Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data.

The most common gynecologic cancer, behind cervical and uterine, is ovarian cancer. Ovarian cancer is a severe concern for women. Abnormal cells form and spread throughout the body. Ovarian cancer microarray data can diagnose and prognosis. Typically, ovarian cancer microarray data contains tens of thousands of genes. In order to reduce computational complexity, selecting the most critical genes or attributes in the entire dataset is necessary. Because microarray datasets have limited samples and many characteristics, classifier detection lags. So, dimensionality reduction measures are essential to protect disease classification genes. In this research, initially the ANOVA method is used for gene selection and then two clustering-based and three transform-based feature extraction methods, namely, Fuzzy C Means, Softmax Discriminant Algorithm (SDA), Hilbert Transform, Fast Fourier Transform (FFT), and Discrete Cosine Transform (DCT), respectively, are used to select relevant genes further. Six classifiers further classify the features as normal and abnormal. The NLR classifier gives the highest accuracy for SDA features at 92%, and KNN gives the lowest accuracy of 55% for SDA, Hilbert, and DCT features. With correlation distance feature selection, the NLR classifier attains the lowest accuracy of 53%, and the highest accuracy of 88% is obtained by the GMM classifier.

Biodegradation of naphthalene by biocatalysts isolated from the contaminated environment under optimal conditions.

Polycyclic aromatic hydrocarbons (PAHs) pollution occurs in freshwater and marine environment by anthropogenic activities. Moreover, analysis of the PAHs-degradation by the indigenous bacterial strains is limited, compared with other degraders. In this study, naphthalene (NAP) biodegrading bacteria were screened by enrichment culture method. Three bacterial strains were obtained for NAP degradation and identified as Bacillus cereus CK1, Pseudomonas aeruginosa KD4 and Enterobacter aerogenes SR6. The amount of hydrogen, carbon, sulphur and nitrogen of wastewater were analyzed. Total bacterial count increased at increasing incubation time (6-60 days) and moderately decreased at higher NAP concentrations. The bacterial population increased after 48 days at 250 ppm NAP (519 ± 15.3 MPM/mL) concentration and this level increased at 500 ppm NAP concentration (541 ± 12.5 MPM/mL). NAP was degraded by bacterial consortium within 36 h-99% at 30 °C. PAHs degrading bacteria were grown optimally at 4% inoculum concentrations. Bacterial consortium was able to degrade 98% NAP at pH 7.0 after 36 h incubation and degradation potential was improved (100%) after 34 h (pH 8.0). Also at pH 9.0, 100% biodegradation was registered after 36 h incubation. When the agitation speed enhanced from 50 ppm to 150 ppm, increased bacteria growth and increased NAP degradation within 42 h incubation. Among the nutrient sources, beef extract, peptone and glucose supplemented medium supported complete degradation of PAHs within 30 h, whereas peptone supported 94.3% degradation at this time. Glucose supplemented medium showed only 2.8% NAP degradation after 6 h incubation and reached maximum (100%) within 42 h incubation. Bacterial consortium can be used to reduce NAP under optimal process conditions and this method can be used for the removal of various hydrocarbon-compounds.

Cow dung is an ideal fermentation medium for amylase production in solid-state fermentation by Bacillus cereus.

Amylase production by Bacillus cereus IND4 was investigated by solid state fermentation (SSF) using cow dung substrate. The SSF conditions were optimized by using one-variable-at-a-time approach and two level full factorial design. Two level full factorial design demonstrated that moisture, pH, fructose, yeast extract and ammonium sulphate have significantly influenced enzyme production (p < 0.05). A central composite design was employed to investigate the optimum concentration of these variables affecting amylase production. Maximal amylase production of 464 units/ml of enzyme was observed in the presence of 100% moisture, 0.1% fructose and 0.01% ammonium sulphate. The enzyme production increased three fold compared to the original medium. The optimum pH and temperature for the activity of amylase were found to be 8.0 and 50 °C, respectively. This enzyme was highly stable at wide pH range (7.0-9.0) and showed 32% enzyme activity after initial denaturation at 50 °C for 1 h. This is the first detailed report on the production of amylase by microorganisms using cow dung as the low cost medium.