Comparative studies on the cultivation, yield, and nutritive value of an edible mushroom, Pleurotus tuber-regium (Rumph. ex Fr.) Singer, grown under different agro waste substrates.

In the present study, Pleurotus tuber-regium (Rumph. ex Fr.) Singer collected from Keeriparai forest of Kanyakumari district, South India was cultivated using environmentally benign, low-cost agricultural waste residues (paddy straw, sugarcane bagasse, rice husk, and sawdust) as growth substrates. The main goal of this study was to assess the cultivation, yield, and nutritional value of P. tuber-regium fruiting bodies grown under different growth substrates. Spawn running time and time for primordia formation were found to be shorter in mushroom growing with paddy straw substrate compared to sawdust and sugarcane bagasse. A quick spawn run time was observed in paddy straw substrate (12 ± 1 day) followed by sugarcane bagasse (15 ± 1 day) and sawdust (23 ± 1 day). The primordia was well developed in the macrofungus grown with paddy straw substrate on 18 ± 1 day followed by sugarcane bagasse (22 ± 1 day) and sawdust (32 ± 1 day). Significantly higher yield of fruiting bodies with increased contents of protein and carbohydrate and low level of fat was obtained when P. tuber-regium was cultivated with paddy straw substrate. While, cultivation of P. tuber-regium in sawdust and sugarcane bagasse resulted in increased contents of K, Na, Ca, and Mg along with highest energy value. On the other hand, rice husk did not support the cultivation of this macrofungus. Therefore, it is of significant interest to initiate the commercial production of this macrofungus so as to fight against the problems of malnutrition found in few African and south Asian countries.

Retinal Image Analysis for Ocular Disease Prediction Using Rule Mining Algorithms.

Medical image processing is now gaining a significant momentum in clinical situation to undertake diagnosis of different anatomical defects. However, with regard to eye diseases, there is no such well-defined image processing technique in medical image analysis. The scope of this study is to automate computer analysis of ocular disease-related retinal images, which may ease the job of ophthalmologists to rule out the diseased condition. In this present work, eye images are subjected for developing a reliable tool for processing the eye retinal fundus images. The primary objective is to effectively probe retinal image data for providing a holistic approach in automatic fundus disease detection and screening to help clinicians in addition with a developed reliable image processing technique combined with a rule-based clustering method for automatic analysis of fundus images in a reduced time frame. More than 400 eye images available in online are examined. The images were preprocessed by grayscale conversion, retinal segmentation, ROI and crop ROI, image resizing, and extraction in RGB channels. Then these images were segmented by NRR from RGB channels, centroids of rows and columns, and NRR to binary image conversion. Then extraction of features like cup to disc area, optic cup area, and NRR calculations prior to measuring ISNT. A unique algorithm named as EARMAM was introduced for the prediction of diseased image from healthy eye image pool is envisaged in this paper. The functional significance of the EARMAM algorithm was compared with other common classification algorithm of current practice such as SVM, naïve Bayes, random forest, and SMO. The results of confusion matrix have shown that there was 93% prediction accuracy which was higher than the predictive values of other algorithms. The above results are discussed with future improvement and application in clinical field.