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Background Ganoderma lucidum (G. lucidum) has gained increasing attention as a potential health care product and food source. However, the bitter taste of G. lucidum has limited its development and utilization for the food industry. Methonds The response surface methodology was employed to optimize the inclusion conditions for the debittering of G. lucidum. The effects of 2-hydroxypropyl-β-cyclodextrin concentration (12–14 g/mL), ultrasound temperature (20–40 °C and host—guest ratio (1:1–2:1) on response variables were studied. The physical characteristics of inclusion complexes prepared through spray drying and freeze drying were analyzed. The antioxidant activity of the different treated samples was subsequently investigated. Results Study results showed that, in comparison to the control group, the inclusion solution displayed a significantly enhanced taste profile under optimal processing conditions, exhibiting an 80.74% reduction in bitterness value. Fourier transform infrared spectroscopy (FTIR) and proton nuclear magnetic resonance (NMR) studies indicated the successful formation of inclusion compounds. The moisture content and bulk density of spray-dried powder were found to be significantly superior to those of freeze-dried powder (p

Summary: Background: Highly pathogenic respiratory RNA viruses such as SARS-CoV-2 and its associated syndrome COVID-19 pose a tremendous threat to the global public health. Innate immune responses to SARS-CoV-2 depend mainly upon the NF-κB-mediated inflammation. Identifying unknown host factors driving the NF-κB activation and inflammation is crucial for the development of immune intervention strategies. Methods: Published single-cell RNA sequencing (scRNA-seq) data was used to analyze the differential transcriptome profiles of bronchoalveolar lavage (BAL) cells between healthy individuals (n = 27) and patients with severe COVID-19 (n = 21), as well as the differential transcriptome profiles of peripheral blood mononuclear cells (PBMCs) between healthy individuals (n = 22) and severely ill patients with COVID-19 (n = 45) or influenza (n = 16). Loss-of-function and gain-of-function assays were performed in diverse viruses-infected cells and male mice models to identify the role of TOMM34 in antiviral innate immunity. Findings: TOMM34, together with a list of genes encoding pro-inflammatory cytokines and antiviral immune proteins, was transcriptionally upregulated in circulating monocytes, lung epithelium and innate immune cells from individuals with severe COVID-19 or influenza. Deficiency of TOMM34/Tomm34 significantly impaired the type I interferon responses and NF-κB-mediated inflammation in various human/murine cell lines, murine bone marrow-derived macrophages (BMDMs) and in vivo. Mechanistically, TOMM34 recruits TRAF6 to facilitate the K63-linked polyubiquitination of NEMO upon viral infection, thus promoting the downstream NF-κB activation. Interpretation: In this study, viral induction of TOMM34 is positively correlated with the hyperinflammation in severely ill patients with COVID-19 and influenza. Our findings also highlight the physiological role of TOMM34 in the innate antiviral signallings. Funding: A full list of funding sources can be found in the acknowledgements section.

ObjectiveTo investigate diagnostic approaches for preoperative localization of secondary hyperparathyroidism, as well as to give surgeons with precise parathyroid gland localization and imaging so that surgery can be performed safely.MethodsThe clinical data of 710 patients with secondary hyperparathyroidism who underwent surgery in our center from October 2009 to October 2023 were retrospectively analyzed. The changes in calcium, phosphorus, and parathyroid hormone levels were observed to ascertain the anatomical location and number of parathyroid glands.ResultsAmong the 710 patients, 55 underwent total parathyroidectomy, the others underwent total parathyroidectomy with autotransplantation. In total, 2,658 parathyroid glands were removed, with 43 glands being removed in 35 reoperation cases. The median parathyroid hormone level at 6 months postoperatively was 13.40 (interquartile range, 7.00-29.80) pg/mL. The detection rates of the parathyroid glands before first and repeat surgeries were higher using 99mTc-MIBI SPECT/CT fusion imaging than ultrasound (P

Abstract The response to programmed death-1 (PD-1) blockade varies in hepatocellular carcinoma (HCC). We utilize a panel of 16 serum factors to show that a circulating level of serum amyloid A (SAA) > 20.0 mg/L has the highest accuracy in predicting anti-PD-1 resistance in HCC. Further experiments show a correlation between peritumoral SAA expression and circulating SAA levels in patients with progressive disease after PD-1 inhibition. In vitro experiments demonstrate that SAA induces neutrophils to express PD-L1 through glycolytic activation via an LDHA/STAT3 pathway and to release oncostatin M, thereby attenuating cytotoxic T cell function. In vivo, genetic or pharmacological inhibition of STAT3 or SAA eliminates neutrophil-mediated immunosuppression and enhances antitumor efficacy of anti-PD-1 treatment. This study indicates that SAA may be a critical inflammatory cytokine implicated in anti-PD-1 resistance in HCC. Targeting SAA-induced PD-L1+ neutrophils through STAT3 or SAA inhibition may present a potential approach for overcoming anti-PD1 resistance.

Due to the mismatch between transmit waveforms and receive filters, Cross-Ambiguity Function (CAF) shaping plays an important role in the design of cognitive radar waveforms and allows more freedom for waveform optimization problem than conventional ambiguity function shaping. A CAF shaping method is proposed for designing phase-shift keying transmit waveforms and receive filters jointly to maximize the output Signal-to-Interference-plus-Noise Ratio (SINR), thereby solving the problem of weaking-moving target detection under strong clutter conditions. The optimization problem is first modeled as a quadratic fractional programming problem under the Constant Modulus (CM) constraint of the transmit waveform. The conjugated gradient method is utilized to solve the minimization problem of the Stiefel manifold space through the introduction of auxiliary variables; furthermore the nonconvex optimization problem is converted into a unimodular quadratic programming problem. An algorithm based on alternately iterative maximization and power method-like iteration is proposed to solve the quadratic optimization problem. Since transmit waveforms are limited by hardware and achieving CM is difficult, the nearest vector method is employed under the constraint of a low peak-to-average power ratio. Finally, the experiments with simulated and real measured data under different parameters reveal that the transmit waveforms and receive filters designed using the proposed method exhibit better SINR performance and faster convergence speed compared with other existing algorithms.

Achieving automatic target recognition in synthetic aperture radar (SAR) imagery is a long-standing difficulty because of the limited training samples and its sensitivity to imaging condition. Active target recognition methods can offer an innovative perspective to improve recognition accuracy compared to their passive counterparts. Although prevailing in the optical imagery area, the active target recognition in SAR image processing remains underexplored. This article proposes an active SAR target recognition framework based on deep reinforcement learning for the first time, where we design a simple view-matching task and model it as a Markov decision process. The proximal policy optimization algorithm is used to help the agent learn how to alter the observing azimuth to seek more discriminative target images for the classifier. Furthermore, the single-view feature extractor is trained with the contrastive learning method to help distinguish the target images under different azimuths, allowing the agent to successfully learn the active data collection policy in the training environment and transfer it to the test environment. Lastly, the effectiveness and advancement of the proposed framework are verified on the SAMPLE dataset. When the training samples for the classifier are very scarce, it could bring around 10% more gain in target recognition rate compared to existing active target recognition frameworks.

Synthetic aperture radar automatic target recognition (SAR ATR) has been widely studied in recent years. Most ATR models are designed based on the traditional closed-set assumption. This type of ATR model can only identify target categories existing in the training set, and it will result in missed detection or misclassification of unseen target categories encountered in battlefield reconnaissance, posing a potential threat. Therefore, it is of great significance to design a model that can simultaneously achieve known class classification and unknown class judgment. In addition, researchers usually use the obtained unknown class data for model relearning to enable it to recognize new categories. However, before this process, it is necessary to manually interpret and annotate the obtained unknown class data, which undoubtedly requires a large time cost and is difficult to meet the timeliness requirements. To solve these problems, we propose a framework that integrates the open-set recognition module and the novel class discovery module. By introducing the K-contrast loss, the open-set recognition module can accurately distinguish unknown class data, classify known class data, and then transfer the known class knowledge through deep clustering for clustering annotation of unknown class data. Extensive experimental results on the MSTAR benchmark dataset demonstrate the effectiveness of the proposed methods.

Summary: The importance of trained immunity in antitumor immunity has been increasingly recognized, but the underlying metabolic regulation mechanisms remain incompletely understood. In this study, we find that squalene epoxidase (SQLE), a key enzyme in cholesterol synthesis, is required for β-glucan-induced trained immunity in macrophages and ensuing antitumor activity. Unexpectedly, the shunt pathway, but not the classical cholesterol synthesis pathway, catalyzed by SQLE, is required for trained immunity induction. Specifically, 24(S),25-epoxycholesterol (24(S),25-EC), the shunt pathway metabolite, activates liver X receptor and increases chromatin accessibility to evoke innate immune memory. Meanwhile, SQLE-induced reactive oxygen species accumulation stabilizes hypoxia-inducible factor 1α protein for metabolic switching into glycolysis. Hence, our findings identify 24(S),25-EC as a key metabolite for trained immunity and provide important insights into how SQLE regulates trained-immunity-mediated antitumor activity.

The global prevalence of obesity is a pressing health issue, increasing the medical burden and posing significant health risks to humans. The side effects and complications associated with conventional medication and surgery have spurred the search for anti-obesity drugs from plant resources. Previous studies have suggested that Artemisiae argyi Folium (Aiye) water extracts could inhibit pancreatic lipase activities, control body weight increase, and improve the plasma lipids profile. However, the exact components and mechanisms were not precisely understood. Therefore, this research aims to identify the chemical profile of Aiye and provide a comprehensive prediction of its anti-obesity mechanisms. The water extract of Aiye was subjected to LC-MS analysis, which identified 30 phenolics. The anti-obesity mechanisms of these phenolics were then predicted, employing network pharmacology and molecular docking. Among the 30 phenolics, 21 passed the drug-likeness screening and exhibited 486 anti-obesity targets. The enrichment analysis revealed that these phenolics may combat obesity through PI3K-Akt signaling and MAPK, prolactin, and cAMP signaling pathways. Eight phenolics and seven central targets were selected for molecular docking, and 45 out of 56 docking had a binding affinity of less than −5 kcal/mol. This research has indicated the potential therapy targets and signaling pathways of Aiye in combating obesity.

Vehicle recognition is a fundamentale problem in synthetic aperture radar (SAR) image interpretation. However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations. In addition, the lack of large datasets further complicates the task. Inspired by the analysis of target signature variations and deep learning explainability, this article proposes a novel domain alignment framework, named the hierarchical disentanglement-alignment network (HDANet), to achieve robustness under various operating conditions. Concisely, HDANet integrates feature disentanglement and alignment into a unified framework with three modules: domain data generation; multitask-assisted mask disentanglement; and the domain alignment of target features. The first module generates diverse data for alignment, and three simple but effective data augmentation methods are designed to simulate target signature variations. The second module disentangles the target features from background clutter using the multitask-assisted mask to prevent clutter from interfering with subsequent alignment. The third module employs a contrastive loss for domain alignment to extract robust target features from generated diverse data and disentangled features. Finally, the proposed method demonstrates impressive robustness across nine operating conditions in the MSTAR dataset, and extensive qualitative and quantitative analyses validate the effectiveness of our framework.

As the cereal-producing region of China’s black soil, there are many agricultural activities, mainly including cultivation, straw processing, and harvesting, in Northeast China. In the process of carrying out these agricultural activities, they inevitably lead to large carbon emissions, among which straw burning and wind erosion are two processes that directly lead to carbon emissions from farmland. In this study, we estimated the carbon emissions of these two processes based on two algorithms: the improved Fire Radiative Power and Community Multiscale Air Quality (FENGSHA) algorithms. The results showed that the carbon emissions from straw burning in Northeast China can reach up to 126,651 Gg in 2017, and those from wind erosion of agricultural land can reach up to 80.45 Gg a year. When compared with the carbon emissions in 2017, the implementation of the Action Plan for Straw Disposal in Northeast China resulted in around a 40% decrease in the carbon emissions from straw burning in 2022. However, the carbon emissions from agricultural land wind erosion increased by about 10%. The seasonal characteristics of both straw burning and farmland wind erosion were obvious, with both being concentrated in the spring. In addition, based on the potential impacts of straw burning on wind erosion, we proposed that a Y-shaped integrated monitoring network should be constructed to monitor both straw burning and wind erosion in Northeast China. Thus, the study of carbon emissions from straw burning and wind erosion in Northeast China is of great importance for energy conservation and emission reduction, and the implementation of a straw burning ban policy, straw recycling and reuse, and a black soil protection policy is recommended.

Radar micro-motion signatures help to judge the target’s motion state and threat level, which plays a vital role in space situational awareness. Most of the existing micro-motion feature extraction methods derived from time-frequency (TF) representation cannot simultaneously satisfy the requirements of high resolution and multiple component representation, which has limitations on processing intersected multi-component micro-motion signals. Meanwhile, as the micro-motion features extracted from the TF spectrograms only focus on the global characteristics of the targets and ignore the physical properties of micro-motion components, it leads to poor performance in micro-motion discrimination. To address these challenges, we empirically observed a decrease in the probability of intersection between the components within the time-frequency-frequency rate (TFFR) space, where components appeared as separated and non-intersecting spatial trajectories. This observation facilitates the extraction and association of multiple components. Given the differences in modulation laws among various micro-motions in the TFFR space, we introduced a novel micro-motion identification method based on scatterer-level TFFR representation. Our experimental evaluations of different targets and micro-motion types demonstrate the efficacy and robustness of this proposed method. This method not only underscores the separability of signal components but also expands the scope of micro-motion discrimination within the TFFR domain.

Abstract It is difficult to estimate the effective rotation velocity for non‐triaxial stabilized space targets, which leads to deviations in the estimation of imaging plane vectors and azimuth resolution. This makes it hard to reconstruct the three‐dimensional (3D) of space targets based on inverse synthetic aperture radar (ISAR) image sequence. To solve this problem, a joint estimation method of imaging plane vector and 3D structure based on ISAR image sequences is proposed. First, a compact form of imaging plane vector is defined. Then, the 3D structure of a target is characterized by a fully connected deep network. The volume rendering for ISAR images is redesigned with the analysis of projection formula for ISAR imaging. The network is trained using the discrepancy between the modulus of rendered and observed ISAR images in a self‐supervised manner without 3D supervision. And the 3D structure can be obtained by inquiring all points in the 3D space. Therefore, it can optimize imaging planes and produce more complete and accurate results for complex space targets. Adequate simulation experiments verify its superiority.

Aspergillus cristatus is the dominant fungus during the fermentation of Fuzhuan brick tea; hypotonic conditions only induce its sexual development to produce ascospores, while hypertonic conditions only induce its asexual development to produce conidia, indicating that osmotic stress can regulate spore production in A. cristatus. However, the underlying regulatory mechanism is unclear. In this study, the role of Achog1, which is homologous to hog1 from Saccharomyces cerevisiae, in sporulation, different kinds of stress responses and pigment production was investigated. Deletion mutants of Achog1 were obtained by homologous recombination. Phenotypic observations showed that the time required to produce conidia was delayed, and the number of conidia produced was significantly reduced in the deletion mutants of Achog1 in hypertonic media, indicating that Achog1 plays a positive role in asexual development. Stress sensitivity tests showed that ΔAchog1 strains were sensitive to hyperosmolarity, and the order of the sensitivity of ΔAchog1 to different osmotic regulators was 3 M sucrose >3 M NaCl >3 M sorbitol. Moreover, the deletion mutants were sensitive to high oxidative stress. pH sensitivity tests indicated that Achog1 inhibited the growth of A. cristatus under alkaline stress. Additionally, pigmentation was decreased in the Achog1 deletion mutants compared with the WT. All the above developmental defects were reversed by the reintroduction of the Achog1 gene in ΔAchog1. Pull-down and LC–MS/MS analysis showed that the expression levels of proteins interacting with Achog1 were significantly different under low and high osmotic stress, and proteins related to conidial development were present only in the cultures treated with hyperosmotic stress. Transcription profiling data showed that Achog1 suppressed the expression of several genes related to asexual development, osmotic and oxidative stress resistance. On the basis of gene knockout, pull-down mass spectrometry and RNA-seq analyses, a regulatory pathway for Achog1 was roughly identified in A. cristatus.

A numerical gear rolling test (NGRT) method for actual tooth surfaces was proposed. The non-uniform rational B-spline (NURBS) fitting method was adapted to reconstruct the actual tooth surface, and the instant meshing point as well as the transmission error of two meshing gears were solved. The instant contact ellipse boundary of each meshing point can be accurately searched, and then the contact pattern formed by a series of contact ellipses on the actual tooth surface can be obtained. Finally, the rolling test experiment for a pair of hypoid gears was conducted to verify the feasibility of the proposed method. The result shows that the contact pattern obtained is consistent with the actual rolling test result, the relative errors of the transmission error amplitudes were only 4.5% and 5.3% for the drive and coast sides, respectively, and the contact ellipse of the actual tooth surface is not necessarily a standard ellipse and the instant contact path is not necessarily a straight line. This NGRT method helps to reduce equipment and time costs in closed-loop gear manufacturing, which is of great significance for mass production.

The disadvantages of the traditional Inverse Synthetic Aperture Radar (ISAR) imaging method based on Fourier transform include large data storage and long collection time. The Compressive Sensing (CS) theory can use limited data to restore an image with the sparsity of the image, reducing the cost of data collection. However for multidimensional data, the traditional compressive sensing methods need to convert three-dimensional data into a one-dimensional vector, causing the storage and calculation burden. Therefore, this study proposes a fast MultiDimensional Alternating Direction Method of Multipliers ((MD-ADMM)) sparse reconstruction method for Multiple-Input Multiple-Output ISAR (MIMO-ISAR) imaging. The CS model based on the tensor signal was established, and the model with the ADMM algorithm was optimized. The measured matrix is decomposed into a tensor modal product, and matrix inversion is replaced by tensor element division, significantly reducing memory consumption and computational burden. Fast ISAR imaging can be achieved by a small amount of data sampling by the proposed method. Compared with other tensor compressed sensing methods, this method has the advantages of stronger robustness, higher image quality, and computational efficiency. The effectiveness of the proposed method can be invalidated by simulated and measured data.

Mixed source localization plays an important role in passive radars. Aiming at the problem of low accuracy via phase difference method under a uniform circular array, this paper proposes a matrix differencing method for mixed far-field and near-field source localization. First, a two-dimensional MUltiple SIgnal Classification (MUSIC) method was utilized to estimate the azimuth and elevation angles of far-field sources. Thereafter, the covariance matrix difference method was exploited to extract the difference matrix of near-field sources. The azimuth and elevation angles of the far-field sources were estimated using the Estimation of Signal Parameters via Rotational Invariance Techniques-like (ESPRIT-like) method. Furthermore, the distance of the near-field sources was obtained by the one-dimensional MUSIC method. Finally, simulations were performed to verify the performance of the proposed algorithm. The proposed algorithm could effectively identify the mixed source when the two-dimensional Direction-Of-Arrival (DOA) of the far-field and near-field sources were the same. Moreover, the proposed algorithm could improve the accuracy of the mixed source parameter estimation. Results show that when the signal-to-noise ratio was set to 20 dB, the 2-D DOA estimation error of the near-field source was approximately 0.01°, and the distance error of the near-field source was approximately 0.1 m.

Attitude estimation is a significant task for monitoring noncooperative spacecraft in orbit. Inverse synthetic aperture radar (ISAR) is an effective method to obtain the structure, size, and attitude of spacecraft. Most existing methods of attitude estimation based on ISAR images generally rely on characteristic point matching or rectangular feature matching among ISAR images. They often need high quality ISAR images, so the robustness is usually poor. A novel method for estimating spacecraft's axial attitude in ISAR image is proposed in this article. This method does not need to utilize a simulation database, or to match sequence of specific features, so it has good robustness. Our method bridges Range–Doppler images and attitude parameters with the accommodation of spacecraft's orbit parameters and the ISAR geometric projection model. Based on the ISAR imaging turntable model, the accurate position of the imaging plane of the spacecraft in orbit is derived. Then, attitude parameters are estimated by an optimization of the projection of spacecraft's three-dimensional model to the imaging plane. Experimental results demonstrate the effectiveness and robustness of the proposed method to estimate attitude of spacecraft in orbit by the simulated and measured ISAR images.

With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile (HRRP) data based on Convolutional Neural Networks (CNN). By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed.

Imaging of maneuvering targets has become a challenging topic recently. This study proposes an improved MUSIC-based single-snapshot imaging method for OFDM-MIMO radar. At the transmitter part, for orthogonal waveform generation, the interleaved OFDM structure is developed that each transmitter only radiates at an equidistant set of OFDM subcarriers unique to itself. At the receiver part, a novel preprocessing scheme is presented to separate multiple transmit/receive paths and compensate the reference point. Preprocessed echo matrix can fit the model of two-Dimensional (2D) MUSIC algorithm, except for the coupling between radial/cross-range in the virtual antenna array. Correspondingly, we modify the 2D ElementSpace (ES)-MUSIC. In the meantime, a 2D Reduced-Dimension BeamSpace Unitary-MUSIC (RD-BS-UMUSIC) algorithm is proposed for computational complexity reduction and estimation performance enhancement. Numerical results of a simulated target consists of limited number of scatterers and a complex target (a Boeing 737-800 plane) verify that the proposed algorithm can improve the imaging quality and reduce computational complexity effectively compared with the traditional method.

Spectrum is the indispensable resource for 5G wireless systems and beyond. The dynamic spectrum management (DSM) framework with spectrum sharing among different kinds of users is essential to satisfy the sustainable rapid increase of the bandwidth requirement. Based on the analysis of the DSM as well as the latent applications of the machine learning and artificial intelligence (AI) in the spectrum access, this paper will address the problem of incorporating intelligence into the spectrum management. Firstly, the spectrum opportunity (SOP) related information is layered into the spectrum data, the spectrum information and the spectrum knowledge based on the processing and abstractive level. The spectrum knowledge is formally defined as the extendible and scalable information to reason and predict the SOP usability as well as the outcome of the SOP occupancy. Then the smart spectrum model (SSM) is proposed with the spectrum knowledge as the key enabler and the coupled SOP exploration and exploitation as the core feature. Under the SSM framework, the spectrum knowledge and real-time observing (SKRO) enabled SOP exploration scheme is designed, which can make use of both the historical information and the real-time sensing information for smart SOP exploration. Extensive simulations are provided which demonstrate that the SKRO enabled SSM can achieve much better SOP utilization while satisfying the required legacy assurance. Specifically, in the low signal to noise environment, at least 16.55% gains on the SOP utilization ratio can be obtained compared with the DSM.

In this article, we investigate physical layer security (PLS) in non-orthogonal multiple access-enabled (NOMA-enabled) underlay cognitive radio networks (CRNs) with outdated channel state information (CSI). Considering the influence of outdated CSI on the interference of secondary transmitter (Alice) to primary user (PU), the constraint for the power is adopted to guarantee the quality-of-service (QoS) of PU over Nakagami-m channels. To further analyze the NOMA-enabled underlay CRNs with outdated CSI in PLS perspective, the secrecy performance is evaluated by the closed-form expressions for connection outage probability (COP), the intercept probability (IP) and effective secrecy throughput (EST). In addition, Monte Carlo simulations are provided to verify the derived analytical results. From the analytical results and simulations, it is concluded that a) with the increment of the channel parameter m, the secrecy performance of the considered networks increases in the low SNR region and decreases in the high SNR region, b) the connection performance with the outdated CSI of the interference links only reduce in the high SNR region, because the power margin factor changes significantly in this region, c) considering the impact of the constraint for the power, the secrecy performance and EST performance of the considered networks with the outdated CSI of the interference links increase in the high SNR region, d) the considered networks with NOMA scheme can achieve higher EST than that with orthogonal multiple access (OMA) scheme.

Shiraia bambusicola is a rare medicinal fungus found in China that causes bamboo plants to decay and die with severe infection. Hypocrellin, its main active ingredient, is widely used in several fields, such as medicine, agriculture, and food industry. In this study, to clarify the genomic components, taxonomic status, pathogenic genes, secondary metabolite synthesis pathways, and regulatory mechanisms of S. bambusicola, whole-genome sequencing, assembly, and functional annotation were performed using high-throughput sequencing and bioinformatics approaches. It was observed that S. bambusicola has 33 Mb genome size, 48.89% GC content, 333 scaffolds, 2590 contigs, 10,703 genes, 82 tRNAs, and 21 rRNAs. The total length of the repeat sequence is 2,151,640 bp. The annotation of 5945 proteins was obtained from InterProScan hits based on the Gene Ontology database. Phylogenetic analysis showed that S. bambusicola belongs to Shiraiaceae, a new family of Pleosporales. It was speculated that there are more than two species or genus in Shiraiaceae. According to the annotation, 777 secreted proteins were associated with virulence or detoxification, including 777 predicted by the PHI database, 776 by the CAZY and Fungal CytochromeP450 database, and 441 by the Proteases database. The 252 genes associated with the secondary metabolism of S. bambusicola were screened and enriched into 28 pathways, among which the terpenoids, staurosporine, aflatoxin, and folate synthesis pathways have not been reported in S. bambusicola. The T1PKS was the main gene cluster among the 28 secondary metabolite synthesis gene clusters in S. bambusicola. The analysis of the T3PKS gene cluster related to the synthesis of hypocrellin showed that there was some similarity between S. bambusicola and 10 other species of fungi; however, the similarity was very low wherein the highest similarity was 17%. The genomic information of S. bambusicola obtained in this study was valuable to understand its genetic function and pathogenicity. The genomic information revealed that several enzyme genes and secreted proteins might be related to their host interactions and pathogenicity. The annotation and analysis of its secondary metabolite synthesis genes and gene clusters will be an important reference for future studies on the biosynthesis and regulation mechanism of the secondary metabolites, contributing to the discovery of new metabolites and accelerating drug development and application.

Abstract Smad4, a key mediator of the transforming growth factor‐β signaling, is mutated or deleted in 20% of pancreatic ductal adenocarcinoma (PDAC) cancers and significantly affects cancer development. However, the effect of Smad4 loss on the immunogenicity and tumor immune microenvironment of PDAC is still unclear. Here, a surprising function of Smad4 in suppressing mouse PDAC tumor immunogenicity is identified. Although Smad4 deletion in tumor cells enhances proliferation in vitro, the in vivo growth of Smad4‐deficient PDAC tumor is significantly inhibited on immunocompetent C57BL/6 (B6) mice, but not on immunodeficient mice or CD8+ cell‐depleted B6 mice. Mechanistically, Smad4 deficiency significantly increases tumor cell immunogenicity by promoting spontaneous DNA damage and stimulating STING‐mediated type I interferon signaling,which contributes to the activation of type 1 conventional dendritic cells (cDC1) and subsequent CD8+ T cells for tumor control. Furthermore, retarded tumor growth of Smad4‐deficient PDAC cells on B6 mice is largely reversed when Sting is codeleted, or when the cells are implanted into interferon‐alpha receptor‐deficientmice or cDC1‐deficientmice. Accordingly, Smad4 deficiency promotes PDAC immunogenicity by inducing tumor‐intrinsic DNA damage‐elicited type I interferon signaling.

The first wave of hematopoiesis is the primitive hematopoiesis, which produces embryonic erythroid and myeloid cells. Primitive erythrocytes are thought to be generated from bipotent hemangioblasts, but the molecular basis remains unclear. Transcriptional repressors Gfi1aa and Gfi1b have been shown to cooperatively promote primitive erythrocytes differentiation from hemangioblasts in zebrafish. However, the mechanism of these repressors during the primitive wave is largely unknown. Herein, by functional analysis of zebrafish gfi1aasmu10, gfi1bsmu11, gfi1absmu12 single, double, and triple mutants, we found that Gfi1aa not only plays a predominant role in primitive erythropoiesis but also synergizes with Gfi1ab. To screen Gfi1aa downstream targets, we performed RNA-seq and ChIP-seq analysis and found two endothelial transcription factors, etv2 and sox7, to be repressed by Gfi1aa. Genetic analysis demonstrated Gfi1aa to promote hemangioblast differentiation into primitive erythrocytes by inhibiting both etv2 and sox7 in an Lsd1-dependent manner. Moreover, the H3K4me1 level of etv2 and sox7 were increased in gfi1aa mutant. Taken together, these results suggest that Gfi1aa/Lsd1-dependent etv2/sox7 downregulation is critical for hemangioblast differentiation during primitive hematopoiesis by inhibition of endothelial specification. The different and redundant roles for Gfi1(s), as well as their genetic and epigenetic regulation during primitive hematopoiesis, help us to better know the molecular basis of the primitive hematopoiesis and sheds light on the understanding the Gfi1(s) related pathogenesis.

Wind erosion can cause high dust emissions from agricultural land and can lead to a significant loss of carbon and nutrients from the soil. The carbon balance of farmland soil is an integral part of the carbon cycle, especially under the current drive to develop carbon-neutral practices. However, the amount of global carbon lost due to the wind erosion of farmland is unknown. In this study, global farmland dust emissions were estimated from a dust emission inventory (0.1° × 0.1°, daily) built using the improved Community Multiscale Air Quality Modeling System–FENGSHA (CMAQ-FENGSHA), and global farmland organic carbon losses were estimated by combining this with global soil organic carbon concentration data. The average global annual dust emissions from agricultural land from 2017 to 2021 were 1.75 × 109 g/s. Global dust emissions from agricultural land are concentrated in the UK, Ukraine, and Russia in Europe; in southern Canada and the central US in North America; in the area around Buenos Aires, the capital of Argentina, in South America; and in northeast China in Asia. The global average annual organic carbon loss from agricultural land was 2970 Gg for 2017–2021. The spatial distribution of emissions is roughly consistent with that of dust emissions, which are mainly concentrated in the world’s four major black soil regions. These estimates of dust and organic carbon losses from agricultural land are essential references that can inform the global responses to the carbon cycle, dust emissions, and black soil conservation.

Parameter estimation is significant to prediction and estimation in the field of radar clutter characteristics. Therefore, it is necessary to study the problem of parameter estimation. The K-distribution is a commonly used model in sea clutter, which is a two-parameter model with shape parameters and scale parameters. The value of the shape parameters should be greater than 0. Moment estimation is usually used to estimate the parameters of the K-distribution. It overcomes the disadvantage of large computation compared with the maximum likelihood estimation method. However, the moment estimation usually uses two different order origin moments to solve the parameters. The joint solution of different order will cause large calculation errors, and sometimes the shape parameter is estimated to be less than 0. In the origin moment expression, the order k can be regarded as a continuous variable. By calculating the relationship between the k-order origin moment and its derivative, a parameter estimation method based on the origin moment derivative is proposed. The estimation efficiency and accuracy are compared with some moment estimation methods. Both simulation data and measured clutter data show that this method can achieve 100% estimation efficiency, can obtain higher estimation accuracy, and can also avoid the situation where the estimated value of the shape parameter is less than 0. Using the same idea to estimate the parameters in the two-parameter models, log–normal and Weibull distribution, we can also obtain the parameters with higher estimation accuracy. The experiments show that the higher-order origin moments are sensitive to the data, and the lower-order moments should be selected as far as possible. By selecting the appropriate order k, we can obtain ideal estimation parameters.

Gold catalysts possess the advantages of water and oxygen resistance, with the possibility of catalyzing many novel chemical transformations, especially in the syntheses of small-molecule skeletons, in addition to achieving the rapid construction of multiple chemical bonds and ring systems in one step. In this feature paper, we summarize recent advances in the construction of small-molecule scaffolds, such as benzene, cyclopentene, furan, and pyran, based on gold-catalyzed cyclization of arylalkyne derivatives within the last decade. We hope that this review will serve as a useful reference for chemists to apply gold-catalyzed strategies to the syntheses of related natural products and active molecules, hopefully providing useful guidance for the exploration of additional novel gold-catalyzed approaches.

As a multifunctional signaling molecule, hydrogen sulfide (H2S) has been reported to induce plant responses to a variety of abiotic stresses. However, there are no reports on H2S treatment inducing resistance in apples against Penicillium expansum, a biotic factor, and its possible mechanism of action. In this study, fumigating apples with 5 mM sodium hydrosulfide (NaHS), the exogenous donor of H2S, for 12 h reduced the diameter of lesions in fruit colonized by P. expansum. NaHS treatment markedly promoted the synthesis of endogenous H2S, hydrogen peroxide (H2O2), and nitrogen oxide (NO). In vivo NaHS treatment enhanced the activities of phenylalanine ammonia-lyase, cinnamate 4-hydroxylase, p-coumarate:coenzyme A ligase isoenzymes, caffeoyl-CoA-O-methyltransferase, caffeic acid-O-methyltransferase, ferulic acid-5-hydroxylase, cinnamyl-CoA reductase, and cinnamyl-alcohol dehydrogenase. The treatment also facilitated the production of specific phenolic acids, such as cinnamic acid, p-coumaric acid, caffeic acid, ferulic acid, and sinapic acid; total phenolic compounds; p-coumaryl alcohol; coniferyl alcohol; sinapyl alcohol; and lignin. NaHS treatment induced resistance against P. expansum in apples through H2O2- and NO-mediated activation of phenylpropanoid metabolism.

In this paper, we investigate the secure transmission for differential quadrature spatial modulation (DQSM) with artificial noise (AN) in multiple-input single-output wiretap networks, where a cooperative jammer helps the secure transmission between transceivers in the presence of an eavesdropper. To evaluate the secrecy performance of the DQSM with AN in the considered wiretap networks, assuming Rayleigh fading, we derive exact and asymptotic closed-form expressions for secrecy outage probability and effective secrecy throughput under two practical scenarios, i.e., Scenario I, where the eavesdropper is passive and its channel state information (CSI) is unavailable, and Scenario II, where the eavesdropper is active and its CSI is available. Finally, numerical simulations demonstrate that increasing the transmit power of transmitter or cooperative jammer can improve the secrecy performance, but too large transmit power at the transmitter or cooperative jammer is not helpful. Moreover, increasing the number of antennas at the transmitter or cooperative jammer brings up better secrecy performance.

Direction-of-arrival (DOA) estimation using sparsity-inducing techniques has attracted much interest recently. In this paper, the DOA estimation for the bi-static passive radar is investigated. Under the framework of sparse Bayesian learning (SBL), a joint sparse Bayesian model is established to combine the measurements from both stations and yield improved targets DOA estimation. First, the maximum a posteriori (MAP) estimation of the DOA using the joint data set is derived. With the utilization of more measurements, the joint reconstruction process can produce far more precise estimates. To reduce the computational expense, a fast SBL method based on evidence maximization is also proposed. Using the fix-point method, the fast SBL method tends to converge faster than the MAP estimator. Theoretical results focusing on local convergence property of the fast SBL method are provided. The simulation results show that the proposed methods outperform the conventional SBL methods, especially in harsh scenarios with a limited number of snapshots and low signal-to-noise ratio (SNR).

In this paper, we investigate the secrecy performance of dual-hop randomize-and-forward (RaF) cognitive wiretap networks over Rayleigh fading channels, in which the RaF relay is considered both as half-duplex (HD) and full-duplex (FD) operations. Specifically, for the HD, maximal-ratio combining/maximal-ratio transmission (MRC/MRT) is adopted at the RaF relay. For the FD, the RaF relay can simultaneously receive the signal from the source and transmit jamming signals to eavesdropper, and the selection combining-zero forcing beamforming/maximal-ratio transmit (SC-ZFB/MRT) and SC-ZFB/ZFB schemes are respectively adopted at the FD relay. To thoroughly assess the secrecy performance achieved from the proposed schemes, the closed-form expressions for the secrecy outage probability (SOP) of the dual-hop RaF cognitive wiretap channels with MRC/MRT, SC-ZFB/MRT and SC-ZFB/ZFB schemes are derived, respectively. In addition, we also provide simple asymptotic approximations for the SOP under two distinct scenarios, depending on the quality of the main and wiretap channels. Additionally, our analytical results and numerical simulations show the efficiency of our proposed solutions for both SC-ZFB/MRT and SC-ZFB/ZFB with FD cases and show considerable performance gains over MRC/MRT with HD scheme. Finally, for the proposed schemes, the SC-ZFB/ZFB scheme is beneficial to improve secrecy performance when the eavesdropper's channel is better than the main channel and the RaF relaying strategy achieves better secrecy performance than that of the decode-and-forward (DF) relaying strategy for dual-hop cognitive wiretap networks.

This paper addresses the resource allocation problem for small cells in the two-tier heterogeneous networks (HetNets) with non-independent resource blocks (RBs) usability, which is prevalent in HetNets application. To formulate the correlations of different RBs to facilitate the resource allocation, a normalized pointwise mutual information (NPMI)-based coefficient is proposed, which is easy to compute and adapt based on the historical information about RBs usability. To obtain a fast allocation solution, a two-stage stable matching-based dynamic spectrum allocation algorithm (TSSM) is proposed, which takes both network throughput and user performance as optimization metrics. Specifically, through robust preferable stable matching in the first stage and the iterative matchings with NPMI-based coefficient as the preference profiles in the second stage, the efficient matching solution can be obtained with low complexity and thus is well adapted to the requirement of the resource allocation in HetNets. The validity of the proposed coefficient in modeling the relevance of RBs usability and the benefit of its usage in the allocation algorithm is verified with typical spectrum usability related data. The extensive simulation results on various spectrum environment show the average probability of no RB to use following the proposed TSSM are reduced by more than 13.2%, and the robust requirement for users can be guaranteed better at the same time. Furthermore, the accumulated throughput of all users in the network increases by more than 19.4% on an average under different RBs usability environment.

The fine state of targets can be represented by the extracted micro-Doppler (m-D) components from the radar echo. However, current methods do not consider the specialty of the m-D components, and their performance with non-sinusoidal components is poor. In this paper, a neural network is applied to signal extraction for the first time. Inspired by the semantic line detection in computer vision, the extraction of the m-D components is transformed into the network-based time–frequency curves detection problem. Specifically, a novel dual-branch network-based m-D components extraction method is proposed. According to the property of intersected multiple m-D components, the dual-branch network consisting of a continuous m-D components extraction branch, and a crossing point detection branch is designed to obtain components and cross points at the same time. In addition, a shuffle attention-fast Fourier convolution (SA-FFC) module is proposed to fuse local and global contexts and focus on key features. To solve the error correlation problem of multi-component signals, the first-order parametric continuous condition and cubic spline interpolation are employed to obtain complete and smooth components curves. Simulation and measurement results show that this method of good robustness is a good candidate for separating the non-sinusoidal m-D components with intersections.

Background Dendritic cells (DCs) play a critical role in antitumor immunity, but the therapeutic efficacy of DC-mediated cancer vaccine remains low, partly due to unsustainable DC function in tumor antigen presentation. Thus, identifying drugs that could enhance DC-based antitumor immunity and uncovering the underlying mechanism may provide new therapeutic options for cancer immunotherapy.Methods In vitro antigen presentation assay was used for DC-modulating drug screening. The function of DC and T cells was measured by flow cytometry, ELISA, or qPCR. B16, MC38, CT26 tumor models and C57BL/6, Balb/c, nude, and Batf3−/− mice were used to analyze the in vivo therapy efficacy and impact on tumor immune microenvironment by clotrimazole treatment.Results By screening a group of small molecule inhibitors and the US Food and Drug Administration (FDA)-approved drugs, we identified that clotrimazole, an antifungal drug, could promote DC-mediated antigen presentation and enhance T cell response. Mechanistically, clotrimazole acted on hexokinase 2 to regulate lactate metabolic production and enhanced the lysosome pathway and Chop expression in DCs subsequently induced DC maturation and T cell activation. Importantly, in vivo clotrimazole administration induced intratumor immune infiltration and inhibited tumor growth depending on both DCs and CD8+ T cells and potentiated the antitumor efficacy of anti-PD1 antibody.Conclusions Our findings showed that clotrimazole could trigger DC activation via the lactate-lysosome axis to promote antigen cross-presentation and could be used as a potential combination therapy approach to improving the therapeutic efficacy of anti-PD1 immunotherapy.

Alternaria alternata, the casual agent of black rot of pear fruit, can sense and respond to the physicochemical cues from the host surface and form infection structures during infection. To evaluate the role of cyclic AMP-dependent protein kinase (cAMP-PKA) signaling in surface sensing of A. alternata, we isolated and functionally characterized the cyclic adenosine monophosphate-dependent protein kinase A catalytic subunit gene (AaPKAc). Gene expression results showed that AaPKAc was strongly expressed during the early stages of appressorium formation on hydrophobic surfaces. Knockout mutants ΔAaPKAc were generated by replacing the target genes via homologous recombination events. We found that intracellular cAMP content increased but PKA content decreased in ΔAaPKAc mutant strain. Appressorium formation and infection hyphae were reduced in the ΔAaPKAc mutant strain, and the ability of the ΔAaPKAc mutant strain to recognize and respond to high hydrophobicity surfaces and different surface waxes was lower than in the wild type (WT) strain. In comparison with the WT strain, the appressorium formation rate of the ΔAaPKAc mutant strain on high hydrophobicity and fruit wax extract surface was reduced by 31.6 and 49.3% 4 h after incubation, respectively. In addition, AaPKAc is required for the hypha growth, biomass, pathogenicity, and toxin production of A. alternata. However, AaPKAc negatively regulated conidia formation, melanin production, and osmotic stress resistance. Collectively, AaPKAc is required for pre-penetration, developmental, physiological, and pathological processes in A. alternata.

A solar dish concentrator system has a large windward area and heavy structural mass, and under the action of wind loads and self-weight loads, foundation settlement can easily occur and cause the column (the azimuth axis) to tilt. Upon tilting, the azimuth axis is no longer perpendicular to the horizontal plane, causing a tracking error in the service of the solar dish concentrator system. In this paper, a tracking error model of a solar dish concentrator system is established based on the rigid body motion theory, which considers the azimuth axis tilt error. In this model, a radial angle and tangential angle parameters are used to describe the azimuth axis’s tilt angle and tilt direction. Under the tilt error of the azimuth axis, we analyze, in detail, the initial tracking position of a solar dish concentrator system, the system operation area, and the variation rule of tracking performance in long-term operation. The results show that under the azimuth axis tilt error of the solar dish concentrator system, the deviation of the initial tracking position of the solar dish concentrator system in the horizontal or vertical plane will reduce its tracking performance and the stability of tracking performance compared with the initial tracking position being due east. The tracking performance of a solar dish concentrator system and its stability are better in areas with a relatively low latitude. In different areas with close latitude, the tracking performance of the solar dish concentrator system and its stability are better, particularly with lower longitudes. During a whole year operation period, the tracking performance of an solar dish concentrator system in the first quarter and the fourth quarter is relatively better, and its stability in June and July is relatively better. This work can provide a theoretical basis for the installation, debugging, and error control of solar dish concentrator systems.

To investigate the mechanisms of phospholipase C (PLC)-mediated calcium (Ca2+) signaling in Alternaria alternata, the regulatory roles of PLC were elucidated using neomycin, a specific inhibitor of PLC activity. Three isotypes of PLC designated AaPLC1, AaPLC2, and AaPLC3 were identified in A. alternata through genome sequencing. qRT-PCR analysis showed that fruit wax extracts significantly upregulated the expression of all three PLC genes in vitro. Pharmacological experiments showed that neomycin treatment led to a dose-dependent reduction in spore germination and appressorium formation in A. alternata. Appressorium formation was stimulated on hydrophobic and pear wax-coated surfaces but was significantly inhibited by neomycin treatment. The appressorium formation rates of neomycin treated A. alternata on hydrophobic and wax-coated surfaces decreased by 86.6 and 47.4%, respectively. After 4 h of treatment, exogenous CaCl2 could partially reverse the effects of neomycin treatment. Neomycin also affected mycotoxin production in alternariol (AOH), alternariol monomethyl ether (AME), altenuene (ALT), and tentoxin (TEN), with exogenous Ca2+ partially reversing these effects. These results suggest that PLC is required for the growth, infection structure differentiation, and secondary metabolism of A. alternata in response to physiochemical signals on the pear fruit surface.

Walnuts have high oil content in their kernels, and they have attracted considerable attention in the food, beverage, nutrient, and health fields because of their delicious taste and potential health benefits. Fatty acid profiles of kernels vary depending on walnuts species, ontogenic variations, and planting environments. To determine the key indicators that can be used to distinguish different walnuts using chemometric analyses, the fatty acid compositions and contents of 72 walnut samples were measured and evaluated. Three fatty acids, oleic acid (21.66%), linoleic acid (56.40%), and linolenic acid (10.50%), were the most common fatty acid components in the kernels. Palmitic acid and linolenic acid in kernels were found to be indicators to rank the walnuts into five levels. Three groups were identified based on of several chemometrics. Oleic acid in kernels was typical fatty acid that could be used to distinguish three walnut groups based on the results of discriminant analysis, while oleic acid and linoleic acid were key differential fatty acids on the discrimination of each group based on the result of orthogonal partial least squares discriminant analysis. This study provides information on how to classify walnuts from different geographical locations based on kernel fatty acid profiling and provides an approach to identify possible adulterations in walnuts on the markets. Moreover, the results are potentially relevant to quality assessments of walnuts.

This paper proposes a new high-velocity motion compensation method for intermediate frequency direct sampling (IFDS) data by adaptive parameter adjustment of a matched filter and entropy minimization. First, the influence of high-velocity motion to IFDS data is analyzed. The high-velocity motion brings in aberration to the range profile and blurring the phase of inverse synthetic aperture radar imaging. Then, adaptive parameter adjustment of a matched filter is designed to accomplish joint high-velocity motion compensation and pulse compression. The iterative search for the velocity utilizing golden section search based on waveform entropy minimization is adopted to improve the efficiency. Experimental results demonstrate the validity of the proposed method.

RCS reconstruction is an important way to reduce the measurement time in anechoic chambers and expand the radar original data, which can solve the problems of data scarcity and a high measurement cost. The greedy pursuit, convex relaxation, and sparse Bayesian learning-based sparse recovery methods can be used for parameter estimation. However, these sparse recovery methods either have problems in solving accuracy or selecting auxiliary parameters, or need to determine the probability distribution of noise in advance. To solve these problems, a non-parametric Sparse Iterative Covariance Estimation (SPICE) algorithm with global convergence property based on the sparse Geometrical Theory of Diffraction (GTD) model (GTD–SPICE) is employed for the first time for RCS reconstruction. Furthermore, an improved coarse-to-fine two-stage SPICE method (DE–GTD–SPICE) based on the Damped Exponential (DE) model and the GTD model (DE–GTD) is proposed to reduce the computational cost. Experimental results show that both the GTD–SPICE method and the DE–GTD–SPICE method are reliable and effective for RCS reconstruction. Specifically, the DE–GTD–SPICE method has a shorter computational time.

Abstract β-Thalassemia is a global health issue, caused by mutations in the HBB gene. Among these mutations, HBB −28 (A>G) mutations is one of the three most common mutations in China and Southeast Asia patients with β-thalassemia. Correcting this mutation in human embryos may prevent the disease being passed onto future generations and cure anemia. Here we report the first study using base editor (BE) system to correct disease mutant in human embryos. Firstly, we produced a 293T cell line with an exogenous HBB −28 (A>G) mutant fragment for gRNAs and targeting efficiency evaluation. Then we collected primary skin fibroblast cells from a β-thalassemia patient with HBB −28 (A>G) homozygous mutation. Data showed that base editor could precisely correct HBB −28 (A>G) mutation in the patient’s primary cells. To model homozygous mutation disease embryos, we constructed nuclear transfer embryos by fusing the lymphocyte or skin fibroblast cells with enucleated in vitro matured (IVM) oocytes. Notably, the gene correction efficiency was over 23.0% in these embryos by base editor. Although these embryos were still mosaic, the percentage of repaired blastomeres was over 20.0%. In addition, we found that base editor variants, with narrowed deamination window, could promote G-to-A conversion at HBB −28 site precisely in human embryos. Collectively, this study demonstrated the feasibility of curing genetic disease in human somatic cells and embryos by base editor system.

The effect of dietary supplementation with conjugated linoleic acid (CLA) on lipid peroxidation in the breast muscles (Pectoralis major and Pectoralis minor) and thigh muscles of broiler chickens was investigated. A total of ninety-six 21-day-old Arbor Acres male broiler chickens were assigned to 2 dietary treatments (0 and 1.5% CLA) with 8 replicates per 6 chickens each. The left breast and thigh muscles of broiler chickens were excised on day 42. The broilers receiving the CLA diet exhibited lower malondialdehyde content and reactive oxygen species production (P < 0.01) in the breast and thigh muscles than the broilers receiving the control diet. After the dietary CLA supplementation, the glutathione (GSH) content in the breast and thigh muscles of the broiler chickens fed the CLA diet increased by 21.89 and 21.56%, respectively (P < 0.05) while the γ-glutamylcysteine synthetase（γ-GCS）activity increased by 28.57 and 25.80% (P < 0.05), respectively. Dietary CLA significantly increased the CLA content (P < 0.01) and saturated fatty acid content (P < 0.05), and decreased the monounsaturated fatty acid content (P < 0.01) in the breast and thigh muscles. These results showed that dietary CLA may decrease the lipid peroxidation level in the breast and thigh muscles of broiler chickens perhaps through increasing the γ-GCS activity to induce GSH synthesis and changing the fatty acid composition to increase oxidative stability.

Abstract An efficient and environmentally friendly method was designed for the oxidation of sulfides to sulfoxides with a recyclable, carbon-skeleton-based heterogeneous catalyst developed by titanium sulfate [Ti(SO4)2] mineralization on the surface of graphene oxide foam [Ti(SO4)2@GOF] by using 30 wt% H2O2 as oxidant. Several different substituted sulfides were examined to explore the scope of substrates of the selective oxidation. The excellent reusability and durability of Ti(SO4)2@GOF was demonstrated by recycling experiments and the catalyst was further applied in the preparation of pantoprazole sodium in a one-pot process.

In order to overcome the bandwidth limitations of common control channels, we introduce a multibit quantization scheme to preprocess raw measurements in a collaborative spectrum sensing (CoSS) scheme. After quantifying the raw measurement, the cognitive sensors (CSs) send the multibit sensing information to a sink node (SN), which makes a final decision by using a soft decision fusion rule. In order to better understand the effects of quantization parameters on collaborative spectrum sensing in a cognitive sensor network (CSN), we discuss the following two problems: the average error probability minimum problem (AEPMP) and throughput maximum problem. By identifying the number of quantization bits, number of CSs, and global decision threshold, we can discuss some design considerations for the multibit-quantization-based CoSS scheme. A closed-form expression for the optimal global decision threshold is derived in the process of analyzing the AEPMP. Furthermore, an iterative algorithm with low complexity is proposed to find the optimal parameters for maximizing the throughput of a CSN under a target detection probability constraint. Finally, we investigate the impact of optimized parameters on the system performance of a CSN. Our theoretical analysis is verified through numerical simulations.

We consider a cluster-based collaborative spectrum sensing (CSS) scheme in energy harvesting cognitive wireless communication network (EH-CWCN), where cognitive nodes (CNs) are clustered based on their received power levels for enhancing sensing performance. In CSS scheme, time resource is limited and shared by energy harvesting, spectrum sensing, and data transmission. The purpose of this paper is to maximize the average throughput of EH-CWCN by identifying the optimal parameter set, including the durations of energy harvesting and spectrum sensing, local detection threshold, and the number of CNs. Moreover, it is hard to confirm the optimal local detection threshold at CNs with different received power levels. By constructing a fictitious cognitive node (FCN), which is assumed to have the same sensing performance as the sink node, the process for finding the optimal local detection threshold can be converted into the process for searching the optimal received signal-to-noise ratio of the FCN. Then, we formulate the general optimization problem under the collision constraint and energy constraint. Then, the existence and uniqueness of time fractions for energy harvesting and spectrum sensing are proved in this paper. The optimal parameter set is achieved by using the bisection method and a simplified linear search method. Finally, the theoretical analysis and the impact of the optimized parameters on the system performance are verified and shown through numerical simulations.

This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior. The Bayesian inference with the Laplacian models is a challenge because the Laplacian prior is not conjugate to the Gaussian likelihood. To solve this problem, we first introduce the inverse-gamma prior, which is conjugate to the Laplacian prior, to model the distinctive scaling parameters of the Laplacian priors. Then the posterior of the sparse signal, approximated by the Laplace approximation, is found to be Gaussian distributed with the expectation being the result of maximum a posterior (MAP) estimation. Finally the expectation-maximization (EM)-based variational Bayesian (VB) inference is utilized to accomplish the sparse signal recovery with the LSM prior. Since the proposed algorithm is a full Bayesian inference based on the MAP estimation, it achieves both the ability of avoiding structural error from the sparse Bayesian learning and the robustness to noise from the MAP estimation. Analysis on experimental results based on both simulated and measured data indicates that the proposed algorithm achieves the state-of-art performance in terms of sparse representation and de-noising.

Aspergillus chevalieri var. intermedius is a dominant filamentous fungal species in Fuzhuan tea and is associated with the quality and health benefits of this tea. The sexual or asexual reproduction of this fungus depends on the osmotic pressure of the tea. Efforts to enhance the beneficial effects of A. chevalieri var. intermedius are hampered by difficulties in disrupting its genes. To address this issue, we identified the A. chevalieri var. intermedius homolog (Acku70) of human Ku70 and generated an Acku70 disruption strain (ΔAcku70), aiming to improve the gene replacement efficiency. ΔAcku70 grew at a slightly lower rate in vitro than the wild-type strain; however, the two strains exhibited similar sensitivity to temperature, osmotic pressure and the effects of ethyl methane sulphonate and H2O2. The replacement efficiency of veA and flbA dramatically increased in ΔAcku70 compared to that in the wild type. The efficiency of flbA replacement increased from 2.6% to 80%, whereas the frequency of veA disruption increased from 15.2% to 83.9% and from 30.8% to 86.8%. Thus, ΔAcku70 is suitable for use as a type strain for large-scale functional genomic analysis of A. chevalieri var. intermedius.

Simulating quantum circuits with classical computers requires resources growing exponentially in terms of system size. Real quantum computer with noise, however, may be simulated polynomially with various methods considering different noise models. In this work, we simulate random quantum circuits in one dimension with matrix product density operators (MPDOs), for different noise models such as dephasing, depolarizing, and amplitude damping. We show that the method based on matrix product states (MPSs) fails to approximate the noisy output quantum states for any of the noise models considered, while the MPDO method approximates them well. Compared with the method of matrix product operators (MPOs), the MPDO method reflects a clear physical picture of noise (with inner indices taking care of the noise simulation) and quantum entanglement (with bond indices taking care of two-qubit gate simulation). Consequently, in case of weak system noise, the resource cost of the MPDO will be significantly less than that of the MPO due to a relatively small inner dimension needed for the simulation. In case of strong system noise, a relatively small bond dimension may be sufficient to simulate the noisy circuits, indicating a regime that the noise is large enough for an “easy” classical simulation, which is further supported by a comparison with the experimental results on an IBM cloud device. Moreover, we propose a more effective tensor updates scheme with optimal truncations for both the inner and the bond dimensions, performed after each layer of the circuit, which enjoys a canonical form of the MPDO for improving simulation accuracy. With truncated inner dimension to a maximum value κ and bond dimension to a maximum value χ, the cost of our simulation scales as ∼NDκ^{3}χ^{3}, for an N-qubit circuit with depth D.