Assessment of Allelopathic Potential of Senna garrettiana Leaves and Identification of Potent Phytotoxic Substances

Senna garrettiana (Craib) Irwin & Barneby (Fabaceae) is a medicinal plant known to be rich in biologically active compounds that could be exploited to produce bioherbicides. The present study was conducted to explore the allelopathic potential and phytotoxic substances of S. garrettiana. Extracts of S. garrettiana leaves were found to significantly inhibit the growth of Lepidium sativum L. and Echinochloa crus-galli (L.) P. Beauv. (p ≤ 0.05). The phytotoxic substances were isolated and identified as vanillic acid and ferulic acid by bioassay-directed fractionation and spectral data analysis. The two compounds were shown to significantly inhibit the seed germination, seedling growth, and dry biomass of L. sativum. Based on the concentration required for 50% growth inhibition (defined as IC50), the roots of L. sativum were the most sensitive to the compounds, and the inhibitory effect of ferulic acid (IC50 = 0.62 mM) was >1.3 times more potent than that of vanillic acid (IC50 = 0.82 mM). In addition, a mixture of the two compounds (0.3 mM) resulted in synergistic inhibitory activity against the L. sativum roots compared with the individual compounds. These results suggest that the extracts of S. garrettiana leaves and their phytotoxic compounds have potential as candidate natural herbicides.

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

Cyclic dynamic patterns of Russian macroeconomic indicators found by spectral analysis

The paper proposes a contemporary interdisciplinary method to identify consistent patterns within cyclical dynamics of GDP and its macroeconomics determinants in the Russian Federation. This method may contribute to better recognition of the stages of economic cycle and of potential early predicators to recessions and crises. We first identify the trend component of Russian GDP and then apply the spectral data analysis to its cyclical component which reveals its multi-frequency, and non-linear vibrations. These vibrations are then further investigated by transforming time series data on GDP and its determinants into a frequency spectrum series via Fourier transform techniques. Wavelength scanning of selected macroeconomic indicators shows the basic economic cycle of real GDP with duration time of approx. 3.13 years. Other procyclical indicators nevertheless discover asynchronous behavior towards GDP due to the relative autonomy of the sectors standing behind these indicators. Their autonomy lies behind differences in reaction forces (shifts) and periods (lags) to both internal and external shocks. We estimate differentials between the dynamics of GDP and its determinants by evaluating phase deviations of their pairwise harmonic components, mutual pairwise phase shifts, and by comparison of their pairwise cross-spectrum. The one of output is the quantification of time lags between GDP and key macroeconomic indicators of individual economic sectors. This result reveals the complexity of GDP dynamics that sends an aliased rather than a unit signal to economic agents. Our decomposition of this signal into signals from key economic sectors and quantification of phase discrepancies between sectoral signals may contribute to findings in early crisis predicators. We also estimate the depth and velocity of shocks penetrations into both economy as a whole and its particular sectors.

Spectral Data Analysis and Identification of Vancomycin Hydrochloride

Objective: To establish a method for the determination of the chemical structure of vancomycin hydrochloride.Methods: Nuclear magnetic resonance spectroscopy and mass spectrometry were conducted to analyze the chemical structure of vancomycin hydrochloride.Results: In this study, the target compound (1) was identified as (Sα)-(3S, 6R, 7R, 22R, 23S, 26S, 36R, 38αR)-44-[[2-O-(3-amino-2, 3, 6-trideoxy-3-C-methyl-α-L-lyso-hexopyranosyl)-β-D-glucopyranosyl] oxy]-3-(carbamoylmethyl)-10, 19-dichloro-7, 22, 28, 30, 32-pentahydroxy-6-[[(2R)-4-methyl-2-(methylamino) pentanoyl] amino]-2, 5, 24, 38, 39-pentaoxo-2, 3, 4, 5, 6, 7, 23, 24, 25, 26, 36, 37, 38, 38α-tetradecahydro-22H-8, 11: 18, 21-dietheno-23, 36-(iminomethano)-13, 16: 31, 35-dimetheno-1H, 13H-[1, 6, 9] oxadiazacyclohexadecino [4, 5-m] [10, 2, 16]-benzoxadiazacyclotetracosine-26-carboxylic acid hydrochloride.Conclusion: The method used in this study is accurate and can be used for the production and structural elucidation of vancomycin hydrochloride.

Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA

Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system.

Synthesis of pyridine clubbed 2,5-disubstituted-1,3,4-oxadiazole derivatives shows potent antimicrobial activity.

: In the present study, series of 2,5-disubstituted-1,3,4 oxadiazole analogues retaining pyridine moiety were synthesized (4a-j) by reacting various substituted aromatic acids and isonicotinohydrazide by using POCl3 as cycling agent. The structure elucidation of all the synthesized compounds was done by chromatographic data and the spectral data analysis. The synthesized compounds were evaluated for their in-vitro antimicrobial activity against various strains of ESKAPE pathogens. Antibacterial activity was performed against Bacillus subtilis, Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa, while antifungal activity was assayed against Candida albicans and Aspergillus spp. The result of invitro antimicrobial studies of all the synthesized compounds revealed that compound 4d and 4f exhibited prompt activity against selected microbial strains equally compared to Cefixime and Econazole used as reference drugs.

Mumunine - A New Carbazole Alkaloid from Murraya koenigii (Linn.) Spreng

The plant Murraya koenigii, commonly known as curry leaf tree is a rich source of carbazole alkaloids. A number of monomeric as well as dimeric carbazoles with C13, C18 and C23 skeleton have been isolated from the plant. In my present work, a new carbazole alkaloid, designated as mumunine, was isolated from the bark of Murraya koenigii (Linn) Spreng, along with a known carbazole alkaloid, viz. mahanimbine. The structure of the new alkaloid 1 was elucidated on the basis of 1D and 2D NMR spectral data analysis. In this paper, the isolation and structure elucidation of the new compound will be discussed in detail.