uncertainty from the Bayesian inference is related to confidence and can be used for screening DPI data points. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/QHwan/PretrainDPI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Direct measurement of curvature-dependent surface tension of an alcohol nanomeniscus.

Surface tension is a key parameter for understanding nucleation in the very initial stage of phase transformation. Although surface tension has been predicted to vary with the curvature of the liquid-vapor interface, particularly at the large curvature of, e.g., the subnanometric critical nucleus, experimental study still remains challenging due to inaccessibility to such a small cluster. Here, by directly measuring the critical size of a single capillary-condensed nanomeniscus using atomic force microscopy, we address the curvature dependence of surface tension of alcohols and observe that the surface tension is doubled for ethanol and n-propanol with a radius-of-curvature of ∼-0.46 nm. We also find that the interface of larger negative (positive) curvature exhibits larger (smaller) surface tension, which evidently governs nucleation at the ∼1 nm scale and below, indicating more facilitated nucleation than normally expected. Such well characterized curvature effects contribute to better understanding and accurate analysis of nucleation occurring in various fields including materials science and atmospheric science.

Universal Theory of Dynamic Force Microscopy for Exact and Robust Force Reconstruction Using Multiharmonic Signal Analysis.

Force reconstruction in dynamic force microscopy (DFM) is a nontrivial problem that requires the deconvolution of integrals. However, conventional reconstruction methods, which recover forces from single-frequency motion of the cantilever at its resonance, exhibit non-negligible error and reconstruction instability in the highly nonlinear force regime when the tip oscillates with its amplitude comparable to the decay length of the interaction. Here, we develop a theoretical platform of DFM based on multiharmonic signal analysis for exact and robust reconstruction of conservative and dissipative forces, valid for all oscillation amplitudes and entire tip-sample distances in both amplitude- and frequency-modulation atomic force microscopy. We achieve accuracy improvement by an order of magnitude for oscillation amplitudes comparable to or larger than the decay length, and by 2 orders of magnitude for smaller amplitudes at the force minimum, even in cases where conventional methods show poor accuracy (≳5%). Moreover, we obtain greater robustness with respect to the oscillation amplitude error, resulting in a fivefold increase in reconstruction precision. Our results demonstrate a fast and versatile reconstruction scheme for nanomechanical force characterization, with higher harmonics measured with sufficient signal-to-noise ratio, which provides unprecedented accuracy and stability beyond conventional methods.

Exploring the Hydration Water Character on Atomically Dislocated Surfaces by Surface Enhanced Raman Spectroscopy.

Hydration is ubiquitous in any kind of water-substance interaction such as in various interfacial and biological processes. Despite substantial progress made to date, however, still less explored is the hydration behavior on complex heterogeneous surfaces, such as the water surrounding the protein, which requires a platform that enables systematic investigation at the atomic scale. Here, we realized a heterogeneous self-assembled monolayer system that allows both controllable mixing with hydrophobic or hydrophilic groups and precise distance control of the functional carboxyl groups from the surface by methylene spacer groups. Using surface-enhanced Raman spectroscopy (SERS), we first demonstrated the hydrophobic (or hydrophilic) mixing ratio-dependent pK a variation of the carboxyl group. Interestingly, we observed a counterintuitive, non-monotonic behavior that a fractionally mixed hydrophobic group can induce significant enhancement of dielectric strength of the interfacial water. In particular, such a fractional mixing substantially decreases the amide coupling efficiency at the surface, as manifested by the corresponding pK a decrease. The SERS-based platform we demonstrated can be widely applied for atomically precise control and molecular-level characterization of hydration water on various heterogeneous surfaces of biological and industrial importance.

GCIceNet: a graph convolutional network for accurate classification of water phases.

Understanding the phases of water molecules based on local structure is essential for understanding their anomalous properties. However, due to complicated structural motifs formed via hydrogen bonds, conventional order parameters represent water molecules incompletely. In this paper, we develop GCIceNet, which automatically generates machine-based order parameters for classifying the phases of water molecules via supervised and unsupervised learning. The multiple graph convolutional layers in GCIceNet can learn topological information on the complex hydrogen bond networks. It shows a substantial improvement in accuracy for predicting the phase of water molecules in a bulk system and an ice/vapor interface system. A relative importance analysis shows that GCIceNet can capture the structural features of the given system hidden in the input data. Augmented with the vast amount of data provided by molecular dynamics simulations, GCIceNet is expected to serve as a powerful tool for the fields of glassy liquids and hydration layers around biomolecules.

Evidence for Nonlinear Isotope Shift in Yb

We measure isotope shifts for five Yb^{+} isotopes with zero nuclear spin on two narrow optical quadrupole transitions ^{2}S_{1/2}→^{2}D_{3/2}, ^{2}S_{1/2}→^{2}D_{5/2} with an accuracy of ∼300 Hz. The corresponding King plot shows a 3×10^{-7} deviation from linearity at the 3σ uncertainty level. Such a nonlinearity can indicate physics beyond the Standard Model (SM) in the form of a new bosonic force carrier, or arise from higher-order nuclear effects within the SM. We identify the quadratic field shift as a possible nuclear contributor to the nonlinearity at the observed scale, and show how the nonlinearity pattern can be used in future, more accurate measurements to separate a new-boson signal from nuclear effects.

Interfacial thermodynamics of spherical nanodroplets: molecular understanding of surface tension via a hydrogen bond network.

Surface tension plays a ubiquitous role in phase transitions including condensation or evaporation of atmospheric liquid droplets. In particular, understanding of interfacial thermodynamics of the critical nucleus of 1 nm scale is important for molecular characterization of the activation energy barrier of nucleation. Here, we investigate surface tension of spherical nanodroplets with both molecular dynamics and density functional theory and find that surface tension decreases appreciably below 1 nm radius, whose analytical expression is consistently derived from the classic Tolman's equation. In particular, the free energy analysis of nanodroplets shows that the change of surface tension originates dominantly from the configurational energy of interfacial molecules, which is evidenced by the increasingly disrupted hydrogen bond network as the droplet size decreases. Our result can be applied to the interface-related phenomena associated with molecular fluctuations such as biomolecule adsorption at the sub-nm scale where macroscopic thermodynamic quantities are ill-defined.

Theoretical study of buckling-based nonlinear transition near a static bifurcation point.

We theoretically investigate the nonlinear behavior of a buckled tip near the bifurcation point under external stress. We present a mechanical model for the buckled tip and derive the governing equation that describes the "buckling-to-flipping" nonlinear transition of the tip motion. Our minimal mechanistic model fully captures the velocity-dependent flipping phenomena, in which the flip position of the tip varies with the speed of the surface motion, as consistently observed in previous experiments. The present study could be applicable for sensitive detection of directional surface motion such as seismic waves.

Dynamic Responses of Electrically Driven Quartz Tuning Fork and qPlus Sensor: A Comprehensive Electromechanical Model for Quartz Tuning Fork.

A quartz tuning fork and its qPlus configuration show different characteristics in their dynamic features, including peak amplitude, resonance frequency, and quality factor. Here, we present an electromechanical model that comprehensively describes the dynamic responses of an electrically driven tuning fork and its qPlus configuration. Based on the model, we theoretically derive and experimentally validate how the peak amplitude, resonance frequency, quality factor, and normalized capacitance are changed when transforming a tuning fork to its qPlus configuration. Furthermore, we introduce two experimentally measurable parameters that are intrinsic for a given tuning fork and not changed by the qPlus configuration. The present model and analysis allow quantitative prediction of the dynamic characteristics in tuning fork and qPlus, and thus could be useful to optimize the sensors' performance.

Nanopipette/Nanorod-Combined Quartz Tuning Fork⁻Atomic Force Microscope.

We introduce a nanopipette/quartz tuning fork (QTF)-atomic force microscope (AFM) for nanolithography and a nanorod/QTF-AFM for nanoscratching with in situ detection of shear dynamics during performance. Capillary-condensed nanoscale water meniscus-mediated and electric field-assisted small-volume liquid ejection and nanolithography in ambient conditions are performed at a low bias voltage (~10 V) via a nanopipette/QTF-AFM. We produce and analyze Au nanoparticle-aggregated nanowire by using nanomeniscus-based particle stacking via a nanopipette/QTF-AFM. In addition, we perform a nanoscratching technique using in situ detection of the mechanical interactions of shear dynamics via a nanorod/QTF-AFM with force sensor capability and high sensitivity.

Ice-VII-like molecular structure of ambient water nanomeniscus.

Structural transformations originating from diverse rearrangements of the hydrogen bonding in water create various phases. Although most phases have been well investigated down to the molecular level, the molecular structure of the nanomeniscus, a ubiquitous form of nanoscale water in nature, still remains unexplored. Here, we demonstrate that the water nanomeniscus exhibits the stable, ice-VII-like molecular structure in ambient condition. Surface-enhanced Raman spectroscopy on trace amounts of water, confined in inter-nanoparticle gaps, shows a narrowed tetrahedral peak at 3340 cm-1 in the OH-stretching band as well as a lattice-vibrational mode at 230 cm-1. In particular, the ice-VII-like characteristics are evidenced by the spectral independence with respect to temperature variations and differing surface types including the material, size and shape of nanoparticles. Our results provide un unambiguous identification of the molecular structure of nanoconfined water, which is useful for understanding the molecular aspects of water in various nanoscale, including biological, environments.

Buckling-Based Non-Linear Mechanical Sensor.

Mechanical sensors provide core keys for high-end research in quantitative understanding of fundamental phenomena and practical applications such as the force or pressure sensor, accelerometer and gyroscope. In particular, in situ sensitive and reliable detection is essential for measurements of the mechanical vibration and displacement forces in inertial sensors or seismometers. However, enhancing sensitivity, reducing response time and equipping sensors with a measurement capability of bidirectional mechanical perturbations remains challenging. Here, we demonstrate the buckling cantilever-based non-linear dynamic mechanical sensor which addresses intrinsic limitations associated with high sensitivity, reliability and durability. The cantilever is attached on to a high-Q tuning fork and initially buckled by being pressed against a solid surface while a flexural stress is applied. Then, buckling instability occurs near the bifurcation region due to lateral movement, which allows high-sensitive detection of the lateral and perpendicular surface acoustic waves with bandwidth-limited temporal response of less than 1 ms.

Adhesive force measurement of steady-state water nano-meniscus: Effective surface tension at nanoscale.

When the surface of water is curved at nanoscale as a bubble, droplet and meniscus, its surface tension is expected to be smaller than that of planar interface, which still awaits experimental studies. Here, we report static and dynamic force spectroscopy that measures the capillary force of a single nanoscale water meniscus at constant curvature condition. Based on the Young-Laplace equation, the results are used to obtain the effective surface tension (ST) of the meniscus, which decreases to less than 20% of the bulk value at the radius-of-curvature (ROC) below 25 nm, while indicating the bulk behaviour above ~130 nm ROC. Interestingly, such a possibility provides a qualitative resolution of the unsettled discrepancies between experiments and theories in the thermodynamic activation processes for the mentioned three types of nano-curvatured water. Our results may not only lead to development of microscopic theories of ST as well as further experimental investigations, but also help better understanding of the ST-induced nanoscale dynamics such as cluster growth or protein folding, and the ST-controlled design of nano-biomaterials using the nano-meniscus.

Bifurcation-enhanced ultrahigh sensitivity of a buckled cantilever.

Buckling, first introduced by Euler in 1744 [Euler L (1744) Opera Omnia I 24:231], a sudden mechanical sideways deflection of a structural member under compressive stress, represents a bifurcation in the solution to the equations of static equilibrium. Although it has been investigated in diverse research areas, such a common nonlinear phenomenon may be useful to devise a unique mechanical sensor that addresses the still-challenging features, such as the enhanced sensitivity and polarization-dependent detection capability. We demonstrate the bifurcation-enhanced sensitive measurement of mechanical vibrations using the nonlinear buckled cantilever tip in ambient conditions. The cantilever, initially buckled with its tip pinned, flips its buckling near the bifurcation point (BP), where the buckled tip becomes softened. The enhanced mechanical sensitivity results from the increasing fluctuations, unlike the typical linear sensors, which facilitate the noise-induced buckling-to-flipping transition of the softened cantilever. This allows the in situ continuous or repeated single-shot detection of the surface acoustic waves of different polarizations without any noticeable wear of the tip. We obtained the sensitivity above 106 V(m/s)-1, a 1,000-fold enhancement over the conventional seismometers. Our results lead to development of mechanical sensors of high sensitivity, reproducibility, and durability, which may be applied to detect, e.g., the directional surface waves on the laboratory as well as the geological scale.