AGRICULTURAL BIOTECHNOLOGY JOURNAL
Year:2024 | Volume:16 | Issue:3|Papers Count:18

Objective This study was conducted to evaluate the effects of trace elements on growth, oils content, fatty acid profiles, and biodiesel properties of Dunaliella salina. Materials and methods Algae cells were grown in Guillard medium (f/2) with a salinity of 117 g/L and with 19 nutrient treatments containing different concentrations of trace elements (25, 50 and 75% of the initial concentration of Guillard medium), with 3 repetitions. At the end of the experiment, dry weight and some selected growth parameters, lipids content, fatty acids, and biodiesel fuel properties were determined. Results Dry weight of algae was significantly influenced by addition levels of trace elements. The maximum algal biomass and fat content were observed in CuSO4 with 75% of standard concentration (1425.1 mg/L and 374 mg/biomass, respectively) and was significantly higher than the control treatment (P < 0.05). Majority of the fatty acid methyl esters were between C14-18 and C22:1n9., The highest cetane number (82.7) and the lowest iodine value (123.5) were found in CoCl2 with 25% of the standard concentration treatment (P < 0.05). Among all treatments, the highest degree of unsaturation was observed in the 25% manganese treatment (P<0.05). The highest and lowest amount of CFPP was observed in cobalt 75% (4.50°C) and cobalt 25% (-2.86°C) treatments, respectively; being significantly higher than the control treatment (P<0.05). Conclusions High-quality biodiesel that was obtained from D. salina grown in CoCl2 with 25% of the standard concentration (higher cetane number and lower long-chain unsaturated fatty acids) showed the great potential for biodiesel production due to higher lipids content (197.92 mg/total biomass) than the control treatment (193.44 mg/total biomass) from lower mass quantity. The exposure of D. salina to trace elements deficiency revealed the ability of this algae to grow the same rate as control (in case of zinc, manganese, cobalt, and molybdate treatments 75%) or even higher growth (75% copper; 1425.1 mg/d.w.). The use of modified Guillard medium (f/2) could be beneficial during the cultivation period of D. salina to prevent biomass loss to some degree (e.g.  nitrogen and phosphorus shock), resulting in higher lipids production.

ObjectiveTomato (Solanum lycopersicum L.) is divided into two botanical varieties cerasiforme and lycopersicum. Many researchers believe that during domestication process, some changes in key genes governing flower properties in cherry tomato (S. lycopersicum var. cerasiforme) led to the development of the variety lycopersicum. The aim of this research was transferring lc and fas genes to the cherry tomato genetic background and assessment the effects of these two loci on flower characteristics in cherry tomato background. Materials and methodsIn this survey the genes lc and fas were transferred to a cherry tomato line p-33 via marker-assisted backcrossing. In each back cross generation, mutated alleles were chosen using specific molecular markers. Backcrossing was continued up to BC3F1. Afterward, the generation BC3F2 was produced and homozygote near-isogenic lines (NILs) were generated for assessment of genes lc and fas in p-33 genetic background. The flower traits including the number of petals, number of sepals, number of stamens, primary fruit weight and locule number were measured.  ResultsThe results showed that the effects of genes lc and fas on the trait's number of petals, sepals, and stamens were statistically significant at 5% level. While the effects of the mentioned genes on the rest of traits was significant at 1% level. The number of sepals, petals, and stamens in NILs-fas increased 31.28%, 36.79%, and 40.48% with respect to line p-33, respectively. Also, the number of locules increased in NILs-lc and NILs-fas with respect to line p-33 (37.88% and 47.63% respectively). Moreover, the primary fruit diameter showed increment in NILs-lc and NILs-fas respect to line p-33 (14.91% and 11.81% respectively). lc and fas loci had also a magnificent effect on flower size and showed incredible increase in NILs-lc and NILs-fas respect to line p-33. ConclusionsThe result of this study displayed that lc and fas genes have dramatic effect of in flower characteristics of tomato var. cerasiforme. The results also showed that the studied genes had key role in giving rise to S. lycopersicum var. lycopersicum form S. lycopersicum var. cerasiforme during tomato domestication.

Objective Phylogeny as a science that study the evolutionary relationships between species and populations of organisms, as well as their evolutionary history, diversity and patterns of population changes, by integrating genomic data, has become a strong approach in the study of the systematic of species named phylogenomics. The aim of the present study were divergence study an percent genetic similarity along with phylogenetic analysis of the seven existence species of the Equidae family based on mitochondrial genome sequences. Materials and methods In order to investigate genetic divergence and similarity, the most up-to-date complete sequences of the mitochondrial genome along with the separate sequences of 13 protein coding genes per each genome from seven main Equidae species including E. africanus, E. ferus, E. grevyi, E. hemionus, E. kiang, E. quagga and E. zebra were extracted from NCBI database site and compared to each other. Results The results of sequence distance analysis based on the complete mitochondrial genome showed that the highest genetic similarity (99.9%) was observed between Asian wild donkey (E. hemionus) and Tibetan donkey (E. kiang) and the lowest genetic similarity (92.3%) was observed between Asian wild donkey (E. hemionus) and Przewalski's horse (E. przewalskii). In terms of phylogenetic analysis, E. przewalskii species was placed in a separate cluster, and E. africanus, E. grevyi, E. hemionus, E. kiang, E. quagga and E. zebra were placed in a another cluster. The analysis of the nucleotide sequences of 13 protein encoding genes per each genome showed that in all these genes E.kiang and E.hemionus species had the most genetic similarity to each other and also the least genetic similarity was observed between E.przewalskii and E.asinus species. In terms of phylogenetic analysis, E.przewalskii species had the highest distance from other species and was not included in the main cluster, which results were similar to the analysis of complete mitochondrial genome sequences. Also, the results of the analysis for the amino acid sequences of 13 protein-coding genes showed that unlike the nucleotide sequences, the highest and least genetic similarity may be different in the species. Although, E. kiang and E. hemionus species had the most genetic similarities, and the lowest genetic similarity of the species was related to E. przewalskii and E. zebra species, and in terms of phylogenetic analysis, E. przewalskii had the highest distance from other species and was not included in the main cluster. Conclusions This research showed that the examination of mitochondrial genomes and sequences of Equidae species can help to determine the evolution and biological processes in the past of these species, as well as to determine the distribution and relationship between different species of the Equus genus and the possibility of correct clustering of the species exists based on the mitochondrial genome.

Objective Fennel (Foeniculum vulgare) has important medicinal compounds with the ability to inhibit DNA oxidative damage. In the present study, metal nanoparticles were first biosynthesized from the aqueous extract of the aerial parts of the fennel plant, and after evaluating the structure and size of the nanoparticles, the effect of the aqueous extract obtained from the aerial parts of the fennel plant and the metal nanoparticles biosynthesized from it was investigated on the growth and survival of bacteria. E. coli and S. aureus were investigated. Materials and methods The size of the resulting nanoparticles was analyzed using scanning electron microscope (SEM) and nanoparticle size distribution (DLS), and the chemical structure and bonds in the composition of nanoparticles were investigated using FTIR. The amount of antibacterial activity of the resulting extract (at a concentration of 20, 10, 5 and 2.5 µl/ml) and iron oxide nanoparticles (at a concentration of 10, 5, 2.5 and 1.25 µl/ml), copper, zinc oxide and silver (in concentrations of 2, 1, 0.5 and 0.25 µl/ml) on culture media containing E. coli and S. aureus bacteria were investigated using the diffusion plate method and the minimum inhibitory concentration (MIC). Results According to the SEM results, biosynthesized iron oxide, copper, silver and zinc oxide nanoparticles had an average size of 25, 22, 23 and 24 nm, respectively, which can be highly effective in the process of dealing with different bacteria. According to the results, E. coli bacteria were more sensitive than S. aureus to concentrations equal to the treatments used and created larger halos. Also, the size of the formed halos depended on the concentration and their size increased with the increase of the concentration. Among the treatments used, the use of biosynthesized silver nanoparticles (especially in concentrations of 10, 5, 2.5 and 1.25 mg) had more antibacterial properties. In terms of the survival rate and growth of bacteria cultured using the MIC method, the IC50 of E. coli bacteria is lower than that of S. aureus bacteria at concentrations equal to the applied treatments, indicating the high sensitivity of the bacterial strain. E. coli compared to S. aureus bacteria. Conclusions Aqueous extract of fennel plant and metal nanoparticles biosynthesized from it have high properties in controlling S. aureus bacteria as gram positive bacteria and E. coli as gram negative bacteria. In this research, it was found that silver nanoparticles biosynthesized from the aqueous extract of fennel plant have high antibacterial properties compared to iron oxide, zinc oxide and copper nanoparticles.

Objective The aim of this study was to propose an efficient algorithm to predict antimicrobial peptides using artificial intelligence algorithms. Materials and methods In this study, an updated AMP and non-AMP data set including physico-chemical characteristics at the level of amino acids and protein sequence in three animal species and humans was extracted. After data exploration and data pre-processing steps, four methods Supervised learning including Decision Tree, Random Forest, Naive Bayes and SVM on the AMP dataset with 10-fold cross-validation to build models and predict the AMP label using the evaluation criteria of specificity, sensitivity, rate Accuracy, precision, recall, F1 score and area under the rock curve (AUC) were evaluated. Results In this study, using an up-to-date dataset, a machine learning model has been successfully trained to predict antimicrobial peptides. A comprehensive set of features has been subjected to feature selection to identify key features of antimicrobial peptides. After selecting the feature, among the different generated models, the model based on the RF model classifier showed the best performance with Accuracy (95 percent), Precision (96 percent), Recall (95 percent), F1 Score (95 percent). the four classification of algorithms, Random Forest algorithm and SVM are the most accurate. The Decision Tree classification algorithm had the least accuracy. Conclusions According to the obtained results, the proposed RF model has a better performance than other models for AMP prediction. This model predicted some peptides as peptides with antimicrobial properties. This predictive approach can be useful in extracting AMPs with antimicrobial properties from AMP libraries in useful clinical applications before moving on to experimental studies. On the other hand, several features in the final selection properties indicate that these features are critical determinants of peptide properties and should be considered in the development of models to predict peptide activity. The executable code is available in the attached file.

Objective Drought conditions at the terminal of grain filling stage are one of the main factors limiting barley yield. In stressful conditions, due to the limitation of current photosynthesis, the importance of storing more stems and its contribution to the production of the final product will be significant. Among other internodes of the stem, the penultimate internode plays the most important role in remobilization. The aim of this study is to investigate the drought tolerance of two cultivars and one line of barley with different degrees of remobilize of substances and also to investigate the comparative metabolic analysis on the penultimate internodes of three genotypes of barley Yousef (tolerant), Morocco (sensitive) and line PBYT17 (semi-tolerant) under the terminal of grain filling stage Materials and methods Evaluation of metabolomics analysis of barley genotypes (Yousef, Morocco and line PBYT17) in response to drought stress was carried out 21 and 28 days after the application of drought stress in the grain filling stage. Also, the ability to remobilize stem penultimates of Yusuf, Morocco and PBYT17 genotypes in response to drought was investigated. Identification of metabolites profile which included 17 metabolites including soluble sugars, sugar phosphate, sugar nucleotide, organic acids, phosphorylated metabolites and nucleotides. Results The results obtained from the metabolomics data showed that Youssef cultivar had a significantly higher efficiency than other cultivars in retransmission, related to penultimate. Among the investigated genotypes, 17 metabolites were identified. The amount of transferred reserves and the amount of remobilization efficiency were significantly different among different genotypes. Two cultivars Yousef and Morocco had the highest and lowest remobilization efficiency under stress conditions, respectively. Our results showed that the tolerant genotype, by increasing UDP-glucose and glucose-6-phosphate, led to the synthesis of trehalose-6-phosphate, which plays a key role in remobilization materials in the stem. Conclusions In general, the results of this research show that the effective metabolites in the remobilze of stem reserves can play a major role in maintaining the performance of barley plants during the drought stress at the end of the season, and this issue can be targeted solutions to increase the genotype performance. The desired varieties of barley that face drought stress at the end of the season should be used. This study may provide potential directions and valuable resources for further study of drought stress in the barley.

Objective Over the last decade, interest in detection of genes or genomic regions that are targeted by selection has been growing. Identifying signatures of selection can provide valuable insights about the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. The aim of this study was to identify genomic region with a positive signature of selection related to wool quality in sheep breeds using selection signature FST and hapFLK methods. Materials and methods In this study, data from 146 Zandi and Merino animals genotyped using 50 K BeadChip were used to identify genomic regions under selection associated with wool quality traits. After quality control of the initial data using plink software, 46,793 SNP markers in 144 animals of sheep were finally entered for further analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under FST and hapFLK software packages, respectively. Candidate genes were identified by SNPs located at 0.1% upper range of FST and hapFLK values. GeneCards and UniProtKB databases were also used to interpret the function of the obtained genes. Results Using FST approach, we identified eight genomic regions on chromosomes 1 (two region), 1, 3 (two region), 10, 13, and 19 chromosomes, which were in the 99.9 percentile of all FST values. The identified candidate genes associated with wool trait in these genomic regions included POU1F1, FGF12, GNAS, LHX2, TMTC3, NBEA, MITF. Genes located in identified regions under selection were associated with the crimp of wool, growth and development of skin, fiber diameter, hair follicle development, growth and development of fiber, stimulation of collagen, regulation of epithelial cell and skin pigments, which can be directly and indirectly related to the trait of the wool quality. Also, the results of hapFLK statistics in this research led to the identification of four genomic regions on chromosomes 7, 10, 14, and 19. The identified candidate genes associated with the wool trait in these genomic regions included DUOX1, RHPN2 and LOC106991379. It was determined that they had different functions in collagen differentiation and gene expression of keratinocytes. Conclusions Various genes that were founded within these regions can be considered as candidates for selection based on function. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. The results of our research can be used to understand the genetic mechanism controlling wool traits and using these findings could potentially be useful for genetic selection in sheep for better clean fleece yield and mean fiber diameters.

Objectives Environmental contamination and its mitigation are significant global challenges. A wide range of contaminants (CM), including pesticides, hydrocarbons, heavy metals, and dyes, are the primary contributors to environmental contamination. Removal of these contaminants can pose significant problems because such efforts often lead to additional release of contaminants into the aqueous environment. Bioremediation (BR) is a very effective technology for reducing environmental CMs and restoring damaged sites to their original conditions. Only a limited amount of cultivable Genetically Engineered Microorganisms (GEMs) has been used, while a vast array of microbial diversity still needs to be explored.   Materials and methods A range of BR techniques have been extensively employed to improve the ability of GEMs to metabolize substances and restore ecological balance by breaking down persistent CM. These include chemotaxis, bio-stimulation, bioaugmentation, biofilm formation, the application of GEMs, and sophisticated omics methods.   Results There has been significant progress in understanding how bacteria effectively break down and clean up environmental contamination. GEMs aid in the remediation of polluted environments by efficiently decomposing waste materials ecologically soundly and generating harmless by-products.   Conclusions The understanding of the dangerous effects of various chemical CMs has led to an increase in research efforts to develop remediation technologies that can effectively eradicate these CMs. BR is a safe, cost-effective, and sustainable procedure that enhances the biological process of biological degradation to eliminate CMs. Thus, to expand the knowledge of the communities of bacteria and their response to environmental CMs, it is crucial to have a deeper comprehension of molecular genetics. This will enable the researchers to boost the CM degradation capabilities of the most successful species. BR is a process that creates a route towards a more sustainable and pollution-free earth.

ObjectivesGrowing crops is always the primary goal of agricultural operations. Still, worldwide agricultural systems are under increasing stress because of climate change and the growing number of people worldwide who need food. Dealing with climate change, making crops that produce more, protecting the environment, and adapting to changing conditions have become difficult to ensure that the world's population can keep growing. Climate-resilient smart Food Crops (CRSFC) are also needed to control biomass output, a crucial part of keeping the environment working properly globally. Pure-line selections, mass selection, back cross breeding, recurrent selection for improving agricultural CRSFC are limited and time-consuming. Careful selection processes are needed to grow new and better crop types. There is an urgent need to accelerate the process of CRSFC breeding by using artificial intelligence to replicate some features of human intelligence using technology. AI offers significant computing capabilities and a wide range of novel instruments and methods for foreseeable plant breeding (PB) due to the neural network training and classification module.ResultsThis review will discuss the use of AI technology in current breeding practices to address challenges in large-scale phenotyping and gene functionality analysis. AI algorithms make it easy for researchers to quickly look at genetic data, find complicated trends, and build predictive models that help with crop breeding and selecting the most beneficial features. It will also explore how advancements in AI technologies create fresh possibilities for subsequent breeding by promoting the widespread utilization of envirotyping information. It is hard to connect gene to trait with the breeding methods we have now. This makes it harder to use high-volume field phenotyping, genomics, and enviromics effectively.ConclusionsThis paper discusses the use of AI as the preferred a method for improving the reliability of high-volume crop phenotyping, genotyping, and envirotyping information. Additionally, we examine the emerging methodologies and obstacles in integrating large multi-omics computational data. Hence, combining AI with "omics" might facilitate swift gene discovery and ultimately expedite agricultural enhancement initiatives.

Objectives Antibiotic-resistant bacteria have become a worldwide concern due to the unintentional use of antibiotics, which has resulted in bacterial strains resistant to many or all available antibiotics. The primary and secondary metabolites found in algae play a major role in the conversion of silver nitrate to silver nanoparticles (AgNPs).   Materials and methods Hexane extract of some freshwater algae was used in the process of making these nanoparticles. The reaction solution's color changing from yellow to dark brown due to the surface plasmon resonance's excitation serves as evidence for this. AgNPs were identified using UV-Vis spectroscopy, proteins and phenols were found to play a significant role in the formation of AgNPs, according to research done using Fourier Transformation-infrared (FTIR) to identify the effective algae group that contributes to the formation of those nanoparticles. A scanning electron microscope (SEM) was used to characterize the shapes and sizes of the synthesized AgNPs, which included spherical, rod-like, and hexagonal structures. Vitek Compact 2 system-diagnosed Multi-Drug Resistant (MDR) bacteria were used to test AgNPs' antibacterial activity.       Results A study was conducted on the antibacterial effectiveness of biosynthetic silver nanoparticles against selected isolates of MDR bacteria. The results showed that silver nanoparticles prepared from hexane extract of the isolated algae at a concentration of 100% showed greater inhibition than crude extract of all types of pathogenic bacteria, with statistically significant differences (P<0.05).   Conclusions The silver nanoparticles prepared from hexane extract was more effective against G-ve and G+Ve MDR bacterial isolates (E. coli, P. aeruginosa, S. aureus, K. Pneumoniae, and E. faecalis) at concentrations 100 μg/mL than those prepared without silver nanoparticles hexane extract, the extract from Cladophora neglecta silver nanoparticles demonstrated highly significant inhibition in all species of bacteria.

Objectives Given the rapid growth of the global human population, it is imperative to enhance agricultural productivity to fulfill the increasing demand for crops. Enhancing crops through selective plant breeding (PB) is a sustainable method to augment both the quantity and consistency of yields without escalating the need for fertilizers and pesticides. Moreover, data generation in agriculture and biotechnology has greatly increased in recent years due to the very rapid development of high-performance technologies. Similar to advancements in genomics, there are encouraging progressions in plant phenotyping technologies, including mechanized phenotyping apparatus and sophisticated picture analysis. This has led to an unparalleled understanding of PB, structure, and function. Therefore, the aim of this study was to investigate a combined method of genomics and bioinformatics for plant breeding programs.   Results The latest advancements in genomics and bioinformatics offer possibilities for expediting crop enhancement. Third-generation sequencing (TGS) techniques are aiding in the resolution of difficulties in plant genome assembly arising from polyploidy and the presence of repetitive regions. There is a growing availability of superior crop-referencing genomes, which significantly aids in the analysis of genetic variations and the identification of specific PB objectives within the genome. Machine learning (ML) aids in identifying genomic areas of agricultural significance by assisting in the annotation of genomes and allowing for the efficient measurement of agronomic variables in controlled and natural environments.   Conclusions Crop datasets that combine the increasing amount of genotype and phenotypic data offer a valuable tool for breeders and a chance for data mining methods to discover new candidate genes related to traits. Moreover, with the increasing understanding of agricultural genetics, the techniques of genomic selections and genome engineering provide the potential for developing crops resistant to illnesses and adaptable to stress while achieving high production.

ObjectivesDifferent countries are interested in jojoba seed as a possible new energy source because it can grow well in harsh conditions like extreme weather, salty water, deserts, and high temperatures. Biodiesel is a fuel that can be used in motor engines, stoves, and home heating oil systems. It is recyclable, biodegradable, and safe. A cleaner-burning substitute for diesel fuel derived from petroleum, it is manufactured from animal fats, recycled cooking grease, or vegetable oils. Biodiesel (BD) is made in this study using jojoba oil under pressure. The inherent huge parallelism of neural networks (NNs) makes them a promising optimization tool. Commercial biodiesel production that is both efficient and environmentally friendly needs AI-powered process modelling and optimization.Materials and methodsPredicting the ideal process parameters for biodiesel synthesis from jojoba oil was accomplished using artificial neural network-genetic algorithm (ANN-GA). With the help of the Integrated Artificial Neural Network - Genetic Algorithm (IANN-GA), this study aims to improve the transesterification process for changing Hyper Critical Methanol (HCM) into BD. The temperature range for IANN-GA optimization was 240–355°C, and the time range was set to 7–21 minutes.ResultsThe primary composite design (PCD) for ANN modelling was used to create the initial studies. The best ANN structure with the right number of concealed neurons was found using a heuristic evaluation of the coefficient of determination (R) values. The R values obtained for training and testing demonstrate the high accuracy of the ANN framework.ConclusionsThe process variables for HCM transesterification have been optimized using GA with an ANN as the fitness coefficient. When taken as a whole, the findings demonstrated that ANN-GA is superior to the model that had been provided before, and that it is a trustworthy modeling and optimization approach for the manufacture of biodiesel from jojoba oil that is both practical and sustainable.

Objective The uniformity of fertilizer application across fields is a common practice, driven by local legislation or by expert opinion. However, this approach might lead to over-application of nitrogen in areas with poor yields. Human health, ecological functions, biodiversity, climate change, and long-term stability are all adversely affected by the increasing release of reactive nitrogen into the environment that may result from the excessive use of fertilizers. The purpose of this work was to show that throughout the growth season, location-specific N proposals may be generated using non-invasive crop status monitoring that is built on Remote Sensing Technologies (RST). This tracking system can accurately assess the position of crop N. Materials and methods In this study, two frameworks—Support Vector Machine (SVM) and Artificial Neural Networks (ANN)—that rely solely on data collected from crop sensors, with the goal of improving our ability to predict crop N Nutrition Index (NNI) and crop yield throughout the growing season were compared. This was performed by combining data from soil, weather, and cultivation with information from present detectors using Random Forest (RF). Results Through RST, a simple and low-cost tool known as a fixed-wing Unmanned Aerial Vehicle (UAV) may capture wavelength reflection images. This collection of images is invaluable to polystyrene nanoparticles (PNSP). Applying ML techniques enhanced the NNI estimate, as seen by the results. Conclusions Utilizing machine learning techniques presents a valuable opportunity to maximize the use of RST data, enabling more effective monitoring of agricultural production factors and directing PNSP strategies

Objective Smart agriculture (SA) is a revolutionary method of farming that maximizes crop productivity and reduces environmental damage by using cutting-edge technology like sensors, robots, and data analytics. By using less pesticides, fertilizers, and other materials that damage ecosystems, it seeks to increase production efficiency and make agricultural methods more environmentally friendly. The combination of Wireless Sensor Networks (WSN) with microfluidic lab-on-a-chip technologies for real-time plant health monitoring and management is one of the most novel developments in SA.   Results Agricultural biotechnology (ABT) in conjunction with networked microfluidic detectors may improve the identification and control of plant diseases. These biosensors can identify even minute levels of pathogens in plant tissues or environmental samples since they are designed to be very sensitive, inexpensive, and portable. Precision agricultural methods and a thorough image of disease propagation are made possible by the integration of these biosensors into a Wireless Sensor Network (WSN), which allows data to be wirelessly sent to a central server for real-time analysis.       Conclusions In order to identify plant diseases, traditional agricultural systems often depend on time-consuming techniques including visual inspections, manual sampling, and diagnostic laboratory testing. Micro-fluidic biosensors provide a quicker and more accurate way to detect plant diseases locally and in real time. These technologies, when integrated with Wireless Sensor Networks (WSN), provide an effective framework for ongoing plant health monitoring, allowing farmers to identify diseases early and take prompt action.

Objective The Food Supply Chain (FSC) internationalization has caused significant problems for the food system. These problems include fraud, security, dependability, and quality, all caused by a lack of fair data. Globalization makes dealing with these problems more complex and complicated, which makes FSC more successful. Blockchain (BC) has shown that it can change FSC by using its strengths. Much research is still being done on using BC in FSC, but it is still in its early stages. This work takes a close look at all the previous research that has been done on BC and Biotechnology (BT) integrated FSCs (BT-FSC).   Results A review using well-known sources was part of the research. Publications from 2018 to 2024 were included in the study. Fifty pieces were looked at to find the things that make BC possible and its pros and cons. Based on the facts in this study, a theoretical framework was created to help with the adoption of BC in the BT-FSC.   Conclusions The poll found that the main things that stop people from using BC are its scalability, interaction, higher prices, inadequate knowledge, and laws. It adds to what is already known by giving helpful information about how BC is used in the BT-FSC. It provides other businesses with evidence-based advice on how to make plans for adopting BC.

Objective Infections caused by viruses and bacteria are the primary microbe-related problems that significantly decrease agricultural productivity worldwide. Currently, the identification of pathogens is particularly difficult due to the prevailing living conditions. Biosensors are now widely used for the surveillance of microbial and viral particles.   Materials and Methods Preventing crop loss and reducing economic and environmental effects requires early plant disease identification. Tracking plant infection nanoparticles has made early disease diagnosis possible due to nanotechnology and biosensors. Pathogens including bacteria, fungi, and viruses form nanoparticles with unique chemical traces that may be detected by sensitive nano biosensors. Precision agriculture now allows faster responses and more specific disease control. Deep Learning (DL) methods, particularly Convolutional Neural Networks (CNNs), can learn hierarchical patterns in nano biosensor data and accurately distinguish healthy and infected plants, even in early infection stages. This expands precision agriculture and disease management.   Results The study utilizes the ECPD-CNN-NBS model to identify Bacterial Spot (BS) disease in peach plants by analyzing their leaf images. The model can also be employed for ECPD detection. The experiments conducted in this paper utilize the publicly accessible PlantVillage dataset to obtain leaf pictures of peach plants.   Conclusions The proposed system attains a learning accuracy of 99.55% and a testing accuracy of 99.01% by utilizing 10,013 learning parameters.

Objective Food contamination and food waste have contributed significantly to the rising incidence of foodborne illnesses and food scarcity globally. There is a pressing necessity to create a more intelligent food traceability system. Recent breakthroughs in biosensor (BS) technology have resulted in the development of user-friendly, fast, specific, highly sensitive, and affordable BSs. These improvements hold significant potential in addressing the urgent need for on-site and prompt detection and treatment of food security and quality control issues, commonly referred to as point-of-care technologies. Blockchain (BC) offers an unchangeable and easily visible record that brings benefits in the traceability of fresh food quality and the real-time monitoring of it.   Results This paper explores the correlation between BC, traceability, and real-time food quality monitoring. It highlights the latest technological advancements in food tracing and continuous surveillance that can offer valuable data for BC implementation. The discussion focuses on using BSs in farming at the pre-harvest phase to identify and manage plant diseases or stresses at an early phase.          Conclusions This article focuses on the recent progress in BSs within the Food Supply Chain (FSC), specifically in the post-harvest phase. It highlights the advancements in detecting various types of food pollutants and the development of intelligent food packaging.

ObjectiveThe Raini cashmere goat is one of the most momentous Iranian goat breeds. These animals are bred for production of not only cashmere but also meat. Factors such as lipid peroxidation, oxidative stress, and changes in membrane properties are among the factors that cause the death of germ cells at different stages of development and ultimately have a negative effect on production and reproduction. To investigate this process, the UCP2 gene, a protein from the inner membrane of the mitochondrial organelle that pertains to uncoupling proteins family that performs a dominant function in reducing the membrane potential of the mitochondrial organelle and the loss of metabolic energy by preventing the accumulation of oxidative stress, was studied. Moreover, this gene is implicated in the physiology of several tissues, and the dominant isoform of its unconjugated proteins performs a dominant function in preventing inflammation and inhibiting cell death. The goal of the present study was therefore to consider the relative expression level of the UCP2 gene in several tissues of the Raini cashmere goat applying the Real-Time PCR technique.Materials and MethodsFor this purpose, samples of humeral muscle, back muscle, femur muscle, spleen, heart, testis, liver and back fat tissues (three replicates of each tissue) were collected from four Raini cashmere goats of approximately the same weight at the time of slaughter. Total RNA of these tissues was extracted and their cDNA amplified. Real-Time PCR reaction using the non-specific SYBR Green dye method was performed to examine the relative gene expression. In this study, the GAPDH gene was used as an internal control. Prism software was applied to analyze the raw data, analyze the data got from Real-Time PCR and calculate the extent of changes in gene expression.Results The results of current investigation showed that the UCP2 gene was expressed in all tissues examined, with the highest expression level in the humeral muscle tissue (9.5-fold) and back muscle (4.5-fold), while the lowest expression level was observed in the back fat tissue (1.3-fold) and liver (1.5-fold) (P<0.01).ConclusionsConsidering that the UCP2 gene was expressed in all tissues examined and showed different expression levels in the different tissues, it could be inferred that this gene performs a function in different mechanisms and activities. Therefore, by conducting further experiments and understanding the relevant mechanisms, the cause of the change in expression can be found and action can be taken to improve livestock performance by changing the expression of this gene.