AtPIP1;4 and AtPIP2;4 Cooperatively Mediate H2O2 Transport to Regulate Plant Growth and Disease Resistance.

The rapid production of hydrogen peroxide (H2O2) is a hallmark of plants' successful recognition of pathogen infection and plays a crucial role in innate immune signaling. Aquaporins (AQPs) are membrane channels that facilitate the transport of small molecular compounds across cell membranes. In plants, AQPs from the plasma membrane intrinsic protein (PIP) family are utilized for the transport of H2O2, thereby regulating various biological processes. Plants contain two PIP families, PIP1s and PIP2s. However, the specific functions and relationships between these subfamilies in plant growth and immunity remain largely unknown. In this study, we explore the synergistic role of AtPIP1;4 and AtPIP2;4 in regulating plant growth and disease resistance in Arabidopsis. We found that in plant cells treated with H2O2, AtPIP1;4 and AtPIP2;4 act as facilitators of H2O2 across membranes and the translocation of externally applied H2O2 from the apoplast to the cytoplasm. Moreover, AtPIP1;4 and AtPIP2;4 collaborate to transport bacterial pathogens and flg22-induced apoplastic H2O2 into the cytoplasm, leading to increased callose deposition and enhanced defense gene expression to strengthen immunity. These findings suggest that AtPIP1;4 and AtPIP2;4 cooperatively mediate H2O2 transport to regulate plant growth and immunity.

MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder.

BACKGROUND: Integrating multi-omics data is emerging as a critical approach in enhancing our understanding of complex diseases. Innovative computational methods capable of managing high-dimensional and heterogeneous datasets are required to unlock the full potential of such rich and diverse data. METHODS: We propose a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoders (MOCAT) to utilize intra- and inter-omics information comprehensively. Additionally, attention mechanisms with confidence learning are incorporated for enhanced feature representation and trustworthy prediction. RESULTS: Extensive experiments were conducted on four benchmark datasets to evaluate the effectiveness of our proposed model, including BRCA, ROSMAP, LGG, and KIPAN. Our model significantly improved most evaluation measurements and consistently surpassed the state-of-the-art methods. Ablation studies showed that the auxiliary classifiers significantly boosted classification accuracy in the ROSMAP and LGG datasets. Moreover, the attention mechanisms and confidence evaluation block contributed to improvements in the predictive accuracy and generalizability of our model. CONCLUSIONS: The proposed framework exhibits superior performance in disease classification and biomarker discovery, establishing itself as a robust and versatile tool for analyzing multi-layer biological data. This study highlights the significance of elaborated designed deep learning methodologies in dissecting complex disease phenotypes and improving the accuracy of disease predictions.

Neuroprotective effects of dipeptidyl peptidase 4 inhibitor on Alzheimer's disease: a narrative review.

Insulin resistance in brain and amyloidogenesis are principal pathological features of diabetes-related cognitive decline and development of Alzheimer's disease (AD). A growing body of evidence suggests that maintaining glucose under control in diabetic patients is beneficial for preventing AD development. Dipeptidyl peptidase 4 inhibitors (DDP4is) are a class of novel glucose-lowering medications through increasing insulin excretion and decreasing glucagon levels that have shown neuroprotective potential in recent studies. This review consolidates extant evidence from earlier and new studies investigating the association between DPP4i use, AD, and other cognitive outcomes. Beyond DPP4i's benefits in alleviating insulin resistance and glucose-lowering, underlying mechanisms for the potential neuroprotection with DPP4i medications were categorized into the following sections: (Ferrari et al., Physiol Rev, 2021, 101, 1,047-1,081): the benefits of DPP4is on directly ameliorating the burden of ß-amyloid plaques and reducing the formation of neurofibrillary tangles; DPP4i increasing the bioactivity of neuroprotective DPP4 substrates including glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic peptide (GIP), and stromal-derived factor-1α (SDF-1α) etc.; pleiotropic effects of DPP4is on neuronal cells and intracerebral structure including anti-inflammation, anti-oxidation, and anti-apoptosis. We further revisited recently published epidemiological studies that provided supportive data to compliment preclinical evidence. Given that there remains a lack of completed randomized trials that aim at assessing the effect of DPP4is in preventing AD development and progression, this review is expected to provide a useful insight into DPP4 inhibition as a potential therapeutic target for AD prevention and treatment. The evidence is helpful for informing the rationales of future clinical research and guiding evidence-based clinical practice.

Growth performance, lipid metabolism, and systemic immunity of weaned piglets were altered by buckwheat protein through the modulation of gut microbiota.

Tartary buckwheat protein (BWP) is well known for the wide-spectrum antibacterial activity and the lipid metabolism- regulating property; therefore, BWP can be applied as feed additives to improve the animal's nutritional supply. With the aim to investigate the bioactive actions of the BWP, growth performance, lipid metabolism and systemic immunity of the weaned piglets were measured, and the alterations of pig gut microbiota were also analyzed. According to the results, the growth performances of the weaned piglets which were calculated as the average daily gain (ADG) and the average daily feed intake (ADFI) were significantly increased when compared to the control group. Simultaneously, the serum levels of the total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) were decreased, while the levels of high-density lipoprotein cholesterol (HDL-C) were increased in the BWP group. Moreover, the relative abundances of Lactobacillus, Prevotella_9, Subdoligranulum, Blautia, and other potential probiotics in the gut microbiota of weaned piglets were obviously increased in the BWP group. However, the relative abundances of Escherichia-Shigella, Campylobacter, Rikenellaceae_RC9_gut_group and other opportunistic pathogens were obviously decreased in the BWP group. In all, BWP was proved to be able to significantly improve the growth performance, lipid metabolism, and systemic immunity of the weaned piglets, and the specific mechanism might relate to the alterations of the gut microbiota. Therefore, BWP could be explored as a prospective antibiotic alternative for pig feed additives.

TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction.

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.

Trinuclear Cu-based covalent organic framework: π-conjugated framework regulating electron delocalization to promote photoreduction CO2.

Sunlight-driven CO2 reduction to value-added chemicals is an effective strategy to promote carbon recycling. The exploration of catalysts with efficient charge separation is crucially important for highly efficient CO2 photoreduction. In this work, the preparation of metal-cluster-based covalent organic framework (CuABD) integrated features from both metal organic frameworks (MOFs) and covalent organic frameworks (COFs) through the condensation of diamines and functionalized trinuclear copper clusters demonstrate a thoughtful design strategy. The reported yield of 1.3 mmol g-1 h-1 for formic acid (HCOOH) under simulated solar irradiation is impressive, surpassing the performance of many COF- and MOF-based catalysts previously reported. Compared to its isomorphic metal-free structure (named BDFTD) and bare trinuclear Cu cluster which present extremely poor catalytic activities, CuABD displays remarkably enhanced CO2 reduction activity. Experimental and theoretical investigations reveal that the efficient charge transfer between diamine monomer and cyclic trinuclear copper (I) units, and the electron delocalization of the π-conjugated framework are responsible for the appealing catalytic performance. In summary, the work presents a well-structured and scientifically sound exploration of a metal-cluster-based covalent organic framework for efficient CO2 reduction under sunlight.

Genetic Characteristics of Wuxiang Virus in Shanxi Province, China.

Wuxiang virus (WUXV) is the first sandfly-borne Phlebovirus isolated from Phlebotomus chinensis collected in China and has been established as a consistent viral presence in the local sandfly populations of both Wuxiang County and Yangquan City. However, its distribution in the Shanxi Province remains unclear. In this study, three novel WUXV strains were isolated from sandflies collected from Jiexiu City, Shanxi Province, China, in 2022. Subsequently, whole-genome sequences of these novel strains were generated using next-generation sequencing. The open reading frame (ORF) sequences of the WUXV strains from the three locations were subjected to gene analysis. Phylogenetic analysis revealed that WUXV belongs to two distinct clades with geographical differences. Strains from Wuxiang County and Yangquan City belonged to clade 1, whereas strains from Jiexiu City belonged to clade 2. Reassortment and recombination analyses indicated no gene reassortment or recombination between the two clades. However, four reassortments or recombination events could be detected in clade 1 strains. By aligning the amino acid sequences, eighty-seven mutation sites were identified between the two clades, with seventeen, sixty, nine, and one site(s) in the proteins RdRp, M, NSs, and N, respectively. Additionally, selection pressure analysis identified 17 positively selected sites across the entire genome of WUXV, with two, thirteen, one, and one site(s) in the proteins RdRp, M, NSs, and N, respectively. Notably, sites M-312 and M-340 in the M segment not only represented mutation sites but also showed positive selective pressure effects. These findings highlight the need for continuous nationwide surveillance of WUXV.

The response of rhubarb to smut infection is revealed through a comparative transcriptome and metabolome study.

MAIN CONCLUSION: Integrated transcriptome and metabolome analysis have unveiled the physiological and molecular responses of rhubarb to infection by smut fungi. Rhubarb is an important medicinal plant that is easily infected by smut fungi during its growth. Thus far, no research on the influence of smut fungi on the growth of rhubarb and its secondary metabolism has been conducted. In this study, petioles of Chinese rhubarb (Rheum officinale) [healthy or infected with smut fungus (Thecaphora schwarzmaniana)] were characterized. Microscopic structure, global gene expression profiling, global metabolic profiling, and key enzyme activity and metabolite levels in infected plants were analyzed. Infection by smut fungi resulted in numerous holes inside the petiole tissue and led to visible tumors on the external surface of the petiole. Through metabolic changes, T. schwarzmaniana induced the production of specific sugars, lipids, and amino acids, and inhibited the metabolism of phenolics and flavonoids in R. officinale. The concentrations of key medicinal compounds (anthraquinones) were decreased because of smut fungus infection. In terms of gene expression, the presence of T. schwarzmaniana led to upregulation of the genes associated with nutrient (sugar, amino acid, etc.) transport and metabolism. The gene expression profiling showed a stimulated cell division activity (the basis of tumor formation). Although plant antioxidative response was enhanced, the plant defense response against pathogen was suppressed by T. schwarzmaniana, as indicated by the expression profiling of genes involved in biotic and abiotic stress-related hormone signaling and the synthesis of plant disease resistance proteins. This study demonstrated physiological and molecular changes in R. officinale under T. schwarzmaniana infection, reflecting the survival tactics employed by smut fungus for parasitizing rhubarb.

Nacre-inspired, strong, tough silk fibroin hydrogels based on biomineralization and the layer-by-layer assembly of ordered silk fabric.

Hydrogels are attractive materials with structures and functional properties similar to biological tissues and widely used in biomedical engineering. However, traditional synthetic hydrogels possess poor mechanical strength, and their applications are limited. Herein, a multidimensional material design method is developed; it includes the in situ gelation of silk fabric and nacre-inspired layer-by-layer assembly, which is used to prepare silk fibroin (SF) hydrogels. The in situ gelation method of silk fabric introduces a directionally ordered fabric network in a silk substrate, considerably enhancing the strength of hydrogels. Based on the nacre structure, the layer-by-layer assembly method enables silk hydrogels to break through the size limit and increase the thickness, realizing the longitudinal extension of the hydrogels. The application of the combined biomineralization and hot pressing method can effectively reduce interface defects and improve the interaction between organic and inorganic interfaces. The multidimensional material design method helps increase the strength (287.78 MPa), toughness (18.43 MJ m-3), and fracture energy (50.58 kJ m-2) of SF hydrogels; these hydrogels can weigh 2000 times their own weight. Therefore, SF hydrogels designed using the aforementioned combined method can realize the combination of strength and toughness and be used in biological tissue engineering and structural materials.

Regenerated silk protein based hybrid film electrode with large area specific capacitance, high flexibility and light weight towards high-performance wearable energy storage.

Planar wearable supercapacitors (PWSCs) have sparked intense interest owing to their hopeful application in smart electronics. However, current PWSCs suffered from poor electrochemical property, weak flexibility and/or large weight. To relieve these defects, in this study, we fabricated a high-performance PWSC using silk protein derived film electrodes (PPy/RSF/MWCNTs-2; RSF, PPy and MWCNTs represent regenerated silk film, polypyrrole and multi-walled carbon nanotubes, respectively, while 2 is the mass ratio of silk to MWCNTs), which were developed by 'dissolving-mixing-evaporating' and in situ polymerization. In three-electrode, PPy/RSF/MWCNTs-2 showed a superb area specific capacitance of 8704.7 mF cm-2 at 5 mA cm-2, which surpassed numerous reported PWSC electrodes, and had a decent durability with a capacitance retention of 90.7 % after 5000 cycles. The PPy/RSF/MWCNTs-2 derived PWSC showed a largest energy density of 281.3 µWh cm-2 at 1660.1 µW cm-2, and a power density as high as 13636.4 µW cm-2 at 125.6 µWh cm-2. Furthermore, impressive capacitive-mechanical stability with a capacitance retention of 92 % under bending angles from 0 to 150 was depicted. Thanks to the rational and affordable preparation, our study for the first time prepared RSF electrode that had great capacitive property, high mechanical flexibility and light weight, simultaneously. The encouraging results can not only open up a new path to manufacture high-performance flexible electrodes, but may also help to realize the high-value-added utilization of silk.

Transcriptomic sequencing analysis of key long noncoding RNAs and mRNAs expression profiles in postoperative recurrence of hepatocellular carcinoma.

BACKGROUND: Recurrence is the main cause of death in hepatocellular carcinoma (HCC) patients after liver resection. OBJECTIVE: The long non-coding RNAs (lncRNAs) have been reported participated in progression and prognosis of HCC, however, the vital role of lncRNA in postoperative recurrence of HCC has rarely been systematically identified. METHODS: RNA-sequencing (RNA-seq) was performed between orthotopic model of HCC and hepatoma postoperative recurrent model to comprehensively analyze the integrated transcriptome expression profiles of lncRNA and mRNA. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was then conducted to quantify the expression levels of DElncRNAs and their target mRNAs. RESULTS: In our study, 211 lncRNAs (P-value < 0.05) and 1125 mRNAs (P-adjust < 0.05) were significantly differentially expressed (DE) between two groups. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DElncRNAs and DEmRNAs were mainly enriched in lipid metabolism, including Arachidonic acid metabolism, PPAR signaling pathway, Steroid hormone biosynthesis, Linoleic acid metabolism, Inflammatory mediator regulation of TRP channels, and Fatty acid degradation. Furthermore, we constructed lncRNA-mRNA interaction networks and protein-protein interaction (PPI) network, and verified by qRT-PCR, suggesting that increased DEIncRNAs (XLOC_063499 and XLOC_042016) may prevent HCC recurrence after surgery by upregulating on targeted cytochrome P450 (CYP) family genes in the lipid metabolism pathway, such as cyp3a16, cyp3a44, cyp2c39, cyp2c40 and cyp2c68. CONCLUSION: Overall, Our findings provided new insights for further investigation of biological function in lncRNA related HCC recurrence.

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

Reduction in gefitinib resistance mediated by Yi-Fei San-Jie pill in non-small cell lung cancer through regulation of tyrosine metabolism, cell cycle, and the MET/EGFR signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: The Chinese herbal prescription Yi-Fei San-Jie pill (YFSJ) has been used for adjuvant treatment in patients with lung cancer for a long time. AIM OF THE STUDY: Reports have indicated that the combination of gefitinib (Gef) with YFSJ inhibits the proliferation of EGFR-TKI-resistant cell lines by enhancing cellular apoptosis and autophagy in non-small cell lung cancer (NSCLC). However, the molecular mechanisms underlying the effect of YFSJ on EGFR-TKI resistance and related metabolic pathways remain to be explored. MATERIALS AND METHODS: In our report, ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), metabolomics, network pharmacology, bioinformatics, and biological analysis methods were used to investigate the mechanism. RESULTS: The UPLC-MS/MS data identified 42 active compounds of YFSJ extracts. YFSJ extracts can enhance the antitumor efficacy of Gef without hepatic and renal toxicity in vivo. The analysis of the metabolomics pathway enrichment revealed that YFSJ mainly affected the tyrosine metabolism pathway in rat models. Moreover, YFSJ has been shown to reverse Gef resistance and improve the effects of Gef on the cellular viability, migration capacity, and cell cycle arrest of NSCLC cell lines with EGFR mutations. The results of network pharmacology and molecular docking analyses revealed that tyrosine metabolism-related active compounds of YFSJ affect EGFR-TKIs resistance in NSCLC by targeting cell cycle and the MET/EGFR signaling pathway; these findings were validated by western blotting and immunohistochemistry. CONCLUSIONS: YFSJ inhibits NSCLC by inducing cell cycle arrest in the G1/S phase to suppress tumor growth, cell viability, and cell migration through synergistic effects with Gef via the tyrosine metabolic pathway and the EGFR/MET signaling pathway. To summarize, the findings of the current study indicate that YFSJ is a prospective complementary treatment for Gef-resistant NSCLC.

MYB44 regulates PTI by promoting the expression of EIN2 and MPK3/6 in Arabidopsis.

The plant signaling pathway that regulates pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) involves mitogen-activated protein kinase (MAPK) cascades that comprise sequential activation of several protein kinases and the ensuing phosphorylation of MAPKs, which activate transcription factors (TFs) to promote downstream defense responses. To identify plant TFs that regulate MAPKs, we investigated TF-defective mutants of Arabidopsis thaliana and identified MYB44 as an essential constituent of the PTI pathway. MYB44 confers resistance against the bacterial pathogen Pseudomonas syringae by cooperating with MPK3 and MPK6. Under PAMP treatment, MYB44 binds to the promoters of MPK3 and MPK6 to activate their expression, leading to phosphorylation of MPK3 and MPK6 proteins. In turn, phosphorylated MPK3 and MPK6 phosphorylate MYB44 in a functionally redundant manner, thus enabling MYB44 to activate MPK3 and MPK6 expression and further activate downstream defense responses. Activation of defense responses has also been attributed to activation of EIN2 transcription by MYB44, which has previously been shown to affect PAMP recognition and PTI development. AtMYB44 thus functions as an integral component of the PTI pathway by connecting transcriptional and posttranscriptional regulation of the MPK3/6 cascade.

Cartilage structure-inspired elastic silk nanofiber network hydrogel for stretchable and high-performance supercapacitors.

Flexible supercapacitors are an important portable energy storage but suffer from low capacitance, inability to stretch, etc. Therefore, flexible supercapacitors must achieve higher capacitance, energy density, and mechanical robustness to expand the applications. Herein, a hydrogel electrode with excellent mechanical strength was created by simulating the collagen fiber network and proteoglycan in cartilage using silk nanofiber (SNF) network and polyvinyl alcohol (PVA). The Young's modulus and breaking strength of the hydrogel electrode increased by 205 % and 91 % compared with PVA hydrogel owing to the enhanced effect of the bionic structure, respectively, which are 1.22 MPa and 1.3 MPa. The fracture energy and fatigue threshold reached 1813.5 J/m2 and 1585.2 J/m2, respectively. The SNF network effectively connected carbon nanotubes (CNTs) and polypyrrole (PPy) in series, affording a capacitance of 13.62 F/cm2 and energy density of 1.2098 mWh/cm2. This capacitance is the highest among currently reported PVA hydrogel capacitors, which can maintain >95.2 % after 3000 charge-discharge cycles. This capacitance Notably, the cartilage-like structure endowed the supercapacitor with high resilience; thus, the capacitance remained >92.1 % under 150 % deformation and >93.35 % after repeated stretching (3000 times), which was far superior to that of other PVA-based supercapacitors. Overall, this effective bionic strategy can endow supercapacitors with ultrahigh capacitance and effectively ensure the mechanical reliability of flexible supercapacitors, which will help expand the applications of supercapacitors.

Persistence of Tembusu Virus in Culex tritaeniorhynchus in Yunnan Province, China.

The Tembusu virus (TMUV), a member of the Flaviviridae family, can be transmitted via mosquitoes and cause poultry disease. In 2020, a strain of TMUV (YN2020-20) was isolated from mosquito samples collected in Yunnan province, China. In vitro experiments showed that TMUV-YN2020-20 produced a significant cytopathic effect (CPE) in BHK, DF-1, and VERO cells, while the CPE in C6/36 cells was not significant. Phylogenetic analysis revealed that the strain belonged to Cluster 3.2 and was closely related to the Yunnan mosquito-derived isolates obtained in 2012 and the Shandong avian-derived isolate obtained in 2014. Notably, TMUV-YN2020-20 developed five novel mutations (E-V358I, NS1-Y/F/I113L, NS4A-T/A89V, NS4B-D/E/N/C22S, and NS5-E638G) at loci that were relatively conserved previously. The results of this study demonstrate the continuous circulation and unique evolution of TMUV in mosquitoes in Yunnan province and suggest that appropriate surveillance should be taken.

Brain-wide and cell-specific transcriptomic insights into MRI-derived cortical morphology in macaque monkeys.

Integrative analyses of transcriptomic and neuroimaging data have generated a wealth of information about biological pathways underlying regional variability in imaging-derived brain phenotypes in humans, but rarely in nonhuman primates due to the lack of a comprehensive anatomically-defined atlas of brain transcriptomics. Here we generate complementary bulk RNA-sequencing dataset of 819 samples from 110 brain regions and single-nucleus RNA-sequencing dataset, and neuroimaging data from 162 cynomolgus macaques, to examine the link between brain-wide gene expression and regional variation in morphometry. We not only observe global/regional expression profiles of macaque brain comparable to human but unravel a dorsolateral-ventromedial gradient of gene assemblies within the primate frontal lobe. Furthermore, we identify a set of 971 protein-coding and 34 non-coding genes consistently associated with cortical thickness, specially enriched for neurons and oligodendrocytes. These data provide a unique resource to investigate nonhuman primate models of human diseases and probe cross-species evolutionary mechanisms.

Characterization of small plasmids carrying florfenicol resistance gene floR in Actinobacillus pleuropneumoniae and Pasteurella multocida isolates from swine in China.

Actinobacillus pleuropneumoniae and Pasteurella multocida are two important bacterial pathogens in swine industry. In the present study, resistance profiles of nine commonly used antibiotics of A. pleuropneumoniae and P. multocida isolates of swine origin from different regions of China were investigated by determination of minimum inhibitory concentrations (MICs). In addition, genetic relationship of the florfenicol-resistant A. pleuropneumoniae and P. multocida isolates was determined by pulsed-field gel electrophoresis (PFGE). The genetic basis of florfenicol resistance in these isolates were explored by floR detection and whole genome sequencing. High resistance rates (>25%) of florfenicol, tetracycline and trimethoprim- sulfamethoxazole were observed for both bacteria. No ceftiofur- and tiamulin- resistant isolates were detected. Furthermore, all the 17 florfenicol-resistant isolates (nine for A. pleuropneumoniae and eight for P. multocida) were positive for floR gene. The presence of similar PFGE types in these isolates suggested that clonal expansion of some floR-producing strains occurred in the pig farms from same regions. WGS and PCR screening showed that three plasmids, named pFA11, pMAF5, and pMAF6, were the cargos of the floR genes in the 17 isolates. Plasmid pFA11 exhibited novel structure and carried several resistance genes, including floR, sul2, aacC2d, strA, strB, and bla ROB - 1. Plasmids pMAF5 and pMAF6 were presented in A. pleuropneumoniae and P. multocida isolates from different regions, suggesting horizontal transfer of the two plasmids are important for the floR dissemination in these Pasteurellaceae pathogens. Further studies of florfenicol resistance and its transfer vectors in Pasteurellaceae bacteria of veterinary origin are warranted.

Expression and Clinical Significance of MDM2 in Non-Functioning PitNETs.

Background and Objective: Non-functioning pituitary neuroendocrine tumors (NF-PitNETs) represent a heterogeneous tumor type that lacks effective medical treatment. MDM2, the main negative regulator of p53, binds to and forms a stable complex with p53 to regulate its activity. In this study, we measured the expression levels and role of MDM2 in non-functioning PitNET patients' combined clinical features and investigated the effect of etoposide on the cell bioactivity of the GT1-1 cell line in vivo and in vitro. Methods: RT-PCR and immunochemistry measured the expression levels and role of MDM2 in 103 NF-PitNET patients' combined clinical features. Cell proliferation, migration, colony and apoptosis experiments measured the effect of etoposide on the GT1-1 cell line in vivo and in vitro. Results: There was more invasive behavior (p = 0.013) in patients with high MDM2, who were also younger (p = 0.007), were more frequently female (p = 0.049) and had larger tumor sizes (p = 0.018) compared with patients with low MDM2. Patients with high p53 were younger (p = 0.017) and had larger tumor sizes (p = 0.034) compared with patients with low p53. Univariate (p = 0.018) and multivariate (p = 0.023) Cox regression analysis showed that MDM2 was the independent factor for invasive behavior in NF-PitNET patients. Log-rank analysis showed that the average progression-free survival (PFS) time in the low MDM2 patients was longer than that in the high MDM2 patients (p = 0.044). Functional studies indicated that etoposide inhibited cell proliferation and cell migration and induced apoptosis in p53 independence in GT1-1 cells. Furthermore, etoposide significantly inhibited the growth of GT1-1-xenograft in BALB/c nude mice. The tumor growth inhibition rate of etoposide was 67.4 ± 4.6% after 14 d of treatment, which suggested the anti-tumor activity of etoposide. Conclusions: MDM2 played the role of tumorigenesis of NF-PitNET in a p53 independence manner, and an MDM2 inhibitor could be a potential choice for the treatment of NF-PitNET patients.

Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks.

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.