Correcting Optical Aberration via Depth-Aware Point Spread Functions.

Optical aberration is a ubiquitous degeneration in realistic lens-based imaging systems. Optical aberrations are caused by the differences in the optical path length when light travels through different regions of the camera lens with different incident angles. The blur and chromatic aberrations manifest significant discrepancies when the optical system changes. This work designs a transferable and effective image simulation system of simple lenses via multi-wavelength, depth-aware, spatially-variant four-dimensional point spread functions (4D-PSFs) estimation by changing a small amount of lens-dependent parameters. The image simulation system can alleviate the overhead of dataset collecting and exploiting the principle of computational imaging for effective optical aberration correction. With the guidance of domain knowledge about the image formation model provided by the 4D-PSFs, we establish a multi-scale optical aberration correction network for degraded image reconstruction, which consists of a scene depth estimation branch and an image restoration branch. Specifically, we propose to predict adaptive filters with the depth-aware PSFs and carry out dynamic convolutions, which facilitate the model's generalization in various scenes. We also employ convolution and self-attention mechanisms for global and local feature extraction and realize a spatially-variant restoration. The multi-scale feature extraction complements the features across different scales and provides fine details and contextual features. Extensive experiments demonstrate that our proposed algorithm performs favorably against state-of-the-art restoration methods.

Multi-scale, multi-dimensional binocular endoscopic image depth estimation network.

During invasive surgery, the use of deep learning techniques to acquire depth information from lesion sites in real-time is hindered by the lack of endoscopic environmental datasets. This work aims to develop a high-accuracy three-dimensional (3D) simulation model for generating image datasets and acquiring depth information in real-time. Here, we proposed an end-to-end multi-scale supervisory depth estimation network (MMDENet) model for the depth estimation of pairs of binocular images. The proposed MMDENet highlights a multi-scale feature extraction module incorporating contextual information to enhance the correspondence precision of poorly exposed regions. A multi-dimensional information-guidance refinement module is also proposed to refine the initial coarse disparity map. Statistical experimentation demonstrated a 3.14% reduction in endpoint error compared to state-of-the-art methods. With a processing time of approximately 30fps, satisfying the requirements of real-time operation applications. In order to validate the performance of the trained MMDENet in actual endoscopic images, we conduct both qualitative and quantitative analysis with 93.38% high precision, which holds great promise for applications in surgical navigation.

TXNL4B regulates radioresistance by controlling the PRP3-mediated alternative splicing of FANCI.

Ionizing radiation (IR) has been extensively used for cancer therapy, but the radioresistance hinders and undermines the radiotherapy efficacy in clinics greatly. Here, we reported that the spliceosomal protein thioredoxin-like 4B (TXNL4B) is highly expressed in lung tissues from lung cancer patients with radiotherapy. Lung cancer cells with TXNL4B knockdown illustrate increased sensitivity to IR. Mechanistically, TXNL4B interacts with RNA processing factor 3 (PRP3) and co-localizes in the nucleus post-IR. Nuclear localization of PRP3 promotes the alternative splicing of the Fanconi anemia group I protein (FANCI) transcript variants, FANCI-12 and FANCI-13. PRP3 regulates alternative splicing of FANCI toward the two variants, FANCI-12 and FANCI-13. Radioresistance was greatly enhanced through the combination of PRP31 and PRP8, the critical components of core spliceosome promoted by PRP3. Notably, the inhibition of PRP3 to suppress the production of FANCI-12 would deprive PRP31 and PRP8 of such interaction. As a result, cell cycle G2/M arrest was induced, DNA damage repair was delayed, and radiosensitivity was improved. Collectively, our study highlights potential novel underlying mechanisms of the involvement of TXNL4B and alternative splicing in radioresistance. The results would benefit potential cancer radiotherapy.

Generalization of learned Fourier-based phase-diversity wavefront sensing.

Proper initialization of the nonlinear optimization is important to avoid local minima in phase diversity wavefront sensing (PDWS). An effective neural network based on low-frequency coefficients in the Fourier domain has proved effective to determine a better estimate of the unknown aberrations. However, the network relies significantly on the training settings, such as imaging object and optical system parameters, resulting in a weak generalization ability. Here we propose a generalized Fourier-based PDWS method by combining an object-independent network with a system-independent image processing procedure. We demonstrate that a network trained with a specific setting can be applied to any image regardless of the actual settings. Experimental results show that a network trained with one setting can be applied to images with four other settings. For 1000 aberrations with RMS wavefront errors bounded within [0.2 λ, 0.4 λ], the mean RMS residual errors are 0.032 λ, 0.039 λ, 0.035 λ, and 0.037 λ, respectively, and 98.9% of the RMS residual errors are less than 0.05 λ.

Fabrication of large-scale scaffolds with microscale features using light sheet stereolithography.

The common characteristics that make scaffolds suitable for human tissue substitutes include high porosity, microscale features, and pores interconnectivity. Too often, however, these characteristics are limiting factors for the scalability of different fabrication approaches, particularly in bioprinting techniques, in which either poor resolution, small areas, or slow processes hinder practical use in certain applications. An excellent example is bioengineered scaffolds for wound dressings, in which microscale pores in large surface-to-volume ratio scaffolds must be manufactured - ideally fast, precise, and cheap, and where conventional printing methods do not readily meet both ends. In this work, we propose an alternative vat photopolymerization technique to fabricate centimeter-scale scaffolds without losing resolution. We used laser beam shaping to first modify the profile of the voxels in 3D printing, resulting in a technology we refer to as light sheet stereolithography (LS-SLA). For proof of concept, we developed a system from commercially available off-the-shelf components to demonstrate strut thicknesses up to 12.8 ± 1.8 µm, tunable pore sizes ranging from 36 µm to 150 µm, and scaffold areas up to 21.4 mm × 20.6 mm printed in a short time. Furthermore, the potential to fabricate more complex and three-dimensional scaffolds was demonstrated with a structure composed of six layers, each rotated by 45° with respect to the previous. Besides the demonstrated high resolution and achievable large scaffold sizes, we found that LS-SLA has great potential for scaling-up of applied oriented technology for tissue engineering applications.

Freeform optical system design with differentiable three-dimensional ray tracing and unsupervised learning.

Optical systems have been crucial for versatile applications such as consumer electronics, remote sensing and biomedical imaging. Designing optical systems has been a highly professional work due to complicated aberration theories and intangible rules-of-thumb, hence neural networks are only coming into this realm until recent years. In this work, we propose and implement a generic, differentiable freeform raytracing module, suitable for off-axis, multiple-surface freeform/aspheric optical systems, paving the way toward a deep learning-based optical design method. The network is trained with minimal prior knowledge, and it can infer numerous optical systems after a one-time training. The presented work unlocks great potential for deep learning in various freeform/aspheric optical systems, and the trained network could serve as an effective, unified platform for generating, recording, and replicating good initial optical designs.

Miniaturized, high numerical aperture confocal fluorescence detection enhanced with pyroelectric droplet accumulation for sub-attomole analyte diagnosis.

To meet the growing demand for early fatal disease screening among large populations, current fluorescence detection instruments aiming at point-of-care diagnosis have the tendency to be low cost and high sensitivity, with a high potential for the analysis of low-volume, multiplex analytes with easy operation. In this work, we present the development of a miniaturized, high numerical aperture confocal fluorescence scanner for sub-micro-liter fluid diagnosis. It is enhanced with high-rate analyte accumulation using a pyroelectro-hydrodynamic dispensing system for generating tiny, stable sample droplets. The simplified confocal fluorescence scanner (numerical aperture 0.79, working distance 7.3 mm) uses merely off-the-shelf mass-production optical components. Experimental results show that it can achieve a high-sensitive, cost-efficient detection for sub-micro-liter, low-abundant (0.04 µL, 0.67 attomoles) fluid diagnosis, promising for point-of-care diagnosis.

Phase-diversity wavefront sensing enhanced by a Fourier-based neural network.

Phase diversity wavefront sensing (PDWS) has been a successful approach to quantifying wavefront aberrations with only a few intensity measurements and nonlinear optimization. However, the inherent non-convexity of the inverse problem may lead to stagnation at a local minimum far from the true solution. Proper initialization of the nonlinear optimization is important to avoid local minima and improve wavefront retrieval accuracy. In this paper, we propose an effective neural network based on low-frequency coefficients in the Fourier domain to determine a better estimate of the unknown aberrations. By virtue of the proposed network, only a small amount of simulation data suffice for a robust training, two orders of magnitude less than those in existing work. Experimental results show that, when compared with some existing methods, our method achieves the highest accuracy while drastically reducing the training time to 1.4 min. The minimum, maximum, and mean values of the root mean square (RMS) residual errors for 800 aberrations are 0.017λ, 0.056λ, and 0.039λ, respectively, and 95% of the RMS residual errors are less than 0.05λ.

A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging.

Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.

Subacute dermal toxicity study of bensulfuron-methyl in Sprague-Dawley rats.

Background: Bensulfuron-methyl has recently attracted attention given its widespread use as an herbicide in crops, especially its transdermal safety. However, no dermal toxicity study of this pesticide to mammals has been reported. The present study aims to investigate subacute dermal toxicity of bensulfuron-methyl following repeated doses exposure.Methods: Forty-eight Sprague-Dawley rats were randomly divided into four groups: a normal control group and bensulfuron-methyl groups of different concentrations (250, 500, 1000 mg/kg.bw/day). The rats were topically applied with the substance dermally for 6 h per day for 28 days. At the end of the experiment, all rats were monitored for any changes in their hematological, biochemical parameters, and pathological and histological sections.Results: There were no statistically significant differences (P ≥ 0.05) in the hematological parameters and biochemical parameters. The pathological histological results of rats in the control and the highest concentration group showed no significant abnormalities. The NOAEL of subacute dermal toxicity study was found to be 1000 mg/kg.bw/day in both female and male rats.Conclusion: The result indicated that bensulfuron-methyl is probably safe for humans as a pesticide.

Compact Shortwave Infrared Imaging Spectrometer Based on a Catadioptric Prism.

This article demonstrates a compact prism imaging spectrometer method. A catadioptric curved prism is located at the secondary mirror position of the spectrometer and used to balance the aberrations, enlarge the dispersion width, and decrease the volume. A mathematical model of the prism and spectrometer is derived, which provides an optimal initial structure for a non-coaxial spectrometer, simplifying the optical design process and reducing the system volume. Using this method, a compact shortwave infrared imaging spectrometer with a 16° field of view is designed with an F-number/3, and the measured spectrum ranges from 0.95 to 2.5 µm. The performance is analyzed and evaluated. Laboratory testing results prove the excellent optical performance, and under the same specifications, the spectrometer length decreases by 40%.

Miniaturized cost-effective broadband spectrometer employing a deconvolution reconstruction algorithm for resolution enhancement.

We demonstrate a miniaturized broadband spectrometer employing a reconstruction algorithm for resolution enhancement. We use an opto-digital co-design approach, by firstly designing an optical system with certain residual aberrations and then correcting these aberrations with a digital algorithm. The proposed optical design provides an optical resolution less than 1.7 nm in the VIS-channel (400-790 nm) and less than 3.4 nm in the NIR-channel (760-1520 nm). Tolerance analysis results show that the components are within a commercial class, ensuring a cost-efficient design. We build the prototype with a size of 37x30x26 mm3 and demonstrate that by applying a restoration algorithm, the optical resolution can be further improved to less than 1.3 nm (VIS-channel) and less than 2.3 nm (NIR-channel).

Label-Free Protein Analysis by Pyro-Electrohydrodynamic Jet Printing of Gold Nanoparticles.

The pyro-electrohydrodynamic jet (p-jet) printing technology has been used for the fabrication of confined assemblies of gold nanoparticles with a round shape and a diameter ranging between 100 and 200 µm. The surface-enhanced Raman spectroscopy (SERS) performance of the p-jet substrate was evaluated by using Rhodamine 6G (R6G) as a reference. The results demonstrate that this kind of SERS substrate exhibits strong plasmonic effects and a significant reproducibility of the signal with a coefficient of variation below 15%. We tested the signal behavior also in case of the bovine serum albumin (BSA) as a model analyte, to demonstrate the affinity with biomolecules. Strong SERS activity was measured also for BSA across the whole spot area. The spectral patterns collected in different locations of the sensing area were highly reproducible. This observation was substantiated by multivariate analysis of the imaging datasets and opens the route towards a potential application of this kind of SERS substrate in biosensing.

Video Super-Resolution via a Spatio-Temporal Alignment Network.

Deep convolutional neural network based video super-resolution (SR) models have achieved significant progress in recent years. Existing deep video SR methods usually impose optical flow to wrap the neighboring frames for temporal alignment. However, accurate estimation of optical flow is quite difficult, which tends to produce artifacts in the super-resolved results. To address this problem, we propose a novel end-to-end deep convolutional network that dynamically generates the spatially adaptive filters for the alignment, which are constituted by the local spatio-temporal channels of each pixel. Our method avoids generating explicit motion compensation and utilizes spatio-temporal adaptive filters to achieve the operation of alignment, which effectively fuses the multi-frame information and improves the temporal consistency of the video. Capitalizing on the proposed adaptive filter, we develop a reconstruction network and take the aligned frames as input to restore the high-resolution frames. In addition, we employ residual modules embedded with channel attention as the basic unit to extract more informative features for video SR. Both quantitative and qualitative evaluation results on three public video datasets demonstrate that the proposed method performs favorably against state-of-the-art super-resolution methods in terms of clearness and texture details.

STAT3 and SPI1, may lead to the immune system dysregulation and heterotopic ossification in ankylosing spondylitis.

OBJECTIVE: This study was aimed to identify the biomarkers for diagnosis and reveal the immune microenvironment changes in ankylosing spondylitis (AS). METHODS: GSE73754 was downloaded for the co-expression network construction and immune cell analyses. Flow cytometric analysis was performed to validate the results of bioinformatics analysis. Gene set enrichment analysis (GSEA) was performed to investigate the potential biological characteristic between different phenotypes. Pearson correlation analysis between the hub genes and the xCell score of immune cell types was performed. RESULTS: Signal transducer and activator of transcription 3 (STAT3) and Spi-1 proto-oncogene (SPI1) was identified as the hub genes in the datasets GSE73754. And the t-test showed that the expression level of STAT3 and SPI1 in the GSE73754 was significantly higher in AS and human leukocyte antigen (HLA)-B27(+) groups. Flow cytometric analysis showed that natural killer T cells (NKT) cells were upregulated, while Th1 cells were down-regulated in AS, which was consistent with the results obtained from bioinformatics analysis. STAT3 and SPI1 was correlated with the NKT cells and Th1 cells. CONCLUSION: STAT3 and SPI1 may be a key cytokine receptor in disease progression in AS.

Epithelial-mesenchymal transition interaction with CD8+ T cell, dendritic cell and immune checkpoints in the development of melanoma.

BACKGROUND: Melanoma is fatal cancer originating from melanocytes, whose high metastatic potential leads to an extremely poor prognosis. OBJECTIVE: This study aimed to reveal the relationship among EMT, TIICs, and immune checkpoints in melanoma. METHODS: Gene expression data and clinical data of melanoma were downloaded from TCGA, UCSC Xena and GEO databases. EMT-related DEGs were detected for risk score calculation. "ESTIMATE" and "xCell" were used for estimating TIICs and obtaining 64 immune cell subtypes, respectively. Moreover, we evaluated the relationship between the risk score and immune cell subtypes and immune checkpoints. RESULTS: Seven EMT-related genes were selected to establish a risk scoring system because of their integrated prognostic relevance. The results of GSEA revealed that most of the gene sets focused on immune-related pathways in the low-risk score group. The risk score was significantly correlated with the xCell score of some TIICs, which significantly affected the prognosis of melanoma. Patients with a low-risk score may be associated with a better response to ICI therapy. CONCLUSION: The individualized risk score could effectively conduct risk stratification, overall survival prediction, ICI therapy prediction, and TME judgment for patients with melanoma, which would be conducive to patients' precise treatment.

Ferroptosis-related gene SOCS1, a marker for tuberculosis diagnosis and treatment, involves in macrophage polarization and facilitates bone destruction in tuberculosis.

This study was aimed to reveal the role of ferroptosis in tuberculosis infection. To elucidate the ferroptosis-related DEGs, GEO datasets associated with tuberculosis infection were downloaded. The two external validation GEO datasets were exploited for subsequent verification of the ferroptosis-related DEGs. We further evaluated the correlation among the ferroptosis-related DEGs, therapeutic effects, and drug resistance. Finally, we tried to reveal the engagement of the ferroptosis-related DEGs in bone destruction during TB infection. The present study identified SOCS1 as the only ferroptosis-related DEGs. Compared to the non-TB patients, up-regulation of SOCS1 was evident in the TB patients. After receiving standard anti-TB treatment, significant down-regulation of SOCS1 confirmed its acceptance as the marker for therapeutic efficacy. The involvement of SOCS1 has also been suggested in the regulation of the micro immune environment in TB. Furthermore, SOCS1 might play an important role in causing bone destruction during TB infection. FRGs-SOCS1 may be the key gene involved in the pathogenesis and progression of TB infection.

Hierarchical Density-Aware Dehazing Network.

The commonly used atmospheric model in image dehazing cannot hold in real cases. Although deep end-to-end networks were presented to solve this problem by disregarding the physical model, the transmission map in the atmospheric model contains significant haze density information, which cannot simply be ignored. In this article, we propose a novel hierarchical density-aware dehazing network, which consists of a the densely connected pyramid encoder, a density generator, and a Laplacian pyramid decoder. The proposed network incorporates density estimation but alleviates the constraint of the atmospheric model. The predicted haze density then guides the Laplacian pyramid decoder to generate a haze-free image in a coarse-to-fine fashion. In addition, we introduce a multiscale discriminator to preserve global and local consistency for dehazing. We conduct extensive experiments on natural and synthetic hazy images, which prove that the proposed model performs favorably against the state-of-the-art dehazing approaches.

STAT1 and CXCL10 involve in M1 macrophage polarization that may affect osteolysis and bone remodeling in extrapulmonary tuberculosis.

OBJECTIVE: This study was aimed to reveal the molecular mechanism of bone destruction due to macrophage polarization leading to during extrapulmonary tuberculosis (EPTB) infection. METHODS: The dataset GSE83456 was downloaded from the GEO database, and the xCell tool was used to obtain the 64 types of immune cells. The flow cytometry was performed to identified the differences between M1 and M2 macrophages between EPTB and the healthy controls (HCs). The enrichment analyses were performed on the differentially expressed genes (DEGs) and their functionally related modules. The hub genes were screened out, and their relationships with EPTB and the immune cell subtypes were further analyzed. RESULTS: The flow cytometric analysis validated this hypothesis of M1-macrophage polarization correlated with the pathogenesis of EPTB. Of the obtained 103 DEGs, 97 genes were upregulated, and 6 genes were downregulated. The GO and KEGG pathway analyses showed that the DEGs were particularly involved in the immune-related processes. The hub genes (STAT1 and CXCL10) might be involved in M1-macrophage polarization and correlated with the pathogenesis of EPTB. STAT1 and CXCL10 could also behave as biomarkers for EPTB. CONCLUSION: STAT1 and CXCL10 were involved in the M1-macrophage polarization and correlated with the pathogenesis of EPTB. Besides, both of them could also behave as biomarkers for EPTB diagnosis and provide the required clues for targeted therapy in the future.

Platelet-to-Lymphocyte Ratio as an Independent Factor Was Associated With the Severity of Ankylosing Spondylitis.

The study was aimed to determine the association of the platelet-lymphocyte ratio (PLR) with the disease activity of ankylosing spondylitis (AS). A total of 275 patients, including 180 AS patients and 95 non-AS patients, participated in the study. We assessed a full blood count for each participant. Platelet to monocyte ratio (PMR), monocytes to lymphocyte ratio (MLR), monocyte to neutrophil ratio (MNR), platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), and platelet to neutrophil ratio (PNR) were calculated. LASSO and logistic regression analyses were performed to establish the nomogram. Receiver operating characteristic (ROC) analysis was performed to evaluate the clinical value of the nomogram. We constructed a novel nomogram, which incorporated easily accessible clinical characteristics like sex, PLR, WBC, EOS, and ESR for AS diagnosis. The AUC value of this nomogram was 0.806; also, the calibration curves indicated a satisfactory agreement between nomogram prediction and actual probabilities. Furthermore, PLR was positively correlated with the severity of AS. PLR was identified as an independent factor for the diagnosis of AS and was associated with the severity of AS.