A terpenoids database with the chemical content as a novel agronomic trait.

Natural products play a pivotal role in drug discovery, and the richness of natural products, albeit significantly influenced by various environmental factors, is predominantly determined by intrinsic genetics of a series of enzymatic reactions and produced as secondary metabolites of organisms. Heretofore, few natural product-related databases take the chemical content into consideration as a prominent property. To gain unique insights into the quantitative diversity of natural products, we have developed the first TerPenoids database embedded with Content information (TPCN) with features such as compound browsing, structural search, scaffold analysis, similarity analysis and data download. This database can be accessed through a web-based computational toolkit available at http://www.tpcn.pro/. By conducting meticulous manual searches and analyzing over 10 000 reference papers, the TPCN database has successfully integrated 6383 terpenoids obtained from 1254 distinct plant species. The database encompasses exhaustive details including isolation parts, comprehensive molecule structures, chemical abstracts service registry number (CAS number) and 7508 content descriptions. The TPCN database accentuates both the qualitative and quantitative dimensions as invaluable phenotypic characteristics of natural products that have undergone genetic evolution. By acting as an indispensable criterion, the TPCN database facilitates the discovery of drug alternatives with high content and the selection of high-yield medicinal plant species or phylogenetic alternatives, thereby fostering sustainable, cost-effective and environmentally friendly drug discovery in pharmaceutical farming. Database URL: http://www.tpcn.pro/.

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment.

Navigating robots through large-scale environments while avoiding dynamic obstacles is a crucial challenge in robotics. This study proposes an improved deep deterministic policy gradient (DDPG) path planning algorithm incorporating sequential linear path planning (SLP) to address this challenge. This research aims to enhance the stability and efficiency of traditional DDPG algorithms by utilizing the strengths of SLP and achieving a better balance between stability and real-time performance. Our algorithm generates a series of sub-goals using SLP, based on a quick calculation of the robot's driving path, and then uses DDPG to follow these sub-goals for path planning. The experimental results demonstrate that the proposed SLP-enhanced DDPG path planning algorithm outperforms traditional DDPG algorithms by effectively navigating the robot through large-scale dynamic environments while avoiding obstacles. Specifically, the proposed algorithm improves the success rate by 12.33% compared to the traditional DDPG algorithm and 29.67% compared to the A*+DDPG algorithm in navigating the robot to the goal while avoiding obstacles.

A chemoinformatic analysis on natural glycosides with respect to biological origin and structural class.

Covering: up to 202216.19% of reported natural products (NPs) in the Dictionary of Natural Products (DNP) are glycosides. As one of the most important NPs' structural modifications, glycosylation can change the NPs' polarity, making the aglycones more amphipathic. However, until now, little is known about the general distribution profile of the natural glycosides in different biological sources or structural types. The reason, structural or species preferences of the natural glycosylation remain unclear. In this highlight, chemoinformatic methods were employed to analyze the natural glycosides from DNP, the most comprehensively annotated NP database. We found that the glycosylation ratios of NPs from plants, bacteria, animals and fungi decrease successively, which are 24.99%, 20.84%, 8.40% and 4.48%, respectively. Echinoderm-derived NPs (56.11%) are the most frequently glycosylated, while those produced by molluscs (1.55%), vertebrates (2.19%) and Rhodophyta (3.00%) are the opposite. Among the diverse structural types, a large proportion of steroids (45.19%), tannins (44.78%) and flavonoids (39.21%) are glycosides, yet aminoacids and peptides (5.16%), alkaloids (5.66%) are comparatively less glycosylated. Even within the same biological source or structural type, their glycosylation rates fluctuate drastically between sub- or cross-categories. The substitute patterns of flavonoid and terpenoid glycosides and the most frequently glycosylated scaffolds were identified. NPs with different glycosylation levels occupy different chemical spaces of physicochemical property and scaffold. These findings could help us to interpret the preference of NPs' glycosylation and investigate how NP glycosylation could aid NP-based drug discovery.

Identification and Analysis of Hub Genes in Diabetic Cardiomyopathy: Potential Role of Cytochrome P450 1A1 in Mitochondrial Metabolism and STZ-Induced Myocardial Dysfunction.

Diabetic cardiomyopathy (DCM) is a primary cause of death in diabetic patients; however, its molecular mechanism is not yet clear, and there is no uniform standard for diagnosis. The aim of this study is to discover the pathogenesis and potential therapeutic targets of DCM through screening and analysis of differentially expressed genes (DEGs) in heart ventricles of DCM, and to testify the role of key hub genes in DCM-induced myocardial dysfunction. Datasets GSE4745 and GSE6880 were downloaded from the GEO database. The difference analysis, visual analysis, cluster analysis and enrichment analysis were performed by using R language, python scripts and bioinformatics software followed by the construction of protein-protein interaction (PPI) network to obtain hub genes. The DCM models were established by streptozocin (STZ) injection to the male mice. The cardiac function and the expressions of hub genes were examined by using echocardiography and real-time quantitative poly-merase chain reaction (RT-qPCR), followed by multiple statistical analyses. Bioinformatic results indicate that mitochondrial dysfunction, disturbed lipid metabolism and decreased collagen synthesis are the main causes of the DCM development. In particular, the hub gene Cyp1a1 that encodes Cytochrome P450 1A1 (CYP4501A1) enzyme has the highest connectivity in the interaction network, and is associated with mitochondrial homeostasis and energy metabolism. It plays a critical role in the oxidation of endogenous or exogenous substrates. Our RT-qPCR results confirmed that ventricular Cyp1a1 mRNA level was nearly 12-fold upregulated in DCM model compared to normal control, which was correlated with abnormal cardiac function in diabetic individuals. CYP4501A1 protein expression in mitochondria was also increased in diabetic hearts. However, we found no significant changes in collagen expressions in cardiac ventricles of mice with DCM. This study provided compact data support for understanding the pathogenesis of DCM. CYP4501A1 might be considered as a potential candidate targeting for DCM therapy. Follow-up animal and clinical verifications need to be further explored.

Statistical theory of linear adsorption capillary chromatography with porous-layer stationary phase.

A set of accurate expressions of elution-curve moments are derived from the moments of residence time and displacement in a step based on probability theory. Then the problems about residence time and displacement in a step of a solute molecule in the porous layer of capillary columns and in the moving mobile phase are described by a set of mass-balance equations respectively. The set of equations are solved in Fourier-Laplace domain, and the characteristic functions of residence time of a step, as well as the moments, are obtained by means of computing software Mathematica. At last, using numerical inverse Laplace transform, the elution curves for various conditions are calculated. In the case of large desorption constant the results entirely coincide with those of mass-balance-equation theory and in the case of small desorption constant they are equivalent to those of stochastic theory.

Statistical theory of multiple-site linear wall-adsorption capillary Chromatography.

Based on the mass-balance principle, a particular diffusion equation to describe the movement of solute molecules in the stagnant layer of multiple-site solid surfaces is constructed. From the equation, the moments of residence time in a step on multiple-site surfaces are derived. Similarly, the moments in a step in the mobile phase are also derived from a diffusion-drift equation. According to the probability theory, there exists a general relationship between the moments of an elution curve and the moments in a step. Through this relationship, the expressions of the elution-curve moments are derived from the step moments. In this paper, the details related to multiple-site linear wall-adsorption capillary chromatography are described and added in the equations to determine the step moments. The resultant expressions of the elution-curve moments involve various factors, such as adsorption-desorption rate constants, equilibrium constants, axial and radial dispersions in the mobile phase. Afterwards, the moment expressions are used to analyze the peak tailing. The results show that a small quantity of sites with a slow desorption rate will lead to a large peak asymmetry.

The statistical theory of linear capillary chromatography with uniform stationary phase.

Based on the random walk model and probability theory, general relations between the moments of column residence time and the moments of step sojourn time and step displacement are established. And starting from the mass-balances principle of solute molecules in the mobile and stationary phases, the moments of step sojourn time and step displacement are derived and expressed in terms of the basic parameters. Substituting the step moments into the general relations, the moments of column residence time are then obtained. The expression of retention time is completely identical to the well-known, the expressions of second moment or HETP unite and generalize various expressions of stochastic theory and mass balance theory, and the third and forth moments are given in more exact form.