Exogenous application of 5-azacitidin, royal jelly and folic acid regulate plant redox state, expression level of DNA methyltransferases and alleviate adverse effects of salinity stress on Vicia faba L. plants.

DNA methylation is one of induced changes under salinity stress causing reduction in the expression of several crucial genes required for normal plant's operation. Potential use of royal jelly (RJ), folic acid (FA) and 5-azacitidine (5-AZA) on two Egyptian faba bean varieties (Sakha-3 and Giza-716) grown under saline conditions was investigated. Salinity stress affects negatively on seeds germination (G %), mitotic index, membrane stability and induced a significant increase in chromosomal abnormalities (CAs). DNA methyltransferases genes (MT1 and MT2) were highly up-regulated (∼23 and 8 folds for MT1 and MT2 in shoots of Giza-716 stressed plants). On the other hand, down regulation of other studied stress related genes: superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), heat shock protein (HSP-17.9) and proline-rich protein (GPRP) were detected in stressed plants of both studied varieties. Treating plants with RJ and FA increase G%, chlorophyll content, improves membrane properties and reduces CAs compared to non-treated stressed plants. Exogenous application of 5-AZA, RJ and FA on salinity stressed plants was associated with a significant reduction in the transcription of MT1 and MT2 which was associated with significant up regulation in the expression of Cu/Zn-SOD, CAT, GR, GPRP and HSP-17.9 encoding genes. The Lowest expression of MT1 and MT2 were induced with 5-AZA treatment in both studied varieties. Exogenous application of the FA, RJ and 5-AZA modified the methylation state of stressed plants by regulation the expression of DNA methyltransferases, subsequently, modulated the expression of studied genes and could be proposed as a promising treatment to ameliorate hazardous effects of salt stress on different plants.

Ameliorating effect of nanoparticles and seeds' heat pre-treatment on soybean plants exposed to sea water salinity.

Impairing plant growth and reducing crop production, salinity is considered as major problem in modern agriculture. The current study aimed to investigate the role of seeds' heat pretreatment at 45 °C as well as application of two different nanoparticles nanosilica (N1) and nanoselenium (N2) in reducing salinity stress in three genotypes of Egyptian commercial soybeans (Glycine max L.). Two levels of salt stress using diluted sea water (1/12 and 1/6) were tested either alone or in combination with protective treatments. Obtained results revealed that salinity caused a significant reduction in all tested physiological parameters such as germination rate and membrane stability in soybean plants. A significant reduction in mitotic index and arrest in metaphase were recorded under both tested levels of salinity. It was also revealed that chromosomal abnormalities in soybean plants were positively correlated with the applied salinity concentrations. The fragmentation effect of salinity on the nuclear DNA was investigated and confirmed using Comet assay analysis. Seeds heat pre-treatment (45 °C) and both types of nanoparticles' treatments yielded positive effects on both the salt-stressed and unstressed plants. Quantitative real-time reverse transcription PCR (qRT-PCR) analysis for salt stress responsive marker genes revealed that most studied genes (CAT, APX, DHN2, CAB3, GMPIPL6 and GMSALT3) responded favorably to protective treatments. The modulation in gene expression pattern was associated with improving growth vigor and salinity tolerance in soybean plants. Our results suggest that seeds' heat pretreatment and nanoparticle applications support the recovery against oxidative stresses and represent a promising strategy for alleviating salt stress in soybean genotypes.

Identifying Effective Feature Selection Methods for Alzheimer's Disease Biomarker Gene Detection Using Machine Learning.

Alzheimer's disease (AD) is a complex genetic disorder that affects the brain and has been the focus of many bioinformatics research studies. The primary objective of these studies is to identify and classify genes involved in the progression of AD and to explore the function of these risk genes in the disease process. The aim of this research is to identify the most effective model for detecting biomarker genes associated with AD using several feature selection methods. We compared the efficiency of feature selection methods with an SVM classifier, including mRMR, CFS, the Chi-Square Test, F-score, and GA. We calculated the accuracy of the SVM classifier using validation methods such as 10-fold cross-validation. We applied these feature selection methods with SVM to a benchmark AD gene expression dataset consisting of 696 samples and 200 genes. The results indicate that the mRMR and F-score feature selection methods with SVM classifier achieved a high accuracy of around 84%, with a number of genes between 20 and 40. Furthermore, the mRMR and F-score feature selection methods with SVM classifier outperformed the GA, Chi-Square Test, and CFS methods. Overall, these findings suggest that the mRMR and F-score feature selection methods with SVM classifier are effective in identifying biomarker genes related to AD and could potentially lead to more accurate diagnosis and treatment of the disease.

Brain MRI Analysis for Alzheimer's Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning.

Alzheimer's disease is the most common form of dementia and the fifth-leading cause of death among people over the age of 65. In addition, based on official records, cases of death from Alzheimer's disease have increased significantly. Hence, early diagnosis of Alzheimer's disease can increase patients' survival rates. Machine learning methods on magnetic resonance imaging have been used in the diagnosis of Alzheimer's disease to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on MRI images is complicated, requiring the involvement of an expert user. Therefore, implementing deep learning as an automatic feature extraction method could minimize the need for feature extraction and automate the process. In this study, we propose a pre-trained CNN deep learning model ResNet50 as an automatic feature extraction method for diagnosing Alzheimer's disease using MRI images. Then, the performance of a CNN with conventional Softmax, SVM, and RF evaluated using different metric measures such as accuracy. The result showed that our model outperformed other state-of-the-art models by achieving the higher accuracy, with an accuracy range of 85.7% to 99% for models with MRI ADNI dataset.

Identification and expression analysis of two small heat shock protein cDNAs from developing seeds of biodiesel feedstock plant Jatropha curcas.

Plant small heat shock proteins (sHSPs) are known to be important for environmental stress tolerance and involved in various developmental processes. In this study, two full-length cDNAs encoding sHSPs, designated JcHSP-1 and JcHSP-2, were identified and characterized from developing seeds of a promising biodiesel feedstock plant Jatropha curcas by expressed sequence tag (EST) sequencing of embryo cDNA libraries and rapid amplification of cDNA ends (RACE). JcHSP-1 and JcHSP-2 contained open-reading frames encoding sHSPs of 219 and 157 amino acids, with predicted molecular weights of 24.42kDa and 18.02kDa, respectively. Sequence alignment indicated that both JcHSP-1 and JcHSP-2 shared high similarity with other plant sHSPs. Real-time quantitative RT-PCR analysis showed that the transcriptional level of both JcHSP-1 and JcHSP-2 increased along with natural dehydration process during seed development. A sharp increase of JcHSP-2 transcripts occurred in response to water content dropping from 42% in mature seeds to 12% in dry seeds. Western blot analysis revealed that the accumulation profile of two cross-reacting proteins, whose molecular weight corresponding to the calculated size of JcHSP-1 and JcHSP-2, respectively, was well consistent with the mRNA expression pattern of JcHSP-1 and JcHSP-2 in jatropha seeds during maturation and natural dehydration. These results indicated that both JcHSPs might play an important role in cell protection and seed development during maturation of J. curcas seeds.