High-resolution 3D imaging in light-field microscopy through Stokes matrices and data fusion.

The trade-off between the lateral and vertical resolution has long posed challenges to the efficient and widespread application of Fourier light-field microscopy, a highly scalable 3D imaging tool. Although existing methods for resolution enhancement can improve the measurement result to a certain extent, they come with limitations in terms of accuracy and applicable specimen types. To address these problems, this paper proposed a resolution enhancement scheme utilizing data fusion of polarization Stokes vectors and light-field information for Fourier light-field microscopy system. By introducing the surface normal vector information obtained from polarization measurement and integrating it with the light-field 3D point cloud data, 3D reconstruction results accuracy is highly improved in axial direction. Experimental results with a Fourier light-field 3D imaging microscope demonstrated a substantial enhancement of vertical resolution with a depth resolution to depth of field ratio of 0.19%. This represented approximately 44 times the improvement compared to the theoretical ratio before data fusion, enabling the system to access more detailed information with finer measurement accuracy for test samples. This work not only provides a feasible solution for breaking the limitations imposed by traditional light-field microscope hardware configurations but also offers superior 3D measurement approach in a more cost-effective and practical manner.

Grass carp (Ctenopharyngodon idella) deacetylase SIRT1 targets p53 to suppress apoptosis in a KAT8 dependent or independent manner.

Sirtuin1 (SIRT1) is known as a deacetylase to control various physiological processes. In mammals, SIRT1 inhibits apoptotic process, but the detailed mechanism is not very clear. Here, our study revealed that grass carp (Ctenopharyngodon idella) SIRT1 (CiSIRT1, MN125614.1) inhibits apoptosis through targeting p53 in a KAT8-dependent or a KAT8-independent manner. In CIK cells, CiSIRT1 over-expression results in significant decrease of some apoptotic gene expressions, including Bax/Bcl2, caspase3 and caspase9, whereas CiKAT8 or Cip53 facilitates the induction of apoptosis. Because CiSIRT1 separately interacted with CiKAT8 and Cip53, we speculated that CiSIRT1 blocked apoptosis may be by virtue of KAT8-p53 axis or directly by p53. In a KAT8-dependent manner, CiSIRT1 interacted with CiKAT8, then reduced the acetylation of CiKAT8 and subsequently promoted its degradation. Then, CiKAT8 acetylated p53 and induced p53-mediated apoptosis. MYST domain of CiKAT8 was critical in this pathway. In a KAT8-independent manner, CiSIRT1 also inhibited p53-induced apoptosis by directly deacetylating p53 and promoting the degradation of p53. Generally, these findings uncovered two pathways in which CiSIRT1 decreases the acetylation of p53 via a KAT8-dependent or a KAT8-independent manner.

Mask information-based gamma correction in fringe projection profilometry.

For fringe projection profilometry (FPP), the gamma effect of the camera and projector will cause non-sinusoidal distortion of the fringe patterns, leading to periodic phase errors and ultimately affecting the reconstruction accuracy. This paper presents a gamma correction method based on mask information. Since the gamma effect will introduce higher-order harmonics into the fringe patterns, on top of projecting two sequences of phase-shifting fringe patterns having different frequencies, a mask image is projected to provide enough information to determine the coefficients of higher-order fringe harmonics using the least-squares method. The true phase is then calculated using Gaussian Newton iteration to compensate for the phase error due to the gamma effect. It does not require projecting a large number of images, and only 2 × 3 phase shift patterns and 1 mask pattern minimum are required. Simulation and experimental results demonstrate that the method can effectively correct the errors caused by the gamma effect.

Single-shot 3D measurement of highly reflective objects with deep learning.

Three-dimensional (3D) measurement methods based on fringe projection profilometry (FPP) have been widely applied in industrial manufacturing. Most FPP methods adopt phase-shifting techniques and require multiple fringe images, thus having limited application in dynamic scenes. Moreover, industrial parts often have highly reflective areas leading to overexposure. In this work, a single-shot high dynamic range 3D measurement method combining FPP with deep learning is proposed. The proposed deep learning model includes two convolutional neural networks: exposure selection network (ExSNet) and fringe analysis network (FrANet). The ExSNet utilizes self-attention mechanism for enhancement of highly reflective areas leading to overexposure problem to achieve high dynamic range in single-shot 3D measurement. The FrANet consists of three modules to predict wrapped phase maps and absolute phase maps. A training strategy directly opting for best measurement accuracy is proposed. Experiments on a FPP system showed that the proposed method predicted accurate optimal exposure time under single-shot condition. A pair of moving standard spheres with overexposure was measured for quantitative evaluation. The proposed method reconstructed standard spheres over a large range of exposure level, where prediction errors for diameter were 73 µm (left) and 64 µm (right) and prediction error for center distance was 49 µm. Ablation study and comparison with other high dynamic range methods were also conducted.

Ultra-precision manufacturing of microlens arrays using an optimum machining process chain.

There are still significant challenges in the accurate and uniform manufacturing of microlens arrays (MLAs) with advanced ultra-precision diamond cutting technologies due to increasingly stringent requirements and shape complexity. In this paper, an optimum machining process chain is proposed based on the integration of a micro-abrasive fluid jet polishing (MAFJP) process to improve the machining quality by single point diamond turning (SPDT). The MLAs were first machined and compensated by SPDT until the maximum possible surface quality was obtained. The MAFJP was used to correct the surface form error and reduce the nonuniformity for each lens. The polishing characterization was analyzed based on the computational fluid dynamics (CFD) method to enhance the polishing efficiency. To better polish the freeform surface, two-step tool path generation using a regional adaptive path and a raster and cross path was employed. Moreover, the compensation error map was also investigated by revealing the relationship between the material removal mechanism and the surface curvature and polishing parameters. A series of experiments were conducted to prove the reliability and capability of the proposed method. The results indicate that the two integrated machining processes are capable of improving the surface form accuracy with a decrease in PV value from 1.67 µm to 0.56 µm and also elimination of the nonuniform surface error for the lenses.

Chromatic confocal sensor-based on-machine measurement for microstructured optical surfaces featuring a self-aligned spiral center.

Chromatic confocal sensor-based on-machine measurement is effective for identifying and compensating for form errors of the ultra-precisely machined components. In this study, an on-machine measurement system was developed for an ultra-precision diamond turning machine to generate microstructured optical surfaces, for which the sensor probe adopts a uniform spiral scanning motion. To avoid the tedious spiral center alignment, a self-alignment method was proposed without additional equipment or artefact, which identified the deviation of the optical axis to the spindle axis by matching the measured surface points and the designed surface. The feasibility of the proposed method was demonstrated by numerical simulation with full consideration of noises and system dynamics. Practically, taking a typical microstructured surface as an example, the on-machine measured points were reconstructed after calibrating the alignment deviation, which was then verified by off-machine white light interferometry measurement. Avoiding tedious operations and special artefacts may significantly simplify the on-machine measurement process, thereby greatly improving the efficiency and flexibility for the measurement.

Non-paraxial region adaptive aberration compensation using the phase transfer function.

The optical transfer function is crucial for imaging system design and characterization. However, practical optical systems often deviate from linear spatial invariance due to aberrations and field-of-view considerations, posing challenges for optical transfer function characterization and aberration compensation in non-paraxial region imaging. Partitioning the field-of-view into isoplanatic regions and measuring the optical transfer function for each region is a potential solution, but practical implementation is hindered by the lack of field-of-view information. This Letter introduces a compensation method for the phase modulation function based on spatial frequency domain division, specifically tailored for scenarios where high imaging quality is not essential. The proposed method addresses the challenge by filling the phase transfer function in an annular form corresponding to aberrations in different isoplanatic regions, offers a valuable solution for adaptive aberration compensation in non-paraxial region imaging, and presents a practical illustration of its effectiveness.

Single photon compressive imaging with enhanced quality using an untrained neural network.

Traditional single photon compressive imaging has poor imaging quality. Although the method of deep learning can alleviate the problem, the harsh training sets have become a problem. In this paper, an untrained neural network is used to address this problem. A whole imaging system was established, and simulation studies based on the Monte Carlo method have been undertaken. The results show that the proposed method has improved the image quality and solved the troublesome training sets problem while ensuring imaging speed. At the same time, the discussion of input pictures, imaging type, and anti-noise capability provide a way to prove CNN's tendency to natural images. It is also found that the network changes the sensitivity of the system to the photon numbers. The research work will provide some basis for subsequent study on single compressive photon imaging and untrained neural networks.

Light field measurement of specular surfaces by multi-polarization and hybrid modulated illumination.

Specular highlights present a challenge in light field microscopy imaging fields, leading to loss of target information and incorrect observation results. Existing highlight elimination methods suffer from computational complexity, false information and applicability. To address these issues, an adaptive multi-polarization illumination scheme is proposed to effectively eliminate highlight reflections and ensure uniform illumination without complex optical setup or mechanical rotation. Using a multi-polarized light source with hybrid modulated illumination, the system achieved combined multi-polarized illumination and physical elimination of specular highlights. This was achieved by exploiting the different light contributions at different polarization angles and by using optimal solution algorithms and precise electronic control. Experimental results show that the proposed adaptive illumination system can efficiently compute control parameters and precisely adjust the light source output in real time, resulting in a significant reduction of specular highlight pixels to less than 0.001% of the original image. In addition, the system ensures uniform illumination of the target area under different illumination configurations, further improving the overall image quality. This study presents a multi-polarization-based adaptive de-highlighting system with potential applications in miniaturization, biological imaging and materials analysis.

Development of a multi-sensor system for defects detection in additive manufacturing.

Defects detection technology is essential for monitoring and hence maintaining the product quality of additive manufacturing (AM) processes; however, traditional detection methods based on single sensor have great limitations such as low accuracy and scarce information. In this study, a multi-sensor defect detection system (MSDDS) was proposed and developed for defect detection with the fusion of visible, infrared, and polarization detection information. The assessment criteria for imaging quality of the MSDDS have been optimized and evaluated. Meanwhile, the feasibility of processing and assembly of each sensor module has been demonstrated with tolerance sensitivity and the Monte Carlo analysis. Moreover, multi-sensor image fusion processing, super-resolution reconstruction, and feature extraction of defects are applied. Simulation and experimental studies indicate that the developed MSDDS can obtain high contrast and clear key information, and high-quality detected images of AM defects such as cracking, scratches, and porosity can be effectively extracted. The research provides a helpful and potential solution for defect detection and processing parameter optimization in AM processes such as Selective Laser Melting.

Robust and accurate measurement of optical freeform surfaces with wavefront deformation correction.

The non-null test to detect the modulated wavefront is a widely used method in optical freeform surface measurement. In this study, the wavefront deformation in the non-null test of an optical freeform surface measurement was corrected based on the wavefront propagation model to improve measurement accuracy. A freeform surface wavefront correction (FSWC) measurement system was established to validate the proposed method. Simulation and experimental studies indicated that the proposed method can reduce the influence of freeform surface wavefront deformation in space propagation. Moreover, the freeform surface form accuracy measured by FSWC can reach a root-mean-squared value of 10 nm.

Transcriptome analysis of host response to porcine epidemic diarrhea virus nsp15 in IPEC-J2 cells.

BACKGROUND: Porcine epidemic diarrhea virus (PEDV) is an enveloped positive-sense ssRNA virus which is highly lethal to piglets, causing enormous economic losses to swine industry worldwide. Nsp15 protein is an endoribonuclease of PEDV and plays an indispensable role in the viral proliferation. We reported the transcription files of nsp15 transfected IPEC-J2 cells for the first time to broaden our understanding of PEDV pathogenesis. METHODS: RNA-seq was performed to compare gene expression profiles between pCAGGS-HA-nsp15 transfected IPEC-J2 cells and pCAGGS-HA (empty vector) transfected IPEC-J2 cells. Immune-related genes and pathways were identified and analyzed to deepen our understanding of nsp15 for PEDV pathogenicity. IPEC-J2 cells transfected with pCAGGS-HA-CCL5/CXCL8/CXCL10 were infected with CV777 and the virus load of PEDV was detected by qRT-PCR. RESULTS: A total of 21,654 genes were obtained by RNA-Seq and 415 differential expressed genes (DEGs) were identified, including 136 up-regulated and 279 down-regulated genes. A number of effect genes involved in immune responses and inflammation were differentially expressed. GO and KEGG enrichment analysis showed that 32 GO terms were significantly enriched and the DEGs were mainly enriched in immune-related pathways such as TNF signaling pathway, RIG-I-like receptor signaling pathway and Cytosolic DNA-sensing pathway. qRT-PCR results indicated the overexpression of selected chemokines, CCL5/CXCL8/CXCL10, can inhibit PEDV proliferation in IPEC-J2 cells. CONCLUSIONS: Our transcriptome profile illustrated a number of genes involving in immune responses and inflammation were inhibited by nsp15, such as CCL5, CXCL8, CXCL10, OAS, MXs, STAT1 and IRF9. The results suggested that nsp15 can antagonize IFNs and block chemokine system to provide an adequate intracellular environment for viral proliferation.

Defect extraction method for additive manufactured parts with improved learning-based image super-resolution and the Canny algorithm.

Additive manufacturing (AM) is a highly competitive, low-cost, and high-degree-of-manufacturing technology. However, AM still has limitations because of some defects. Thus, defect detection technology is essential for quality enhancement in the AM process. Super-resolution (SR) technology can be utilized to improve defect image quality and enhance defect extraction performance. This study proposes a defect extraction method for additive manufactured parts with improved learning-based image SR and the Canny algorithm (LSRC), which is based on direct mapping methodology. The LSRC method is compared with the bicubic interpolation algorithm and the neighbor embedding (NE) algorithm in SR reconstruction quality and robustness. The experimental results show that the proposed LSRC method achieves satisfactory performance in terms of the averaged information entropy (E), standard deviation (SD), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM), which are 7.259, 45.301, 27.723, and 0.822, respectively. The accordingly average improvement rates of the E, SD, PSNR, and SSIM, are 0.45%, 7.15%, 5.85%, and 6.35% in comparison with the bicubic interpolation algorithm, while the comparison data are 0.97%, 13.40%, 10.55%, and 15.35% in terms of the NE algorithm. This indicates that the LSRC method is significantly better than the comparison algorithm in reconstruction quality and robustness, which is of great significance for the extraction and analysis of key defect information of additive manufactured parts.

Design of a composite lighting system based on a freeform and a rod lens for machine vision.

Light-emitting diodes (LEDs) have been widely utilized in machine vision lighting systems such as the process monitoring system in the additive manufacturing field, owing to their long life, high illumination efficiency, and controllable dimming. The quality of the lighting system directly affects the efficiency and accuracy of the entire monitoring system. However, existing designs cannot meet the optical efficiency and uniformity requirements at short lighting distances and small inspection areas with mixed multi-spectrum channels. This paper thus proposes a novel, to the best of our knowledge, design method of integrating a freeform surface lens and a square-shaped rod lens. The optical characteristics under different working distances and targeting surface types have been optimized and evaluated. Meanwhile, tolerance analysis has been utilized to demonstrate the feasibility of installation. With the use of the software Tracepro, simulation results showed that the designed composite machine vision lighting system can obtain an optical efficiency of 81.704% and an illuminance uniformity of 95.804% within the inspection area at a distance of 250 mm. Furthermore, verification experiments with a prototype were performed, demonstrating the feasibility of the proposed machine vision lighting system.

Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion.

Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process.

Surfeit 4 Contributes to the Replication of Hepatitis C Virus Using Double-Membrane Vesicles.

A number of positive-strand RNA viruses, such as hepatitis C virus (HCV) and poliovirus, use double-membrane vesicles (DMVs) as replication sites. However, the role of cellular proteins in DMV formation during virus replication is poorly understood. HCV NS4B protein induces the formation of a "membranous web" structure that provides a platform for the assembly of viral replication complexes. Our previous screen of NS4B-associated host membrane proteins by dual-affinity purification, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), and small interfering RNA (siRNA) methods revealed that the Surfeit 4 (Surf4) gene, which encodes an integral membrane protein, is involved in the replication of the JFH1 subgenomic replicon. Here, we investigated in detail the effect of Surf4 on HCV replication. Surf4 affects HCV replication in a genotype-independent manner, whereas HCV replication does not alter Surf4 expression. The influence of Surf4 on HCV replication indicates that while Surf4 regulates replication, it has no effect on entry, translation, assembly, or release. Analysis of the underlying mechanism showed that Surf4 is recruited into HCV RNA replication complexes by NS4B and is involved in the formation of DMVs and the structural integrity of RNA replication complexes. Surf4 also participates in the replication of poliovirus, which uses DMVs as replication sites, but it has no effect on the replication of dengue virus, which uses invaginated/sphere-type vesicles as replication sites. These findings clearly show that Surf4 is a novel cofactor that is involved in the replication of positive-strand RNA viruses using DMVs as RNA replication sites, which provides valuable clues for DMV formation during positive-strand RNA virus replication.IMPORTANCE Hepatitis C virus (HCV) NS4B protein induces the formation of a membranous web (MW) structure that provides a platform for the assembly of viral replication complexes. The main constituents of the MW are double-membrane vesicles (DMVs). Here, we found that the cellular protein Surf4, which maintains endoplasmic reticulum (ER)-Golgi intermediate compartments and the Golgi compartment, is recruited into HCV RNA replication complexes by NS4B and is involved in the formation of DMVs. Moreover, Surf4 participates in the replication of poliovirus, which uses DMVs as replication sites, but has no effect on the replication of dengue virus, which uses invaginated vesicles as replication sites. These results indicate that the cellular protein Surf4 is involved in the replication of positive-strand RNA viruses that use DMVs as RNA replication sites, providing new insights into DMV formation during virus replication and potential targets for the diagnosis and treatment of positive-strand RNA viruses.

Chromatic confocal sensor-based sub-aperture scanning and stitching for the measurement of microstructured optical surfaces.

The noncontact optical probe-based surface scanning is promising for the measurement of complex-shaped optical surfaces. In this study, by combining a chromatic confocal sensor and a planar nano-positioning stage, a sub-aperture scanning and stitching method is developed for the noncontact measurement of the microstructured optical surfaces, with the measured form accuracy being irrespective of the accuracy of the global scanning stage. After the scanning, the Gaussian process-based denoising is employed to remove the measurement noises, and a hybrid registration algorithm is proposed to achieve a 6-DOF alignment of any neighbored sub-apertures. For the registration, the differential evolution-based minimization is implemented to find a coarse transformation which then serves as the initial value for the iterative closest point-based fine registration. The hybrid method is beneficial in finding an optimal registration with a greatly reduced computation burden. Finally, the effectiveness of the developed measurement system, as well as the stitching algorithm, is comprehensively demonstrated through practically measuring a sinusoidal micro-grid surface.

Measuring corner cube reflectors through ray tracing of a reflected wavefront.

The corner cube plays a key role in many advanced optical systems as the critical retrieval component, while the errors such as dihedral deviation will lead to unacceptable results. We present a method to obtain the right-angle plane deviation of the corner cube only by measuring and calculating the normal incident reflection wavefront. The calculation process is an iterative method of ray tracing based on the corner cube reflection process. The three-dimensional shape of the right-angle plane of the corner cube can be obtained accurately by this method. The proposed method is easy to implement and reliable, and it avoids the complicated operation of the traditional measurement method, which can also be applied to the measurement of the errors in the assembly and adjustment process where corner cube reflectors are used.

Japanese Encephalitis Virus Induces Apoptosis and Encephalitis by Activating the PERK Pathway.

Accumulated evidence demonstrates that Japanese encephalitis virus (JEV) infection triggers endoplasmic reticulum (ER) stress and neuron apoptosis. ER stress sensor protein kinase R-like endoplasmic reticulum kinase (PERK) has been reported to induce apoptosis under acute or prolonged ER stress. However, the precise role of PERK in JEV-induced apoptosis and encephalitis remains unknown. Here, we report that JEV infection activates the PERK-ATF4-CHOP apoptosis pathway both in vitro and in vivo PERK activation also promotes the formation of stress granule, which in turn represses JEV-induced apoptosis. However, PERK inhibitor reduces apoptosis, indicating that JEV-activated PERK predominantly induces apoptosis via the PERK-ATF4-CHOP apoptosis pathway. Among JEV proteins that have been reported to induce ER stress, only JEV NS4B can induce PERK activation. PERK has been reported to form an active molecule by dimerization. The coimmunoprecipitation assay shows that NS4B interacts with PERK. Moreover, glycerol gradient centrifugation shows that NS4B induces PERK dimerization. Both the LIG-FHA and the LIG-WD40 domains within NS4B are required to induce PERK dimerization, suggesting that JEV NS4B pulls two PERK molecules together by simultaneously interacting with them via different motifs. PERK deactivation reduces brain cell damage and encephalitis during JEV infection. Furthermore, expression of JEV NS4B is sufficient to induce encephalitis via PERK in mice, indicating that JEV activates PERK primarily via its NS4B to cause encephalitis. Taken together, our findings provide a novel insight into JEV-caused encephalitis.IMPORTANCE Japanese encephalitis virus (JEV) infection triggers endoplasmic reticulum (ER) stress and neuron apoptosis. ER stress sensor protein kinase R-like endoplasmic reticulum kinase (PERK) has been reported to induce apoptosis under acute or prolonged ER stress. However, whether the PERK pathway of ER stress response plays important roles in JEV-induced apoptosis and encephalitis remains unknown. Here, we found that JEV infection activates ER stress sensor PERK in neuronal cells and mouse brains. PERK activation induces apoptosis via the PERK-ATF4-CHOP apoptosis pathway upon JEV infection. Among the JEV proteins prM, E, NS1, NS2A, NS2B, and NS4B, only NS4B activates PERK. Moreover, activated PERK participates in apoptosis and encephalitis induced by JEV and NS4B. These findings provide a novel therapeutic approach for JEV-caused encephalitis.

Fiducial-aided calibration of a displacement laser probing system for in-situ measurement of optical freeform surfaces on an ultra-precision fly-cutting machine.

Surface metrology is an essential operation to determine whether the quality of manufactured surfaces meets the design requirements. In order to improve the surface accuracy and machining efficiency in the manufacturing of optical freeform surfaces, in-situ surface measurement without re-positioning the workpiece is considered as a promising technique in advanced manufacturing. In this study, a displacement laser scanner is integrated into an ultra-precision fly-cutting machine in order to perform as a coordinate measuring machine. However, some inevitable errors such as motion errors of the machine tool, thermal drift, vibrations, and errors of the laser sensor are introduced due to the manufacturing environment. To improve the performance of the measurement system, calibration of the main error sources is investigated with consideration of the characteristics of the built laser scanner system. Hence, the relationship between the moving speed of the laser scanner and the vibration of the tested signals is studied. Following that, the errors of the z-axis scale could be corrected by measuring a four-step heights artefact. Furthermore, volumetric positioning errors are identified by the proposed modified chi-square method and Gaussian processing prediction method. Simulation and measurement experiments are conducted, and the results indicate that the calibrated measuring system can measure ultra-precision freeform surfaces with micrometre form accuracy.