Search results for: Aparna Ravichandran

Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

Procedia PDF Downloads 63

Authors: Priyanka Mokashi, Aparna Khanna, Nancy Pandita

Abstract:

Diabetes mellitus is a chronic metabolic disorder which will be the 7th leading cause of death by 2030. The current line of treatment for the diabetes mellitus is oral antidiabetic drugs (biguanides, sulfonylureas, meglitinides, thiazolidinediones and alpha-glycosidase inhibitors) and insulin therapy depending upon the type 1 or type 2 diabetes mellitus. But, these treatments have their disadvantages, ranging from the developing of resistance to the drugs and adverse effects caused by them. Alternative to these synthetic agents, natural products provides a new insight for the development of more efficient and safe drugs due to their therapeutic values. Enicostema littorale blume (A. Raynal) is a traditional Indian plant belongs to the Gentianaceae family. It is widely distributed in Asia, Africa, and South America. There are few reports on Swrtiamarin, major component of this plant for its antidiabetic activity. However, the antidiabetic activity of flavonoids from E. littorale and their mechanism of action have not yet been elucidated. Flavonoids have a positive relationship with disease prevention and can act on various molecular targets and regulate different signaling pathways in pancreatic β-cells, adipocytes, hepatocytes and skeletal myofibers. They may exert beneficial effects in diabetes by (i) improving hyperglycemia through regulation of glucose metabolism in hepatocytes; (ii) enhancing insulin secretion and reducing apoptosis and promoting proliferation of pancreatic β-cells; (iii) increasing glucose uptake in hepatocytes, skeletal muscle and white adipose tissue (iv) reducing insulin resistance, inflammation and oxidative stress. Therefore, we have isolated four flavonoid rich fractions, Fraction A (FA), Fraction B (FB), Fraction C (FC), Fraction D (FD) from crude alcoholic hot (AH) extract from E. littorale, identified by LC/MS. Total eight flavonoids were identified on the basis of fragmentation pattern. Flavonoid FA showed the presence of swertisin, isovitexin, and saponarin; FB showed genkwanin, quercetin, isovitexin, FC showed apigenin, swertisin, quercetin, 5-O-glucosylswertisin and 5-O-glucosylisoswertisin whereas FD showed the presence of swertisin. Further, these fractions were assessed for their antidiabetic activity on stimulating glucose uptake in insulin-resistant HepG2 cell line model (IR/HepG2). The results showed that FD containing C-glycoside Swertisin has significantly increased the glucose uptake rate of IR/HepG2 cells at the concentration of 10 µg/ml as compared to positive control Metformin (0.5mM) which was determined by glucose oxidase- peroxidase method. It has been reported that enhancement of glucose uptake of cells occurs due the translocation of Glut4 vesicles to cell membrane through IR/IRS1/AKT pathway. Therefore, we have studied expressions of three genes IRS1, AKT and Glut4 by real-time PCR to evaluate whether they follow the same pathway or not. It was seen that the glucose uptake rate has increased in FD treated IR/HepG2 cells due to the activation of insulin receptor substrate-1 (IRS1) followed by protein kinase B (AKT) through phosphoinositide 3-kinase (PI3K) leading to translocation of Glut 4 vesicles to cell membrane, thereby enhancing glucose uptake and insulin sensitivity of insulin resistant HepG2 cells. Hence, the up-regulation indicated the mechanism of action through which FD (Swertisin) acts as antidiabetic candidate in the treatment of type 2 diabetes mellitus.

Keywords: E. littorale, glucose transporter, glucose uptake rate, insulin resistance

Procedia PDF Downloads 290