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Cardiac fibrosis is a serious public health problem worldwide that is closely linked to progression of many cardiovascular diseases (CVDs) and adversely affects both the disease process and clinical prognosis. Numerous studies have shown that the TGF-β/Smad signaling pathway plays a key role in the progression of cardiac fibrosis. Therefore, targeted inhibition of the TGF-β/Smad signaling pathway may be a therapeutic measure for cardiac fibrosis. Currently, as the investigation on non-coding RNAs (ncRNAs) move forward, a variety of ncRNAs targeting TGF-β and its downstream Smad proteins have attracted high attention. Besides, Traditional Chinese Medicine (TCM) has been widely used in treating the cardiac fibrosis. As more and more molecular mechanisms of natural products, herbal formulas, and proprietary Chinese medicines are revealed, TCM has been proven to act on cardiac fibrosis by modulating multiple targets and signaling pathways, especially the TGF-β/Smad. Therefore, this work summarizes the roles of TGF-β/Smad classical and non-classical signaling pathways in the cardiac fibrosis, and discusses the recent research advances in ncRNAs targeting the TGF-β/Smad signaling pathway and TCM against cardiac fibrosis. It is hoped, in this way, to give new insights into the prevention and treatment of cardiac fibrosis.

Citrate exudation mediated by a citrate transporter of the MATE protein family is critical for resisting aluminum (Al) toxicity in soybeans. However, the expression patterns of citrate transporter genes differ under Al stress. Thus, exploring the responsive pattern of GmMATEs in response to Al stress is of great importance to understand the Al resistance mechanism in soybeans. In the present study, the phylogenetic analysis, transcriptionally expressed pattern, and function of GmMATE13 were investigated. The results show that soybean GmMATE13 is highly homologous to known citrate transporter proteins from other plants. Under Al exposure, the transcript abundance of GmMATE13 was increased during a 24 h Al treatment period. The expression of GmMATE13 is specifically induced by Al exposure, but not by the status of Fe, Cu, Cd, or La. Moreover, it was also highly increased when soybean seedlings were grown on acidic soil with a high Al content. Subcellular localization showed that GmMATE13 was localized on the plasma membrane when it was transiently expressed in Arabidopsis protoplasts. Investigation of tissue localization of GmMATE13 expression by investigating GUS activity staining under control of the GmMATE13 promoter showed that it was mainly expressed in the central cylinder in the root tips of the soybean under Al-free conditions, yet extended to cortical and epidermis cells under Al stress. Finally, overexpressing GmMATE13 in soybean hairy roots enhanced Al resistance by increasing citrate efflux. Collectively, we conclude that GmMATE13 is a promising candidate to improve the resistance of soybean to Al toxicity in acidic soil.

Fractional differential equations are used to construct mathematical models and can describe the characteristics of real systems. In this paper, the parameter estimation problem of a fractional Wiener system is studied by designing linear filters which can obtain smaller tunable parameters and maintain the stability of the parameters in any case. To improve the identification performance of the stochastic gradient algorithm, this paper derives two modified stochastic gradient algorithms for the fractional nonlinear Wiener systems with colored noise. By introducing the forgetting factor, a forgetting factor stochastic gradient algorithm is deduced to improve the convergence rate. To achieve more efficient and accurate algorithms, we propose a multi-innovation forgetting factor stochastic gradient algorithm by means of the multi-innovation theory, which expands the scalar innovation into the innovation vector. To test the developed algorithms, a fractional-order dynamic photovoltaic model is employed in the simulation, and the dynamic elements of this photovoltaic model are estimated using the modified algorithms. Concurrently, a numerical example is given, and the simulation results verify the feasibility and effectiveness of the proposed procedures.

Plants employ sophisticated mechanisms to control developmental processes and to cope with environmental changes at transcriptional and post-transcriptional levels. MicroRNAs (miRNAs) and long noncoding RNAs (lncRNAs), two classes of endogenous noncoding RNAs, are key regulators of gene expression in plants. Recent studies have identified the interplay between miRNAs and lncRNAs as a novel regulatory layer of gene expression in plants. On one hand, miRNAs target lncRNAs for the production of phased small interfering RNAs (phasiRNAs). On the other hand, lncRNAs serve as origin of miRNAs or regulate the accumulation or activity of miRNAs at transcription and post-transcriptional levels. Theses lncRNA-miRNA interplays are crucial for plant development, physiology and responses to biotic and abiotic stresses. In this review, we summarize recent advances in the biological roles, interaction mechanisms and computational predication methods of the interplay between miRNAs and lncRNAs in plants.

Abstract Background Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. Results The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. Conclusions DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

This paper investigates the problem of the level control for two tank liquid level system(TTLLS) with parametric uncertainties and lumped disturbances. It is achieved by a novel adaptive disturbance attenuation control method based on the Port-Controlled Hamiltonian (PCH) model. Firstly, the model of TTLLS is established according to the mass balance principle and the PCH model is achieved. Based on the PCH model, the PCH controller of TTLLS is designed and the stability of the closed-loop system is ensured. To reduce the impact of disturbances and unmeasurable parameters, adaptive L2 disturbance attenuation technology is integrated. To achieve the robustness and simplify the calculation, the parameter estimation vector is designed by splitting complex mathematical expressions. Utilizing the properties of the PCH method and merits of the adaptive disturbance attenuation technology, the integrated controller achieves good performance. Moreover, simulation and experimental results are given to show the effectiveness and strong robustness of the proposed control algorithm.

Modeling errors, external loads and output constraints will affect the tracking control of the n-joint manipulator driven by the permanent magnet synchronous motor. To solve the above problems, the smooth-switching for backstepping gain control strategy based on the Barrier Lyapunov Function and adaptive neural network (BLF-ANBG) is proposed. First, the adaptive neural network method is established to approximate modeling errors, unknown loads and unenforced inputs. Then, the gain functions based on the error and error rate of change are designed, respectively. The two gain functions can respectively provide faster response speed and better tracking stability. The smooth-switching for backstepping gain strategy based on the Barrier Lyapunov Function is proposed to combine the advantages of both gain functions. According to the above strategy, the BLF-ANBG strategy is proposed, which not only solves the influence of multiple constraints, unknown loads and modeling errors, but also enables the manipulator system to have better dynamic and steady-state performances at the same time. Finally, the proposed controller is applied to a 2-DOF manipulator and compared with other commonly used methods. The simulation results show that the BLF-ANBG strategy has good tracking performance under multiple constraints and model errors.

This paper proposes a fresh state error feedback linearization control method with disturbance observer (DOB) and L2 gain for a quadruple-tank liquid-level system. Firstly, in terms of the highly nonlinear and strong coupling characteristics of the quadruple-tank system, a state error feedback linearization technique is employed to design the controller to achieve liquid-level position control and tracking control. Secondly, DOB is purposed to estimate uncertain exogenous disturbances and applied to compensation control. Moreover, an L2-gain disturbance attenuation technology is designed to resolve one class of disturbance problem by uncertain parameter perturbation existing in the quadruple-tank liquid-level system. Finally, compared with the classical proportion integration differentiation (PID) and sliding mode control (SMC) methods, the extensive experimental results validate that the proposed strategy has good position control, tracking control, and disturbance rejection performances.

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3-Hydroxy-3-methylglutaryl-CoA synthase (HMGS) is one of the rate-limiting enzymes in the mevalonate pathway as it catalyzes the condensation of acetoacetyl-CoA to form 3-hydroxy-3-methylglutaryl-CoA. In this study, A HMGS gene (designated as GbHMGS1) was cloned from Ginkgo biloba for the first time. GbHMGS1 contained a 1422-bp open-reading frame encoding 474 amino acids. Comparative and bioinformatics analysis revealed that GbHMGS1 was extensively homologous to HMGSs from other plant species. Phylogenetic analysis indicated that the GbHMGS1 belonged to the plant HMGS superfamily, sharing a common evolutionary ancestor with other HMGSs, and had a further relationship with other gymnosperm species. The yeast complement assay of GbHMGS1 in HMGS-deficient Saccharomyces cerevisiae strain YSC6274 demonstrated that GbHMGS1 gene encodes a functional HMGS enzyme. The recombinant protein of GbHMGS1 was successfully expressed in E. coli. The in vitro enzyme activity assay showed that the kcat and Km values of GbHMGS1 were 195.4 min−1 and 689 μM, respectively. GbHMGS1 was constitutively expressed in all tested tissues, including the roots, stems, leaves, female flowers, male flowers and fruits. The transcript accumulation for GbHMGS1 was highest in the leaves. Expression profiling analyses revealed that GbHMGS1 expression was induced by abiotic stresses (ultraviolet B and cold) and hormone treatments (salicylic acid, methyl jasmonate, and ethephon) in G. biloba, indicating that GbHMGS1 gene was involved in the response to environmental stresses and plant hormones.

Ginkgolides and bilobalide, collectively termed terpene trilactones (TTLs), are terpenoids that form the main active substance of Ginkgo biloba. Terpenoids in the mevalonate (MVA) biosynthetic pathway include acetyl-CoA C-acetyltransferase (AACT) and mevalonate kinase (MVK) as core enzymes. In this study, two full-length (cDNAs) encoding AACT (GbAACT, GenBank Accession No. KX904942) and MVK (GbMVK, GenBank Accession No. KX904944) were cloned from G. biloba. The deduced GbAACT and GbMVK proteins contain 404 and 396 amino acids with the corresponding open-reading frame (ORF) sizes of 1215 bp and 1194 bp, respectively. Tissue expression pattern analysis revealed that GbAACT was highly expressed in ginkgo fruits and leaves, and GbMVK was highly expressed in leaves and roots. The functional complementation of GbAACT in AACT-deficient Saccharomyces cerevisiae strain Δerg10 and GbMVK in MVK-deficient strain Δerg12 confirmed that GbAACT mediated the conversion of mevalonate acetyl-CoA to acetoacetyl-CoA and GbMVK mediated the conversion of mevalonate to mevalonate phosphate. This observation indicated that GbAACT and GbMVK are functional genes in the cytosolic mevalonate (MVA) biosynthesis pathway. After G. biloba seedlings were treated with methyl jasmonate and salicylic acid, the expression levels of GbAACT and GbMVK increased, and TTL production was enhanced. The cloning, characterization, expression and functional analysis of GbAACT and GbMVK will be helpful to understand more about the role of these two genes involved in TTL biosynthesis.