Deep Learning based Early Prediction Scheme for Breast Cancer

Wireless Personal Communications - Breast cancer is one of the rapid spreading diseases resulting in the death of younger age group of women. Unfortunately, as the detection of cancer is at later...     

Separation and Purification of Bromelain by Reverse Micellar Extraction Coupled Ultrafiltration and Comparative Studies with Other Methods

Reverse micellar extraction (RME) is a promising liquid-liquid extraction technique for downstream processing of biomolecules from dilute solutions. An integrated approach of coupling RME with ultrafiltration is attempted to improve the overall efficiency of extraction and purification of bromelain from aqueous extract of pineapple core. The performance of RME is compared with aqueous two-phase extraction (ATPE), another potential liquid-liquid extraction technique and conventional ammonium sulphate precipitation technique. The reverse micellar system of cationic surfactant cetyltrimethylammoniumbromide/isooctane/hexanol/butanol used for RME resulted in an activity recovery of 95.8% and purification of 5.9-fold. The purification of bromelain increased to 8.9-fold after ultrafiltration. Alteration of aqueous phase pH during RME facilitated the differential partitioning of bromelain and polyphenoloxidase. Comparison of RME results with ATPE (activity recovery of 93.1% and purification of 3.2-fold) and the conventional ammonium sulphate precipitation (activity recovery of 82.1% and purification of 2.5-fold) indicated the improved performance of RME.     

Reverse micellar extraction of bromelain from Ananas comosus L. Merryl

BACKGROUND: A reverse micellar system (RMS) of ionic surfactants is used for the first time for the extraction and primary purification of fruit bromelain (EC 3.4.22.33) from the aqueous extract of pineapple (Ananas comosus L. Merryl). The effect of various process parameters on both forward and back extraction of bromelain is studied to improve the extraction efficiency of RMS. Most of the reverse micellar extraction (RME) studies reported so far are on model systems and its application to enzyme extraction from a natural source is rarely reported. RESULTS: Studies carried out with ionic surfactants sodium bis(2‐ethylhexyl)sulfosuccinate (AOT) and cetyltrimethylammonium bromide (CTAB) confirmed that electrostatic interaction was the main driving force for the extraction of fruit bromelain. Among the two surfactants studied, CTAB was found to be the most suitable for the extraction of fruit bromelain with respect to activity recovery (97.56%) and degree of purification (4.54 fold) when employed as a 150 mmol L^?1 CTAB/iso‐octane/5% (v/v) hexanol/15% (v/v) butanol system. Activity recovery with a counterionic system is higher (94.30%) in comparison with isopropyl alcohol added system (85.35%). CONCLUSION: RME could be used as an efficient primary purification step for the recovery of bromelain from pineapple juice. Reverse micellar phase components can easily be recovered and efficiently reused for fresh or subsequent extraction, which contributes favorably to the process economics and environmental issues. Copyright © 2007 Society of Chemical Industry     

A Novel Regression Based Clustering Technique for Wireless Sensor Networks

The objective of this paper is to develop a new adaptive iterative linear regression based clustering algorithm for wireless sensor network. According to this, the initial cluster is classified horizontally and vertically in parallel, each resulting in two sub-clusters. Of these two, the best is selected based on the proposed similarity index and with this selected cluster as reference, iteration continues until the convergence criterion ‘Delta’ is met. The similarity index is designed based on the intra cluster similarity and inter cluster dissimilarity. Delta is the difference between the similarity index of the current iteration and the previous iteration. The proposal is implemented in MATLAB and simulations are carried out under different network scenarios. The cluster quality is evaluated through external and internal indices using the Cluster Validity Analysis Platform tool. The cluster obtained by the proposal is studied and its quality is compared with the well-established k-means and hierarchical clustering. The performance indices confirm the supremacy of the proposal.