BAB-GSL: Using Bayesian influence with attention mechanism to optimize graph structure in basic views.

In recent years, Graph Neural Networks (GNNs) have garnered significant attention, with a notable focus on Graph Structure Learning (GSL), a branch dedicated to optimizing graph structures to enhance network training performance. Current GSL methods primarily involve constructing optimized graph representations by analyzing one or more initial graph sources to improve performance in subsequent application tasks. Despite these advancements, achieving high-quality graphs that accurately and robustly reflect node relationships remains challenging. This paper introduces a novel approach, termed BAB-GSL, designed to approximate an ideal graph structure through a systematic process. Specifically, two basic views are extracted from the original graph and utilized as inputs for the model, where the preliminary optimized view is generated through the view fusion module. The Attention mechanism is then applied to the optimized view to improve nodes' connectivity and expressiveness. Subsequently, the trained view is re-structured using a Bayesian optimizer to produce the final graph structure. Extensive experiments were conducted across multiple datasets, both in undisturbed and attacked scenarios, to thoroughly evaluate the proposed method, demonstrating the effectiveness and robustness of the BAB-GSL approach.

Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design.

Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

Structure characteristics and gasification reactivity of co-pyrolysis char from lignocellulosic biomass and waste plastics: Effect of polyethylene.

The rate limiting stage is char reactivity during gasification that can be influenced by its physicochemical structural characteristics. In this study, the effects of feedstock share, rice straw (RS) and polyethylene (PE), on the physicochemical properties and gasification reactivity of chars were investigated and their relationships were discussed. The char gasification reactivity was investigated via isothermal experiments using a thermal analyzer. The results indicated that the PE addition improved the specific surface area (SSA) and pore volume (Vp) of the char obtained from co-pyrolysis RS with PE. The SSA of the char increased by 1.31 times when the PE content was 60 wt%, compared with that of RS char. The order degree and gasification reactivity of the co-pyrolysis char samples increased with increasing PE content beyond 40 wt%. The char reactivity in the early stage of co-gasification was primarily determined by the order degree of carbonaceous and pore structure. The char reactivity in the later stage was influenced by these two factors and the silicon dioxide content could inhibit the char co-gasification reactivity.

Metamaterial-Assisted Illumination Nanoscopy with Exceptional Axial Resolution.

Recent advancements in optical metamaterials have opened new possibilities in the exciting field of super-resolution microscopies. The far-field metamaterial-assisted illumination nanoscopies (MAINs) have, very recently, enhanced the lateral resolution to one-fifteenth of the optical wavelength. However, the axial localization accuracy of fluorophores in the MAINs remains rarely explored. Here, a MAIN with a nanometer-scale axial localization accuracy is demonstrated by monitoring the distance-dependent photobleaching dynamics of the fluorophores on top of an organic hyperbolic metamaterial (OHM) substrate under a wide-field single-objective microscope. With such a regular experimental configuration, 3D imaging of various biological samples with the resolution of ≈40 nm in the lateral dimensions and ≈5 nm in the axial dimension is realized. The demonstrated imaging modality enables the resolution of the 3D morphology of nanoscopic cellular structures with a significantly simplified experimental setup.

Giant optical second- and third-order nonlinearities at a telecom wavelength.

A material platform that excels in both optical second- and third-order nonlinearities at a telecom wavelength is theoretically and experimentally demonstrated. In this TiN-based coupled metallic quantum well structure, electronic subbands are engineered to support doubly resonant inter-subband transitions for an exceptionally high second-order nonlinearity and provide single-photon transitions for a remarkable third-order nonlinearity within the 1400-1600 nm bandwidth. The second-order susceptibility χ(2) reaches 2840 pm/V at 1440 nm, while the Kerr coefficient n2 arrives at 2.8 × 10-10 cm2/W at 1460 nm. The achievement of simultaneous strong second- and third-order nonlinearities in one material at a telecom wavelength creates opportunities for multi-functional advanced applications in the field of nonlinear optics.

Infrared color-sorting metasurfaces.

The process of sorting light based on colors (photon energy) is a prerequisite in broadband optical systems, typically achieved in the form of guiding incoming signals through a sequence of spectral filters. The assembly of filters often leads to lengthy optical trains and consequently, large system footprints. In this work, we address this issue by proposing a flat color-sorting device comprising a diffraction grating and a dielectric Huygens' metasurface. Upon the incidence of a broadband beam, the grating disperses wavelengths to a continuous range of angles in accordance with the law of diffraction. The following metasurface with multiple paired Huygens' resonances corrects the dispersion and binds wavelengths to the corresponding waveband with a designated output angle. We demonstrate the sorting efficacy by designing a device with a color-sorting metasurface with two discrete dispersion-compensated outputs (10.8 ± 0.3 µm and 11.9 ± 0.3 µm), based on the proposed approach. The optimized metasurface possesses an overall transmittance exceeding 57% and reduces lateral dispersion by 90% at the output. The proposed color-sorting mechanism provides a solution that benefits the designing of metasurfaces for miniature multi-band systems.

Inverse design of metasurface based off-axis image relay.

The rapid advancement of portable electronics has created enormous demand for compact optical imaging systems. Such systems often require folded optical systems with beam steering and shaping components to reduce sizes and minimize image aberration at the same time. In this study, we present a solution that utilizes an inverse-designed dielectric metasurface for arbitrary-angle image-relay with aberration correction. The metasurface phase response is optimized by a series of artificial neural networks to compensate for the severe aberrations in the deflected images and meet the requirements for device fabrication at the same time. We compare our results to the solutions found by the global optimization tool in Zemax OpticStudio and show that the proposed method can predict better point-spread functions and images with less distortion. Finally, we designed a metasurface to achieve the optimized phase profile.

Engineering the Mechanical Stability of a Therapeutic Affibody/PD-L1 Complex by Anchor Point Selection.

Protein-protein complexes can vary in mechanical stability depending on the direction from which force is applied. Here we investigated the anisotropic mechanical stability of a molecular complex between a therapeutic non-immunoglobulin scaffold called Affibody and the extracellular domain of the immune checkpoint protein PD-L1. We used a combination of single-molecule AFM force spectroscopy (AFM-SMFS) with bioorthogonal clickable peptide handles, shear stress bead adhesion assays, molecular modeling, and steered molecular dynamics (SMD) simulations to understand the pulling point dependency of mechanostability of the Affibody:(PD-L1) complex. We observed diverse mechanical responses depending on the anchor point. For example, pulling from residue #22 on Affibody generated an intermediate unfolding event attributed to partial unfolding of PD-L1, while pulling from Affibody's N-terminus generated force-activated catch bond behavior. We found that pulling from residue #22 or #47 on Affibody generated the highest rupture forces, with the complex breaking at up to ~ 190 pN under loading rates of ~104-105 pN/sec, representing a ~4-fold increase in mechanostability as compared with low force N-terminal pulling. SMD simulations provided consistent tendencies in rupture forces, and through visualization of force propagation networks provided mechanistic insights. These results demonstrate how mechanostability of therapeutic protein-protein interfaces can be controlled by informed selection of anchor points within molecules, with implications for optimal bioconjugation strategies in drug delivery vehicles.

Establishing a nutrition calculation model for catering food according to the influencing factors of energy and nutrient content in food processing.

Objective: This study aimed to establish an accurate and efficient scientific calculation model for the nutritional composition of catering food to estimate energy and nutrient content of catering food. Methods: We constructed a scientific raw material classification database based on the Chinese food composition table by calculating the representative values of each food raw material type. Using China's common cooking methods, we cooked 150 dishes including grains, meat, poultry, fish, eggs, and vegetables and established a database showing the raw and cooked ratios of various food materials by calculating the ratio of raw to cooked and the China Total Diet Research database. The effects of various cooking methods on the nutritional composition of catering food were analyzed to determine correction factors for such methods on the nutritional components. Finally, we linked the raw material classification, raw and cooked ratio, and nutritional component correction factor databases to establish a model for calculating the nutritional components of catering food. The model was verified with nine representative Chinese dishes. Results: We have completed the construction of an accurate and efficient scientific calculation model for the nutritional composition of catering food, which improves the accuracy of nutrition composition calculation. Conclusion: The model constructed in this study was scientific, accurate, and efficient, thereby promising in facilitating the accurate calculation and correct labeling of nutritional components in catering food.

Eagle-Eye Inspired Meta-Device for Phase Imaging.

The dual-focus vision observed in eagles' eyes is an intriguing phenomenon captivates scientists since a long time. Inspired by this natural occurrence, the authors' research introduces a novel bifocal meta-device incorporating a polarized camera capable of simultaneously capturing images for two different polarizations with slightly different focal distances. This innovative approach facilitates the concurrent acquisition of underfocused and overfocused images in a single snapshot, enabling the effective extraction of quantitative phase information from the object using the transport of intensity equation. Experimental demonstrations showcase the application of quantitative phase imaging to artificial objects and human embryonic kidney cells, particularly emphasizing the meta-device's relevance in dynamic scenarios such as laser-induced ablation in human embryonic kidney cells. Moreover, it provides a solution for the quantification during the dynamic process at the cellular level. Notably, the proposed eagle-eye inspired meta-device for phase imaging (EIMPI), due to its simplicity and compact nature, holds promise for significant applications in fields such as endoscopy and headsets, where a lightweight and compact setup is essential.

The engineering of defect-modified boron nitride ribbons: an effective adsorbent to rapid capture for tetracycline.

Antibiotics, the persistent organic pollutants, have brought serious pollution to the aquatic environment. Therefore, it is necessary to select rapid adsorbents to remove them from their long-term threat. Herein, the introduction of defects in BN was employed to enhance its surface chemical activity for rapid capture of tetracycline via hydrothermal and calcination methods. The defect content in BN can be controlled by adjusting the volume ratio of ethanol to water. Among them, when the volume ratio of H2O/ethanol is 4/1 (BN-3), BN-3 has the most N defects at 33%, which increases the adsorption rate of h-BN for TC and promotes the adsorption capacity to 302.15 mg g-1, which is due to the introduction of nitrogen defects significantly regulates the electronic structure of BN. The corresponding theoretical calculations confirm that BN with N defects decreases the absorption energy of BN for TC. Additionally, the adsorption removal rate of tetracycline still reached 95.5% after 5 cycles of TC adsorption by BN-3, indicating that the defect-modified BN has good reusability and is beneficial for its use in pollutant adsorption.

A Biophysics Toolbox for Reliable Data Acquisition and Processing in Integrated Force-Confocal Fluorescence Microscopy.

Integrated single-molecule force-fluorescence spectroscopy setups allow for simultaneous fluorescence imaging and mechanical force manipulation and measurements on individual molecules, providing comprehensive dynamic and spatiotemporal information. Dual-beam optical tweezers (OT) combined with a confocal scanning microscope form a force-fluorescence spectroscopy apparatus broadly used to investigate various biological processes, in particular, protein:DNA interactions. Such experiments typically involve imaging of fluorescently labeled proteins bound to DNA and force spectroscopy measurements of trapped individual DNA molecules. Here, we present a versatile state-of-the-art toolbox including the preparation of protein:DNA complex samples, design of a microfluidic flow cell incorporated with OT, automation of OT-confocal scanning measurements, and the development and implementation of a streamlined data analysis package for force and fluorescence spectroscopy data processing. Its components can be adapted to any commercialized or home-built dual-beam OT setup equipped with a confocal scanning microscope, which will facilitate single-molecule force-fluorescence spectroscopy studies on a large variety of biological systems.

Tap water microbiome shifts in secondary water supply for high-rise buildings.

In high-rise buildings, secondary water supply systems (SWSSs) are pivotal yet provide a conducive milieu for microbial proliferation due to intermittent flow, low disinfectant residual, and high specific pipe-surface area, raising concerns about tap water quality deterioration. Despite their ubiquity, a comprehensive understanding of bacterial community dynamics within SWSSs remains elusive. Here we show how intrinsic SWSS variables critically shape the tap water microbiome at distal ends. In an office setting, distinct from residential complexes, the diversity in piping materials instigates a noticeable bacterial community shift, exemplified by a transition from α-Proteobacteria to γ-Proteobacteria dominance, alongside an upsurge in bacterial diversity and microbial propagation potential. Extended water retention within SWSSs invariably escalates microbial regrowth propensities and modulates bacterial consortia, yet secondary disinfection emerges as a robust strategy for preserving water quality integrity. Additionally, the regularity of water usage modulates proximal flow dynamics, thereby influencing tap water's microbial landscape. Insights garnered from this investigation lay the groundwork for devising effective interventions aimed at safeguarding microbiological standards at the consumer's endpoint.

Engineering an artificial catch bond using mechanical anisotropy.

Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin E (DocG:CohE) adhesion complex found on human gut bacteria. Using AFM single-molecule force spectroscopy in combination with bioorthogonal click chemistry, we mechanically dissociate the complex using five precisely controlled anchor geometries. When tension is applied between residue #13 on CohE and the N-terminus of DocG, the complex behaves as a two-state catch bond, while in all other tested pulling geometries, including the native configuration, it behaves as a slip bond. We use a kinetic Monte Carlo model with experimentally derived parameters to simulate rupture force and lifetime distributions, achieving strong agreement with experiments. Single-molecule FRET measurements further demonstrate that the complex does not exhibit dual binding mode behavior at equilibrium but unbinds along multiple pathways under force. Together, these results show how mechanical anisotropy and anchor point selection can be used to engineer artificial catch bonds.

Super resolution label-free dark-field microscopy by deep learning.

Dark-field microscopy (DFM) is a powerful label-free and high-contrast imaging technique due to its ability to reveal features of transparent specimens with inhomogeneities. However, owing to the Abbe's diffraction limit, fine structures at sub-wavelength scale are difficult to resolve. In this work, we report a single image super resolution DFM scheme using a convolutional neural network (CNN). A U-net based CNN is trained with a dataset which is numerically simulated based on the forward physical model of the DFM. The forward physical model described by the parameters of the imaging setup connects the object ground truths and dark field images. With the trained network, we demonstrate super resolution dark field imaging of various test samples with twice resolution improvement. Our technique illustrates a promising deep learning approach to double the resolution of DFM without any hardware modification.

BI-FedGNN: Federated graph neural networks framework based on Bayesian inference.

The development of the Industrial Internet of Things (IIoT) in recent years has resulted in an increase in the amount of data generated by connected devices, creating new opportunities to enhance the quality of service for machine learning in the IIoT through data sharing. Graph neural networks (GNNs) are the most popular technique in machine learning at the moment because they can learn extremely precise node representations from graph-structured data. Due to privacy issues and legal restrictions of clients in industrial IoT, it is not permissible to directly concentrate vast real-world graph-structured datasets for training on GNNs. To resolve the aforementioned difficulties, this paper proposes a federal graph learning framework based on Bayesian inference (BI-FedGNN) that performs effectively in the presence of noisy graph structure information or missing strong relational edges. BI-FedGNN extends Bayesian Inference (BI) to the process of Federal Graph Learning (FGL), adding random samples with weights and biases to the client-side local model training process, improving the accuracy and generalization ability of FGL in the training process by rendering the graph structure data involved in GNNs training more similar to the graph structure data existing in the real world. Through extensive experimental tests, the results show that BI-FedGNN has about 0.5%-5.0% accuracy improvement over other baselines of federal graph learning. In order to expand the applicability of BI-FedGNN, experiments are carried out on heterogeneous graph datasets, and the results indicate that BI-FedGNN can also have at least 1.4% improvement in classification accuracy.

De novo fabrication of custom-sequence plasmids for the synthesis of long DNA constructs with extrahelical features.

DNA constructs for single-molecule experiments often require specific sequences and/or extrahelical/noncanonical structures to study DNA-processing mechanisms. The precise introduction of such structures requires extensive control of the sequence of the initial DNA substrate. A commonly used substrate in the synthesis of DNA constructs is plasmid DNA. Nevertheless, the controlled introduction of specific sequences and extrahelical/noncanonical structures into plasmids often requires several rounds of cloning on pre-existing plasmids whose sequence one cannot fully control. Here, we describe a simple and efficient way to synthesize 10.1-kb plasmids de novo using synthetic gBlocks that provides full control of the sequence. Using these plasmids, we developed a 1.5-day protocol to assemble 10.1-kb linear DNA constructs with end and internal modifications. As a proof of principle, we synthesize two different DNA constructs with biotinylated ends and one or two internal 3' single-stranded DNA flaps, characterize them using single-molecule force and fluorescence spectroscopy, and functionally validate them by showing that the eukaryotic replicative helicase Cdc45/Mcm2-7/GINS (CMG) binds the 3' single-stranded DNA flap and translocates in the expected direction. We anticipate that our approach can be used to synthesize custom-sequence DNA constructs for a variety of force and fluorescence single-molecule spectroscopy experiments to interrogate DNA replication, DNA repair, and transcription.

Low-dimensional heat conduction in surface phonon polariton waveguide.

Heat conduction in solids is typically governed by the Fourier's law describing a diffusion process due to the short wavelength and mean free path for phonons and electrons. Surface phonon polaritons couple thermal photons and optical phonons at the surface of polar dielectrics, possessing much longer wavelength and propagation length, representing an excellent candidate to support extraordinary heat transfer. Here, we realize clear observation of thermal conductivity mediated by surface phonon polaritons in SiO2 nanoribbon waveguides of 20-50 nm thick and 1-10 µm wide and also show non-Fourier behavior in over 50-100 µm distance at room and high temperature. This is enabled by rational design of the waveguide to control the mode size of the surface phonon polaritons and its efficient coupling to thermal reservoirs. Our work laid the foundation for manipulating heat conduction beyond the traditional limit via surface phonon polaritons waves in solids.

Iterative Machine Learning for Classification and Discovery of Single-Molecule Unfolding Trajectories from Force Spectroscopy Data.

We report the application of machine learning techniques to expedite classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression, and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) learning where a user classifies a small number of repeatable unfolding patterns encoded as images, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multidomain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways that were undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis and generate new insights in protein biophysics.

A chromatinized origin reduces the mobility of ORC and MCM through interactions and spatial constraint.

Chromatin replication involves the assembly and activity of the replisome within the nucleosomal landscape. At the core of the replisome is the Mcm2-7 complex (MCM), which is loaded onto DNA after binding to the Origin Recognition Complex (ORC). In yeast, ORC is a dynamic protein that diffuses rapidly along DNA, unless halted by origin recognition sequences. However, less is known about the dynamics of ORC proteins in the presence of nucleosomes and attendant consequences for MCM loading. To address this, we harnessed an in vitro single-molecule approach to interrogate a chromatinized origin of replication. We find that ORC binds the origin of replication with similar efficiency independently of whether the origin is chromatinized, despite ORC mobility being reduced by the presence of nucleosomes. Recruitment of MCM also proceeds efficiently on a chromatinized origin, but subsequent movement of MCM away from the origin is severely constrained. These findings suggest that chromatinized origins in yeast are essential for the local retention of MCM, which may facilitate subsequent assembly of the replisome.