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Porcine epidemic diarrhea virus (PEDV) has emerged in American countries, and it has reemerged in Asia and Europe, causing significant economic losses to the pig industry worldwide. In the present study, the 17GXCZ-1ORF3d strain, which has a naturally large deletion at the 172–554 bp position of the ORF3 gene, together with the 17GXCZ-1ORF3c strain, was serially propagated in Vero cells for up to 120 passages. The adaptability of the two strains gradually increased through serial passages in vitro. Genetic variation analysis of the variants of the two strains from different generations revealed that the naturally truncated ORF3 gene in the 17GXCZ-1ORF3d variants was stably inherited. Furthermore, the survival, viral shedding and histopathological lesions following inoculation of piglets demonstrated that the virulence of 17GXCZ-1ORF3d-P120 was significantly attenuated. These results indicate that the naturally truncated ORF3 gene may accelerate the attenuation of virulence and is involved in PEDV virulence together with mutations in other structural genes. Importantly, immunization of sows with G2b 17GXCZ-1ORF3d-P120 increased PEDV-specific IgG and IgA antibody levels in piglets and conferred partial passive protection against heterologous G2a PEDV strains. Our findings suggest that an attenuated strain with a truncated ORF3 gene may be a promising candidate for protection against PEDV.

Pyruvic acid has numerous applications in the food, chemical, and pharmaceutical industries. The high costs of chemical synthesis have prevented the extensive use of pyruvate for many applications. Metabolic engineering and traditional strategies for mutation and selection have been applied to microorganisms to enhance their ability to produce pyruvate. In the past decades, different microbial strains were generated to enhance their pyruvate production capability. In addition to the development of genetic engineering and metabolic engineering in recent years, the metabolic transformation of wild-type yeast, E. coli, and so on to produce high-yielding pyruvate strains has become a hot spot. The strategy and the understanding of the central metabolism directly related to pyruvate production could provide valuable information for improvements in fermentation products. One of the goals of this review was to collect information regarding metabolically engineered strains and the microbial fermentation processes used to produce pyruvate in high yield and productivity.

Maintaining the homeostasis balance of trace elements is crucial for the health of organisms. Human health is threatened by diseases caused by a lack of trace elements. Saccharomyces cerevisiae has a wide and close relationship with human daily life and industrial applications. It can not only be used as fermentation products and single-cell proteins, but also as a trace elements supplement that is widely used in food, feed, and medicine. Trace-element-enriched yeast, viz., chromium-, iron-, zinc-, and selenium-enriched yeast, as an impactful microelements supplement, is more efficient, more environmentally friendly, and safer than its inorganic and organic counterparts. Over the last few decades, genetic engineering has been developing large-scaled genetic re-design and reconstruction in yeast. It is hoped that engineered yeast will include a higher concentration of trace elements. In this review, we compare the common supplement forms of several key trace elements. The mechanisms of detoxification and transport of trace elements in yeast are also reviewed thoroughly. Moreover, genes involved in the transport and detoxification of trace elements are summarized. A feasible way of metabolic engineering transformation of S. cerevisiae to produce trace-element-enriched yeast is examined. In addition, the economy, safety, and environmental protection of the engineered yeast are explored, and the future research direction of yeast enriched in trace elements is discussed.

Since 2010, porcine epidemic diarrhea virus (PEDV) has swept across China and spread throughout the country, causing huge economic losses. In this study, 673 diarrhea samples from 143 pig farms in Guangxi during 2017–2022 were collected and detected for PEDV. Ninety-eight strains were selected for S1 gene analyses and these strains were classified into four subgroups (G1b, G2a, G2b and G2c), accounting for 1.02 (1/98), 75.51 (74/98), 16.33 (16/98) and 7.14% (7/98) of the total, respectively. Importantly, an increased number of strains in the G2c subgroup was found from 2019 onwards. Bayesian analysis revealed that Guigang may have been the epicenter of PEDVs in Guangxi. In addition, Guigang was identified as the primary hub from which PEDVs spread via two routes, namely Guigang–Wuzhou and Guigang–Laibin. Moreover, several coinfections of novel PEDV variants bearing large deletions in the partial S1 protein and PEDVs possessing an intact partial S1 protein were found in pigs. Further recombination analyses indicated that two of the strains, 18-GXNN-6 and 19-GXBH-2, originated from intra-genogroup recombination. Together, our data revealed a new profile of PEDV in Guangxi, China, which enhances our understanding of the distribution, genetic characteristics and evolutionary profile of the circulating PEDV strains in China.

Steel surface defect recognition is an important part of industrial product surface defect detection, which has attracted more and more attention in recent years. In the development of steel surface defect recognition technology, there has been a development process from manual detection to automatic detection based on the traditional machine learning algorithm, and subsequently to automatic detection based on the deep learning algorithm. In this paper, we discuss the key hardware of steel surface defect detection systems and offer suggestions for related options; second, we present a literature review of the algorithms related to steel surface defect recognition, which includes traditional machine learning algorithms based on texture features and shape features as well as supervised, unsupervised, and weakly supervised deep learning algorithms (Incomplete supervision, inexact supervision, imprecise supervision). In addition, some common datasets and algorithm performance evaluation metrics in the field of steel surface defect recognition are summarized. Finally, we discuss the challenges of the current steel surface defect recognition algorithms and the corresponding solutions, and our future work focus is explained.

RGB-T salient object detection (SOD) has received considerable attention in the field of computer vision. Although existing methods have achieved notable detection performance in certain scenarios, challenges remain. Many methods fail to fully utilize high-frequency and low-frequency features during information interaction among different scale features, limiting detection performance. To address this issue, we propose a method for RGB-T salient object detection that enhances performance through wavelet transform and channel-wise attention fusion. Through feature differentiation, we effectively extract spatial characteristics of the target, enhancing the detection capability for global context and fine-grained details. First, input features are passed through the channel-wise criss-cross module (CCM) for cross-modal information fusion, adaptively adjusting the importance of features to generate rich fusion information. Subsequently, the multi-scale fusion information is input into the feature selection wavelet transforme module (FSW), which selects beneficial low-frequency and high-frequency features to improve feature aggregation performance and achieves higher segmentation accuracy through long-distance connections. Extensive experiments demonstrate that our method outperforms 22 state-of-the-art methods.

A bstract The Higgs boson can mix with a singlet scalar that dynamically generates the Majorana mass of the right-handed neutrino N R . We show that even a tiny mixing between the Higgs boson and a ‘decoupled’ singlet scalar allows for Higgs-mediated pair production of N R without significant mixings between the active neutrinos and N R , and thus testable at colliders via a characteristic signal of two same-sign same-flavor lepton pairs, plus missing energy. We demonstrate that this search channel is mostly background- free in pp -collision and can be a highly sensitive probe of the Higgs-singlet mixing at the current and future pp colliders. Such channel provides a clean signal to discover the singlet scalar and explore the origin of neutrino masses.

As the core components of fifth‐generation (5G) communication technology, optical modules should be consistently miniaturized in size while improving their level of integration. This inevitably leads to a dramatic spike in power consumption and a consequent increase in heat flow density when operating in a confined space. To ensure a successful start‐up and operation of 5G optical modules, active cooling and precise temperature control via the Peltier effect in confined space is essential yet challenging. In this work, p‐type Bi0.5Sb1.5Te3 and n‐type Bi2Te2.7Se0.3 bulk thermoelectric (TE) materials are used, and a micro thermoelectric thermostat (micro‐TET) (device size, 2 × 9.3 × 1.1 mm3; leg size, 0.4 × 0.4 × 0.5 mm3; number of legs, 44) is successfully integrated into a 5G optical module with Quad Small Form Pluggable 28 interface. As a result, the internal temperature of this kind of optical module is always maintained at 45.7°C and the optical power is up to 7.4 dBm. Furthermore, a multifactor design roadmap is created based on a 3D numerical model using the ANSYS finite element method, taking into account the number of legs (N), leg width (W), leg length (L), filling atmosphere, electric contact resistance (Rec), thermal contact resistance (Rtc), ambient temperature (Ta), and the heat generated by the laser source (QL). It facilitates the integrated fabrication of micro‐TET, and shows the way to enhance packaging and performance under different operating conditions. According to the roadmap, the micro‐TET (2 × 9.3 × 1 mm3, W = 0.3 mm, L = 0.4 mm, N = 68 legs) is fabricated and consumes only 0.89 W in cooling mode (QL = 0.7 W, Ta = 80°C) and 0.36 W in heating mode (Ta = 0°C) to maintain the laser temperature of 50°C. This research will hopefully be applied to other microprocessors for precise temperature control and integrated manufacturing. This work draws a multifactor roadmap, including thermoelectric leg width, leg length, number of legs, laser source heat generation, filling atmosphere, electric contact resistance, thermal contact resistance, and ambient temperature, for designing micro thermoelectric thermostat in a closed‐loop integrated fifth‐generation (5G) optical module.

Surface inspection of aero-engine blades is critical for aero-engine production and maintenance. However, composite materials like titanium alloys and superalloys, as well as thermal barrier coatings on blades, exhibit distinct optical reflection properties, while their complex curved surfaces cause severe image reflections leading to overexposure, underexposure, edge blurring and reduced measurement accuracy. To solve this, we propose ELANet, a deep-learning-based multi-exposure image fusion method with DenseNet as the backbone. Its key innovations include two parts: first, an Efficient Channel Attention mechanism to capture reflection feature differences between substrate and coating, prioritizing resource allocation to anti-reflection channels; second, an Ultra-Lightweight Subspace Attention Mechanism with only one-fifth the parameters of traditional spatial attention that adaptively assigns weights to local features based on curved surface reflection laws, enhancing edge and detail extraction while reducing computational cost. The Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism synergistically address exposure and blurring issues. Validated against 12 mainstream methods via 9 quantitative metrics, ELANet achieves state-of-the-art performance: MEF-SSIM reaches 0.9472, which is 1.3% higher than the best comparative method, PSNR reaches 21.48 dB, which is 2.2 percent higher than the second-best method, and the average processing time is 0.48 s. Ablation experiments confirm the necessity of the Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism. This method effectively supports high-precision blade inspection.

Critical bone defects continue to pose a critical challenge in clinical settings. Facilitating rapid and thorough vascularization during bone regeneration is advantageous for the repair of substantial bone defects. Presently, the application of carriers to deliver pharmacological agents or growth factors to bone deficiency areas is an effective strategy for promoting vascularization within bone tissue, with the selection of appropriate carriers being of paramount importance. Because of its unique breathing characteristics and flexible framework structure, metal-organic framework (MOF) materials MIL-53(Fe), make promising nanocarrier for delivery applications. This work aimed to investigate the role of MIL-53(Fe) in the neovascularization of bone regeneration and the possible molecular mechanisms. The results validated that MIL-53(Fe) has excellent dispersion stability and biocompatibility. Angiogenic sprouting requires the activation of endothelial tip cells. By increasing the glycolytic activity of endothelial cells through self-assembling glucose, the nanocarrier MIL-53(Fe) markedly increased endothelial tip cell activation and boosted angiogenesis, ultimately encouraging bone repair. Through the Hippo/Yes-associated protein (YAP) pathway, the MIL-53(Fe)@Glucose complex was demonstrated to increase glycolytic activity and enhance activation of tip cell phenotype. This study demonstrated that the nanocarrier MIL-53(Fe) serves as an effective strategy for promoting rapid and sufficient angiogenesis during bone regeneration. Graphical abstract