International Journal of Biology and Biomedical Engineering


ISSN: 1998-4510
Volume 12, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Main Page

Submit a paper | Submission terms | Paper format

 


Volume 12, 2018


Title of the Paper: Fast Virus and Bacteria Genome Sequencing by Compatible Restriction Enzyme Fingerprinting

 

Authors: Peter Z. Revesz, Dipty Singh

Pages: 18-27

Abstract: Early identification of a dangerous strain of a virus or bacteria that may cause a pandemic requires practical methods for sequencing of their genomes. This paper describes the concept of compatible restriction enzymes, and a fast and cost-efficient genome map assembly and sequencing method. Computer experiments on plasmid and virus genomes show that the genome map assembly and sequencing can be done in an approximately linear time in the sizes of the genomes.


Title of the Paper: Locked-in Patients’ Activities Enhancement via Brain-Computer Interface System Using Neural Network

 

Authors: Anas A. Magour, K. Sayed, Wael A. Mohamed, M. M. El Bahy

Pages: 7-17

Abstract: Nowadays, there are millions of people around the world suffer from the disability caused by big stroke. In recent years we have seen a rising interest in brain computer interface (BCI) systems that help those patients to practice their normal lives. Therefore, this work presents a GUI application based on an offline BCI system to test their mental capacities. This application was designed based on three tests are alphabet, arithmetic operations and Raven’s progressive matrices. The success of this system depends on the choice of the processing techniques. Therefore, Discrete Wavelet Transform (DWT) and Principal Components Analysis (PCA) were used to extract a set of statistical features from the recorded brain signals. These features were classified into four classes are head movement to up, down, right or left using three classifiers are Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). The performance of classifiers was measured using the most frequently statistical parameters: the sensitivity, specificity, precision, classification accuracy, and area under receiver operating characteristics (ROC) curve (AUC). It was concluded that when DWT was used as a feature extraction, ANN and SVM achieved the highest classification accuracy with a value of 95.24% but when using PCA, ANN achieved the highest classification accuracy with a value of 92.86%. On the other hand, LDA classifier was the worst among the three classifiers.


Title of the Paper: Variations in the Functional Properties of Soybean Flour Fermented with Lactic Acid Bacteria

 

Authors: Alloysius Chibuike Ogodo, Ositadinma Chinyere Ugbogu, Reginald Azu Onyeagba

Pages: 1-6

Abstract: The functional properties of soybean flour fermented with lactic acid (LAB)-consortium was evaluated. Soybean was processed into flour, fermented spontaneously and with LAB-consortium previously isolated from maize (Lactobacillus plantarum WCFS1+Lactobacillus rhamnosus GG, ATCC 53/03+Lactobacillus nantensis LP33+Lactobacillus fermentum CIP 102980+Lactobacillus reuteri DSM 20016) and sorghum (Pediococcus acidilactici DSM 20284+Lactobacillus fermentum CIP 102980+Lactobacillus brevis ATCC 14869+Lactobacillus nantensis LP33+Lactobacillus plantarum WCFS1) to evaluate their effects on the functional properties of the flour at 12 h intervals using standard techniques. The result shows gradual decrease in bulk density with increasing fermentation period ranging from 0.74±0.03 g/mL to 0.72±0.03 g/mL (natural), from 0.74±0.03 g/mL to 0.70±0.02 g/mL (LAB-consortium from maize) and from 0.74±0.03 g/mL to 0.70±0.02 g/mL (LAB-consortium from sorghum) fermentation. The swelling capacity decreased from 0.77±0.03 g/mL to 0.64±0.01 g/mL, from 0.77±0.03 g/mL to 0.59±0.01 g/mL and from 0.77±0.03 g/mL to 0.61±0.03 g/mL in natural, LAB-consortium from maize and sorghum fermentations respectively. Water holding capacity decreased from 2.4±0.03 mL/g to 1.9±0.03 mL/g, from 2.4±0.03 mL/g to 2.0±0.03 mL/g and from 2.4±0.03 mL/g to 1.9±0.03 mL/g in natural, LAB-consortium from maize and sorghum fermentation respectively. Oil holding capacity increased significantly (p<0.05) with increasing fermentation time, ranging from 8.92 ± 0.02 mL/g to 9.30±0.03 mL/g (natural), 8.92±0.01 mL/g to 9.63±0.03 mL/g (LAB-consortium from maize) and from 8.92±0.03 mL/g to 9.69±0.03 mL/g (LAB-consortium from sorghum) fermentations. The least gelation concentration ranged from 3.0% (unfermented) to 6.0% (other fermentation products). Emulsion capacity (EC) increased from 35.88±3.12% to 44.33±1.33%, from 35.88±3.12% to 46.83±3.18% and from 35.88±3.12 % to 45.99±2.21% in natural, LAB-consortium from maize and sorghum fermentations respectively. This suggests the potentials of LAB-consortia fermentation in improving nutritional and functional properties of soybean flour.