Integrated Transcriptomic and Metabolomic Analysis Reveal the Underlying Mechanism of Anthocyanin Biosynthesis in Toona sinensis Leaves.

Toona sinensis, commonly known as Chinese Toon, is a plant species that possesses noteworthy value as a tree and vegetable. Its tender young buds exhibit a diverse range of colors, primarily determined by the presence and composition of anthocyanins and flavonoids. However, the underlying mechanisms of anthocyanin biosynthesis in Toona sinensis have been rarely reported. To explore the related genes and metabolites associated with composition of leaf color, we conducted an analysis of the transcriptome and metabolome of five distinct Toona clones. The results showed that differentially expressed genes and metabolites involved in anthocyanin biosynthesis pathway were mainly enriched. A conjoint analysis of transcripts and metabolites was carried out in JFC (red) and LFC (green), resulting in the identification of 510 genes and 23 anthocyanin-related metabolites with a positive correlation coefficient greater than 0.8. Among these genes and metabolites, 23 transcription factors and phytohormone-related genes showed strong coefficients with 13 anthocyanin derivates, which mainly belonged to the stable types of delphinidin, cyanidin, peonidin. The core derivative was found to be Cyanidin-3-O-arabinoside, which was present in JFC at 520.93 times the abundance compared to LFC. Additionally, the regulatory network and relative expression levels of genes revealed that the structural genes DFR, ANS, and UFGT1 might be directly or indirectly regulated by the transcription factors SOC1 (MADS-box), CPC (MYB), and bHLH162 (bHLH) to control the accumulation of anthocyanin. The expression of these genes was significantly higher in red clones compared to green clones. Furthermore, RNA-seq results accurately reflected the true expression levels of genes. Overall, this study provides a foundation for future research aimed at manipulating anthocyanin biosynthesis to improve plant coloration or to derive human health benefits.

Modified Noise-Immune Fuzzy Neural Network for Solving the Quadratic Programming With Equality Constraint Problem.

Quadratic programming with equality constraint (QPEC) problems have extensive applicability in many industries as a versatile nonlinear programming modeling tool. However, noise interference is inevitable when solving QPEC problems in complex environments, so research on noise interference suppression or elimination methods is of great interest. This article proposes a modified noise-immune fuzzy neural network (MNIFNN) model and use it to solve QPEC problems. Compared with the traditional gradient recurrent neural network (TGRNN) and traditional zeroing recurrent neural network (TZRNN) models, the MNIFNN model has the advantage of inherent noise tolerance ability and stronger robustness, which is achieved by combining proportional, integral, and differential elements. Furthermore, the design parameters of the MNIFNN model adopt two disparate fuzzy parameters generated by two fuzzy logic systems (FLSs) related to the residual and residual integral term, which can improve the adaptability of the MNIFNN model. Numerical simulations demonstrate the effectiveness of the MNIFNN model in noise tolerance.

Regional variation of polybrominated diphenyl ethers in East Asian finless porpoises in the East China Sea.

Certain polybrominated diphenyl ethers (PBDEs) have been banned for years, however, they still possess the potential to harm marine cetaceans. In this study, 56 East Asian finless porpoises (EAFPs) collected from three locations of the East China Sea between 2009 and 2011, were analyzed to determine the presence of typical PBDE congeners. Among all the samples, BDE47 was the main congener, constituting ∼48.3 % of the ΣPBDEs. Significant variations (p < 0.01) in PBDE abundance were observed among different regions (Pingtan: 172.8 ng/g, Lvsi: 61.2 ng/g and Ningbo: 32.9 ng/g). In addition, there was a significant positive correlation between PBDE abundance and male body length. The general ΣPBDEs concentration of this population was lower compared to other populations and cetaceans. Although combined risk assessments indicated a low risk to porpoise health, long-term surveillance is essential as PBDEs are not completely banned.

Willingness intensity and co-evolution of decision rationality depending on aspiration enhance cooperation in the spatial public goods game.

The Fermi rule states that rational or irrational sentiment affects individual decision-making. Existing studies have assumed that individuals' irrational sentiments and behavior willingness have fixed values and do not change with time. In reality, people's rationality sentiment and behavior willingness may be influenced by some factors. Therefore, we propose a spatial public goods game mechanism, in which individuals' rational sentiment is co-evolution synchronously depending on the difference between aspiration and payoff. Moreover, the intensity of their subjective willingness to change the status quo depends on the gap between aspiration and payoff. We likewise compare the combined promotion effect of the stochastic "Win-Stay-Lose-Shift" (WSLS) and random imitation (IM) rules. Simulation experiments indicate that high enhancement factors are not conducive to cooperation under the IM rules. When aspiration is small, WSLS is more conducive to promoting cooperation than IM, while increasing aspiration, and the opposite phenomenon will appear. The heterogeneous strategic update rule is beneficial to the evolution of cooperation. Lastly, we find that this mechanism performs better than the traditional case in enhancing cooperation.

Novel combined endoscopic and laparoscopic surgery for advanced T2 gastric cancer: Two case reports.

BACKGROUND: The standard treatment for advanced T2 gastric cancer (GC) is laparoscopic or surgical gastrectomy (either partial or total) and D2 lymphadenectomy. A novel combined endoscopic and laparoscopic surgery (NCELS) has recently been proposed as a better option for T2 GC. Here we describe two case studies demonstrating the efficacy and safety of NCELS. CASE SUMMARY: Two T2 GC cases were both resected by endoscopic submucosal dissection and full-thickness resection and laparoscopic lymph nodes dissection. This method has the advantage of being more precise and minimally invasive compared to current methods. The treatment of these 2 patients was safe and effective with no complications. These cases were followed up for nearly 4 years without recurrence or metastasis. CONCLUSION: This novel method provides a minimally invasive treatment option for T2 GC, and its potential indications, effectiveness and safety needs to be further evaluated in controlled studies.

A Segmented Variable-Parameter ZNN for Dynamic Quadratic Minimization With Improved Convergence and Robustness.

As a category of the recurrent neural network (RNN), zeroing neural network (ZNN) can effectively handle time-variant optimization issues. Compared with the fixed-parameter ZNN that needs to be adjusted frequently to achieve good performance, the conventional variable-parameter ZNN (VPZNN) does not require frequent adjustment, but its variable parameter will tend to infinity as time grows. Besides, the existing noise-tolerant ZNN model is not good enough to deal with time-varying noise. Therefore, a new-type segmented VPZNN (SVPZNN) for handling the dynamic quadratic minimization issue (DQMI) is presented in this work. Unlike the previous ZNNs, the SVPZNN includes an integral term and a nonlinear activation function, in addition to two specially constructed time-varying piecewise parameters. This structure keeps the time-varying parameters stable and makes the model have strong noise tolerance capability. Besides, theoretical analysis on SVPZNN is proposed to determine the upper bound of convergence time in the absence or presence of noise interference. Numerical simulations verify that SVPZNN has shorter convergence time and better robustness than existing ZNN models when handling DQMI.

Design and Analysis of a Self-Adaptive Zeroing Neural Network for Solving Time-Varying Quadratic Programming.

In order to solve the time-varying quadratic programming (TVQP) problem more effectively, a new self-adaptive zeroing neural network (ZNN) is designed and analyzed in this article by using the Takagi-Sugeno fuzzy logic system (TSFLS) and thus called the Takagi-Sugeno (T-S) fuzzy ZNN (TSFZNN). Specifically, a multiple-input-single-output TSFLS is designed to generate a self-adaptive convergence factor to construct the TSFZNN model. In order to obtain finite- or predefined-time convergence, four novel activation functions (AFs) [namely, power-bi-sign AF (PBSAF), tanh-bi-sign AF (TBSAF), exp-bi-sign AF (EBSAF), and sinh-bi-sign AF (SBSAF)] are developed and applied in the TSFZNN model for solving the TVQP problem. Both theoretical proofs and experimental simulations show that the TSFZNN model using PBSAF or TBSAF has the property of converging in a finite time, and the TSFZNN model using EBSAF or SBSAF has the property of converging in a predefined time, which have superior convergence performance compared to the traditional ZNN model.

Role of endoscopic ultrasound in the evaluation of unexplained extrahepatic bile duct dilatation.

OBJECTIVE: This study was performed to assess the diagnostic performance of endoscopic ultrasonography (EUS) in patients with extrahepatic bile duct (EBD) dilatation and develop a novel model incorporating EUS-based signature with clinical parameters for distinguishing the malignant dilation of EBD. METHODS: The EUS data and clinical parameters of the patients were collected and analyzed retrospectively. First, we evaluated the diagnostic performance of EUS in detecting the cause of EBD dilatation. Then, we performed univariate and multivariate binary logistic regression analyses based on clinical and EUS features. Finally, a nomogram was established to aid in distinguishing between malignant dilation and noncalculous benign dilatation of EBD in patients. RESULTS: A total of 184 patients were enrolled. For the diagnosis of malignant dilation, EUS achieved an accuracy of 90.76%, sensitivity of 85.96%, and specificity of 92.91%. For the diagnosis of calculous dilation, EUS achieved an accuracy of 100%, sensitivity of 100%, and specificity of 100%. For the diagnosis of noncalculous benign dilatation, EUS achieved an accuracy of 90.76%, sensitivity of 90.90%, and specificity of 90.58%. Multivariable logistic regression analyses indicated that abnormal liver function test, elevated tumor markers, and EUS findings were the well-diagnostic factors of malignant EBD dilation. The nomogram established by these factors showed good calibration and discrimination. CONCLUSION: EUS is a useful examinational modality in the work-up of EBD dilatation. In combination with abnormal liver function test and elevated tumor markers, EUS may provide additional information for the detection of malignant dilation of EBD and should be further investigated.

Identification of HMGB2 associated with proliferation, invasion and prognosis in lung adenocarcinoma via weighted gene co-expression network analysis.

BACKGROUND: High mobility group protein B2 (HMGB2) is a multifunctional protein that plays various roles in different cellular compartments. Moreover, HMGB2 serves as a potential prognostic biomarker and therapeutic target for lung adenocarcinoma (LUAD). METHODS: In this study, the expression pattern, prognostic implication, and potential role of HMGB2 in LUAD were evaluated using the integrated bioinformatics analyses based on public available mRNA expression profiles from The Cancer Genome Atlas and Gene Expression Omnibus databases, both at the single-cell level and the tissue level. Further study in the patient-derived samples was conducted to explore the correlation between HMGB2 protein expression levels with tissue specificity, (tumor size-lymph node-metastasis) TNM stage, pathological grade, Ki-67 status, and overall survival. In vitro experiments, such as CCK-8, colony-formation and Transwell assay, were performed with human LUAD cell line A549 to investigate the role of HMGB2 in LUAD progression. Furthermore, xenograft tumor model was generated with A549 in nude mice. RESULTS: The results showed that the HMGB2 expression was higher in the LUAD samples than in the adjacent normal tissues and was correlated with high degree of malignancy in different public data in this study. Besides, over-expression of HMGB2 promoted A549 cells proliferation and migration while knocking down of HMGB2 suppressed the tumor promoting effect. CONCLUSIONS: Our study indicated that HMGB2 was remarkably highly expressed in LUAD tissues, suggesting that it is a promising diagnostic and therapeutic marker for LUAD in the future.

WD repeat domain 43 promotes malignant progression of non-small cell lung cancer by regulating CDK2.

Non-small cell lung cancer (NSCLC) ranks highly among malignant tumors in the world in terms of morbidity and mortality. By using bioinformatics, we screened and obtained a novel oncogene WDR43, a member of the WD-repeat protein encoding family that is closely related to tumor progression. PCR and immunohistochemistry showed that WDR43 is highly expressed in NSCLC. High WDR43 expression in NSCLC was associated with worse clinical symptoms and prognosis. Knocked down expression of WDR43 significantly impaired the migration and proliferation and cell-cycle arrest in G1 phase in NSCLC cell lines. WDR43 can directly interact with cyclin-dependent kinase 2 and induce the expression of cyclin proteins. Our results suggest that WDR43 is a promising target of protein-protein interaction inhibitors for treatment of NSCLC.

Design and Analysis of a Novel Distributed Gradient Neural Network for Solving Consensus Problems in a Predefined Time.

In this article, a novel distributed gradient neural network (DGNN) with predefined-time convergence (PTC) is proposed to solve consensus problems widely existing in multiagent systems (MASs). Compared with previous gradient neural networks (GNNs) for optimization and computation, the proposed DGNN model works in a nonfully connected way, in which each neuron only needs the information of neighbor neurons to converge to the equilibrium point. The convergence and asymptotic stability of the DGNN model are proved according to the Lyapunov theory. In addition, based on a relatively loose condition, three novel nonlinear activation functions are designed to speedup the DGNN model to PTC, which is proved by rigorous theory. Computer numerical results further verify the effectiveness, especially the PTC, of the proposed nonlinearly activated DGNN model to solve various consensus problems of MASs. Finally, a practical case of the directional consensus is presented to show the feasibility of the DGNN model and a corresponding connectivity-testing example is given to verify the influence on the convergence speed.

Chinese medical dialogue information extraction via contrastive multi-utterance inference.

Medical Dialogue Information Extraction (MDIE) is a promising task for modern medical care systems, which greatly facilitates the development of many real-world applications such as electronic medical record generation, automatic disease diagnosis, etc. Recent methods have firstly achieved considerable performance in Chinese MDIE but still suffer from some inherent limitations, such as poor exploitation of the inter-dependencies in multiple utterances, weak discrimination of the hard samples. In this paper, we propose a contrastive multi-utterance inference (CMUI) method to address these issues. Specifically, we first use a type-aware encoder to provide an efficient encode mechanism toward different categories. Subsequently, we introduce a selective attention mechanism to explicitly capture the dependencies among utterances, which thus constructs a multi-utterance inference. Finally, a supervised contrastive learning approach is integrated into our framework to improve the recognition ability for the hard samples. Extensive experiments show that our model achieves state-of-the-art performance on a public benchmark Chinese-based dataset and delivers significant performance gain on MDIE as compared with baselines. Specifically, we outperform the state-of-the-art results in F1-score by 2.27%, 0.55% in Recall and 3.61% in Precision (The codes that support the findings of this study are openly available in CMUI at https://github.com/jc4357/CMUI.).

SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions.

Capturing comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, graph neural networks (GNNs) have received increasing attention in the drug discovery domain due to their capability of integrating drugs profiles and the network structure into a low-dimensional feature space for predicting links and classification. Most of GNN models for DDI predictions are built on an unsigned graph, which tends to represent associated nodes with similar embedding results. However, semantic correlation between drugs, such as degressive effects, or even adverse side reactions should be disassortative. In this study, we put forward signed GNNs to model assortative and disassortative relationships within drug pairs. Since negative links exclude direct generalization of spectral filters on unsigned graph, we divide the signed graph into two unsigned subgraphs to dedicate two spectral filters, which captures both commonality and difference of drug pairs. For drug representations we derive two signed graph filtering-based neural networks (SGFNNs) which integrate signed graph structures and drug node attributes. Moreover, we use an end-to-end framework for learning DDIs, where an SGFNN together with a discriminator is jointly trained under a problem-specific loss function. The experimental results on two prediction problems show that our framework can obtain significant improvements compared with baselines. The case study further verifies the validation of our method.

Comparative Mitogenomics of True Frogs (Ranidae, Anura), and Its Implications for the Phylogeny and Evolutionary History of Rana.

The true frogs of the genus Rana are a complex and diverse group, containing approximately 60 species with wide distribution across Eurasia and the Americas. Recently, many new species have been discovered with the help of molecular markers and morphological traits. However, the evolutionary history in Rana was not well understood and might be limited by the absence of mitogenome information. In this study, we sequenced and annotated the complete mitochondrial genome of R. longicrus and R. zhenhaiensis, containing 22 tRNAs, 13 protein-coding genes, two ribosomal RNAs, and a non-coding region, with 17,502 bp and 18,006 bp in length, respectively. In 13 protein codon genes, the COI was the most conserved, and ATP8 had a fast rate of evolution. The Ka/Ks ratio analysis among Rana indicated the protein-coding genes were suffering purify selection. There were three kinds of gene arrangement patterns found. The mitochondrial gene arrangement was not related to species diversification, and several independent shifts happened in evolutionary history. Climate fluctuation and environmental change may have played an essential role in species diversification in Rana. This study provides mitochondrial genetic information, improving our understanding of mitogenomic structure and evolution, and recognizes the phylogenetic relationship and taxonomy among Rana.

Complete chloroplast genome of Toona ciliata Roem. var. pubescens (Franch.) Hand.-Mazz (Meliaceae), 'Chinese mahogany'.

Toona ciliata var. pubescens is classified as Toona subgenus of Meliaceae family, which belongs to a large deciduous tree species. It is also a kind of precious timber tree species and has a certain medicinal value. Here, the first complete chloroplast genome (cpDNA) sequence of T. ciliata var. pubescens was determined using the Illumina sequencing platform. The cpDNA genome is 159,481 bp in length, containing a large single-copy region (LSC) of 87,176 bp and a small single-copy region (SSC) of 18,381 bp, which were separated by a pair of inverted repeats (IRs) regions of 26,962 bp. The genome contains 138 genes, including 90 protein-coding genes, eight ribosomal RNA genes, and 40 transfer RNA genes. The phylogenetic analysis based on 19 cpDNA genomes showed a close relationship with Toona ciliate.

A survey of circular RNAs in complex diseases: databases, tools and computational methods.

Circular RNAs (circRNAs) are a category of novelty discovered competing endogenous non-coding RNAs that have been proved to implicate many human complex diseases. A large number of circRNAs have been confirmed to be involved in cancer progression and are expected to become promising biomarkers for tumor diagnosis and targeted therapy. Deciphering the underlying relationships between circRNAs and diseases may provide new insights for us to understand the pathogenesis of complex diseases and further characterize the biological functions of circRNAs. As traditional experimental methods are usually time-consuming and laborious, computational models have made significant progress in systematically exploring potential circRNA-disease associations, which not only creates new opportunities for investigating pathogenic mechanisms at the level of circRNAs, but also helps to significantly improve the efficiency of clinical trials. In this review, we first summarize the functions and characteristics of circRNAs and introduce some representative circRNAs related to tumorigenesis. Then, we mainly investigate the available databases and tools dedicated to circRNA and disease studies. Next, we present a comprehensive review of computational methods for predicting circRNA-disease associations and classify them into five categories, including network propagating-based, path-based, matrix factorization-based, deep learning-based and other machine learning methods. Finally, we further discuss the challenges and future researches in this field.

Performance Analysis and Applications of Finite-Time ZNN Models With Constant/Fuzzy Parameters for TVQPEI.

Based on extensive applications of the time-variant quadratic programming with equality and inequality constraints (TVQPEI) problem and the effectiveness of the zeroing neural network (ZNN) to address time-variant problems, this article proposes a novel finite-time ZNN (FT-ZNN) model with a combined activation function, aimed at providing a superior efficient neurodynamic method to solve the TVQPEI problem. The remarkable properties of the FT-ZNN model are faster finite-time convergence and preferable robustness, which are analyzed in detail, where in the case of the robustness discussion, two kinds of noises (i.e., bounded constant noise and bounded time-variant noise) are taken into account. Moreover, the proposed several theorems all compute the convergent time of the nondisturbed FT-ZNN model and the disturbed FT-ZNN model approaching to the upper bound of residual error. Besides, to enhance the performance of the FT-ZNN model, a fuzzy finite-time ZNN (FFT-ZNN), which possesses a fuzzy parameter, is further presented for solving the TVQPEI problem. A simulative example about the FT-ZNN and FFT-ZNN models solving the TVQPEI problem is given, and the experimental results expectably conform to the theoretical analysis. In addition, the designed FT-ZNN model is effectually applied to the repetitive motion of the three-link redundant robot and image fusion to show its potential practical value.

A Variable-Parameter Noise-Tolerant Zeroing Neural Network for Time-Variant Matrix Inversion With Guaranteed Robustness.

Matrix inversion frequently occurs in the fields of science, engineering, and related fields. Numerous matrix inversion schemes are often based on the premise that the solution procedure is ideal and noise-free. However, external interference is generally ubiquitous and unavoidable in practice. Therefore, an integrated-enhanced zeroing neural network (IEZNN) model has been proposed to handle the time-variant matrix inversion issue interfered with by noise. However, the IEZNN model can only deal with small time-variant noise interference. With slightly larger noise interference, the IEZNN model may not converge to the theoretical solution exactly. Therefore, a variable-parameter noise-tolerant zeroing neural network (VPNTZNN) model is proposed to overcome shortcomings and improve the inadequacy. Moreover, the excellent convergence and robustness of the VPNTZNN model are rigorously analyzed and proven. Finally, compared with the original zeroing neural network (OZNN) model and the IEZNN model for matrix inversion, numerical simulations and a practical application reveal that the proposed VPNTZNN model has the best robust property under the same external noise interference.

NSL2CD: identifying potential circRNA-disease associations based on network embedding and subspace learning.

Many studies have evidenced that circular RNAs (circRNAs) are important regulators in various pathological processes and play vital roles in many human diseases, which could serve as promising biomarkers for disease diagnosis, treatment and prognosis. However, the functions of most of circRNAs remain to be unraveled, and it is time-consuming and costly to uncover those relationships between circRNAs and diseases by conventional experimental methods. Thus, identifying candidate circRNAs for human diseases offers new opportunities to understand the functional properties of circRNAs and the pathogenesis of diseases. In this study, we propose a novel network embedding-based adaptive subspace learning method (NSL2CD) for predicting potential circRNA-disease associations and discovering those disease-related circRNA candidates. The proposed method first calculates disease similarities and circRNA similarities by fully utilizing different data sources and learns low-dimensional node representations with network embedding methods. Then, we adopt an adaptive subspace learning model to discover potential associations between circRNAs and diseases. Meanwhile, an integrated weighted graph regularization term is imposed to preserve local geometric structures of data spaces, and L1,2-norm constraint is also incorporated into the model to realize the smoothness and sparsity of projection matrices. The experiment results show that NSL2CD achieves comparable performance under different evaluation metrics, and case studies further confirm its ability to discover potential candidate circRNAs for human diseases.