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Mangroves are an important component of coastal wetland ecosystems, and low temperature is the main factor that limits their extension to higher latitudes. Kandelia obovata as one of the most cold-tolerant species in mangrove ecosystems can provide basis for the northward migration of mangrove ecosystems. We took K. obovata seedlings from Zhoushan (Zhoushan, Zhejiang, China) as the research object in this study. Transcriptome sequencing based on the Illumina HiSeqTM 2500 platform was performed to compare the transcriptome changes of roots, stems, and leaves before and after freezing and to reveal the molecular mechanisms of frost resistance. A total of 1560, 370, and 416 genes were differentially expressed in the roots, stems, and leaves before and after cold snaps, respectively. Among these differentially expressed genes, 13 positive and negative regulators were attributed to the CBL-CIPK signaling network and MAPK cascade, which might be related to the frost resistance mechanism of K. obovata Transcription factors such as AP2/EREBP and bHLH were involved in regulating the synthesis pathways of ethylene, cytokinin, growth hormone, and flavonoids. Results provide new insights into the frost resistance mechanism of K. obovata seedlings.

BackgroundParkinson's disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson's disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model.MethodsWe downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples).ResultsUltimately, we screened for four immune-related Parkinson's disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson's disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson's disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes.ConclusionIn this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.

Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates s-line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph’s s-line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the s-line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation.

The pressurized capsule structure provides the pressure environment for astronauts or payloads in space, which is thus considered as the most crucial structural component for manned spacecraft. The manned deep space exploration mission (MDSEM) brings new challenges to the pressurized capsule structure: extremely low structural weight, long service life, reusability and adaptability to the harsh deep space environment. The conventional welded panel pressurized capsule structure (WPPCS) is not able to meet these new requirements. To address the above challenges, this paper comprehensively expounds why the current WPPCS cannot meet the requirements of MDSEMs based on the analysis of the vibration environment and structural characteristics of the pressurized capsule structure. Furthermore, a new type of integrated panel pressurized capsule structure (IPPCS) is proposed, showing the lightweight advantage compared with WPPCS. Finally, the technical details and research results of the strength criterion, design method, material upgrading and structural integrity manufacturing process of the IPPCS are fully introduced. The conclusions drawn in this paper will provide useful and meaningful references for the future development of large-size, lightweight pressurized capsule structures.

Ischemic stroke (IS) is a high-incidence disease that seriously threatens human life and health. Neuroinflammation and immune responses are key players in the pathophysiological processes of IS. However, the underlying immune mechanisms are not fully understood. In this study, we attempted to identify several immune biomarkers associated with IS. We first retrospectively collected validated human IS immune-related genes (IS-IRGs) as seed genes. Afterward, potential IS-IRGs were discovered by applying random walk with restart on the PPI network and the permutation test as a screening strategy. Doing so, the validated and potential sets of IS-IRGs were merged together as an IS-IRG catalog. Two microarray profiles were subsequently used to explore the expression patterns of the IS-IRG catalog, and only IS-IRGs that were differentially expressed between IS patients and controls in both profiles were retained for biomarker selection by the Random Forest rankings. CLEC4D and CD163 were finally identified as immune biomarkers of IS, and a classification model was constructed and verified based on the weights of two biomarkers obtained from the Neural Network algorithm. Furthermore, the CIBERSORT algorithm helped us determine the proportions of circulating immune cells. Correlation analyses between IS immune biomarkers and immune cell proportions demonstrated that CLEC4D was strongly correlated with the proportion of neutrophils (r = 0.72). These results may provide potential targets for further studies on immuno-neuroprotection therapies against reperfusion injury.

ObjectiveAs a chronic neurodegenerative disorder, Alzheimer’s disease (AD) is the most common form of progressive dementia. The purpose of this study was to identify diagnostic signatures of AD and the effect of immune cell infiltration in this pathology.MethodsThe expression profiles of GSE109887, GSE122063, GSE28146, and GSE1297 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between AD and control brain samples. Functional enrichment analysis was performed to reveal AD-associated biological functions and key pathways. Besides, we applied the Least Absolute Shrinkage Selection Operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) analysis to screen potential diagnostic feature genes in AD, which were further tested in AD brains of the validation cohort (GSE5281). The discriminatory ability was then assessed by the area under the receiver operating characteristic curves (AUC). Finally, the CIBERSORT algorithm and immune cell infiltration analysis were employed to assess the inflammatory state of AD.ResultsA total of 49 DEGs were identified. The functional enrichment analysis revealed that leukocyte transendothelial migration, cytokine receptor interaction, and JAK-STAT signaling pathway were enriched in the AD group. MAF basic leucine zipper transcription factor F (MAFF), ADCYAP1, and ZFP36L1 were identified as the diagnostic biomarkers of AD with high discriminatory ability (AUC = 0.850) and validated in AD brains (AUC = 0.935). As indicated from the immune cell infiltration analysis, naive B cells, plasma cells, activated/resting NK cells, M0 macrophages, M1 macrophages, resting CD4+ T memory cells, resting mast cells, memory B cells, and resting/activated dendritic cells may participate in the development of AD. Additionally, all diagnostic signatures presented different degrees of correlation with different infiltrating immune cells.ConclusionMAFF, ADCYAP1, and ZFP36L1 may become new candidate biomarkers of AD, which were closely related to the pathogenesis of AD. Moreover, the immune cells mentioned above may play crucial roles in disease occurrence and progression.

Abstract Myasthenia gravis (MG) is an autoimmune disease and the most common type of neuromuscular disease. Genes and miRNAs associated with MG have been widely studied; however, the molecular mechanisms of transcription factors (TFs) and the relationship among them remain unclear. A TF–miRNA–gene network (TMGN) of MG was constructed by extracting six regulatory pairs (TF–miRNA, miRNA–gene, TF–gene, miRNA–TF, gene–gene and miRNA–miRNA). Then, 3/4/5-node regulatory motifs were detected in the TMGN. Then, the motifs with the highest Z-score, occurring as 3/4/5-node composite feed-forward loops (FFLs), were selected as statistically significant motifs. By merging these motifs together, we constructed a 3/4/5-node composite FFL motif-specific subnetwork (CFMSN). Then, pathway and GO enrichment analyses were performed to further elucidate the mechanism of MG. In addition, the genes, TFs and miRNAs in the CFMSN were also utilized to identify potential drugs. Five related genes, 3 TFs and 13 miRNAs, were extracted from the CFMSN. As the most important TF in the CFMSN, MYC was inferred to play a critical role in MG. Pathway enrichment analysis showed that the genes and miRNAs in the CFMSN were mainly enriched in pathways related to cancer and infections. Furthermore, 21 drugs were identified through the CFMSN, of which estradiol, estramustine, raloxifene and tamoxifen have the potential to be novel drugs to treat MG. The present study provides MG-related TFs by constructing the CFMSN for further experimental studies and provides a novel perspective for new biomarkers and potential drugs for MG.

As the key load-bearing component of spacecraft, the strength evaluation of stiffened plate structures faces two challenges. On the one hand, the simulation results are sometimes inaccurate, due to the simplification of the true loading conditions and modeling details. On the other hand, data from the sensors cannot provide the full-field strength information of the structure, which may result in the misjudgment of the structural state. To this end, a digital twin modeling method of multi-source data fusion based on transfer learning is proposed in this paper. In transfer learning, simulation data and sensor data are utilized as the source dataset and the target dataset, respectively. First, a pre-trained deep neural network (DNN) model is established based on the source dataset. Then, the pre-trained DNN model is fine-tuned based on the target dataset using a lower learning rate and fewer training epochs. Finally, a digital twin model can be built, which is capable of visualizing the full-field strength information of the stiffened plate structure. To verify the effectiveness of the proposed method, an experimental study on a hierarchical stiffened plate is carried out. Compared with the traditional data fusion method, the results verify the high prediction accuracy and efficiency of the proposed method, demonstrating its potential for the strength health monitoring of spacecraft in orbit.

As one of the most complex and critical components of spacecraft, the structural design of the curved cabin door faces two challenges. On the one hand, it is difficult to obtain innovative configurations for the cabin door in the preliminary design stage. On the other hand, the traditional optimization design algorithm is inefficient in the detailed design stage. In this paper, a two-stage intelligent method for the layout design of the curved cabin door is proposed. In the first stage, the innovative stiffener layout of the cabin door is obtained based on the topology optimization method. Then the mesh deformation method is used for rapid modeling and geometric reconstruction. In the second stage, a recently proposed powerful evolutionary algorithm, named elite-driven surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (ES-CMA-ES), is employed to optimize the parameters of the cabin door and its surrounding thin-wall structure. To verify the effectiveness of the proposed method, a curved cabin door example from the spacecraft (cargo spaceship) is carried out. Compared with the traditional orthogrid stiffener design, the mass of the optimal design is reduced by 52.21% while satisfying the constraints, which indicates the excellent optimization ability of the proposed method and demonstrates huge potential for improving the carrying capacity and efficiency of the spacecraft.

The complete mitochondrial genome of Chrysochir aureus was sequenced. The full length of the mitochondrial genome was 16,501 bp, including 13 protein-coding genes (PCGs), two ribosomal RNAs, 22 transfer RNA genes, a non-coding control region (CR) and one origin of replication on the light-strand (OL). The total nucleotide composition of mitochondrial DNA was 26.95% A, 29.99% C, 26.29% T, and 16.77% G. Twelve PCGs used the canonical ATG as their initiation codon, whereas COI gene started with an alternative start codon GTG. The mitochondrial genome of C. aureus described in this study could be a useful basis for management of this species and laid a foundation for further research involved with phylogenetic relationship within Sciaenidae.

A fast vibration reduction optimization approach accelerated by the global proper orthogonal decomposition (POD) reduced-order model (ROM) is proposed, aiming at increasing the efficiency of frequency response analysis and vibration reduction optimization of complex thin-walled shells. At the offline stage, the global POD ROM is adaptively updated using the sample configurations generated by the CV (cross validation)–Voronoi sequence sampling method. In comparison to the traditional direct sampling method, the proposed approach achieves higher global prediction accuracy. At the online stage, the fast vibration reduction optimization is performed by combining the surrogate-based efficient global optimization (EGO) method and the proposed ROM. Two representative examples are carried out to verify the effectiveness and efficiency of the proposed approach, including examples of an aerospace S-shaped curved stiffened shell and a Payload Attach Fitting. The results indicate that the proposed approach achieves high prediction accuracy and efficiency through the verification by FOM and obtains better optimization ability over the direct optimization method based on FOM.

In this paper, the integrated design of curvilinearly grid-stiffened panels is investigated, including stiffener layout design and profile design. The layout design includes the curvilinear path and non-uniform distribution of stiffeners. Firstly, the path of curvilinear stiffeners is defined by the piecewise linear function, and the non-uniform distribution of curvilinear stiffener cluster is described by the proposed representation method based on the PCHIP interpolation function. In addition to the layout design, the variable profile design method of grid-stiffened structures is also proposed in this paper. The variations of the thickness and height of stiffeners are represented by curves respectively. Similarly, the variable profile design of stiffeners is realized by designing the curves. On this basis, various novel configuration of curvilinearly grid-stiffened panels can be obtained by the integrated layout and profile design. The effectiveness of the proposed integrated design framework is validated by numerical examples. Compared with the initial design, the final integrated design improves the critical buckling factor of the grid-stiffened panel by 73.09%, which shows a great design potential of this kind of variable-stiffness structure compared to constant-stiffness structures. Keywords: Curvilinearly grid-stiffened panel, Non-uniform distribution, Variable stiffener profile, Integrated design

The Indian driftfish (Cubiceps squamiceps) is one of the most important commercial fish species in China, Japan and India. The complete mitogenome of Cubiceps squamiceps was determined in this study. The assembled mitogenome was 16,507 bp and consisted of 13 protein-coding genes, 22 tRNAs, two rRNAs, and a control region. Nucleotide composition of the complete mitogenome was 27.5% A, 28.5% C, 17.5% G, and 26.5% T, with an A + T bias of 53.9%. The maximum-likelihood tree based on 13 protein-coding genes showed that Cubiceps pauciradiatus and Psenes pellucidus were the closest to C. squamiceps.

We establish a coincidence theorem in -KKM class by means of the basic defining property for multifunctions in -KKM. Based on this coincidence theorem, we deduce some useful corollaries and investigate the fixed point problem on uniform spaces.