Expression and immunogenicity of non-structural protein 8 of porcine epidemic diarrhea virus.

The non-structural protein (nsp) 8 of the porcine epidemic diarrhea virus (PEDV) is highly stable across different PEDV strains and plays an important role in PEDV virulence. In current study, nsp8 prokaryotic expression vectors were constructed based on parental vectors pMAL-c2x-maltose binding protein (MBP) and pET-28a (+). Subsequently, the optimization of expression conditions in Escherichia coli, including induced temperature, time and isopropyl ß-D-thiogalactopyranoside concentration were performed to obtain a stable expression of MBP-nsp8 and nsp8. The nsp8 fused with MBP increased the water solubility of the expressed products. Target proteins were further purified from E. coli culture and their immunogenicities were evaluated in vivo by mice. The antibody titers of serum from nsp8 immunized mice were up to 1:7,750,000 when measured by indirect enzyme-linked immunosorbent assay; meanwhile, the mice immunized with MBP-nsp8 gave an antibody titer reaching 1:1,000,000. In all, the expression and purification system of PEDV nsp8 and MBP-nsp8 were successfully established in this work and a strong immune response was elicited in mice by both purified nsp8 and MBP-nsp8, providing a basis for the study of the structure and function of PEDV nsp8.

Transcriptome and proteomic analysis of mpox virus F3L-expressing cells.

Background: Monkeypox or mpox virus (mpox) is a double-stranded DNA virus that poses a significant threat to global public health security. The F3 protein, encoded by mpox, is an apoenzyme believed to possess a double-stranded RNA-binding domain (dsRBD). However, limited research has been conducted on its function. In this study, we present data on the transcriptomics and proteomics of F3L-transfected HEK293T cells, aiming to enhance our comprehension of F3L. Methods: The gene expression profiles of pCAGGS-HA-F3L transfected HEK293T cells were analyzed using RNA-seq. Proteomics was used to identify and study proteins that interact with F3L. Real-time PCR was used to detect mRNA levels of several differentially expressed genes (DEGs) in HEK293T cells (or Vero cells) after the expression of F3 protein. Results: A total of 14,822 genes were obtained in cells by RNA-Seq and 1,672 DEGs were identified, including 1,156 up-regulated genes and 516 down-regulated genes. A total of 27 cellular proteins interacting with F3 proteins were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and 19 cellular proteins with large differences in abundance ratios were considered to be candidate cellular proteins. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the DEGs were significantly enriched in immune-related pathways, including type I interferon signaling pathway, response to virus, RIG-I-like receptor signaling pathway, NOD-like receptor signaling pathway, etc. Moreover, some selected DEGs were further confirmed by real-time PCR and the results were consistent with the transcriptome data. Proteomics data show that cellular proteins interacting with F3 proteins are mainly related to RNA splicing and protein translation. Conclusions: Our analysis of transcriptomic and proteomic data showed that (1) F3L up-regulates the transcript levels of key genes in the innate immune signaling pathway, such as RIGI, MDA5, IRF5, IRF7, IRF9, ISG15, IFNA14, and elicits a broad spectrum of antiviral immune responses in the host. F3L also increases the expression of the FOS and JNK genes while decreasing the expression of TNFR2, these factors may ultimately induce apoptosis. (2) F3 protein interacts with host proteins involved in RNA splicing and protein translation, such as SNRNP70, POLR2H, HNRNPA1, DDX17, etc. The findings of this study shed light on the function of the F3 protein.

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.

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.

Reverse Genetics Systems for Emerging and Re-Emerging Swine Coronaviruses and Applications.

Emerging and re-emerging swine coronaviruses (CoVs), including porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV), and swine acute diarrhea syndrome-CoV (SADS-CoV), cause severe diarrhea in neonatal piglets, and CoV infection is associated with significant economic losses for the swine industry worldwide. Reverse genetics systems realize the manipulation of RNA virus genome and facilitate the development of new vaccines. Thus far, five reverse genetics approaches have been successfully applied to engineer the swine CoV genome: targeted RNA recombination, in vitro ligation, bacterial artificial chromosome-based ligation, vaccinia virus -based recombination, and yeast-based method. This review summarizes the advantages and limitations of these approaches; it also discusses the latest research progress in terms of their use for virus-related pathogenesis elucidation, vaccine candidate development, antiviral drug screening, and virus replication mechanism determination.

Measurement and Characterization of Rotational Errors of Aerostatic Bearings in Subnanometer Accuracy.

Measuring the running accuracy of aerostatic bearings is challenging because of the high-precision requirements in rotational motion. This paper presents an ultra-high precision measurement method for aerostatic bearings using atomic force microscopy (AFM) as the displacement sensor. The Donaldson reversal method was used to separate the artifact form errors from the measurement results. A measurement system was developed with the integration of an AFM module. The effects of sensor nonlinearity, environmental noise, and structural vibration on the measurement results were effectively suppressed in the system. The experimental results show that the measurement achieves up to subnanometer accuracy.

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.

Expression and immunogenicity of recombinant porcine epidemic diarrhea virus Nsp9.

Porcine epidemic diarrhea virus (PEDV) causes acute diarrhea, vomiting, dehydration, and high mortality in newborn piglets, which leads to significant economic losses. Coronavirus nonstructural protein 9 (Nsp9) is an essential RNA binding protein for coronavirus replication, which renders it a promising candidate for developing antiviral drugs and diagnosis targeting PEDV. In this study, PEDV Nsp9 protein fused with MBP protein and His-tag were expressed and purified in Escherichia coli. Furthermore, immunization of MBP-Nsp9 enhances both humoral and cellular immunity responses as compared with that of His-Nsp9 protein. Finally, the swine immunization showed that Nsp9 protein could stimulate the swine immunity system to carry out humoral immunity, and the generated antibody could inhibit the proliferation of PEDV in Vero cells. Altogether, our data provide direct evidence for the immunogenicity of PEDV Nsp9, which sheds light on the future developments of anti-PEDV drugs and vaccines for PED prevention.

[Protein expression, purification and mouse antiserum preparation of monkeypox virus A23R].

Objective To express the monkeypox virus (MPXV) A23R protein in Escherichia coli and purify by Ni-NTA affinity column, and to prepare mouse antiserum against MPXV A23R. Methods The recombinant plasmid pET-28a-MPXV-A23R was constructed and transformed into Escherichia coli BL21 to induce the expression of A23R protein. After optimizing the conditions of expression, A23R protein was highly expressed. Recombinant A23R protein was purified by Ni-NTA affinity column and identified by Western blot analysis. The purified protein was used to immunize mice for preparing the A23R polyclonal antibody, and the antibody titer was detected by ELISA. Results The expression of A23R recombinant protein reached the peak under the induced conditions of 0.6 mmol/L isopropyl-ß-D-thiogalactoside (IPTG), 37 DegreesCelsius and 20 hours. The purity of the protein was about 96.07% and was identified by Western blot analysis. The mice were immunized with recombinant protein, and the titer of antibody reached 1:102 400 at the 6th week after immunization. Conclusion MPXV A23R is expressed highly and purified with a high purity and its antiserum from mouse is obtained with a high titre.

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.

A Review of In Situ Defect Detection and Monitoring Technologies in Selective Laser Melting.

The additive manufacturing (AM) technique has received considerable industrial attention, as it is capable of producing complex functional parts in the aerospace and defense industry. Selective laser melting (SLM) technology is a relatively mature AM process that can manufacture complex structures both directly and efficiently. However, the quality of SLM parts is affected by many factors, resulting in a lack of repeatability and stability of this method. Therefore, several common and advanced in situ monitoring as well as defect detection methods are utilized to improve the quality and stability of SLM processes. This article aims at documenting the various defects that occurred in SLM processes and their influences on the final parts. Various types of in situ monitoring and defect detection methods and their applications are reviewed, and their integrations with the SLM processes are also discussed.

Fast and Accurate Defects Detection for Additive Manufactured Parts by Multispectrum and Machine Learning.

Traditional defect detection methods for metal additive manufacturing (AM) have the problems of low detection efficiency and accuracy, while the existing machine learning detection algorithms are of poor adaptability and complex structure. To address the above problems, this article proposed an improved You Only Look Once version 3 (YOLOv3) algorithm to detect the surface defects of metal AM based on multispectrum. The weighted k-means algorithm is used to cluster the target samples to improve the matching degree between the prior frame and the feature layer. The network structure of YOLOv3 is modified by using the lightweight MobileNetv3 to replace the Darknet-53 in the original YOLOv3 algorithm. Dilated convolution and Inceptionv3 are added to improve the detection capability for surface defects. A multispectrum measuring system was also developed to obtain the AM surface data with defects for experimental verification. The results show that the detection accuracy in the test set by YOLOv3-MobileNetv3 network is 11% higher than that by the original YOLOv3 network on average. The detection accuracy for cracking defects of the three types of defects is significantly increased by 23.8%, and the detection speed is also increased by 18.2%. The experimental results show that the improved YOLOv3 algorithm realizes the end-to-end surface defect detection for metal AM with high accuracy and fast speed, which can be further applied for online defect detection.

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.

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.

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.

Immunogenicity analysis of the E. coli expressed structural protein VP1 of persistent infection foot-and-mouth disease virus.

The persistent infection of FMDV in cloven hoofed animals has made the epidemic prevention and control more difficult. VP1 is the main immunogenic protein and first candidate of vaccine development for FMDV prevention. However, the mutation of VP1 in host cell with persistent infection FMDV (PI-FMDV) caused the change of its immunogenicity. Hence, it is imperative to establish the expression system for VP1 of PI-FMDV (PI-VP1) and re-evaluate its immunogenicity. In this study, the PI-VP1 with His-tag was cloned into pET-28a vector. PI-VP1 protein was expressed and purified in E. coli, and further the antiserum of immunized mice was analyzed. Results showed that purified PI-VP1 protein produced a good humoral and cellular immune response after immunizing mice. Furthermore, our study showed that the antiserum could not only neutralize PI-FMDV, but also prevent the adsorption of WT-FMDV. In summarize, our work provides valuable implications for the FMDV vaccines and therapeutics development.

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.