SMYD4 monomethylates PRMT5 and forms a positive feedback loop to promote hepatocellular carcinoma progression.

Both lysine and arginine methyltransferases are thought to be promising therapeutic targets for malignant tumors, yet how these methyltransferases function in malignant tumors, especially hepatocellular carcinoma (HCC), has not been fully elucidated. Here, we reported that SMYD4, a lysine methyltransferase, acts as an oncogene in HCC. SMYD4 was highly upregulated in HCC and promoted HCC cell proliferation and metastasis. Mechanistically, PRMT5, a well-known arginine methyltransferase, was identified as a SMYD4-binding protein. SMYD4 monomethylated PRMT5 and enhanced the interaction between PRMT5 and MEP50, thereby promoting the symmetrical dimethylation of H3R2 and H4R3 on the PRMT5 target gene promoter and subsequently activating DVL3 expression and inhibiting expression of E-cadherin, RBL2, and miR-29b-1-5p. Moreover, miR-29b-1-5p was found to inversely regulate SMYD4 expression in HCC cells, thus forming a positive feedback loop. Furthermore, we found that the oncogenic effect of SMYD4 could be effectively suppressed by PRMT5 inhibitor in vitro and in vivo. Clinically, high coexpression of SMYD4 and PRMT5 was associated with poor prognosis of HCC patients. In summary, our study provides a model of crosstalk between lysine and arginine methyltransferases in HCC and highlights the SMYD4-PRMT5 axis as a potential therapeutic target for the treatment of HCC.

PE-RASP: range image stitching of photon-efficient imaging through reconstruction, alignment, stitching integration network based on intensity image priors.

Single photon imaging integrates advanced single photon detection technology with Laser Radar (LiDAR) technology, offering heightened sensitivity and precise time measurement. This approach finds extensive applications in biological imaging, remote sensing, and non-visual field imaging. Nevertheless, current single photon LiDAR systems encounter challenges such as low spatial resolution and a limited field of view in their intensity and range images due to constraints in the imaging detector hardware. To overcome these challenges, this study introduces a novel deep learning image stitching algorithm tailored for single photon imaging. Leveraging the robust feature extraction capabilities of neural networks and the richer feature information present in intensity images, the algorithm stitches range images based on intensity image priors. This innovative approach significantly enhances the spatial resolution and imaging range of single photon LiDAR systems. Simulation and experimental results demonstrate the effectiveness of the proposed method in generating high-quality stitched single-photon intensity images, and the range images exhibit comparable high quality when stitched with prior information from the intensity images.

Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar.

Micro-Doppler time-frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones. We propose a new method for parameter extraction by combining the range profile of a micro-motion cone and the micro-Doppler time-frequency spectrum. This method can effectively decouple and accurately extract the structure and the micro-motion parameters of cones. Compared with traditional time-frequency analysis methods, the accuracy of parameter extraction is higher, and the information is richer. Firstly, the range profile of the micro-motion cone was obtained by using an FMCW (Frequency Modulated Continuous Wave) lidar based on simulation. Secondly, quantitative analysis was conducted on the edge features of the range profile and the micro-Doppler time-frequency spectrum. Finally, the parameters of the micro-motion cone were extracted based on the proposed decoupling parameter extraction method. The results show that our method can effectively extract the cone height, the base radius, the precession angle, the spin frequency, and the gravity center height within the range of a lidar LOS (line of sight) angle from 20° to 65°. The average absolute percentage error can reach below 10%. The method proposed in this paper not only enriches the detection information regarding micro-motion cones, but also improves the accuracy of parameter extraction and establishes a foundation for classification and recognition. It provides a new technical approach for laser micro-Doppler detection in accurate recognition.

Comprehensive analysis of lipid metabolism in influenza virus infection.

Influenza A virus (IAV) exploits host metabolic pathways to support its replication. To improve the understanding of lipid metabolic changes that could occur upon IAV infection, a comprehensive analysis of lipid metabolites in A549 cells infected with the avian H9N2 virus at the different time points was performed. It was found that H9N2 infection could largely promote the level of lipid metabolites. Further, these metabolites were mainly included in glycerophospholipids (GPs), sphingolipids (SPs), glycerolipids (GLs), fatty acids (FAs), sterollipids (STs), triglycerides (TGs), and prenol lipids (PRs). Specifically, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that these metabolites were mainly associated with the glycerphospholipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and autophagy. Furthermore, it is interesting to note that these metabolites, including FFA(19:1), PE(P-17:0_20:3), PE(P-18:1_20:2), LPC(14:0/0:0), PE(O-18:0_20:3), and MGDG(16:0_18:1), are upregulated and shared in the top 10 at 12 h, 24 h, 36 h, and 48 h after H9N2 infection, indicative of the possibility of acting as biomarkers for the diagnosis in the lung infected with influenza virus. These pathways and altered metabolites could provide new understandings about biological characteristics and pathogenicity of influenza virus and have the potential to serve as biomarkers for influenza.

Otus scops adenovirus: the complete genome sequence of a novel aviadenovirus discovered in a wild owl.

We present the complete genome sequence of an aviadenovirus obtained by metagenomics from cloacal swabs taken from a free-living Eurasian scops owl (Otus scops, a small raptor distributed in Europe and several parts of Asia) in China. Thirty protein coding genes were predicted in this 40,239-bp-long genome, which encodes the largest fiber protein among all reported aviadenoviruses. The viral genome sequence is highly divergent, and the encoded proteins have an average of only 55% amino acid sequence identity to those of other adenoviruses. In phylogenetic analysis, the new owl virus grouped with members of the genus Aviadenovirus and formed a common clade with another owl adenovirus reported previously in Japan. This is the second complete genome sequence of an aviadenovirus discovered in owls, and its proteins have an average of 62% amino acid sequence identity to those of the previously reported owl adenovirus. Combining this result with comparative genomic analysis of all aviadenoviruses, we propose that this owl virus and the previously described Japanese owl adenovirus can be assigned to two new species in the genus Aviadenovirus. This study provides new data on the diversity of aviadenoviruses in wild birds.

Generated Image Editing Method Based on Global-Local Jacobi Disentanglement for Machine Learning.

Accurate semantic editing of the generated images is extremely important for machine learning and sample enhancement of big data. Aiming at the problem of semantic entanglement in generated image latent space of the StyleGAN2 network, we proposed a generated image editing method based on global-local Jacobi disentanglement. In terms of global disentanglement, we extract the weight matrix of the style layer in the pre-trained StyleGAN2 network; obtain the semantic attribute direction vector by using the weight matrix eigen decomposition method; finally, utilize this direction vector as the initialization vector for the Jacobi orthogonal regularization search algorithm. Our method improves the speed of the Jacobi orthogonal regularization search algorithm with the proportion of effective semantic attribute editing directions. In terms of local disentanglement, we design a local contrast regularized loss function to relax the semantic association local area and non-local area and utilize the Jacobi orthogonal regularization search algorithm to obtain a more accurate semantic attribute editing direction based on the local area prior MASK. The experimental results show that the proposed method achieves SOTA in semantic attribute disentangled metrics and can discover more accurate editing directions compared with the mainstream unsupervised generated image editing methods.

Centroid Optimization of DNN Classification in DOA Estimation for UAV.

Classifications based on deep learning have been widely applied in the estimation of the direction of arrival (DOA) of signal. Due to the limited number of classes, the classification of DOA cannot satisfy the required prediction accuracy of signals from random azimuth in real applications. This paper presents a Centroid Optimization of deep neural network classification (CO-DNNC) to improve the estimation accuracy of DOA. CO-DNNC includes signal preprocessing, classification network, and Centroid Optimization. The DNN classification network adopts a convolutional neural network, including convolutional layers and fully connected layers. The Centroid Optimization takes the classified labels as the coordinates and calculates the azimuth of received signal according to the probabilities of the Softmax output. The experimental results show that CO-DNNC is capable of acquiring precise and accurate estimation of DOA, especially in the cases of low SNRs. In addition, CO-DNNC requires lower numbers of classes under the same condition of prediction accuracy and SNR, which reduces the complexity of the DNN network and saves training and processing time.

HbBIN2 Functions in Plant Cold Stress Resistance through Modulation of HbICE1 Transcriptional Activity and ROS Homeostasis in Hevea brasiliensis.

Cold stress poses significant limitations on the growth, latex yield, and ecological distribution of rubber trees (Hevea brasiliensis). The GSK3-like kinase plays a significant role in helping plants adapt to different biotic and abiotic stresses. However, the functions of GSK3-like kinase BR-INSENSITIVE 2 (BIN2) in Hevea brasiliensis remain elusive. Here, we identified HbBIN2s of Hevea brasiliensis and deciphered their roles in cold stress resistance. The transcript levels of HbBIN2s are upregulated by cold stress. In addition, HbBIN2s are present in both the nucleus and cytoplasm and have the ability to interact with the INDUCER OF CBF EXPRESSION1(HbICE1) transcription factor, a central component in cold signaling. HbBIN2 overexpression in Arabidopsis displays decreased tolerance to chilling stress with a lower survival rate and proline content but a higher level of electrolyte leakage (EL) and malondialdehyde (MDA) than wild type under cold stress. Meanwhile, HbBIN2 transgenic Arabidopsis treated with cold stress exhibits a significant increase in the accumulation of reactive oxygen species (ROS) and a decrease in the activity of antioxidant enzymes. Further investigation reveals that HbBIN2 inhibits the transcriptional activity of HbICE1, thereby attenuating the expression of C-REPEAT BINDING FACTOR (HbCBF1). Consistent with this, overexpression of HbBIN2 represses the expression of CBF pathway cold-regulated genes under cold stress. In conclusion, our findings indicate that HbBIN2 functions as a suppressor of cold stress resistance by modulating HbICE1 transcriptional activity and ROS homeostasis.

Fourier single pixel imaging reconstruction method based on the U-net and attention mechanism at a low sampling rate.

There exists the contradiction between imaging efficiency and imaging quality for Fourier single-pixel imaging (FSI). Although the deep learning approaches have solved this problem to some extent, the reconstruction quality at low sampling rate is still not enough to meet the practical requirements. To solve this problem, inspired by the idea of super-resolution, this paper proposes the paralleled fusing of the U-net and attention mechanism to improve the quality of FSI reconstruction at a low sampling rate. This paper builds a generative adversarial network structure to achieve recovery of high-resolution target images from low-resolution FSI reconstruction results under low sampling rate conditions. Compared with conventional FSI and other deep learning methods based on FSI, the proposed method can get better quality and higher resolution results at low sampling rates in simulation and experiments. This approach is particularly important to high-speed Fourier single pixel imaging applications.

Deep-learning based photon-efficient 3D and reflectivity imaging with a 64 × 64 single-photon avalanche detector array.

A single photon avalanche diode (SPAD) is a high sensitivity detector that can work under weak echo signal conditions (≤1 photon per pixel). The measured digital signals can be used to invert the range and reflectivity images of the target with photon-efficient imaging reconstruction algorithm. However, the existing photon-efficient imaging reconstruction algorithms are susceptible to noise, which leads to poor quality of the reconstructed range and reflectivity images of target. In this paper, a non-local sparse attention encoder (NLSA-Encoder) neural network is proposed to extract the 3D information to reconstruct both the range and reflectivity images of target. The proposed network model can effectively reduce the influence of noise in feature extraction and maintain the capability of long-range correlation feature extraction. In addition, the network is optimized for reconstruction speed to achieve faster reconstruction without performance degradation, compared with other existing deep learning photon-efficient imaging reconstruction methods. The imaging performance is verified through numerical simulation, near-field indoor and far-field outdoor experiments with a 64 × 64 SPAD array. The experimental results show that the proposed network model can achieve better results in terms of the reconstruction quality of range and reflectivity images, as well as reconstruction speed.

The Structure and Function of Microbial Community in Rhizospheric Soil of American Ginseng (Panax quinquefolius L.) Changed with Planting Years.

The changes of microbial communities of rhizospheric soil in different ages are speculated to cause soil-borne diseases and replanting problem in American ginseng (Panax quinquefolius L.) cultivation. This study analyzed the physicochemical properties and microbial communities of rhizospheric soil during the planting of American ginseng in the Wendeng area of Weihai, China. The water content and organic matter content of American ginseng rhizospheric soil decreased year by year. A decline in the diversity of bacteria and fungi was observed in the rhizospheric soils planting American ginseng compared with the traditional crop wheat in the control group. During the later planting stage, the abundances of Proteobacteria, Actinobacteria, and Basidiomycota were lower, whereas that of Acidobacteria, Firmicutes, and Mucoromycota were higher. Through the correlation analysis between environmental factors and microbial community, it was found that the content of soil phosphorus was significantly positively correlated with the root rot pathogen Fusarium. The results of functional prediction showed that the decrease of secondary metabolite synthesis of rhizospheric soil bacteria and the increase of plant pathogenic fungi may be the important reasons for the increase of diseases in the later stage of American ginseng planting. This study revealed the evolution of rhizosphere microbial community and function in the process of American ginseng planting, which is valuable for planting management.

Interstitial lung disease in Primary Sjögren's syndrome.

BACKGROUND: Interstitial lung disease (ILD) may cause life-threatening complications of primary Sjogren's syndrome (pSS), and has a poor prognosis in terms of survival and quality of life. To date, few studies have investigated the risk factors for ILD detected by high-resolution computed tomography (HRCT) in pSS patients with or without respiratory symptoms. METHODS: Data of 333 patients with newly diagnosed pSS were retrospectively analysed. Interstitial lung disease involvement was defined as typical abnormalities on HRCT and/or pulmonary function tests. Multivariate regression model was used to evaluate the association between interstitial lung disease and pSS characteristics. RESULTS: Sixty-six patients (19.82%) were diagnosed with pSS-ILD. Ground glass opacities (87.88%) and septal/sub pleural lines (81.82%) were most frequent. Based on pulmonary high-resolution computed tomography, patients were divided into nonspecific (n = 42), usual (n = 20), lymphocytic interstitial pneumonia (n = 3) and cryptogenic organising pneumonia (n = 1) groups. There was a strong association between erythrocyte sedimentation rate (ESR)/C-reactive protein (CRP) and the HRCT-score. Pulmonary function tests revealed impaired diffusion capacity for carbon monoxide and total lung capacity, and coexistence of small airway lesions in pSS-interstitial lung disease. On logistic regression analysis, age, Raynaud's phenomenon, lymphopenia, cough, dyspnoea and rampant dental caries were risk factors associated with pSS-interstitial lung disease. CONCLUSIONS: Interstitial lung disease involvement in pSS is a common clinical occurrence. The clinical manifestation is nonspecific and variable; Raynaud's phenomenon and lymphopenia may predict its onset. pSS patients with advanced age, dry cough and dyspnoea should be systematically evaluated for ILD involvement and managed according to their symptoms.

Object Relocation Visual Tracking Based on Histogram Filter and Siamese Network in Intelligent Transportation.

Target detection and tracking algorithms are one of the key technologies in the field of autonomous driving in intelligent transportation, providing important sensing capabilities for vehicle localization and path planning. Siamese network-based trackers formulate the visual tracking mission as an image-matching process by regression and classification branches, which simplifies the network structure and improves the tracking accuracy. However, there remain many problems, as described below. (1) The lightweight neural networks decrease the feature representation ability. It is easy for the tracker to fail under the disturbing distractors (e.g., deformation and similar objects) or large changes in the viewing angle. (2) The tracker cannot adapt to variations of the object. (3) The tracker cannot reposition the object that has failed to track. To address these issues, we first propose a novel match filter arbiter based on the Euclidean distance histogram between the centers of multiple candidate objects to automatically determine whether the tracker fails. Secondly, the Hopcroft-Karp algorithm is introduced to select the winners from the dynamic template set through the backtracking process, and object relocation is achieved by comparing the Gradient Magnitude Similarity Deviation between the template and the winners. The experiments show that our method obtains better performance on several tracking benchmarks, i.e., OTB100, VOT2018, GOT-10k, and LaSOT, compared with state-of-the-art methods.

Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning.

Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging.

Underwater ghost imaging based on generative adversarial networks with high imaging quality.

Ghost imaging is widely used in underwater active optical imaging because of its simple structure, long distance, and non-local imaging. However, the complexity of the underwater environment will greatly reduce the imaging quality of ghost imaging. To solve this problem, an underwater ghost imaging method based on the generative adversarial networks is proposed in the study. The generator of the proposed network adopts U-Net with the double skip connections and the attention module to improve the reconstruction quality. In the network training process, the total loss function is the sum of the weighted adversarial loss, perceptual loss, and pixel loss. The experiment and simulation results show that the proposed method effectively improves the target reconstruction performance of underwater ghost imaging. The proposed method promotes the further development of active optical imaging of underwater targets based on ghost imaging technology.

Black Soldier Fly (Hermetia illucens) Larvae Significantly Change the Microbial Community in Chicken Manure.

Using black soldier fly (Hermetia illucens) larvae in treatment of livestock manure is a promising technology. In this study, high-throughput sequencing was used to analyze the microbial community in chicken manure before and after treatment with H. illucens larvae. In fresh chicken manure, the most abundant bacterial phylum was Firmicutes (55.58%) followed by Bacteroidetes (24.52%) and then Proteobacteria (12.29%). After treatment of the manure with H. illucens larvae for 15 days, the abundance of Firmicutes increased to 97.72% while that of Bacteroidetes and Proteobacteria decreased. Concomitantly, the most abundant genera of fungi in chicken manure changed from Kernia (46.19%) and Microascus (17.22%) to Penicillium (46.82%) and Aspergillus (45.22%). Correlation-network analysis showed the existence of strong and complex correlations between the dominant operational taxonomic units (OUT) of bacteria and fungi. While most of these correlations were positive, three specific genera, namely g_norank_f_Bacillaceae, Penicillium, and Aspergillus exhibited negative correlations with the remaining genera. These three genera were highly abundant in the intestines of H. illucens and in chicken manure treated with H. illucens larvae. Based on 16S rDNA microbiome-function predictions, the metabolic pathways associated with sugars, amino acids, and organic pollutants inside the intestinal tract of H. illucens were enriched versus those of the other three groups. In summary, the treatment of chicken manure with H. illucens larvae significantly reduced the microbial diversity, while strongly increasing organic metabolism in the intestinal bacteria. This technology shows the potential for applications in livestock manure treatment.

Pig Weight and Body Size Estimation Using a Multiple Output Regression Convolutional Neural Network: A Fast and Fully Automatic Method.

Pig weight and body size are important indicators for producers. Due to the increasing scale of pig farms, it is increasingly difficult for farmers to quickly and automatically obtain pig weight and body size. Due to this problem, we focused on a multiple output regression convolutional neural network (CNN) to estimate pig weight and body size. DenseNet201, ResNet152 V2, Xception and MobileNet V2 were modified into multiple output regression CNNs and trained on modeling data. By comparing the estimated performance of each model on test data, modified Xception was selected as the optimal estimation model. Based on pig height, body shape, and contour, the mean absolute error (MAE) of the model to estimate body weight (BW), shoulder width (SW), shoulder height (SH), hip width (HW), hip width (HH), and body length (BL) were 1.16 kg, 0.33 cm, 1.23 cm, 0.38 cm, 0.66 cm, and 0.75 cm, respectively. The coefficient of determination (R2) value between the estimated and measured results was in the range of 0.9879-0.9973. Combined with the LabVIEW software development platform, this method can estimate pig weight and body size accurately, quickly, and automatically. This work contributes to the automatic management of pig farms.

Snapshot spectral polarimetric light field imaging using a single detector.

In this Letter, we investigate a snapshot spectral-polarimetric-volumetric imaging (SSPVI) system using a single detector. Through compressed acquisition and reconstruction, SSPVI can achieve spectral imaging (x,y,λ), polarization imaging (x,y,ψ,χ), and light field imaging (x,y,θ,φ) simultaneously. The newly discovered performance is showcased by attaining the spectral-polarimetric-volumetric video and different laboratory accuracy experiments. These never-seen-before capacities of the camera open new prospects for many applications, such as biological analysis, object recognition, and remote sensing.

Cytotoxic Secondary Metabolites Isolated from the Marine Alga-Associated Fungus Penicillium chrysogenum LD-201810.

A new pentaketide derivative, penilactonol A (1), and two new hydroxyphenylacetic acid derivatives, (2'R)-stachyline B (2) and (2'R)-westerdijkin A (3), together with five known metabolites, bisabolane-type sesquiterpenoids 4-6 and meroterpenoids 7 and 8, were isolated from the solid culture of a marine alga-associated fungus Penicillium chrysogenum LD-201810. Their structures were elucidated based on extensive spectroscopic analyses, including 1D/2D NMR and high resolution electrospray ionization mass spectra (HRESIMS). The absolute configurations of the stereogenic carbons in 1 were determined by the (Mo2(OAc)4)-induced circular dichroism (CD) and comparison of the calculated and experimental electronic circular dichroism (ECD) spectra, while the absolute configuration of the stereogenic carbon in 2 was established using single-crystal X-ray diffraction analysis. Compounds 2 and 3 adapt the 2'R-configuration as compared to known hydroxyphenylacetic acid-derived and O-prenylated natural products. The cytotoxicity of 1-8 against human carcinoma cell lines (A549, BT-549, HeLa, HepG2, MCF-7, and THP-1) was evaluated. Compound 3 exhibited cytotoxicity to the HepG2 cell line with an IC50 value of 22.0 µM. Furthermore, 5 showed considerable activities against A549 and THP-1 cell lines with IC50 values of 21.2 and 18.2 µM, respectively.

Characterization and Complete Genome Analysis of the Carbazomycin B-Producing Strain Streptomyces luteoverticillatus SZJ61.

Members of marine Actinobacteria have been highly regarded as potentially important sources of antimicrobial compounds. Here, we isolated a strain of Actinobacteria, SZJ61, and showed that it inhibits the in vitro growth of fungi pathogenic to plants. This new isolate was identified as Streptomyces luteoverticillatus by morphological, biochemical and genetic analyses. Antifungal compounds were isolated from S. luteoverticillatus strain SZJ61 and characterized as carbazomycin B by nuclear magnetic resonance spectra. We then sequenced the genome of the S. luteoverticillatus SZJ61 strain, which consists of only one 7,367,863 bp linear chromosome that has a G+C content of 72.05%. Thirty-five putative biosynthetic gene clusters for secondary metabolites, including a variety of bioactive products, were found. Mining of the genome sequence information revealed the putative biosynthetic gene cluster of carbazomycin B. This genomic information is valuable for interpreting the biosynthetic mechanisms of diverse bioactive compounds that have potential applications in the pharmaceutical industry.