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Ocimum basilicum L. leaves extract-mediated green synthesis of MnO NPs: Phytochemical profile, characterization, catalytic and thrombolytic activities
Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)
The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.
Identyfikacja markerów molekularnych sprzężonych z genami warunkującymi odporność na suchą zgniliznę kapustnych (Leptosphaeria spp.) z wykorzystaniem zaawansowanych technik molekularnych.
Brassica napus haploid and double haploid production and its latest applications
Rapeseed is one of the most important oil crops in the world. Increasing demand for oil and limited agronomic capabilities of present-day rapeseed result in the need for rapid development of new, superior cultivars. Double haploid (DH) technology is a fast and convenient approach in plant breeding as well as genetic research. Brassica napus is considered a model species for DH production based on microspore embryogenesis; however, the molecular mechanisms underlying microspore reprogramming are still vague. It is known that morphological changes are accompanied by gene and protein expression patterns, alongside carbohydrate and lipid metabolism. Novel, more efficient methods for DH rapeseed production have been reported. This review covers new findings and advances in Brassica napus DH production as well as the latest reports related to agronomically important traits in molecular studies employing the double haploid rapeseed lines.
Biosynthesis of CuNP Using Phragmites australis Rhizomes: Biological Activity and Cytotoxicity Against MCF-7 Cell Line
Three-Dimensional Convolutional Neural Networks (3D-CNN) in the Classification of Varieties and Quality Assessment of Soybean Seeds (Glycine max L. Merrill)
The precise identification, classification, sorting, and rapid and accurate quality assessment of soybean seeds are extremely important in terms of the continuity of agricultural production, varietal purity, seed processing, protein extraction, and food safety. Currently, commonly used methods for the identification and quality assessment of soybean seeds include morphological analysis, chemical analysis, protein electrophoresis, liquid chromatography, spectral analysis, and image analysis. The use of image analysis and artificial intelligence is the aim of the presented research, in which a method for the automatic classification of soybean varieties, the assessment of the degree of damage, and the identification of geometric features of soybean seeds based on numerical models obtained using a 3D scanner has been proposed. Unlike traditional two-dimensional images, which only represent height and width, 3D imaging adds a third dimension, allowing for a more realistic representation of the shape of the seeds. The research was conducted on soybean seeds with a moisture content of 13%, and the seeds were stored in a room with a temperature of 20–23 °C and air humidity of 60%. Individual soybean seeds were scanned to create 3D models, allowing for the measurement of their geometric parameters, assessment of texture, evaluation of damage, and identification of characteristic varietal features. The developed 3D-CNN network model comprised an architecture consisting of an input layer, three hidden layers, and one output layer with a single neuron. The aim of the conducted research is to design a new, three-dimensional 3D-CNN architecture, the main task of which is the classification of soybean seeds. For the purposes of network analysis and testing, 22 input criteria were defined, with a hierarchy of their importance. The training, testing, and validation database of the SB3D-NET network consisted of 3D models obtained as a result of scanning individual soybean seeds, 100 for each variety. The accuracy of the training process of the proposed SB3D-NET model for the qualitative classification of 3D models of soybean seeds, based on the adopted criteria, was 95.54%, and the accuracy of its validation was 90.74%. The relative loss value during the training process of the SB3D-NET model was 18.53%, and during its validation process, it was 37.76%. The proposed SB3D-NET neural network model for all twenty-two criteria achieves values of global error (GE) of prediction and classification of seeds at the level of 0.0992.
Molecular selection of soybean towards adaptation to Central European agroclimatic conditions
AbstractEurope is highly dependent on soybean meal imports and anticipates an increase of domestic plant protein production. Ongoing climate change resulted in northward shift of plant hardiness zones, enabling spring-sowing of freezing-sensitive crops, including soybean. However, it requires efficient reselection of germplasm adapted to relatively short growing season and long-day photoperiod. In the present study, a PCR array has been implemented, targeting early maturity (E1–E4, E7, E9, and E10), pod shattering (qPHD1), and growth determination (Dt1) genes. This array was optimized for routine screening of soybean diversity panel (204 accessions), subjected to the 2018–2020 survey of phenology, morphology, and yield-related traits in a potential cultivation region in Poland. High broad-sense heritability (0.84–0.88) was observed for plant height, thousand grain weight, maturity date, and the first pod height. Significant positive correlations were identified between the number of seeds and pods per plant, between these two traits and seed yield per plant as well as between flowering, maturity, plant height, and first pod height. PCR array genotyping revealed high genetic diversity, yielding 98 allelic combinations. The most remarkable correlations were identified between flowering and E7 or E1, between maturity and E4 or E7 and between plant height and Dt1 or E4. The study demonstrated high applicability of this PCR array for molecular selection of soybean towards adaptation to Central Europe, designating recessive qPHD1 and dominant Dt1, E3, and E4 alleles as major targets to align soybean growth season requirements with the length of the frost-free period, improve plant performance, and increase yield.
Ocimum basilicum L. : A Systematic Review on Pharmacological Actions and Molecular Docking Studies for Anticancer Properties
Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed
The main objective of this study is to develop an automatic classification model for winter rapeseed varieties, to assess seed maturity and damage based on seed colour using a convolutional neural network (CNN). A CNN with a fixed architecture was built, consisting of an alternating arrangement of five classes Conv2D, MaxPooling2D and Dropout, for which a computational algorithm was developed in the Python 3.9 programming language, creating six models depending on the type of input data. Seeds of three winter rapeseed varieties were used for the research. Each imaged sample was 20.000 g. For each variety, 125 weight groups of 20 samples were prepared, with the weight of damaged or immature seeds increasing by 0.161 g. Each of the 20 samples in each weight group was marked by a different seed distribution. The accuracy of the models’ validation ranged from 80.20 to 85.60%, with an average of 82.50%. Higher accuracy was obtained when classifying mature seed varieties (average of 84.24%) than when classifying the degree of maturity (average of 80.76%). It can be stated that classifying such fine seeds as rapeseed seeds is a complex process, creating major problems and constraints, as there is a distinct distribution of seeds belonging to the same weight groups, which causes the CNN model to treat them as different.
Quantifying Genetic Parameters for Blackleg Resistance in Rapeseed: A Comparative Study
Selection is a fundamental part of the plant breeding process, enabling the identification and development of varieties with desirable traits. Thanks to advances in genetics and biotechnology, the selection process has become more precise and efficient, resulting in faster breeding progress and better adaptation of crops to environmental challenges. Genetic parameters related to gene additivity and epistasis play a key role and can influence decisions on the suitability of breeding material. In this study, 188 rapeseed doubled haploid lines were assessed in field conditions for resistance to Leptosphaeria spp. Through next-generation sequencing, a total of 133,764 molecular markers (96,121 SilicoDArT and 37,643 SNP) were obtained. The similarity of the DH lines at the phenotypic and genetic levels was calculated. The results indicate that the similarity at the phenotypic level was markedly different from the similarity at the genetic level. Genetic parameters related to additive gene action effects and epistasis (double and triple) were calculated using two methods: based on phenotypic observations only and using molecular marker observations. All evaluated genetic parameters (additive, additive-additive and additive-additive-additive) were statistically significant for both estimation methods. The parameters associated with the interaction (double and triple) had opposite signs depending on the estimation method.
Introdukcja genów odporności na choroby i owady oraz męskiej sterylności z pokrewnych gatunków rodzaju Brassica do rzepaku (Brassica napus L.)
Cross-talk between stability parameters and selection models: a new procedure for improving the identification of the superior genotypes in multi-environment trials
Comparison of Six Measures of Genetic Similarity of Interspecific Brassicaceae Hybrids F2 Generation and Their Parental Forms Estimated on the Basis of ISSR Markers
Genetic similarity determines the extent to which two genotypes share common genetic material. It can be measured in various ways, such as by comparing DNA sequences, proteins, or other genetic markers. The significance of genetic similarity is multifaceted and encompasses various fields, including evolutionary biology, medicine, forensic science, animal and plant breeding, and anthropology. Genetic similarity is an important concept with wide application across different scientific disciplines. The research material included 21 rapeseed genotypes (ten interspecific Brassicaceae hybrids of F2 generation and 11 of their parental forms) and 146 alleles obtained using 21 ISSR molecular markers. In the presented study, six measures for calculating genetic similarity were compared: Euclidean, Jaccard, Kulczyński, Sokal and Michener, Nei, and Rogers. Genetic similarity values were estimated between all pairs of examined genotypes using the six measures proposed above. For each genetic similarity measure, the average, minimum, maximum values, and coefficient of variation were calculated. Correlation coefficients between the genetic similarity values obtained from each measure were determined. The obtained genetic similarity coefficients were used for the hierarchical clustering of objects using the unweighted pair group method with an arithmetic mean. A multiple regression model was written for each method, where the independent variables were the remaining methods. For each model, the coefficient of multiple determination was calculated. Genetic similarity values ranged from 0.486 to 0.993 (for the Euclidean method), from 0.157 to 0.986 (for the Jaccard method), from 0.275 to 0.993 (for the Kulczyński method), from 0.272 to 0.993 (for the Nei method), from 0.801 to 1.000 (for the Rogers method) and from 0.486 to 0.993 (for the Sokal and Michener method). The results indicate that the research material was divided into two identical groups using any of the proposed methods despite differences in the values of genetic similarity coefficients. Two of the presented measures of genetic similarity (the Sokal and Michener method and the Euclidean method) were the same.
Genetic Relationship of Brassicaceae Hybrids with Various Resistance to Blackleg Is Disclosed by the Use of Molecular Markers
Brassica napus is an important oil source. Its narrow gene pool can be widened by interspecific hybridization with the Brassicaceae species. One of the agronomically important traits, that can be transferred through the hybridization, is the resistance to blackleg, a dangerous disease mainly caused by Leptosphaeria maculans. Hybrid individuals can be analyzed with various molecular markers, including Simple Sequence Repeats (SSR). We investigated the genetic similarity of 32 Brassicaceae hybrids and 19 parental components using SSR markers to reveal their genetic relationship. Furthermore, we compared the field resistance to blackleg of the interspecific progenies. The tested set of 15 SSR markers proved to be useful in revealing the genetic distances in the Brassicaceae hybrids and species. However, genetic similarity of the studied hybrids could not be correlated with the level of field resistance to L. maculans. Moreover, our studies confirmed the usefulness of the Brassicaceae hybrids in terms of blackleg management.
GC-MS Analysis and Quantification of Some Secondary Metabolites of the Algerian Phragmites australis Leaf Extract and Their Biological Activities
Introdukcja genów odporności na choroby i owady oraz męskiej sterylności z pokrewnych gatunków rodzaju Brassica do rzepaku (Brassica napus L.)
Introdukcja genów odporności na choroby i owady oraz męskiej sterylności z pokrewnych gatunków rodzaju Brassica do rzepaku (Brassica napus L.)
Ocimum basilicum L. extract-loaded liposomes ameliorate metribuzin-induced pulmonary and cardiac dysfunction in Wistar Albino rats
The objective was to develop liposomes as a new drug delivery system (NDDS) containing basil extract and to investigate their therapeutic and protective effects against toxicity caused by exposure to metribuzin in the lungs and heart. For in vivo study, 24 male albino Wistar rats were divided into four groups (n = 6), control group, metribuzin-treated group, Basil extract-treated group (BE) and Basil extract liposomes-treated group (BE-LPs). The weight gain of each organ was measured. Superoxide dismutase (SOD), Glutathion peroxidase (GPx), Glutation S transferase (GSTs), Catalase (CAT), Reduced Glutathion (GSH) and malondialdehyde (MDA) levels in lugs and heart were measured in order to evaluate oxidative stress status. The tissue histology of the organs was examined. Various biochemical parameters and inflammation markers were estimated. Results of the in vivo rats' study showed that treatment with metribuzin induced increase in organs weight, oxidative stress, biochemical toxicity, inflammation, and histological changes in the lungs and heart, as well as a significant amelioration of BE and BE-LPs against the toxic effects induced by metribuzin by reversing all of the previous parameters. In conclusion, the application of BE-LPs appears to be effective in addressing the issues of oxidative stress and inflammation caused by metribuzin in the lungs and heart.
