The Droplet Creeping-Sliding Dynamic Wetting Mechanism on Bionic Self-Cleaning Surfaces.

The dynamic wetting behavior of droplets has been of wide concern due to the hazards of accretion/icing of supercooled droplets on engineering components/systems served in low temperature freezing rain environment; thus, it is urgent to establish the relationship between droplet depinning/removing behaviors and surface characteristics. In this article, the actual rotation conditions of moving components such as wind turbine blades are simulated. The self-cleaning hydrophobic coating surface(S1) and bionic superhydrophobic coating surface(S2) show outstanding droplet removal performance compared to hydrophilic bare steel surface(S0), and the average speed of the droplet removal is increased by 400-500%. The "creeping-sliding" behavior of droplets on self-cleaning coatings is investigated by the change of droplet displacement(ΔD). The effect of the energy storage caused by the droplet creeping process provides initial kinetic energy for the droplet removal. Combined with the experimental data and theoretical model, the critical depinning resistance is calculated. The difference of the wetting interface free energy(ΔEx) during the dynamic wetting process of the droplets on the bionic superhydrophobic self-cleaning surface is researched. And the influence mechanism of the droplet embedded depth(x) on the creeping/sliding behavior in the nanotexture is clarified. Thus, the mechanical criterion of droplet depinning is proposed (the error is about 10%). The results can provide a theoretical basis for the design principle of antifreezing rain coatings on moving components.

Disodium Cromoglycate Templates Anisotropic Short-Chain PEG Hydrogels.

Anisotropic hydrogels have found widespread applications in biomedical engineering, particularly as scaffolds for tissue engineering. However, it remains a challenge to produce them using conventional fabrication methods, without specialized synthesis or equipment, such as 3D printing and unidirectional stretching. In this study, we explore the self-assembly behaviors of polyethylene glycol diacrylate (PEGDA), using disodium cromoglycate (DSCG), a lyotropic chromonic liquid crystal, as a removable template. The affinity between short-chain PEGDA (Mn = 250) and DSCG allows polymerization to take place at the DSCG surface, thereby forming anisotropic hydrogel networks with fibrin-like morphologies. This process requires considerable finesse as the phase behaviors of DSCG depend on a multitude of factors, including the weight percentage of PEGDA and DSCG, the chain length of PEGDA, and the concentration of ionic species. The key to modulating the microstructures of the all-PEG hydrogel networks is through precise control of the DSCG concentration, resulting in anisotropic mechanical properties. Using these anisotropic hydrogel networks, we demonstrate that human dermal fibroblasts are particularly sensitive to the alignment order. We find that cells exhibit a density-dependent activation pattern of a Yes-associated protein, a mechanotransducer, corroborating its role in enabling cells to translate external mechanical and morphological patterns to specific behaviors. The flexibility of modulating microstructure, along with PEG hydrogels' biocompatibility and biodegradability, underscores their potential use for tissue engineering to create functional structures with physiological morphologies.

Measurement Guidance in Diffusion Models: Insight from Medical Image Synthesis.

In the field of healthcare, the acquisition of sample is usually restricted by multiple considerations, including cost, labor- intensive annotation, privacy concerns, and radiation hazards, therefore, synthesizing images-of-interest is an important tool to data augmentation. Diffusion models have recently attained state-of-the-art results in various synthesis tasks, and embedding energy functions has been proved that can effectively guide the pre-trained model to synthesize target samples. However, we notice that current method development and validation are still limited to improving indicators, such as Fréchet Inception Distance score (FID) and Inception Score (IS), and have not provided deeper investigations on downstream tasks, like disease grading and diagnosis. Moreover, existing classifier guidance which can be regarded as a special case of energy function can only has a singular effect on altering the distribution of the synthetic dataset. This may contribute to in-distribution synthetic sample that has limited help to downstream model optimization. All these limitations remind that we still have a long way to go to achieve controllable generation. In this work, we first conducted an analysis on previous guidance as well as its contributions on further applications from the perspective of data distribution. To synthesize samples which can help downstream applications, we then introduce uncertainty guidance in each sampling step and design an uncertainty-guided diffusion models. Extensive experiments on four medical datasets, with ten classic networks trained on the augmented sample sets provided a comprehensive evaluation on the practical contributions of our methodology. Furthermore, we provide a theoretical guarantee for general gradient guidance in diffusion models, which would benefit future research on investigating other forms of measurement guidance for specific generative tasks. Codes and models are available at: https://github.com/yangqy1110/MGDM.

The dynamic risk spillover effects among carbon, renewable energy, and electricity markets based on the TVP-VAR-DY model.

The linkages among carbon, renewable energy, and electricity markets are gradually strengthening. In order to prevent risk transmission among markets, this paper uses the TVP-VAR-DY (Time-Varying Parameter-Vector Auto Regression-Dynamic) model to analyze the dynamic risk spillover effects and network structure of risk transmission among carbon, renewable energy, and electricity markets. The empirical results show that there are significant asymmetric spillover effects among carbon, renewable energy, and electricity markets. The total spillover index shows that spillover effects among carbon, renewable energy, and electricity markets are time-varying, especially during unexpected events. Besides, the net spillover index indicates that the spillover effects are bidirectional, asymmetric, and time-varying. Finally, under the influence of unexpected events, the network structures of risk transmission among carbon, renewable energy, and electricity markets are heterogeneous. Compared to the Russia-Ukraine conflict, the COVID-19 pandemic has a more significant impact on these markets.

Target-Guided Diffusion Models for Unpaired Cross-modality Medical Image Translation.

In a clinical setting, the acquisition of certain medical image modality is often unavailable due to various considerations such as cost, radiation, etc. Therefore, unpaired cross-modality translation techniques, which involve training on the unpaired data and synthesizing the target modality with the guidance of the acquired source modality, are of great interest. Previous methods for synthesizing target medical images are to establish one-shot mapping through generative adversarial networks (GANs). As promising alternatives to GANs, diffusion models have recently received wide interests in generative tasks. In this paper, we propose a target-guided diffusion model (TGDM) for unpaired cross-modality medical image translation. For training, to encourage our diffusion model to learn more visual concepts, we adopted a perception prioritized weight scheme (P2W) to the training objectives. For sampling, a pre-trained classifier is adopted in the reverse process to relieve modality-specific remnants from source data. Experiments on both brain MRI-CT and prostate MRI-US datasets demonstrate that the proposed method achieves a visually realistic result that mimics a vivid anatomical section of the target organ. In addition, we have also conducted a subjective assessment based on the synthesized samples to further validate the clinical value of TGDM.

Coacervate or precipitate? Formation of non-equilibrium microstructures in coacervate emulsions.

Non-equilibrium processing of aqueous polyelectrolyte complex (PEC) coacervates is critical to many applications. In particular, many coacervate-forming systems are known to become trapped in out-of-equilibrium states (e.g., precipitation). The mechanism and conditions under which these states form, and whether they age, is not clearly understood. Here, we elucidate the influence of processing on the PEC coarsening mechanism as it varies with flow during mixing for a model system of poly(allylamine hydrochloride) and poly(acrylic acid sodium salt) in water. We demonstrate that flow conditions can be used to toggle the formation of rough, precipitate-like aggregates of micron-scale PEC structures. These structures form at compositions with viscous-dominant equilibrium rheology, and observations of their formation via optical microscopy suggest that they comprise colloidal aggregates of PEC coacervate droplets. We further show that these aggregates exhibit micron-scale coarsening, with a mixing time-dependent characteristic aging time scale. The results show that the formation of precipitate-like structures is not solely determined by composition, but is instead highly sensitive to mass transport and colloidal instability effects. Our observations suggest that the details of mixing flow can provide non-equilibrium structural control of a broad range of PEC coacervate materials orthogonally to structure-property inspired polymeric design. We anticipate that these findings will open the door for future studies on the control of non-equilibrium PEC formation and structure.

An ultrasensitive colorimetric biosensor for the detection of Gram-positive bacteria by integrating paper-based enrichment and carbon dot-based selective recognition.

Rapid screening of bacteria by low-cost and eco-friendly material-based approaches is still a major challenge. Herein, a colorimetric biosensor was designed for the ultrasensitive and rapid detection of Gram-positive bacteria. The biosensor exploited polydopamine and polyethyleneimine (PDA-PEI)-modified papers for separating bacteria and carbon dots (CDs) for selective colorimetric detection of Gram-positive bacteria. Noble metal-free CDs can target Gram-positive bacteria by binding with peptidoglycan and possess peroxidase-like activity. Thus, they can avert the step of modifying recognition probes, facilitating biosensor fabrication, and reducing the cost. This biosensor can detect S. aureus as low as 1 cfu mL-1, L. monocytogenes as low as 5 cfu mL-1, and B. subtilis as low as 9 cfu mL-1 within 55 min. In addition, a portable device was constructed to enable convenient and on-site quantitative detection of Gram-positive bacteria. The feasibility of the biosensor was verified by detecting Gram-positive bacteria in eggshell and sausage samples with recoveries ranging from 91.2% to 110%.

Molecular-scale substrate anisotropy, crowding and division drive collective behaviours in cell monolayers.

The ability of cells to reorganize in response to external stimuli is important in areas ranging from morphogenesis to tissue engineering. While nematic order is common in biological tissues, it typically only extends to small regions of cells interacting via steric repulsion. On isotropic substrates, elongated cells can co-align due to steric effects, forming ordered but randomly oriented finite-size domains. However, we have discovered that flat substrates with nematic order can induce global nematic alignment of dense, spindle-like cells, thereby influencing cell organization and collective motion and driving alignment on the scale of the entire tissue. Remarkably, single cells are not sensitive to the substrate's anisotropy. Rather, the emergence of global nematic order is a collective phenomenon that requires both steric effects and molecular-scale anisotropy of the substrate. To quantify the rich set of behaviours afforded by this system, we analyse velocity, positional and orientational correlations for several thousand cells over days. The establishment of global order is facilitated by enhanced cell division along the substrate's nematic axis, and associated extensile stresses that restructure the cells' actomyosin networks. Our work provides a new understanding of the dynamics of cellular remodelling and organization among weakly interacting cells.

Genetically engineered bacterium-modified magnetic particles assisted chiral recognition and colorimetric determination of D/L-tryptophan in millets.

Chiral recognition of enantiomers has always been a thorny issue since they exhibit the same properties under an achiral environment. Herein, polydopamine-functionalized magnetic particles (MP@PDA) were synthesized to immobilize the genetically engineered bacterium Escherichia coli DH5α (MP@PDA-E. coli). L-tryptophan (Trp) instead of D-Trp can be stereo-specifically degraded by tryptophanase in E. coli. The degradation product indole reacts with 4-dimethylaminobenzaldehyde to generate a rose-red adduct. Thus, MP@PDA-E. coli was employed to fabricate a chiral colorimetric method for chiral recognition and determination of L-Trp. The method averts the purification of tryptophanase. More importantly, tryptophanase demonstrates excellent enantioselective ability for L-Trp. The method can not only quantitatively detect L-Trp but also realize the measurement of the enantiomer percentage in the enantiomeric mixture. The feasibility was verified by detecting L-Trp in millet samples from different origins. Furthermore, a portable device was fabricated to make the method more convenient.

Quasi-distributed fiber-optic acoustic sensor based on a low coherence light source: publisher's note.

This publisher's note contains a correction to Opt. Lett.47, 3780 (2022)10.1364/OL.464020.

Topological defect-propelled swimming of nematic colloids.

Topological defects on colloids rotating in nematic liquid crystals form far-from-equilibrium structures that perform complex swim strokes in which the defects periodically extend, depin, and contract. These defect dynamics propel the colloid, generating translation from rotation. The swimmer's speed and direction are determined by the topological defect's polarity and extent of elongation. Defect elongation is controlled by a rotating external magnetic field, allowing control over particle trajectories. The swimmers' translational motion relies on broken symmetries associated with lubrication forces between the colloid and the bounding surfaces, line tensions associated with the elongated defect, and anisotropic viscosities associated with the defect elongation adjacent to the colloid. The scattering or effective pair interaction of these swimmers is highly anisotropic, with polarization-dependent dimer stability and motion that depend strongly on entanglement and sharing of their extended defect structures. This research introduces transient, far-from-equilibrium topological defects as a class of virtual functional structures that generate modalities of motion and interaction.

Quasi-distributed fiber-optic acoustic sensor based on a low coherence light source.

A quasi-distributed acoustic sensor using in-line weak reflectors and a low coherence light source is presented. The dynamic strain is retrieved from the phase change of the two interfering light beams reflected by the same weak reflector. In the experiments, two vibrations at different channels along a weak reflector array are successfully detected simultaneously. A strain resolution of 50 pɛ/H z with 20-m interval is achieved in experiments, and no cross talk is observed. With simple system configuration and low cost, this approach provides a new, to the best of our knowledge, solution for quasi-distributed acoustic sensing.

Ischemic stroke and intestinal flora: an insight into brain-gut axis.

Stroke is a type of cerebrovascular disease that significantly endangers human health and lowers quality of life. This understandably places a heavy burden on society and families. In recent years, intestinal flora has attracted increasing attention from scholars worldwide, and its association with ischemic stroke is becoming a hot topic of research amongst researchers in field of stroke. After suffering from a stroke, intestinal microbial dysbiosis leads to increased intestinal permeability and activation of the intestinal immune system, which in turn leads to ectopic intestinal bacteria and pro-inflammatory cells that enter brain tissue through the damaged blood-brain barrier. This exacerbates ischemia-reperfusion injury. Interestingly, after a stroke, some metabolites produced by the intestinal flora attenuate ischemia-reperfusion injury by suppressing the post-stroke inflammatory response and promotes the repair of neurological function. Here we elucidate the changes in gut flora after occurrence of a stroke and highlight the immunomodulatory processes of the post-stroke gut flora.

Microstructure of continuous shear thickening colloidal suspensions determined by rheo-VSANS and rheo-USANS.

Research on shear thickening colloidal suspensions demonstrates that measurements of the microstructure can elucidate the source of the rheological material properties in the shear thickened state as well as critically test simulations and theory based on a variety of mechanisms such as enhanced lubrication hydrodynamics, elastohydrodynamics, and contact friction. Prior work on continuous shear thickening dispersions with a well-defined shear thickened state identified the formation of hydroclusters as characteristic of this state, determined the anisotropy in the nearest neighbor distribution, and used this information to test prevailing theories and simulations. However, important questions remain about the mesoscale (i.e., particle cluster scale) microstructure of the shear thickened state. Here we employ neutron scattering methods applied to shearing colloidal dispersions of spherical particles with two extremes of friction and lubrication surface properties to resolve the longer-length scale microstructure in the shear thickened state. Hydroclusters are shown to be highly localized, in agreement with prior neutron scattering and direct optical measurements, but in disagreement with the most recent simulations that predict a longer-range structure formation. These results combined with prior measurements provide experimental evidence about the length scale of microstructure formation in continuous shear thickening suspensions necessary to improve our understanding of the phenomenon as well as guide theoretical investigations that quantitatively link nanoscale forces to macroscopic properties in the shear thickened state.

High-throughput microscopy to determine morphology, microrheology, and phase boundaries applied to phase separating coacervates.

Evolution of composition, rheology, and morphology during phase separation in complex fluids is highly coupled to rheological and mass transport processes within the emerging phases, and understanding this coupling is critical for materials design of multiphase complex fluids. Characterizing these dependencies typically requires careful measurement of a large number of equilibrium and transport properties that are difficult to measure in situ as phase separation proceeds. Here, we propose and demonstrate a high-throughput microscopy platform to achieve simultaneous, in situ mapping of time-evolving morphology and microrheology in phase separating complex fluids over a large compositional space. The method was applied to a canonical example of polyelectrolyte complex coacervation, whereby mixing of oppositely charged species leads to liquid-liquid phase separation into distinct solute-dense and dilute phases. Morphology and rheology were measured simultaneously and kinetically after mixing to track the progression of phase separation. Once equilibrated, the dense phase viscosity was determined to high compositional accuracy using passive probe microrheology, and the results were used to derive empirical relationships between the composition and viscosity. These relationships were inverted to reconstruct the dense phase boundary itself, and further extended to other mixture compositions. The resulting predictions were validated by independent equilibrium compositional measurements. This platform paves the way for rapid screening and formulation of complex fluids and (bio)macromolecular materials, and serves as a critical link between formulation and rheology for multi-phase material discovery.

Edge-enhancement densenet for X-ray fluoroscopy image denoising in cardiac electrophysiology procedures.

PURPOSE: Reducing X-ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low-dose X-ray fluoroscopy images but may compromise clinically important details required by cardiologists. METHODS: In order to obtain denoised X-ray fluoroscopy images whilst preserving details, we propose a novel deep-learning-based denoising framework, namely edge-enhancement densenet (EEDN), in which an attention-awareness edge-enhancement module is designed to increase edge sharpness. In this framework, a CNN-based denoiser is first used to generate an initial denoising result. Contours representing edge information are then extracted using an attention block and a group of interacted ultra-dense blocks for edge feature representation. Finally, the initial denoising result and enhanced edges are combined to generate the final X-ray image. The proposed denoising framework was tested on a total of 3262 clinical images taken from 100 low-dose X-ray sequences acquired from 20 patients. The performance was assessed by pairwise voting from five cardiologists as well as quantitative indicators. Furthermore, we evaluated our technique's effect on catheter detection using 416 images containing coronary sinus catheters in order to examine its influence as a pre-processing tool. RESULTS: The average signal-to-noise ratio of X-ray images denoised with EEDN was 24.5, which was 2.2 times higher than that of the original images. The accuracy of catheter detection from EEDN denoised sequences showed no significant difference compared with their original counterparts. Moreover, EEDN received the highest average votes in our clinician assessment when compared to our existing technique and the original images. CONCLUSION: The proposed deep learning-based framework shows promising capability for denoising interventional X-ray fluoroscopy images. The results from the catheter detection show that the network does not affect the results of such an algorithm when used as a pre-processing step. The extensive qualitative and quantitative evaluations suggest that the network may be of benefit to reduce radiation dose when applied in real time in the catheter laboratory.

Uncertainty quantification and estimation in differential dynamic microscopy.

Differential dynamic microscopy (DDM) is a form of video image analysis that combines the sensitivity of scattering and the direct visualization benefits of microscopy. DDM is broadly useful in determining dynamical properties including the intermediate scattering function for many spatiotemporally correlated systems. Despite its straightforward analysis, DDM has not been fully adopted as a routine characterization tool, largely due to computational cost and lack of algorithmic robustness. We present statistical analysis that quantifies the noise, reduces the computational order, and enhances the robustness of DDM analysis. We propagate the image noise through the Fourier analysis, which allows us to comprehensively study the bias in different estimators of model parameters, and we derive a different way to detect whether the bias is negligible. Furthermore, through use of Gaussian process regression (GPR), we find that predictive samples of the image structure function require only around 0.5%-5% of the Fourier transforms of the observed quantities. This vastly reduces computational cost, while preserving information of the quantities of interest, such as quantiles of the image scattering function, for subsequent analysis. The approach, which we call DDM with uncertainty quantification (DDM-UQ), is validated using both simulations and experiments with respect to accuracy and computational efficiency, as compared with conventional DDM and multiple particle tracking. Overall, we propose that DDM-UQ lays the foundation for important new applications of DDM, as well as to high-throughput characterization.

Photoinduced Acetylation of Anilines under Aqueous and Catalyst-Free Conditions.

A green and efficient visible-light induced functionalization of anilines under mild conditions has been reported. Utilizing nontoxic, cost-effective, and water-soluble diacetyl as photosensitizer and acetylating reagent, and water as the solvent, a variety of anilines were converted into the corresponding aryl ketones, iodides, and bromides. With advantages of environmentally friendly conditions, simple operation, broad substrate scope, and functional group tolerance, this reaction represents a valuable method in organic synthesis.

Ultra-Dense Denoising Network: Application to Cardiac Catheter-Based X-Ray Procedures.

Reducing radiation dose in cardiac catheter-based X-ray procedures increases safety but also image noise and artifacts. Excessive noise and artifacts can compromise vital image information, which can affect clinical decision-making. Developing more effective X-ray denoising methodologies will be beneficial to both patients and healthcare professionals by allowing imaging at lower radiation dose without compromising image information. This paper proposes a framework based on a convolutional neural network (CNN), namely Ultra-Dense Denoising Network (UDDN), for low-dose X-ray image denoising. To promote feature extraction, we designed a novel residual block which establishes a solid correlation among multiple-path neural units via abundant cross connections in its representation enhancement section. Experiments on synthetic additive noise X-ray data show that the UDDN achieves statistically significant higher peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) than other comparative methods. We enhanced the clinical adaptability of our framework by training using normally-distributed noise and tested on clinical data taken from procedures at St. Thomas' hospital in London. The performance was assessed by using local SNR and by clinical voting using ten cardiologists. The results show that the UDDN outperforms the other comparative methods and is a promising solution to this challenging but clinically impactful task.

How Micro-/Nanostructure Evolution Influences Dynamic Wetting and Natural Deicing Abilities of Bionic Lotus Surfaces.

Anti-icing materials have become increasingly urgent for many fields such as power transmission, aviation, energy, telecommunications, and so on. Bionic lotus hydrophobic surfaces with hierarchical micro-/nanostructures show good potential of delaying ice formation; however, their icephobicity (deicing ability) has been controversial. It is mainly attributed to lack of deep understanding of the correlation between micro-/nanoscale structures, wettability, and icephobicity, as well as effective methods for evaluating the deicing ability close to natural environments. In this article, the natural deicing ability is innovatively proposed on the basis of ice adhesion and the influence of microscale structure evolution on dynamic wetting and deicing ability (both ice adhesion strength and natural deicing time) was systematically investigated. Interestingly, different modes (sticky or slippery) were found in natural deicing of hierarchical hydrophobic surfaces, although their ice adhesion strength was higher than that of smooth surfaces. The mechanism was analyzed from three aspects: mechanics, heat transfer, and dynamic wetting. It is highlighted that the sliding of melted interface is not equal to water droplet sliding (dynamic wetting) before freezing or after deicing but significantly depends on the microscale structure. The fundamental understanding on natural deicing of bionic hydrophobic surfaces will open up a new window for developing new anti-icing materials and technology.