Native bio-control agents from the rice fields of Telangana, India: characterization and unveiling the potential against stem rot and false smut diseases of rice.

The stem rot caused by Sclerotium hydrophilum and false smut caused by Ustilaginoidea virens are two of the major production constraints in rice cultivation in India and other countries. Stem rot and false smut can be effectively controlled with synthetic fungicides. However, the indiscriminate use of chemical fungicides may cause development of resistance among the pathogens. In addition to this, synthetic fungicides also exhibit harmful impacts on the environment. Exploiting microbe-based alternatives for managing plant diseases diminishes public concerns about the ill effects of pesticide usage in crops. In this regard, the present study was designed to investigate the potential of native microbial biocontrol agents (BCAs) from rice rhizosphere for the sustainable management of stem rot and false smut diseases in rice. Potential BCAs and pathogens were identified and characterized through morphological, biochemical, and sanger sequencing techniques. Bio-efficacy tests of potential BCAs against stem rot and false smut diseases on rice under glasshouse conditions indicated higher seed vigour index of the treated seeds, significant improvement in the growth of the seedling, increased dry weight, reduction in percentage disease index viz., 70.03% (stem rot) and 69.24% (false smut) over the control plants. Phytohormones indole acetic acid (IAA), abscisic acid (ABA), gibberellic acid (GA), salicylic acid (SA), and zeatin (tZ) were detected and quantified in the four potential BCAs using liquid chromatography- tandem mass spectrometry (LC-MS/MS). Scanning electron microscopy (SEM) studies revealed the endophytic nature of the strains in rice. The study indicated a positive correlation between the diversity and concentration of phytohormones released by the bioagents and enhanced plant growth promotion and disease suppression in rice.

Alzheimer's Disease Prediction Using Fly-Optimized Densely Connected Convolution Neural Networks Based on MRI Images.

Alzheimer's is a degenerative brain cell disease that affects around 5.8 million people globally. The progressive neurodegenerative disease known as Alzheimer's Disease (AD), affects the frontal cortex, the part of the brain in charge of memory, language, and cognition. As a result, researchers are utilizing a variety of machine-learning techniques to create an automated method for AD detection. The massive data collected during ROI and biomarker identification takes longer to handle using current methods. This study uses metaheuristic-tuned deep learning to detect the AD-affected region. The research utilizes advanced deep learning and image processing techniques to enhance early and accurate diagnosis of Alzheimer's disease, potentially enhancing patient outcomes and prompt therapy. The capacity of deep neural networks to extract complex patterns from magnetic resonance imaging (MRI) scans makes them indispensable in the diagnosis of AD since they allow the detection of minor aberrations and complex alterations in brain structure and composition. An adaptive histogram approach processes the collected photographs, and a weighted median filter is used in place of the noisy pixels. The next step is to identify the issue region using a deep convolution network-based clustering segmentation process. A correlated information theory approach is used to extract various textural and statistical features from the separated regions. Lastly, the selected features are probed by the fly-optimized densely linked convolution neural networks. The method surpasses state-of-the-art techniques in sensitivity (15.52%), specificity (15.62%), accuracy (9.01%), error rate (11.29%), and F-measure (10.52%) for recognizing AD-impacted regions in MRI scans using the Kaggle dataset.

Multi-region minutiae depth value-based efficient forged finger print analysis.

The application of biometrics has expanded the wings to many domains of application. However, various biometric features are being used in different security systems; the fingerprints have their own merits as it is more distinct. A different algorithm has been discussed earlier to improve the security and analysis of fingerprints to find forged ones, but it has a deficiency in expected performance. A multi-region minutiae depth value (MRMDV) based finger analysis algorithm has been presented to solve this issue. The image that is considered as input has been can be converted into noisy free with the help of median and Gabor filters. Further, the quality of the image is improved by sharpening the image. Second, the preprocessed image has been divided into many tiny images representing various regions. From the regional images, the features of ridge ends, ridge bifurcation, ridge enclosure, ridge dot, and ridge island. The multi-region minutiae depth value (MRMDV) has been computed based on the features which are extracted. The test image which has a similarity to the test image is estimated around MRMDV value towards forgery detection. The MRMDV approach produced noticeable results on forged fingerprint detection accuracy up to 98% with the least time complexity of 12 seconds.

Demonstration of the metabolic pathway responsible for nevirapine-induced skin rash.

The reverse transcriptase inhibitor, nevirapine (NVP), causes skin rashes and hepatotoxicity. We used a rat model to determine if the rash is caused by the parent drug or a reactive metabolite. By manipulation of metabolic pathways and testing analogues, we eliminated all but one pathway, 12-hydroxylation, which involves the oxidation of an exocyclic methyl group, as being responsible for the rash. Treatment with 12-OH-NVP caused a rash, and an analogue in which the methyl hydrogens were replaced by deuterium to inhibit the 12-OH pathway did not cause a rash; however, quite unexpectedly, blood levels of the deuterated analogue were very low. This is due to partitioning of the benzylic free radial intermediate between oxygen rebound to form 12-OH-NVP and loss of another hydrogen atom to form a reactive quinone methide, which inactivates P450. Cotreatment with the P450 inhibitor, 1-aminobenzotriazole, led to comparable levels of NVP and the deuterated analogue, and the deuterated analogue still caused a lower rash incidence. These data clearly point to the 12-hydroxy pathway being responsible for NVP skin rash. We propose that the hepatotoxicity of NVP in humans is due to the quinone methide formed by P450 in the liver, while the skin rash may be due to the quinone methide formed in the skin by sulfation of 12-OH metabolite followed by loss of sulfate. This is the first example in which a valid animal model of an idiosyncratic drug reaction was used to determine the metabolic pathway responsible for the reaction.

Engineering d-amino acid containing novel protease inhibitors using catalytic site architecture.

The mechanism of proteolysis by serine proteases is a reasonably well-understood process. Typically, a histidine residue acting as a general base deprotonates the catalytic serine residue and the hydrolytic water molecule. We disclose here, the use of an unnatural d-amino acid as a strategic residue in P1 position, designed de novo based on the architecture of the protease catalytic site to impede the catalytic histidine residue at the stage of acyl-enzyme intermediate. Several probe molecules containing d-homoserine or its derivatives at P1 position are evaluated. Compounds 1, 6, and 8-10 produced up to 57% loss of activity against chymotrypsin. More potent and specific inhibitors could be designed with structure optimization as this strategy is completely general and can be used to design inhibitors against any serine or cysteine protease.