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IntroductionClassical Muscovy duck reovirus (C-MDRV) and goose-origin Muscovy duck reovirus (Go-MDRV) infections cause “Liver white-spots disease” in Muscovy duckling and gosling. It is difficult to differentiate the infections caused by C-MDRV and Go-MDRV using conventional serological methods.MethodsSpecific primers were designed and synthesized according to σNS and λA nucleotide sequences of C-MDRV and Go-MDRV, respectively. The PCR amplified products were cloned into the pMD-18-T vector. The recombinant plasmid DNA was used to establish an SYBR Green І based duplex real-time PCR assay for the simultaneous detection and differentiation of C-MDRV and Go-MDRV using high-resolution melting (HRM) analysis. The specificity, sensitivity, and repeatability of the methodology were examined based on the optimization of the reaction system and amplification conditions.ResultsC-MDRV and Go-MDRV were identified by their distinctive melting temperatures with 84.50 ± 0.25°C for C-MDRV and 87.50 ± 0.20°C for Go-MDRV, respectively. The amplifications were specific, and other non-targeted waterfowl viruses employed in this study did not show normalized melting peaks. The intra- and inter-assay coefficients of variations were between 0.05 and 1.83%, demonstrating good repeatability. The detection limits of this assay were 51.4 copies·μl−1 for C-MDRV and 61.8 copies·μl−1 for Go-MDRV, respectively. A total of 45 clinical samples were tested by RT-qPCR, with positive rates of 15.56% for C-MDRV and 22.22% for Go-MDRV, without co-infections.DiscussionThese results suggest that this duplex RT-qPCR method is highly sensitive, specific, and reproducible. The HRM assay established in this study provides a powerful tool for the differential detection and epidemiological investigation of C-MDRV and Go-MDRV.

This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform (EWT) for a through- type cophase traction direct power supply system, where both sides of a traction network line exhibit a distinctive boundary structure. This approach capitalizes on the boundary’s capacity to attenuate the high-frequency component of fault signals, resulting in a variation in the high-frequency transient energy ratio when faults occur inside or outside the line. During internal line faults, the high-frequency transient energy at the checkpoints located at both ends surpasses that of its neighboring lines. Conversely, for faults external to the line, the energy is lower compared to adjacent lines. EWT is employed to decompose the collected fault current signals, allowing access to the high-frequency transient energy. The longitudinal protection for the traction network line is established based on disparities between both ends of the traction network line and the high-frequency transient energy on either side of the boundary. Moreover, simulation verification through experimental results demonstrates the effectiveness of the proposed protection scheme across various initial fault angles, distances to faults, and fault transition resistances.

Incipient faults (IFs) are abnormal states before the permanent failure of power equipment. IFs are typically transient and generally do not trigger the operation of relay protection devices. This leads the difficulty in capturing IF data from waveform monitoring or recording devices. However, traditional detection methods cannot achieve satisfactory performance when faced with limited data. Besides, some signal analysis methods based on waveform conversion to images cannot obtain understandable image data and cannot analyze both current and voltage signals simultaneously. To resolve these problems, a few-shot meta-learning framework for incipient fault detection (FSMLF-IFD) is proposed in this paper. For better data processing, a waveform image conversion strategy is proposed to convert waveforms into understandable images from the time domain perspective. Then, an adaptive image fusion strategy is developed to concurrently analyze voltage and current images. Next, at the meta-training stage, an adaptability-enhancing weighting initialization strategy is constructed to address the data differences between the meta-training stage and IF detection stage. Finally, an IF detection model based on convolutional neural networks (CNNs) is obtained through the fine-tuning process. In the numerical results, the IF detection and classification accuracy of FSMLF-IFD reached 0.9720 and 0.9840 based on simulation and field IF data, which validates the effectiveness of the proposed method.

Abstract Ammonia is a storage molecule for hydrogen, which can be released by catalytic decomposition. Inexpensive iron catalysts suffer from a low activity due to a too strong iron-nitrogen binding energy compared to more active metals such as ruthenium. Here, we show that this limitation can be overcome by combining iron with cobalt resulting in a Fe-Co bimetallic catalyst. Theoretical calculations confirm a lower metal-nitrogen binding energy for the bimetallic catalyst resulting in higher activity. Operando spectroscopy reveals that the role of cobalt in the bimetallic catalyst is to suppress the bulk-nitridation of iron and to stabilize this active state. Such catalysts are obtained from Mg(Fe,Co)2O4 spinel pre-catalysts with variable Fe:Co ratios by facile co-precipitation, calcination and reduction. The resulting Fe-Co/MgO catalysts, characterized by an extraordinary high metal loading reaching 74 wt.%, combine the advantages of a ruthenium-like electronic structure with a bulk catalyst-like microstructure typical for base metal catalysts.

Synthetic aperture radar (SAR) inter-pulse parameter agility technology involves dynamically adjusting parameters such as the pulse width, chirp rate, carrier frequency, and pulse repetition interval within a certain range; this effectively increases the complexity and uncertainty of radar waveforms, thereby countering active deceptive interference signals from multiple dimensions. With the development of active deceptive interference technology, single-parameter agility can no longer meet the requirements, making multi-parameter joint agility one of the main research directions. However, inter-pulse carrier frequency agility can cause azimuth Doppler chirp rate variation, making azimuth compression difficult and compensation computationally intensive, thus hindering imaging. Additionally, pulse repetition interval (PRI) agility leads to non-uniform azimuth sampling, severely deteriorating image quality. To address these issues, this paper proposes a multi-parameter agile SAR imaging scheme based on traditional frequency domain imaging algorithms. This scheme can handle joint agility of pulse width, chirp rate polarity, carrier frequency, and PRI, with relatively low computational complexity, making it feasible for engineering implementation. By inverting SAR images, the echoes with multi-parameter joint agility are obtained, and active deceptive interference signals are added for processing. The interference-suppressed imaging results verify the effectiveness of the proposed method. Furthermore, simulation results of point targets with multiple parameters under the proposed processing algorithm show that the peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are improved by 12 dB and 10 dB, respectively, compared to the traditional fixed waveform scheme.

IntroductionMacrofungi, such as edible mushrooms, have been used as a valuable medical resource for millennia as a result of their antibacterial and immuno-modulatory components. Mushrooms contain dietary fibers known as β-glucans, a class of polysaccharides previously linked to the induction of Trained Immunity. However, little is known about the ability of mushroom-derived β-glucans to induce Trained Immunity.Methods & resultsUsing various powdered forms of the white button mushroom (Agaricus bisporus), we found that mouse macrophages pre-treated with whole mushroom powder (WMP) displayed enhanced responses to restimulation with TLR ligands, being particularly sensitive to Toll-like receptor (TLR)-2 stimulation using synthetic lipopeptides. This trained response was modest compared to training observed with yeast-derived β-glucans and correlated with the amount of available β-glucans in the WMP. Enriching for β-glucans content using either a simulated in-vitro digestion or chemical fractionation retained and boosted the trained response with WMP, respectively. Importantly, both WMP and digested-WMP preparations retained β-glucans as identified by nuclear magnetic resonance analysis and both displayed the capacity to train human monocytes and enhanced responses to restimulation. To determine if dietary incorporation of mushroom products can lead to Trained Immunity in myeloid cells in vivo, mice were given a regimen of WMP by oral gavage prior to sacrifice. Flow cytometric analysis of bone-marrow progenitors indicated alterations in hematopoietic stem and progenitor cells population dynamics, with shift toward myeloid-committed multi-potent progenitor cells. Mature bone marrow-derived macrophages derived from these mice displayed enhanced responses to restimulation, again particularly sensitive to TLR2.DiscussionTaken together, these data demonstrate that β-glucans from common macrofungi can train innate immune cells and could point to novel ways of delivering bio-available β-glucans for education of the innate immune system.

For the challenges of high-precision mapping in complex terrain, a novel airborne Interferometric Synthetic Aperture Radar (InSAR) system is designed. This system, named ASMIS (Airborne Short-Baseline Millimeter-Wave InSAR System), adopts the coplanar antenna and a pod-type structure. This design makes the system lightweight and highly integrated. It can be compatible with small general aviation flight platforms. The baseline is millimeters in size, which greatly simplifies the unwrapping process. The coplanar antennas have two advantages: they maximize the baseline utilization and minimize the Doppler decorrelation and the motion error inconsistency. Acquisition campaigns of the system have been carried out in Boao, Bayannur, and Chengde, China. In the Chengde experimental area, we designed an antiparallel flight experiment to account for the topographic relief. High-precision Digital Orthophoto Maps (DOMs) and Digital Surface Models (DSMs) at a scale of 1:5000 were obtained. The coordinate Root Mean Square Error (RMSE) of the checkpoints within the obtained DSM is less than 0.82 m in altitude and 3 m horizontally. The RMSE of the Sparse Ground Control Points (GCPs) within the obtained DSM is less than 0.3 m in altitude. Experimental results from different areas, including plains, mountains, and coastlines, demonstrate the system’s performance.

The iron ore industry faces increasing electricity demand due to industrialization, making effective management of electricity demand crucial. This study proposes a temperature compensation model using Support Vector Regression (SVR), aiming to enhance the accuracy of sensors in monitoring electricity demand. An experiment is conducted to assess the impact of temperature on sensor measurements, and a modified Whale Optimization Algorithm is employed to correct the sensor outputs. The proposed model is compared with both PSOSVR and unimproved WOA-SVR. Results show that the proposed model significantly improves accuracy, achieving a determination coefficient of 0.7882 and a relative standard deviation of the error square sum of 4.6412%. The results of this study not only enhance power demand management in iron mining but also hold potential applications across various industries. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is one of the most common malignancies among male patients with cancer pathology worldwide. Owing to the inevitable development of irremediable castration-resistant prostate cancer (CRPC) following intervention with androgen deprivation therapy, new therapeutic strategies are in great demand. Ganoderma lucidum, a medicinal fungus, has recently been reported to be a potential antitumor agent. However, whether the water extract of sporoderm-broken spores of G. lucidum (BSGWE), a special approach to obtain whole wall-broken spore extracts of G. lucidum, can inhibit castration-resistant prostate cancer (CRPC) has not been well characterized. In this study, we found that BSGWE inhibited cell proliferation, caused cell cycle arrest in G2/M phase, and promoted ferroptosis, which resulted in high levels of reactive oxygen species (ROS) in vitro and suppressed the growth of CRPC tumors in xenograft models. In addition, we reported that BSGWE inhibited STAT3 expression, which partly accounted for the inhibition of the transcription of PD-L1 and led to ferroptosis, manifested by the downregulation of SLC7A11 and GPX4 expression. Furthermore, BSGWE exhibited a synergistic effect with 5-fluorouracil (5-Fu) in chemotherapy of CRPC in vitro and in vivo. This study provides biological evidence that BSGWE is a novel STAT3 inhibitor that exerts anti-proliferation, pro-cell death, and pro-immunity effect and indicates a novel utilization of BSGWE in neoadjuvant chemotherapy of CRPC.