Frequency aware high-quality computer-generated holography via multilevel wavelet learning and channel attention.

Deep learning-based computer-generated holography offers significant advantages for real-time holographic displays. Most existing methods typically utilize convolutional neural networks (CNNs) as the basic framework for encoding phase-only holograms (POHs). However, recent studies have shown that CNNs suffer from spectral bias, resulting in insufficient learning of high-frequency components. Here, we propose a novel, to our knowledge, frequency aware network for generating high-quality POHs. A multilevel wavelet-based channel attention network (MW-CANet) is designed to address spectral bias. By employing multi-scale wavelet transformations, MW-CANet effectively captures both low- and high-frequency features independently, thus facilitating an enhanced representation of high-frequency information crucial for accurate phase inference. Furthermore, MW-CANet utilizes an attention mechanism to discern and allocate additional focus to critical high-frequency components. Simulations and optical experiments confirm the validity and feasibility of our method.

Integrative Graph-Based Framework for Predicting circRNA Drug Resistance Using Disease Contextualization and Deep Learning.

Circular RNAs (circRNAs) play a crucial role in gene regulation and have been implicated in the development of drug resistance in cancer, representing a significant challenge in oncological therapeutics. Despite advancements in computational models predicting RNA-drug interactions, existing frameworks often overlook the complex interplay between circRNAs, drug mechanisms, and disease contexts. This study aims to bridge this gap by introducing a novel computational model, circRDRP, that enhances prediction accuracy by integrating disease-specific contexts into the analysis of circRNA-drug interactions. It employs a hybrid graph neural network that combines features from Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN) in a two-layer structure, with further enhancement through convolutional neural networks. This approach allows for sophisticated feature extraction from integrated networks of circRNAs, drugs, and diseases. Our results demonstrate that the circRDRP model outperforms existing models in predicting drug resistance, showing significant improvements in accuracy, precision, and recall. Specifically, the model shows robust predictive capability in case studies involving major anticancer drugs such as Cisplatin and Methotrexate, indicating its potential utility in precision medicine. In conclusion, circRDRP offers a powerful tool for understanding and predicting drug resistance mediated by circRNAs, with implications for designing more effective cancer therapies.

Depth Perception in Optical See-Through Augmented Reality: Investigating the Impact of Texture Density, Luminance Contrast, and Color Contrast.

The immersive augmented reality (AR) system necessitates precise depth registration between virtual objects and the real scene. Prior studies have emphasized the efficacy of surface texture in providing depth cues to enhance depth perception across various media, including the real scene, virtual reality, and AR. However, these studies predominantly focus on black-and-white textures, leaving a gap in understanding the effectiveness of colored textures.To address this gap and further explore texture-related factors in AR, a series of experiments were conducted to investigate the effects of different texture cues on depth perception using the perceptual matching method. Findings indicate that the absolute depth error increases with decreasing contrast under black-and-white texture. Moreover, textures with higher color contrast also contribute to enhanced accuracy of depth judgments in AR. However, no significant effect of texture density on depth perception was observed. The findings serve as a theoretical reference for texture design in AR, aiding in the optimization of virtual-real registration processes.

Real-time simulation for multi-component biomechanical analysis using localized tissue constraint progressive transfer learning.

In virtual surgical training, it is crucial to achieve real-time, high-fidelity simulation of the tissue deformation. The anisotropic and nonlinear characteristics of the organ with multi-component make accurate real-time deformation simulation difficult. A localized tissue constraint progressive transfer learning method is proposed in this paper, where the base-compensated dual-output transfer learning strategy and the localized tissue constraint progressive learning architecture are developed. The proposed strategy enriches the multi-component biomechanical dataset to fully represent complex force-displacement with minimal high-quality data. Meanwhile, the proposed architecture adopts focused and progressive model to accurately describe tissues with varied biomechanical properties rather than singular homogeneous model. We made comparison with 4 state-of-the-art (SOTA) methods in simulating multi-component biomechanical deformations of organs with 100 pairs of testing data. Results show that the accuracy of our method is 50% higher than other methods in different validation matrix. And our method can stably simulate the deformations in 0.005 s per frame, which largely improves the computing efficiency.

Radiationless optical modes in metasurfaces: recent progress and applications.

Non-radiative optical modes attracted enormous attention in optics due to strong light confinement and giant Q-factor at its spectral position. The destructive interference of multipoles leads to zero net-radiation and strong field trapping. Such radiationless states disappear in the far-field, localize enhanced near-field and can be excited in nano-structures. On the other hand, the optical modes turn out to be completely confined due to no losses at discrete point in the radiation continuum, such states result in infinite Q-factor and lifetime. The radiationless states provide a suitable platform for enhanced light matter interaction, lasing, and boost nonlinear processes at the state regime. These modes are widely investigated in different material configurations for various applications in both linear and nonlinear metasurfaces which are briefly discussed in this review.

Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster.

Spatial transcriptomics provides valuable insights into gene expression within the native tissue context, effectively merging molecular data with spatial information to uncover intricate cellular relationships and tissue organizations. In this context, deciphering cellular spatial domains becomes essential for revealing complex cellular dynamics and tissue structures. However, current methods encounter challenges in seamlessly integrating gene expression data with spatial information, resulting in less informative representations of spots and suboptimal accuracy in spatial domain identification. We introduce stCluster, a novel method that integrates graph contrastive learning with multi-task learning to refine informative representations for spatial transcriptomic data, consequently improving spatial domain identification. stCluster first leverages graph contrastive learning technology to obtain discriminative representations capable of recognizing spatially coherent patterns. Through jointly optimizing multiple tasks, stCluster further fine-tunes the representations to be able to capture complex relationships between gene expression and spatial organization. Benchmarked against six state-of-the-art methods, the experimental results reveal its proficiency in accurately identifying complex spatial domains across various datasets and platforms, spanning tissue, organ, and embryo levels. Moreover, stCluster can effectively denoise the spatial gene expression patterns and enhance the spatial trajectory inference. The source code of stCluster is freely available at https://github.com/hannshu/stCluster.

Battery-free optoelectronic patch for photodynamic and light therapies in treating bacteria-infected wounds.

Light therapy is an effective approach for the treatment of a variety of challenging dermatological conditions. In contrast to existing methods involving high doses and large areas of illumination, alternative strategies based on wearable designs that utilize a low light dose over an extended period provide a precise and convenient treatment. In this study, we present a battery-free, skin-integrated optoelectronic patch that incorporates a coil-powered circuit, an array of microscale violet and red light emitting diodes (LEDs), and polymer microneedles (MNs) loaded with 5-aminolevulinic acid (5-ALA). These polymer MNs, based on the biodegradable composite materials of polyvinyl alcohol (PVA) and hyaluronic acid (HA), serve as light waveguides for optical access and a medium for drug release into deeper skin layers. Unlike conventional clinical photomedical appliances with a rigid and fixed light source, this flexible design allows for a conformable light source that can be applied directly to the skin. In animal models with bacterial-infected wounds, the experimental group with the combination treatment of metronomic photodynamic and light therapies reduced 2.48 log10 CFU mL-1 in bactericidal level compared to the control group, indicating an effective anti-infective response. Furthermore, post-treatment analysis revealed the activation of proregenerative genes in monocyte and macrophage cell populations, suggesting enhanced tissue regeneration, neovascularization, and dermal recovery. Overall, this optoelectronic patch design broadens the scope for targeting deep skin lesions, and provides an alternative with the functionality of standard clinical light therapy methods.

Mapping-based design method for high-quality integral projection system.

A general method for designing an integral projection system is proposed, including optical design and digital preprocessing based on the mapping within the projection system. The per-pixel mapping between the sub-images and the integral projection image is generated by incorporating an integral projection imaging model as well as the ray data of all sub-channels. By tracing rays for sparsely sampled field points of the central sub-channel and constructing the mapping between the central sub-channel and other sub-channels, the efficient acquisition of ray data for all sub-channels is achieved. The sub-image preprocessing pipeline is presented to effectively address issues such as overlapping misalignment, optical aberrations, inhomogeneous illumination, and their collective contribution. An integral projection optical system with a field of view (FOV) of 80°, an F-number of 2, and uniform image performance is given as a design example. The ray tracing simulation results and quantitative analysis demonstrate that the proposed system yields distortion-free, uniformly illuminated, and high-quality integral projection images.

DSC-Recon: Dual-Stage Complementary 4D Organ Reconstruction from X-ray Image Sequence for Intraoperative Fusion.

Accurately reconstructing 4D critical organs contributes to the visual guidance in X-ray image-guided interventional operation. Current methods estimate intraoperative dynamic meshes by refining a static initial organ mesh from the semantic information in the single-frame X-ray images. However, these methods fall short of reconstructing an accurate and smooth organ sequence due to the distinct respiratory patterns between the initial mesh and X-ray image. To overcome this limitation, we propose a novel dual-stage complementary 4D organ reconstruction (DSC-Recon) model for recovering dynamic organ meshes by utilizing the preoperative and intraoperative data with different respiratory patterns. DSC-Recon is structured as a dual-stage framework: 1) The first stage focuses on addressing a flexible interpolation network applicable to multiple respiratory patterns, which could generate dynamic shape sequences between any pair of preoperative 3D meshes segmented from CT scans. 2) In the second stage, we present a deformation network to take the generated dynamic shape sequence as the initial prior and explore the discriminate feature (i.e., target organ areas and meaningful motion information) in the intraoperative X-ray images, predicting the deformed mesh by introducing a designed feature mapping pipeline integrated into the initialized shape refinement process. Experiments on simulated and clinical datasets demonstrate the superiority of our method over state-of-the-art methods in both quantitative and qualitative aspects.

Meta-Attention Network Based Spectral Reconstruction with Snapshot Near-Infrared Metasurface.

Near-infrared (NIR) spectral information is important for detecting and analyzing material compositions. However, snapshot NIR spectral imaging systems still pose significant challenges owing to the lack of high-performance NIR filters and bulky setups, preventing effective encoding and integration with mobile devices. This study introduces a snapshot spectral imaging system that employs a compact NIR metasurface featuring 25 distinct C4 symmetry structures. Benefitting from the sufficient spectral variety and low correlation coefficient among these structures, center-wavelength accuracy of 0.05 nm and full width at half maximum accuracy of 0.13 nm are realized. The system maintains good performance within an incident angle of 1°. A novel meta-attention network prior iterative denoising reconstruction (MAN-IDR) algorithm is developed to achieve high-quality NIR spectral imaging. By leveraging the designed metasurface and MAN-IDR, the NIR spectral images, exhibiting precise textures, minimal artifacts in the spatial dimension, and little crosstalk between spectral channels, are reconstructed from a single grayscale recording image. The proposed NIR metasurface and MAN-IDR hold great promise for further integration with smartphones and drones, guaranteeing the adoption of NIR spectral imaging in real-world scenarios such as aerospace, health diagnostics, and machine vision.

Ultracompact polarization multiplexing meta-combiner for augmented reality display.

Augmented reality (AR) display, as a next-generation innovative technology, is revolutionizing the ways of perceiving and communicating by overlaying virtual images onto real-world scenes. However, the current AR devices are often bulky and cumbersome, posing challenges for long-term wearability. Metasurfaces have flexible capabilities of manipulating light waves at subwavelength scales, making them as ideal candidates for replacing traditional optical elements in AR display devices. In this work, we propose and fabricate what we believe is a novel reflective polarization multiplexing gradient metasurface based on propagation phase principle to replace the optical combiner element in traditional AR display devices. Our designed metasurface exhibits different polarization modulations for reflected and transmitted light, enabling efficient deflection of reflected light while minimizing the impact on transmitted light. This work reveals the significant potential of metasurfaces in next-generation optical display systems and provides a reliable theoretical foundation for future integrated waveguide schemes, driving the development of next-generation optical display products towards lightweight and comfortable.

Polarized emission of Cs3Cu2I5 nanowires embedded in nanopores of an anodic aluminum oxide template.

Due to the intrinsic polarized emission property, polarized emissive materials with anisotropic nanostructures are expected to be potential substitutes for polarizers. Herein, by the template-assisted strategy, well-aligned lead-free metal halide Cs3Cu2I5 nanowire (NW) arrays are fabricated by evaporating the precursor ink in the anodic aluminum oxide (AAO) for polarized emission. The Cs3Cu2I5/AAO composite film emits highly polarized light with a degree of polarization (DOP) of 0.50. Furthermore, by changing the molar ratio of CsI/CuI, the stability of Cs3Cu2I5 precursor inks is improved. Finally, an ultraviolet (UV) light-emitting diode (LED) is adopted to pump the composite film to achieve a blue LED device. The reported Cs3Cu2I5/AAO composite film with highly polarized light emissions will have great potential for polarized emission applications such as liquid crystal display backlights, waveguides, and lasers.

postGWAS: A web server for deciphering the causality post the genome-wide association studies.

While genome-wide association studies (GWAS) have unequivocally identified vast disease susceptibility variants, a majority of them are situated in non-coding regions and are in high linkage disequilibrium (LD). To pave the way of translating GWAS signals to clinical drug targets, it is essential to identify the underlying causal variants and further causal genes. To this end, a myriad of post-GWAS methods have been devised, each grounded in distinct principles including fine-mapping, co-localization, and transcriptome-wide association study (TWAS) techniques. Yet, no platform currently exists that seamlessly integrates these diverse post-GWAS methodologies. In this work, we present a user-friendly web server for post-GWAS analysis, that seamlessly integrates 9 distinct methods with 12 models, categorized by fine-mapping, colocalization, and TWAS. The server mainly helps users decipher the causality hindered by complex GWAS signals, including casual variants and casual genes, without the burden of computational skills and complex environment configuration, and provides a convenient platform for post-GWAS analysis, result visualization, facilitating the understanding and interpretation of the genome-wide association studies. The postGWAS server is available at http://g2g.biographml.com/.

Multi-Dimensional Multiplexed Metasurface Holography by Inverse Design.

Multi-dimensional multiplexed metasurface holography extends holographic information capacity and promises revolutionary advancements for vivid imaging, information storage, and encryption. However, achieving multifunctional metasurface holography by forward design method is still difficult because it relies heavily on Jones matrix engineering, which places high demands on physical knowledge and processing technology. To break these limitations and simplify the design process, here, an end-to-end inverse design framework is proposed. By directly linking the metasurface to the reconstructed images and employing a loss function to guide the update of metasurface, the calculation of hologram can be omitted; thus, greatly simplifying the design process. In addition, the requirements on the completeness of meta-library can also be significantly reduced, allowing multi-channel hologram to be achieved using meta-atoms with only two degrees of freedom, which is very friendly to processing. By exploiting the proposed method, metasurface hologram containing up to 12 channels of multi-wavelength, multi-plane, and multi-polarization is designed and experimentally demonstrated, which exhibits the state-of-the-art information multiplexing capacity of the metasurface composed of simple meta-atoms. This method is conducive to promoting the intelligent design of multifunctional meta-devices, and it is expected to eventually accelerate the application of meta-devices in colorful display, imaging, storage and other fields.

Pose estimation via structure-depth information from monocular endoscopy images sequence.

Image-based endoscopy pose estimation has been shown to significantly improve the visualization and accuracy of minimally invasive surgery (MIS). This paper proposes a method for pose estimation based on structure-depth information from a monocular endoscopy image sequence. Firstly, the initial frame location is constrained using the image structure difference (ISD) network. Secondly, endoscopy image depth information is used to estimate the pose of sequence frames. Finally, adaptive boundary constraints are used to optimize continuous frame endoscopy pose estimation, resulting in more accurate intraoperative endoscopy pose estimation. Evaluations were conducted on publicly available datasets, with the pose estimation error in bronchoscopy and colonoscopy datasets reaching 1.43 mm and 3.64 mm, respectively. These results meet the real-time requirements of various scenarios, demonstrating the capability of this method to generate reliable pose estimation results for endoscopy images and its meaningful applications in clinical practice. This method enables accurate localization of endoscopy images during surgery, assisting physicians in performing safer and more effective procedures.

Augmented Reality Navigation System for Biliary Interventional Procedures With Dynamic Respiratory Motion Correction.

OBJECTIVE: Biliary interventional procedures require physicians to track the interventional instrument tip (Tip) precisely with X-ray image. However, Tip positioning relies heavily on the physicians' experience due to the limitations of X-ray imaging and the respiratory interference, which leads to biliary damage, prolonged operation time, and increased X-ray radiation. METHODS: We construct an augmented reality (AR) navigation system for biliary interventional procedures. It includes system calibration, respiratory motion correction and fusion navigation. Firstly, the magnetic and 3D computed tomography (CT) coordinates are aligned through system calibration. Secondly, a respiratory motion correction method based on manifold regularization is proposed to correct the misalignment of the two coordinates caused by respiratory motion. Thirdly, the virtual biliary, liver and Tip from CT are overlapped to the corresponding position of the patient for dynamic virtual-real fusion. RESULTS: Our system is respectively evaluated and achieved an average alignment error of 0.75 ± 0.17 mm and 2.79 ± 0.46 mm on phantoms and patients. The navigation experiments conducted on phantoms achieve an average Tip positioning error of 0.98 ± 0.15 mm and an average fusion error of 1.67 ± 0.34 mm after correction. CONCLUSION: Our system can automatically register the Tip to the corresponding location in CT, and dynamically overlap the 3D virtual model onto patients to provide accurate and intuitive AR navigation. SIGNIFICANCE: This study demonstrates the clinical potential of our system by assisting physicians during biliary interventional procedures. Our system enables dynamic visualization of virtual model on patients, reducing the reliance on contrast agents and X-ray usage.

Design of an image-based BRDF measurement method using a catadioptric multispectral capture and a real-time Lambert calibration.

By utilizing a catadioptric system and a calibration Lambertian sample, a compact measurement method of bidirectional reflectance distribution function (BRDF) has been proposed for rapid and accurate measurement. With the help of an ellipsoidal dome mirror, a hyperboloid mirror, and a high-resolution camera, spatial reflectance distributions from reflected directions with a large field of view (FOV) can be obtained. The built-in Lambertian standard allows for real-time calibration to account for fluctuations in the illumination spectrum, effectively reducing the measurement drift and achieving a high accuracy. Moreover, a multispectral camera captures images at 8 spectral bands for accurate spectral color reconstruction from different directions. To verify the method, a prototype capable of fast, high-resolution measurements with a large FOV has been developed for characterizing the scattering properties of objects. It achieves a measured angular range up to 160°. Multispectral BRDF data for each sample can be obtained within 5 minutes with an angular resolution of less than 0.6°. Eight ceramic samples with different colors were selected for the verification of measurement accuracy, and their mean relative bias of BRDF measurement was found to be as low as 2.5%.

Monocular endoscopy images depth estimation with multi-scale residual fusion.

BACKGROUND: Monocular depth estimation plays a fundamental role in clinical endoscopy surgery. However, the coherent illumination, smooth surfaces, and texture-less nature of endoscopy images present significant challenges to traditional depth estimation methods. Existing approaches struggle to accurately perceive depth in such settings. METHOD: To overcome these challenges, this paper proposes a novel multi-scale residual fusion method for estimating the depth of monocular endoscopy images. Specifically, we address the issue of coherent illumination by leveraging image frequency domain component space transformation, thereby enhancing the stability of the scene's light source. Moreover, we employ an image radiation intensity attenuation model to estimate the initial depth map. Finally, to refine the accuracy of depth estimation, we utilize a multi-scale residual fusion optimization technique. RESULTS: To evaluate the performance of our proposed method, extensive experiments were conducted on public datasets. The structural similarity measures for continuous frames in three distinct clinical data scenes reached impressive values of 0.94, 0.82, and 0.84, respectively. These results demonstrate the effectiveness of our approach in capturing the intricate details of endoscopy images. Furthermore, the depth estimation accuracy achieved remarkable levels of 89.3 % and 91.2 % for the two models' data, respectively, underscoring the robustness of our method. CONCLUSIONS: Overall, the promising results obtained on public datasets highlight the significant potential of our method for clinical applications, facilitating reliable depth estimation and enhancing the quality of endoscopy surgical procedures.

The unusual quadruple bonding of nitrogen in ThN.

Nitrogen has five valence electrons and can form a maximum of three shared electron-pair bonds to complete its octet, which suggests that its maximum bond order is three. With a joint anion photoelectron spectroscopy and quantum chemistry investigation, we report herein that nitrogen presents a quadruple bonding interaction with thorium in ThN. The quadruple Th≣N bond consists of two electron-sharing Th-N π bonds formed between the Th-6dxz/6dyz and N 2px/2py orbitals, one dative Th←N σ bond and one weak Th←N σ bonding interaction formed between Th-6dz2 and N 2s/2pz orbitals. The ThC molecule has also been investigated and proven to have a similar bonding pattern as ThN. Nonetheless, due to one singly occupied σ-bond, ThC is assigned a bond order of 3.5. Moreover, ThC has a longer bond length as well as a lower vibrational frequency in comparison with ThN.