Effect of lamination layer thickness and abutment preparation on the fit of Co-Cr multi-unit prostheses fabricated by additive manufacturing: An in vitro study

STATEMENT OF PROBLEM

Selective laser melting has been increasingly used in the fabrication of metal-ceramic prostheses. The lamination layer thickness may influence the fit of the restoration. However, data regarding its impact remain scarce.

PURPOSE

The purpose of this in vitro study was to evaluate the marginal and internal fit of cobalt-chromium (Co-Cr) 3-unit frameworks fabricated by additive manufacturing with different melting layer thicknesses on different abutments.

MATERIAL AND METHODS

The first right maxillary premolar and molar from a typodont were prepared with a circumferential 1.2-mm chamfer, 2-mm occlusal reduction, and total taper of 8 degrees to receive 3-unit fixed partial dentures. After framework design, the data were sent to a laser machine, and 30 specimens were fabricated from Co-Cr metal powder (Mediloy S-Co) by selective laser melting. Specimens were assigned to 3 groups (n=10) with different lamination layer thicknesses of 25 (L25), 50 (L50), and 100 (L100) μm. Marginal fit and internal fit were evaluated. Results were compared among layer thickness groups and abutments. Data were compared by using the Levene test, t test, and ANOVA (α=.05).

RESULTS

A statistical difference was found between different layer thickness groups (P=.016), but not between abutments (P=.429). On the abutment level, significant difference was found in the gap values for the molars (P=.049) between layer thickness groups, but not for the premolars (P=.126). Group L25 reported the smallest mean discrepancy values, and group L100 reported the highest, with statistical difference found between group L100 and groups L25 (P<.001) and L50 (P<.025), but not between groups L25 and L50 (P=.094).

CONCLUSIONS

The lamination layer thickness affected the adaptation of the metal frameworks. Framework fit did not significantly differ between abutments.

Prediction and Control in DNA Nanotechnology

DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.

Transient electrophoresis of a spherical colloidal particle with a slip surface

We derive the general expression for the transient electrophoretic mobility of a spherical colloidal particle with a slip surface in an electrolyte solution. From the general mobility expression, we derive an analytic mobility expression, which is applicable for low particle zeta potentials and arbitrary Debye length. This expression corresponds to the time-dependent transient Henry function.

Fluctuating landscapes and heavy tails in animal behavior

Animal behavior is shaped by a myriad of mechanisms acting on a wide range of scales. This immense variability hampers quantitative reasoning and renders the identification of universal principles elusive. Through data analysis and theory, we here show that slow non-ergodic drives generally give rise to heavy-tailed statistics in behaving animals. We leverage high-resolution recordings of C. elegans locomotion to extract a self-consistent reduced order model for an inferred reaction coordinate, bridging from sub-second chaotic dynamics to long-lived stochastic transitions among metastable states. The slow mode dynamics exhibits heavy-tailed first passage time distributions and correlation functions, and we show that such heavy tails can be explained by dynamics on a time-dependent potential landscape. Inspired by these results, we introduce a generic model in which we separate faster mixing modes that evolve on a quasi-stationary potential, from slower non-ergodic modes that drive the potential landscape, and reflect slowly varying internal states. We show that, even for simple potential landscapes, heavy tails emerge when barrier heights fluctuate slowly and strongly enough. In particular, the distribution of first passage times and the correlation function can asymptote to a power law, with related exponents that depend on the strength and nature of the fluctuations. We support our theoretical findings through direct numerical simulations.

The ability to sense the environment is heterogeneously distributed in cell populations

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information theoretic framework to quantify the distribution of sensing abilities from single cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an " average cell ". We verify these predictions using live cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information theoretic framework will significantly improve our understanding of how cells sense in their environment.

Structural and spectroscopic details of polysaccharide-bile acid composites from molecular dynamics simulations

Interactions of a prototypical bile acid (cholic acid, 'Ch') and its corresponding sodium salt (sodium cholate, 'NaCh') with a standard dietary β-glucan (β-G), bearing β-D-glucopyranose units having mixed 1-4/1-3 glycosidic linkages are studied using molecular dynamics simulation and density functional theory (DFT) calculations. Self-aggregation of the biliary components and their interaction with fifteen strands of the decameric mixed linkage β-glucan is elucidated by estimating varieties of physical properties like the coordination number, moment of inertia and shape anisotropy of the biggest cluster formed at different time instants. Small angle scattering profiles indicate formation of compact spheroidal aggregates. The simulated results of small angle scattering and 1H NMR chemical shifts are compared to spectroscopic data, wherever available. Density functional theory calculations and estimation of the 1H NMR chemical shifts of Ch-protons lying close to the β-G chains reveal change in chemical shift values from that in absence of the polysaccharide. Hydrogen bonding and non-bonding interactions, primarily short range van der Waals interactions and some extent of inter-molecular charge transfer are found to play significant role in stabilizing the complex soft assemblies of bile acid aggregates and β-G.Communicated by Ramaswamy H. Sarma.

Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients

Estimation of age-dependent transmissibility of COVID-19 patients is critical for effective policymaking. Although the transmissibility of symptomatic cases has been extensively studied, asymptomatic infection is understudied due to limited data. Using a dataset with reliably distinguished symptomatic and asymptomatic statuses of COVID-19 cases, we propose an ordinary differential equation model that considers age-dependent transmissibility in transmission dynamics. Under a Bayesian framework, multi-source information is synthesized in our model for identifying transmissibility. A shrinkage prior among age groups is also adopted to improve the estimation behavior of transmissibility from age-structured data. The added values of accounting for age-dependent transmissibility are further evaluated through simulation studies. In real-data analysis, we compare our approach with two basic models using the deviance information criterion (DIC) and its extension. We find that the proposed model is more flexible for our epidemic data. Our results also suggest that the transmissibility of asymptomatic infections is significantly lower (on average, 76.45% with a credible interval (27.38%, 88.65%)) than that of symptomatic cases. In both symptomatic and asymptomatic patients, the transmissibility mainly increases with age. Patients older than 30 years are more likely to develop symptoms with higher transmissibility. We also find that the transmission burden of asymptomatic cases is lower than that of symptomatic patients.

Advanced Neuromonitoring Modalities on the Horizon: Detection and Management of Acute Brain Injury in Children

Timely detection and monitoring of acute brain injury in children is essential to mitigate causes of injury and prevent secondary insults. Increasing survival in critically ill children has emphasized the importance of neuroprotective management strategies for long-term quality of life. In emergent and critical care settings, traditional neuroimaging modalities, such as computed tomography and magnetic resonance imaging (MRI), remain frontline diagnostic techniques to detect acute brain injury. Although detection of structural and anatomical abnormalities remains crucial, advanced MRI sequences assessing functional alterations in cerebral physiology provide unique diagnostic utility. Head ultrasound has emerged as a portable neuroimaging modality for point-of-care diagnosis via assessments of anatomical and perfusion abnormalities. Application of electroencephalography and near-infrared spectroscopy provides the opportunity for real-time detection and goal-directed management of neurological abnormalities at the bedside. In this review, we describe recent technological advancements in these neurodiagnostic modalities and elaborate on their current and potential utility in the detection and management of acute brain injury.

The sound of blood: photoacoustic imaging in blood analysis

Blood analysis is a ubiquitous and critical aspect of modern medicine. Analyzing blood samples requires invasive techniques, various testing systems, and samples are limited to relatively small volumes. Photoacoustic imaging (PAI) is a novel imaging modality that utilizes non-ionizing energy that shows promise as an alternative to current methods. This paper seeks to review current applications of PAI in blood analysis for clinical use. Furthermore, we discuss obstacles to implementation and future directions to overcome these challenges. Firstly, we discuss three applications to cellular analysis of blood: sickle cell, bacteria, and circulating tumor cell detection. We then discuss applications to the analysis of blood plasma, including glucose detection and anticoagulation quantification. As such, we hope this article will serve as inspiration for PAI's potential application in blood analysis and prompt further studies to ultimately implement PAI into clinical practice.

Identification of potential Staphylococcus aureus dihydrofolate reductase inhibitors using QSAR, molecular docking, dynamics simulations and free energy calculation

Herein we describe the use of molecular docking simulations, quantitative structure-activity relationships studies and ADMETox predictions to analyse the molecular recognition of a series of 7-aryl-2,4-diaminoquinazoline derivatives on the inhibition of Staphylococcus aureus dihydrofolate reductase and conducted a virtual screening to discover new potential inhibitors. A quantitative structure-activity relationship model was developed using 40 compounds and two selected descriptors. These descriptors indicated the importance of pKa and molar refractivity for the inhibitory activity against SaDHFR. The values of R2train, CVLOO and R2test generated by the model were 0.808, 0.766, and 0.785, respectively. The integration between QSAR, molecular docking, ADMETox analysis and molecular dynamics simulations with binding free energies calculation, yielded the compounds PC-124127620, PC-124127795 and PC-124127805 as promising candidates to SaDHFR inhibitors. These compounds presented high potency, good pharmacokinetics and toxicological profile. Thus, these molecules are good potential antimicrobial agent to treatment of infect disease caused by S. aureus.Communicated by Ramaswamy H. Sarma.

Self-organized collective cell behaviors as design principles for synthetic developmental biology

Over the past two decades, molecular cell biology has graduated from a mostly analytic science to one with substantial synthetic capability. This success is built on a deep understanding of the structure and function of biomolecules and molecular mechanisms. For synthetic biology to achieve similar success at the scale of tissues and organs, an equally deep understanding of the principles of development is required. Here, we review some of the central concepts and recent progress in tissue patterning, morphogenesis and collective cell migration and discuss their value for synthetic developmental biology, emphasizing in particular the power of (guided) self-organization and the role of theoretical advances in making developmental insights applicable in synthesis.

Biomechanical models and mechanisms of cellular morphogenesis and cerebral cortical expansion and folding

Morphogenesis of the nervous system involves a highly complex spatio-temporal pattern of physical forces (mainly tension and pressure) acting on cells and tissues that are pliable but have an intricately organized cytoskeletal infrastructure. This review begins by covering basic principles of biomechanics and the core cytoskeletal toolkit used to regulate the shapes of cells and tissues during embryogenesis and neural development. It illustrates how the principle of 'tensegrity' provides a useful conceptual framework for understanding how cells dynamically respond to forces that are generated internally or applied externally. The latter part of the review builds on this foundation in considering the development of mammalian cerebral cortex. The main focus is on cortical expansion and folding - processes that take place over an extended period of prenatal and postnatal development. Cortical expansion and folding are likely to involve many complementary mechanisms, some related to regulating cell proliferation and migration and others related to specific types and patterns of mechanical tension and pressure. Three distinct multi-mechanism models are evaluated in relation to a set of 18 key experimental observations and findings. The Composite Tension Plus (CT+) model is introduced as an updated version of a previous multi-component Differential Expansion Sandwich Plus (DES+) model (Van Essen, 2020); the new CT+ model includes 10 distinct mechanisms and has the greatest explanatory power among published models to date. Much needs to be done in order to validate specific mechanistic components and to assess their relative importance in different species, and important directions for future research are suggested.

Aqueous solubilization of hydrophobic compounds by inorganic nano-ions: An unconventional mechanism

HYPOTHESIS

Solubilization of hydrophobic compounds in water is commonly performed by using organic solubilizers such as hydrotropes, surfactants, co-solvents, and macrocycles to form host-guest complexes. 3,3'-commo-bis[closo-1,2-dicarba-3-cobaltadodecaborane] derivatives (COSANs) are fully inorganic and non-amphiphilic ionic boron clusters with nanometric size (nano-ions) showing superchaotropic properties as they strongly bind to neutral organic molecules. Therefore, we expect COSANs to act as solubilizers of sparingly water-soluble molecules, but with a mechanism different from all other organic solubilizers known so far.

EXPERIMENTS

The aqueous solubilization efficiency of COSANs towards butanol was evaluated by determining phase diagrams and comparing them to classical solubilizers. Nanostructuration of the mixture was studied using UV spectroscopy, small-angle X-ray, and neutron scattering with contrast variation.

FINDINGS

COSANs act as efficient aqueous solubilizers of medium-chain alcohols (0.6 < log P < 1.5). Unlike surfactants, COSAN is an efficient solubilizer in its monomeric state, at concentrations well below its critical aggregation concentration. Solubilization by COSAN takes place with a bi-dimensional anisotropic growth of COSAN/butanol co-assemblies, whereas solubilization by surfactant occurs via an isotropic swelling of micelles. Appealingly, COSANs/2-butanol co-assemblies efficiently solubilize more hydrophobic compounds with log P values up to around 6, offering new opportunities in many applied fields.

In vivo deep-brain 2-photon fluorescent microscopy labeled with near-infrared dyes excited at the 1700 nm window

2-Photon fluorescence microscopy (2PFM) is an indispensable imaging technology for neuroscience. However, the imaging depth is usually limited to the cortical layer in mouse brain in vivo. Here, we demonstrate deep brain 2PFM in vivo excited at the 1700 nm window, using IR780 and aza-IR780 as fluorescent labels. Our detailed characterization of the multiphoton excitation and emission properties of IR780 and aza-IR780 show that: (1) IR780 or aza-IR780 generate 2-photon fluorescence excited at the 1700 nm window and are promising for 2PFM; (2) aza-IR780 exhibits a larger ησ₂ with better anti-photobleaching property compared to IR780; The 2-photon action cross-sections of IR780 and aza-IR780 in plasma are an order-of-magnitude larger than those in PBS; (3) In vivo 2-photon emission spectra for both dyes show a notable red shift compared to those in vitro. Based on these characterization results, we demonstrate deep brain 2PFM labeled by them. A maximum imaging depth of 1585 μm (labeled by IR780) and 1800 μm (labeled by aza-IR780) into the mouse brain in vivo readily penetrates the subcortical region of hippocampus. Besides, a maximum of 1528 μm hemodynamic imaging depth is realized via 2PFM with aza-IR780 labeling, enabling us to measure blood flow speed in the hippocampus.

Solving the crystallographic phase problem using dynamical scattering in electron diffraction

Solving crystal structures from kinematical X-ray or electron diffraction patterns of single crystals requires many more diffracted beams to be recorded than there are atoms in the structure, since the phases of the structure factors can only be retrieved from such data if the atoms can be resolved as sharply peaked objects. Here a method is presented by which the fact that multiple scattering encodes structure factor phases in the diffracted intensities is being used for solving the crystallographic phase problem. The retrieval of both amplitudes and phases of electron structure factors from diffraction patterns recorded with varying angle of incidence will be demonstrated. No assumption about the scattering potential itself is being made. In particular, the resolution in the diffraction data does not need to be sufficient to resolve atoms, making this method potentially interesting for electron crystallography of 2-dimensional protein crystals and other beam-sensitive complex structures.

Surface hydrophilicity-mediated migration of nano/microparticles under temperature gradient in a confined space

HYPOTHESIS

Particle transport by a temperature gradient is prospective in many biomedical applications. However, the prevalence of boundary confinement in practical use introduces synergistic effects of thermophoresis and thermo-osmosis, causing controversial phenomena and great difficulty in understanding the mechanisms.

EXPERIMENTS

We developed a microfluidic chip with a uniform temperature gradient and switchable substrate hydrophilicity to measure the migrations of various particles (d = 200 nm - 2 μm), through which the effects of particle thermophoresis and thermo-osmotic flow from the substrate surface were decoupled. The contribution of substrate hydrophilicity on thermo-osmosis was examined. Thermophoresis was measured to clarify its dependence on particle size and hydrophilicity.

FINDINGS

This paper reports the first experimental evidence of a large enthalpy-dependent thermo-osmotic mobility χ ∼ ΔH on a hydrophobic polymer surface, which is 1-2 orders of magnitude larger than that on hydrophilic surfaces. The normalized Soret coefficient for polystyrene particles, S_T/d = 18.0 K^-1µm^-1, is confirmed to be constant, which helps clarify the controversy of the size dependence. Besides, the Soret coefficient of hydrophobic proteins is approximately-four times larger than that of hydrophilic extracellular vesicles. These findings suggest that the intrinsic slip on the hydrophobic surface could enhance both surface thermo-osmosis and particle thermophoresis.

Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena

Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.

Superatomic states under high pressure

The study of superatoms has attracted great interest since they apparently go beyond the traditional understanding of the periodic table of elements. In this work, we clearly show that superatoms can be extended from conventional structures to states under pressure condition. By studying the compression process of the CH₄@C₆₀ system formed via embedding methane molecules inside fullerene C₆₀, it is found that the system maintains superatomic properties in both static states, and even dynamic rotation situations influenced by quantum tunneling. Remarkably, the simulations reveal the emergence of new superatomic molecular orbitals by decreasing the confined space to approach the van der Waals boundary between CH₄ and C₆₀. Our current results not only establish a complete picture of superatoms from ambient condition to high pressure, but also offer a perspective for the discovery and exploration of new properties in superatom systems under extreme conditions.

Fmoc-diphenylalanine gelating nanoarchitectonics: A simplistic peptide self-assembly to meet complex applications

9-fluorenylmethoxycarbonyl-diphenylalanine (Fmoc-FF), has been has been extensively explored due to its ultrafast self-assembly kinetics, inherent biocompatibility, tunable physicochemical properties, and especially, the capability of forming self-sustained gels under physiological conditions. Consequently, various methodologies to develop Fmoc-FF gels and their corresponding applications in biomedical and industrial fields have been extensively studied. Herein, we systemically summarize the mechanisms underlying Fmoc-FF self-assembly, discuss the preparation methodologies of Fmoc-FF hydrogels, and then deliberate the properties as well as the diverse applications of Fmoc-FF self-assemblies. Finally, the contemporary shortcomings which limit the development of Fmoc-FF self-assembly are raised and the alternative solutions are proposed, along with future research perspectives.

Temperature-dependent slip length for water and electrolyte solution

HYPOTHESIS

The temperature dependence of boundary slip at liquid-solid interface is critical both for the fundamental theory and applications of fluid mechanics on micro and nanoscale, such as sustainable cooling of electronic devices. However, there is a controversy on the temperature dependence of boundary slip which lacks experimental evidence, we aim to resolve it by hypothesizing that the temperature dependent slip length depends on the variation in the interfacial energy barrier.

EXPERIMENTS

Here, we measured l_s - T relation of water and NaCl solution on self-assembled FDTS (Perfluorodecyltrichlorosilane) surface using colloidal probe AFM. The transition of l_s - T monotonicity is found. For water and 0.1 M NaCl solution, l_s is negatively correlated with T, while for 1 M NaCl solution, l_s is positively correlated with T.

FINDINGS

Together with molecular dynamics simulations, such observation is quantitatively explained with an analytical model based on rate theory, where the l_s - T monotonicity depends on the difference between liquid-solid interfacial energy barrier and liquid internal energy barrier. Our results provide not only solid experimental evidence for the boundary slip being a rate process, but also a basis for the thermal-hydrodynamic design of microfluidic and nanofluidic devices.

A generalized scaling theory for spontaneous spreading of Newtonian fluids on solid substrates

HYPOTHESIS

There exists a generalized solution for the spontaneous spreading dynamics of droplets taking into account the influence of interfacial tension and gravity.

EXPERIMENTS

This work presents a generalized scaling theory for the problem of spontaneous dynamic spreading of Newtonian fluids on a flat substrate using experimental analysis and numerical simulations. More specifically, we first validate and modify a dynamic contact angle model to accurately describe the dependency of contact angle on the contact line velocity, which is generalized by the capillary number. The dynamic contact model is implemented into a two-phase moving mesh computational fluid dynamics (CFD) model, which is validated using experimental results.

FINDINGS

We show that the spreading process is governed by three important parameters: the Bo number, viscous timescale τ_viscous, and static advancing contact angle, θ_s. More specifically, there exists a master spreading curve for a specific Bo and θ_s by scaling the spreading time with the τ_viscous. Moreover, we developed a correlation for prediction of the equilibrium shape of the droplets as a function of both Bo and θ_s. The results of this study can be used in a wide range of applications to predict both dynamic and equilibrium shape of droplets, such as in droplet-based additive manufacturing.

Multidimensional dynamic regulation of cellulose coloration for digital recognition and humidity response

Structural color is an eye-catching phenomenon in nature, which originates from the synergistic effect of cholesteric structure inside living organisms and light. However, biomimetic design and green construction of dynamically tunable structural color materials have been a great challenge in the field of photonic manufacturing. In this work, the new ability of L-lactic acid (LLA) to multi-dimensionally modulate the cholesteric structures constructed from cellulose nanocrystals (CNC) is revealed for the first time. By studying the molecular-scale hydrogen bonding mechanism, a novel strategy that electrostatic repulsion and hydrogen bonding forces jointly drive the uniform arrangement of cholesteric structures is proposed. Due to the flexible tunability and uniform alignment of the CNC cholesteric structure, different encoded messages were developed in the CNC/LLA (CL) pattern. Under different viewing conditions, the recognition information of different digits will continue to reversibly and rapidly switch until the cholesteric structure is destroyed. In addition, the LLA molecules facilitated the more sensitive response of the CL film to the humidity environment, making it exhibit reversible and tunable structural colors under different humidity. These excellent properties provide more possibilities for the application of CL materials in the fields of multi-dimensional display, anti-counterfeiting encryption, and environmental monitoring.

The importance of monitoring cerebral oxygenation in non brain injured patients

Over the past few years, the use of non-invasive neuromonitoring in non-brain injured patients has increased, as a result of the recognition that many of these patients are at risk of brain injury in a wide number of clinical scenarios and therefore may benefit from its application which allows interventions to prevent injury and improve outcome. Among these, are post cardiac arrest syndrome, sepsis, liver failure, acute respiratory failure, and the perioperative settings where in the absence of a primary brain injury, certain groups of patients have high risk of neurological complications. While there are many neuromonitoring modalities utilized in brain injured patients, the majority of those are either invasive such as intracranial pressure monitoring, require special skill such as transcranial Doppler ultrasonography, or intermittent such as pupillometry and therefore unable to provide continuous monitoring. Cerebral oximetry using Near infrared Spectroscopy, is a simple non invasive continuous measure of cerebral oxygenation that has been shown to be useful in preventing cerebral hypoxemia both within the intensive care unit and the perioperative settings. At present, current recommendations for standard monitoring during anesthesia or in the general intensive care concentrate mainly on hemodynamic and respiratory monitoring without specific indications regarding the brain, and in particular, brain oximetry. The aim of this manuscript is to provide an up-to-date overview of the pathophysiology and applications of cerebral oxygenation in non brain injured patients as part of non-invasive multimodal neuromonitoring in the early identification and treatment of neurological complications in this population.

Neuronal growth on high-aspect-ratio diamond nanopillar arrays for biosensing applications

Monitoring neuronal activity with simultaneously high spatial and temporal resolution in living cell cultures is crucial to advance understanding of the development and functioning of our brain, and to gain further insights in the origin of brain disorders. While it has been demonstrated that the quantum sensing capabilities of nitrogen-vacancy (NV) centers in diamond allow real time detection of action potentials from large neurons in marine invertebrates, quantum monitoring of mammalian neurons (presenting much smaller dimensions and thus producing much lower signal and requiring higher spatial resolution) has hitherto remained elusive. In this context, diamond nanostructuring can offer the opportunity to boost the diamond platform sensitivity to the required level. However, a comprehensive analysis of the impact of a nanostructured diamond surface on the neuronal viability and growth was lacking. Here, we pattern a single crystal diamond surface with large-scale nanopillar arrays and we successfully demonstrate growth of a network of living and functional primary mouse hippocampal neurons on it. Our study on geometrical parameters reveals preferential growth along the nanopillar grid axes with excellent physical contact between cell membrane and nanopillar apex. Our results suggest that neuron growth can be tailored on diamond nanopillars to realize a nanophotonic quantum sensing platform for wide-field and label-free neuronal activity recording with sub-cellular resolution.

Surface tension models for binary aqueous solutions: a review and intercomparison

The liquid-air surface tension of aqueous solutions is a fundamental quantity in multi-phase thermodynamics and fluid dynamics and thus relevant in many scientific and engineering fields. Various models have been proposed for its quantitative description. This Perspective gives an overview of the most popular models and their ability to reproduce experimental data of ten binary aqueous solutions of electrolytes and organic molecules chosen to be representative of different solute types. In addition, we propose a new model which reproduces sigmoidal curve shapes (Sigmoid model) to empirically fit experimental surface tension data. The surface tension of weakly surface-active substances is well reproduced by all models. In contrast, only few models successfully model the surface tension of aqueous solutions with strongly surface-active substances. For substances with a solubility limit, usually no experimental data is available for the surface tension of supersaturated solutions and the pure liquid solute. We discuss ways in which these can be estimated and emphasize the need for further research. The newly developed Sigmoid model best reproduces the surface tension of all tested solutions and can be recommended as a model for a broad range of binary mixtures and over the entire concentration range.

Charge order driven by multiple-Q spin fluctuations in heavily electron-doped iron selenide superconductors

Intertwined spin and charge orders have been widely studied in high-temperature superconductors, since their fluctuations may facilitate electron pairing; however, they are rarely identified in heavily electron-doped iron selenides. Here, using scanning tunneling microscopy, we show that when the superconductivity of (Li_0.84Fe_0.16OH)Fe_1-xSe is suppressed by introducing Fe-site defects, a short-ranged checkerboard charge order emerges, propagating along the Fe-Fe directions with an approximately 2a_Fe period. It persists throughout the whole phase space tuned by Fe-site defect density, from a defect-pinned local pattern in optimally doped samples to an extended order in samples with lower T_c or non-superconducting. Intriguingly, our simulations indicate that the charge order is likely driven by multiple-Q spin density waves originating from the spin fluctuations observed by inelastic neutron scattering. Our study proves the presence of a competing order in heavily electron-doped iron selenides, and demonstrates the potential of charge order as a tool to detect spin fluctuations.

The effect of thermal photons on exceptional points in coupled resonators

We analyse two quantum systems with hidden parity-time ([Formula: see text]) symmetry: one is an optical device, whereas another is a superconducting microwave-frequency device. To investigate their symmetry, we introduce a damping frame (DF), in which loss and gain terms for a given Hamiltonian are balanced. We show that the non-Hermitian Hamiltonians of both systems can be tuned to reach an exceptional point (EP), i.e., the point in parameter space at which a transition from broken to unbroken hidden [Formula: see text] symmetry takes place. We calculate a degeneracy of a Liouvillian superoperator, which is called the Liouvillian exceptional point (LEP), and show that, in the optical domain, LEP is equivalent to EP obtained from the non-Hermitian Hamiltonian (HEP). We also report breaking the equivalence between LEP and HEP by a non-zero number of thermal photons for the microwave-frequency system.

Quantum transports in two-dimensions with long range hopping

We investigate the effects of disorder and shielding on quantum transports in a two dimensional system with all-to-all long range hopping. In the weak disorder, cooperative shielding manifests itself as perfect conducting channels identical to those of the short range model, as if the long range hopping does not exist. With increasing disorder, the average and fluctuation of conductance are larger than those in the short range model, since the shielding is effectively broken and therefore long range hopping starts to take effect. Over several orders of disorder strength (until [Formula: see text] times of nearest hopping), although the wavefunctions are not fully extended, they are also robustly prevented from being completely localized into a single site. Each wavefunction has several localization centers around the whole sample, thus leading to a fractal dimension remarkably smaller than 2 and also remarkably larger than 0, exhibiting a hybrid feature of localization and delocalization. The size scaling shows that for sufficiently large size and disorder strength, the conductance tends to saturate to a fixed value with the scaling function [Formula: see text], which is also a marginal phase between the typical metal ([Formula: see text]) and insulating phase ([Formula: see text]). The all-to-all coupling expels one isolated but extended state far out of the band, whose transport is extremely robust against disorder due to absence of backscattering. The bond current picture of this isolated state shows a quantum version of short circuit through long hopping.

Confocal interferometric scattering microscopy reveals 3D nanoscopic structure and dynamics in live cells

Bright-field light microscopy and related phase-sensitive techniques play an important role in life sciences because they provide facile and label-free insights into biological specimens. However, lack of three-dimensional imaging and low sensitivity to nanoscopic features hamper their application in many high-end quantitative studies. Here, we demonstrate that interferometric scattering (iSCAT) microscopy operated in the confocal mode provides unique label-free solutions for live-cell studies. We reveal the nanometric topography of the nuclear envelope, quantify the dynamics of the endoplasmic reticulum, detect single microtubules, and map nanoscopic diffusion of clathrin-coated pits undergoing endocytosis. Furthermore, we introduce the combination of confocal and wide-field iSCAT modalities for simultaneous imaging of cellular structures and high-speed tracking of nanoscopic entities such as single SARS-CoV-2 virions. We benchmark our findings against simultaneously acquired fluorescence images. Confocal iSCAT can be readily implemented as an additional contrast mechanism in existing laser scanning microscopes. The method is ideally suited for live studies on primary cells that face labeling challenges and for very long measurements beyond photobleaching times.

A multi-fragment real-time extension of projected density matrix embedding theory: Non-equilibrium electron dynamics in extended systems

In this work, we derive a multi-fragment real-time extension of the projected density matrix embedding theory (pDMET) designed to treat non-equilibrium electron dynamics in strongly correlated systems. As in the previously developed static pDMET, the real time pDMET partitions the total system into many fragments; the coupling between each fragment and the rest of the system is treated through a compact representation of the environment in terms of a quantum bath. The real-time pDMET involves simultaneously propagating the wavefunctions for each separate fragment–bath embedding system along with an auxiliary mean-field wavefunction of the total system. The equations of motion are derived by (i) projecting the time-dependent Schrödinger equation in the fragment and bath space associated with each separate fragment and by (ii) enforcing the pDMET matching conditions between the global 1-particle reduced density matrix (1-RDM) obtained from the fragment calculations and the mean-field 1-RDM at all points in time. The accuracy of the method is benchmarked through comparisons to time-dependent density-matrix renormalization group and time-dependent Hartree–Fock (TDHF) theory; the methods were applied to a one- and two-dimensional single-impurity Anderson model and multi-impurity Anderson models with ordered and disordered distributions of the impurities. The results demonstrate a large improvement over TDHF and rapid convergence to the exact dynamics with an increase in fragment size. Our results demonstrate that the real-time pDMET is a promising and flexible method that balances accuracy and efficiency to simulate the non-equilibrium electron dynamics in heterogeneous systems of large size.

Active layer dynamics drives a transition to biofilm fingering

The emergence of spatial organisation in biofilm growth is one of the most fundamental topics in biofilm biophysics and microbiology. It has long been known that growing biofilms can adopt smooth or rough interface morphologies, depending on the balance between nutrient supply and microbial growth; this 'fingering' transition has been linked with the average width of the 'active layer' of growing cells at the biofilm interface. Here we use long-time individual-based simulations of growing biofilms to investigate in detail the driving factors behind the biofilm-fingering transition. We show that the transition is associated with dynamical changes in the active layer. Fingering happens when gaps form in the active layer, which can cause local parts of the biofilm interface to pin, or become stationary relative to the moving front. Pinning can be transient or permanent, leading to different biofilm morphologies. By constructing a phase diagram for the transition, we show that the controlling factor is the magnitude of the relative fluctuations in the active layer thickness, rather than the active layer thickness per se. Taken together, our work suggests a central role for active layer dynamics in controlling the pinning of the biofilm interface and hence biofilm morphology.

Survival strategies of artificial active agents

Artificial cells can be engineered to display dynamics sharing remarkable features in common with the survival behavior of living organisms. In particular, such active systems can respond to stimuli provided by the environment and undertake specific displacements to remain out of equilibrium, e.g. by moving towards regions with higher fuel concentration. In spite of the intense experimental activity aiming at investigating this fascinating behavior, a rigorous definition and characterization of such "survival strategies" from a statistical physics perspective is still missing. In this work, we take a first step in this direction by adapting and applying to active systems the theoretical framework of Transition Path Theory, which was originally introduced to investigate rare thermally activated transitions in passive systems. We perform experiments on camphor disks navigating Petri dishes and perform simulations in the paradigmatic active Brownian particle model to show how the notions of transition probability density and committor function provide the pivotal concepts to identify survival strategies, improve modeling, and obtain and validate experimentally testable predictions. The definition of survival in these artificial systems paves the way to move beyond simple observation and to formally characterize, design and predict complex life-like behaviors.

Broad Electronic Modulation of Two-Dimensional Metal-Organic Frameworks over Four Distinct Redox States

Two-dimensional (2D) inorganic materials have emerged as exciting platforms for (opto)electronic, thermoelectric, magnetic, and energy storage applications. However, electronic redox tuning of these materials can be difficult. Instead, 2D metal-organic frameworks (MOFs) offer the possibility of electronic tuning through stoichiometric redox changes, with several examples featuring one to two redox events per formula unit. Here, we demonstrate that this principle can be extended over a far greater span with the isolation of four discrete redox states in the 2D MOFs Li_xFe₃(THT)₂ (x = 0-3, THT = triphenylenehexathiol). This redox modulation results in 10,000-fold greater conductivity, p- to n-type carrier switching, and modulation of antiferromagnetic coupling. Physical characterization suggests that changes in carrier density drive these trends with relatively constant charge transport activation energies and mobilities. This series illustrates that 2D MOFs are uniquely redox flexible, making them an ideal materials platform for tunable and switchable applications.

Palmitate-mediated disruption of the endoplasmic reticulum decreases intracellular vesicle motility

Essential cellular processes such as metabolism, protein synthesis, and autophagy require the intracellular transport of membrane-bound vesicles. The importance of the cytoskeleton and associated molecular motors for transport is well documented. Recent research has suggested that the endoplasmic reticulum (ER) may also play a role in vesicle transport through a tethering of vesicles to the ER. We use single-particle tracking fluorescence microscopy and a Bayesian change-point algorithm to characterize vesicle motility in response to the disruption of the ER, actin, and microtubules. This high-throughput change-point algorithm allows us to efficiently analyze thousands of trajectory segments. We find that palmitate-mediated disruption of the ER leads to a significant decrease in vesicle motility. A comparison with the disruption of actin and microtubules shows that disruption of the ER has a significant impact on vesicle motility, greater than the disruption of actin. Vesicle motility was dependent on cellular region, with greater motility in the cell periphery than the perinuclear region, possibly due to regional differences in actin and the ER. Overall, these results suggest that the ER is an important factor in vesicle transport.

Super-exponential growth and stochastic size dynamics in rod-like bacteria

Proliferating bacterial cells exhibit stochastic growth and size dynamics, but the regulation of noise in bacterial growth and morphogenesis remains poorly understood. A quantitative understanding of morphogenetic noise control, and how it changes under different growth conditions, would provide better insights into cell-to-cell variability and intergenerational fluctuations in cell physiology. Using multigenerational growth and width data of single Escherichia coli and Caulobacter crescentus cells, we deduce the equations governing growth and size dynamics of rod-like bacterial cells. Interestingly, we find that both E. coli and C. crescentus cells deviate from exponential growth within the cell cycle. In particular, the exponential growth rate increases during the cell cycle irrespective of nutrient or temperature conditions. We propose a mechanistic model that explains the emergence of super-exponential growth from autocatalytic production of ribosomes coupled to the rate of cell elongation and surface area synthesis. Using this new model and statistical inference on large datasets, we construct the Langevin equations governing cell growth and size dynamics of E. coli cells in different nutrient conditions. The single-cell level model predicts how noise in intragenerational and intergenerational processes regulate variability in cell morphology and generation times, revealing quantitative strategies for cellular resource allocation and morphogenetic noise control in different growth conditions.

A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells

Dendritic cell (DC) migration is crucial for mounting immune responses. Immature DCs (imDCs) reportedly sense infections, while mature DCs (mDCs) move quickly to lymph nodes to deliver antigens to T cells. However, their highly heterogeneous and complex innate motility remains elusive. Here, we used an unsupervised machine learning (ML) approach to analyze long-term, two-dimensional migration trajectories of Granulocyte-macrophage colony-stimulating factor (GMCSF)-derived bone marrow-derived DCs (BMDCs). We discovered three migratory modes independent of the cell state: slow-diffusive (SD), slow-persistent (SP), and fast-persistent (FP). Remarkably, imDCs more frequently changed their modes, predominantly following a unicyclic SD→FP→SP→SD transition, whereas mDCs showed no transition directionality. We report that DC migration exhibits a history-dependent mode transition and maturation-dependent motility changes are emergent properties of the dynamic switching of the three migratory modes. Our ML-based investigation provides new insights into studying complex cellular migratory behavior.

Does Carrier Envelope Phase Affect the Ionization Site in a Neutral Diatomic Molecule?

A recent work shows how to extract the ionization site of a neutral diatomic molecule by comparing Quantum Trajectory Monte Carlo (QTMC) simulations with experimental measurements of the final electron momenta distribution. This method was applied to an experiment using a 40-femtosecond infrared pulse, finding that a downfield atom is roughly twice as likely to be ionized as an upfield atom in a neutral nitrogen molecule. However, an open question remains as to whether an assumption of the zero carrier envelope phase (CEP) used in the above work is still valid for short, few-cycle pulses where the CEP can play a large role. Given experimentalists’ limited control over the CEP and its dramatic effect on electron momenta after ionization, it is desirable to see what influence the CEP may have in determining the ionization site. In this paper, we employ QTMC techniques to simulate strong-field ionization and electron propagation from neutral N2 using an intense 6-cycle laser pulse with various CEP values. Comparing simulated electron momenta to experimental data indicates that the ratio of down-to-upfield ions remains roughly 2:1 regardless of the CEP. This confirms that the ionization site of a neutral molecule is determined predominantly by the laser frequency and intensity, as well as the ground-state molecular wavefunction, and is largely independent of the CEP.

Microfluidic platform for the reproduction of hypoxic vascular microenvironments

Vascular endothelial cells (ECs) respond to mechanical stimuli caused by blood flow to maintain vascular homeostasis. Although the oxygen level in vascular microenvironment is lower than the atmospheric one, the cellular dynamics of ECs under hypoxic and flow exposure are not fully understood. Here, we describe a microfluidic platform for the reproduction hypoxic vascular microenvironments. Simultaneous application of hypoxic stress and fluid shear stress to the cultured cells was achieved by integrating a microfluidic device and a flow channel that adjusted the initial oxygen concentration in a cell culture medium. An EC monolayer was then formed on the media channel in the device, and the ECs were observed after exposure to hypoxic and flow conditions. The migration velocity of the ECs immediately increased after flow exposure, especially in the direction opposite to the flow direction, and gradually decreased, resulting in the lowest value under the hypoxic and flow exposure condition. The ECs after 6-h simultaneous exposure to hypoxic stress and fluid shear stress were generally aligned and elongated in the flow direction, with enhanced VE-cadherin expression and actin filament assembly. Thus, the developed microfluidic platform is useful for investigating the dynamics of ECs in vascular microenvironments.

Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning

X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.