Withania somnifera is an important medicinal plant, which is used in traditional medicine to cure many diseases.Flavonoids were determined in the extracts of W. somnifera root (WSREt) and leaf (WSLEt). The amounts of total flavonoids found in WSREt and WSLEt were 530 and 520 mg/100 g dry weight (DW), respectively. Hypoglycaemic and hypolipidaemic effects of WSREt and WSLEt were also investigated in alloxan-induced diabetic rats. WSREt and WSLEt and the standard drug glibenclamide were orally administered daily to diabetic rats for eight weeks. After the treatment period, urine sugar, blood glucose, haemoglobin (Hb), glycosylated haemoglobin (HbA1C), liver glycogen, serum and tissues lipids, serum and tissues proteins, liver glucose-6-phosphatase (G6P) and serum enzymes like aspartate transaminase (AST), alanine transaminase (ALT), acid phosphatase (ACP) and alkaline phosphatase (ALP) levels were determined. The levels of urine sugar, blood glucose, HbA1C, G6P, AST, ALT, ACP, ALP, serum lipids except high density lipoprotein-bound cholesterol (HDL-c) and tissues like liver, kidney and heart lipids were significantly (p < 0.05) increased, however Hb, total protein, albumin, albumin:globulin (A:G) ratio, tissues protein and glycogen were significantly (p < 0.05) decreased in alloxan-induced diabetic rats. Treatment of the diabetic rats with WSREt, WSLEt and glibenclamide restored the changes of the above parameters to their normal level after eight weeks of treatment, indicating that WSREt and WSLEt possess hypoglycaemic and hypolipidaemic activities in alloxan-induced diabetes mellitus (DM) rats.

With the growth of modern lifestyles and the growing urbanization and reliance on fossil fuels, the need for tracking and monitoring air pollution has become more significant. This research used existing information on significant pollutants to forecast their future condition using time-series modeling. Most studies have used Autoregressive Integrated Moving Average (ARIMA) and Logistic Regression (LR) methods to analyze time-series data. Still, employing an Adaptive Neuro Fuzzy Inference System (ANFIS) for this purpose has been infrequent. Conventional time-series prediction approaches use the assumption that there is a linear connection among variables. However, in air pollution modeling, there are non-linear and intricate factors. This paper used an Adaptive Network Fuzzy Inference System with the help of Improved Genetic Algorithm (ANFIS-IGA) to predict air pollution. This work aimed to address this constraint by enhancing the precision of everyday air pollutant prediction via the analysis of time-series data using ANFIS modeling. Air pollution data, including Fine Particulate Matter (FPM), CO, SO2, O3, and NO2, is gathered from the Air Quality Open Data Platform. This research examines the surveillance and prediction of air pollution concentration in indoor and outdoor situations using the ANFIS-IGA model. The model's effectiveness was enhanced and optimized for using IGA. The results indicate that the proposed ANFIS-IGA framework achieved superior performance compared to other models, as shown by the Root Mean Square Error (RMSE) value of 0.052658.

Challenges are loved by the researchers and there are many lane dataset challenges which are motivating the researchers to implement the algorithms for the complex lane datasets. Lane detection and departure is a broad research area. This paper covers the information related to some lane detection and departure datasets. The straight lines, curved lines, faint lines etc are the part of the dataset as well as the different environmental conditions and time such as day and night time scenes are also covered by some datasets. A comprehensive survey of different lane dataset and their comparison is given in this paper.

The present study was aimed to screen the phytochemicals and quantification of alkaloids, phenolic compounds and flavonoids and to evaluate the nematicidal activity of ethanolic leaf extracts of Datura metel, Datura innoxia and Brugmansia suaveolens against Meloidogyne incognita. Phytochemical screening of leaf extracts of D. metel, D. innoxia and B. suaveolens was carried out by qualitative analysis and the results revealed that the presence of bioactive compounds like alkaloids, steroids, flavonoids, terpenoids, phenolic compounds, tannins, anthroquinone glycosides, saponins and triterpenes. The quantitative phytochemical analysis showed that the maximum amount of alkaloids 12.12 mg/g in leaf of D. innoxia, phenolic compounds 5.675 mg/g in leaf of D. metel and flavonoids 4.945 mg/g in leaf of B. suaveolens. The nematicidal activity of extracts of D. metel, D. innoxia and B. suaveolens were studied against the root knot nematode M. incognita. For nematicidal activity, the leaf extracts were used at different concentrations like 250 ppm, 500 ppm, 750 ppm and 1000 ppm in different exposure time like 24 hrs, 48 hrs and 72 hrs. The in vitro nematicidal activity results suggested that the extracts of D. metel, D. innoxia and B. suaveolens were possessed nematicidal activity. From this study, we concluded that the extracts of D. metel, D. innoxia and B. suaveolens contain phytochemicals and the plants were exhibited nematicidal activity against root knot nematode M. incognita. The present study may be useful for the identification and isolation of novel active nematicidal compounds from the leaf of D. metel, D. innoxia and B. suaveolens.

Safflower (Carthamus tinctorius L.) seeds are used as a folk medicine to enhance bone formation or to prevent osteoporosis in Korea. Therefore, the methanolic extract of safflower seeds (MESS) containing high mineral content, such as calcium (Ca), potassium (K) and phosphorous (P), was evaluated for the role on osteoblast (Ob) markers of Sprague-Dawley rats. In serum of 3 to 11 weeks (wks) old rats, both osteocalcin (OC) content and bone-specific alkaline phosphatase (B-ALP) activity increased to their maximum levels in 4-7 wks. Hence, 3 wks old rats were selected for 8 wks oral treatment of MESS, resulted in the significant increase of Ob markers in serum such as OC content (4-8 wks), B-ALP activity (1-2 wks) and insulin-like growth factor I (IGF-I) level (1 wk), and the growth parameter such as the length of femur (2-8 wks) and tibia (4 wks). On the basis of Pearson’s correlation coefficient, there were a moderate correlation between OC and B-ALP at 8 wks, a low correlation between OC and IGF-I at 1, 4 and 8 wks, a moderate correlation between OC and femur length at 1, 2 and 8 wks, and a moderate correlations between OC and tibia length at 1 and 8 wks of MESS-treated groups. The result reveals that the changes of OC correlated at low to moderate level with the changes of B-ALP activity, IGF-I content and femur and tibia length in the MESS-treatment period. On the other hand, there were a strong correlation between IGF-I and femur length at 2 wks and moderate correlation between IGF-I and tibia length at 1, 2 and 8 wks of MESS-treated groups. Therefore, the effect of MESS on bone formation likely appears to be mediated by IGF-I at the early stage of treatment.