Genetic and Environmental Factors Jointly Impact Leaf Phenolic Profiles of Iris variegata L

A method for durian precise fertilization based on improved radial basis neural network algorithm

Introduction

Durian is one of the tropical fruits that requires soil nutrients in its cultivation. It is important to understand the relationship between the content of critical nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) in the soil and durian yield. How to optimize the fertilization plan is also important to the durian planting.

Methods

Thus, this study proposes an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) in the durian precision fertilization. It uses the gray wolf algorithm to optimize the weights and thresholds of the RBNNA algorithm, which can improve the prediction accuracy of the RBNNA algorithm for the soil nutrient content and its relationship with the durian yield. It also collects the soil nutrients and historical yield data to build the IM-RBNNA model and compare with other similar algorithms.

Results

The results show that the IM-RBNNA algorithm is better than the other three algorithms in the average relative error, average absolute error, and coefficient of determination between the predicted and true values of soil N, K, and P fertilizer contents. It also predicts the relationship between soil nutrients and yield, which is closer to the true value.

Discussion

It shows that the IM-RBNNA algorithm can accurately predict the durian soil nutrient content and yield, which is benefited for farmers to make agronomic plans and management strategies. It uses soil nutrient resources efficiently, which reduces the environmental negative impacts. It also ensures that the durian tree can obtain the appropriate amount of nutrients, maximize its growth potential, reduce production costs, and increase yields.

Exploring the Role of Apigenin in Neuroinflammation: Insights and Implications

Neuroinflammation, a hallmark of various central nervous system disorders, is often associated with oxidative stress and neuronal or oligodendrocyte cell death. It is therefore very interesting to target neuroinflammation pharmacologically. One therapeutic option is the use of nutraceuticals, particularly apigenin. Apigenin is present in plants: vegetables (parsley, celery, onions), fruits (oranges), herbs (chamomile, thyme, oregano, basil), and some beverages (tea, beer, and wine). This review explores the potential of apigenin as an anti-inflammatory agent across diverse neurological conditions (multiple sclerosis, Parkinson's disease, Alzheimer's disease), cancer, cardiovascular diseases, cognitive and memory disorders, and toxicity related to trace metals and other chemicals. Drawing upon major studies, we summarize apigenin's multifaceted effects and underlying mechanisms in neuroinflammation. Our review underscores apigenin's therapeutic promise and calls for further investigation into its clinical applications.

Development of approach for flavonoid profiling of biotechnological raw materials Iris sibirica L. by HPLC with high-resolution tandem mass spectrometry

INTRODUCTION

Iris L. are promising in medicine due to the biological activity of extracts. Iris sibirica L. is spread in Russia but its phytochemical composition has not been studied in detail though it is included in the Red Book. For this reason, I. sibirica L. biotechnology is in high demand. One of the key points in biotechnology is the regulation of plant metabolism using phytohormones. Obtaining of chromatographic metabolite profiles allows to control this process.

OBJECTIVE

The aim of this study was to develop an approach for effective control of biotechnological raw materials of I. sibirica L. by flavonoid profiles using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) and to investigate the influence of phytohormones in nutrient media on content of flavonoids.

METHODOLOGY

Iris sibirica L. regenerated plants were grown on Murashige-Skoog media with 6-benzylaminopurine (6-BAP) and α-naphtylacetic acid (NAA) additives. To optimise extraction conditions, the design of the experiment was used. Profiles of polyphenols were obtained by HPLC-MS/MS in the positive and negative ionisation modes.

RESULTS

The process for efficient extraction from leaves of I. sibirica L. were developed. The factors influencing the extraction efficiency of flavonoids have been determined. A total of 36 compounds were identified by HPLC-MS/MS. Among them isoflavones and their glycosides are the main classes. Addition of an auxin-like hormone increased the non-polar flavonoid levels, but decreased the polar ones. The variation in concentration of cytokinin (6-BAP) affected almost all of the analytes.

CONCLUSION

The methodology for effective control of I. sibirica L. raw plant material biotechnology was developed by analysing obtained chromatographic polyphenol profiles.