Deciphering Winter Sprouting Potential of Erianthus procerus Derived Sugarcane Hybrids under Subtropical Climates.

Winter sprouting potential and red rot resistance are two key parameters for successful sugarcane breeding in the subtropics. However, the cultivated sugarcane hybrids had a narrow genetic base; hence, the present study was planned to evaluate the Erianthus procerus genome introgressed Saccharum hybrids for their ratooning potential under subtropical climates and red rot tolerance under tropical and subtropical climates. A set of 15 Erianthus procerus derived hybrids confirmed through the 5S rDNA marker, along with five check varieties, were evaluated for agro-morphological, quality, and physiological traits for two years (2018-2019 and 2019-2020) and winter sprouting potential for three years (2018-2019, 2019-2020, and 2020-2021). The experimental material was also tested against the most prevalent isolates of the red rot pathogen in tropical (Cf671 and Cf671 + Cf9401) and subtropical regions (Cf08 and Cf09). The E. procerus hybrid GU 12-19 had the highest winter sprouting potential, with a winter sprouting index (WSI) of 10.6, followed by GU 12-22 with a WSI of 8.5. The other top-performing hybrids were as follows: GU 12-21 and GU 12-29 with a WSI of 7.2 and 6.9, respectively. A set of nine E. procerus-derived hybrids, i.e., GU04 (28) EO-2, GU12-19, GU12-21, GU12-22, GU12-23, GU12-26, GU12-27, GU12-30, and GU12-31, were resistant to the most prevalent isolates of red rot in both tropical and subtropical conditions. The association analysis revealed significant correlations between the various traits, particularly the fibre content, with a maximum number of associations, which indicates its multifaceted impact on sugarcane characteristics. Principal component analysis (PCA) summarised the data, explaining 57.6% of the total variation for the measured traits and genotypes, providing valuable insights into the performance and characteristics of the Erianthus procerus derived hybrids under subtropical climates. The anthocyanin content of Erianthus procerus hybrids was better than the check varieties, ranging from 0.123 to 0.179 (2018-2019) and 0.111 to 0.172 (2019-2020); anthocyanin plays a vital role in mitigating cold injury, acting as an antioxidant in cool weather conditions, particularly in sugarcane. Seven hybrids recorded a more than 22% fibre threshold, indicating their industrial potential. These hybrids could serve as potential donors for cold tolerance and a high ratooning ability, along with red rot resistance, under subtropical climates.

Application of Deep Learning in the Diagnosis of Alzheimer's and Parkinson's disease-A Review.

Most neurodegenerative diseases such as Alzheimer's and Parkinson's are life-threatening, critical, and incurable affecting mainly the elderly population. Early diagnosis is challenging as disease phenotype is very crucial for predicting, preventing the progression, and effective drug discovery. In the last few years, Deep learning (DL) based neural networks are the state-of-the-art models deployed in industries and academics across different areas like natural language processing, image analysis, speech recognition, audio classification, and many more. It has been slowly realized that they have a high potential in medical image analysis and diagnostics and medical management in general. As this field is vast and expanding rapidly, we have put focused on existing DL-based models to detect Alzheimer's and Parkinson's in particular. This study gives a summary of related medical examinations for these diseases. Frameworks and applications of many deep learning models have been discussed. We have given precise notes on pre-processing techniques used by various studies for MRI image analysis. An overview of the application of DL-based models in different stages of medical image analysis has been conferred. It has been realized from the review that more studies are focused on Alzheimer's compared to Parkinson's disease. Additionally, we have tabulated the various public datasets available for these diseases. We have highlighted the potential use of a novel biomarker for the early diagnosis of these disorders. Also, some challenges and issues in implementing deep learning techniques for the detection of these diseases have been addressed. Finally, we concluded with some directions for future research regarding deep learning in the diagnosis of these diseases.

A single acute hepatotoxic dose of CCl4 causes oxidative stress in the rat brain.

Carbon tetrachloride (CCl4), a hepatotoxic agent is widely used to study the toxic mechanisms in experimental animals. We have investigated whether oxidative stress is induced in the brain at a single hepatotoxic dosage (1 ml/kg bw) of CCl4. Increased lipid peroxidation (LPO), protein carbonyls (PC) content and glutathione (GSH) depletion were observed in the brain regions of rats treated with CCl4 which was higher than that of liver. A drastic reduction in the activity of glutathione-S-transferase (GST) was seen in the brain regions which was higher than that of liver. Similarly, activities of glutathione peroxidase (GPx), glutathione reductase (GR), superoxide dismutase (SOD), catalase (CAT), NADH- and NADPH-dehydrogenase were reduced in the brain regions similar to that of liver. Higher induction of oxidative stress in the brain compared to that of liver implies vulnerability of the brain for CCl4 neurotoxicity. Our study shows that a single hepatotoxic dose of CCl4 is equally neurotoxic to rats.