Fatty acids distribution in different tissues of wild and reared Seriola dumerili

In this study, we compared the lipid and fatty acids content, between cultured and wild Seriola dumerili, in different edible portions. Results showed that cultured fish contained a higher level of lipids than wild fish. The fatty acids profiles revealed that, among all the split‐fish side, palmitic (C16:0) and oleic (C18:1n‐9) acid were the principal saturated and monounsaturated fatty acids, in cultured and wild S. dumerili. On the other hand, wild S. dumerili contained a higher level of saturated (38.12 ± 0.54% vs. 33.66 ± 0.15% in cultured fish), monounsaturated (33.13 ± 1.07% vs. 26.49 ± 0.17%), n‐3 polyunsaturated fatty acids (PUFA) (23.90 ± 1.02% vs. 19.77 ± 0.51%) particularly docosahexaenoic acid (DHA) (18.83 ± 0.48% vs. 11.77 ± 0.42%). However, the cultured fish showed a higher level of n‐6 PUFA due principally to the higher value of linoleic acid (C18:2n‐6). In fact, changes in fatty acids content between anatomical areas marked differences in the muscle quality of wild and cultured S. dumerili. According to this study, both groups of wild and farmed S. dumerili have nutritional benefits for human health. Cultured fish were characterised by higher hypocholesterolaemic and hypercholesterolaemic fatty acids ratio for all samples studied and a lower n‐3/n‐6 ratio due to the abundance of n‐3 PUFA particularly DHA in wild fish.     

Yeast involved in fermentation of Coffea arabica in East Africa determined by genotyping and by direct denaturating gradient gel electrophoresis

Samples of Coffea arabica were collected during the different stages of the fermentation from two production sites in Tanzania. The yeasts community was identified by genotyping using ITS-PCR and sequence analysis of the D1/D2 domain of the 26S rRNA gene. For confirmation, denaturating gradient gel electrophoresis (DGGE) of PCR-amplified 26S rRNA gene was performed to detect yeast directly from coffee samples without cultivation. Yeast counts were in the range 4.0 x 10(4) - 5.0 x 10(7) CFU/g with an increase during fermentation. Three yeasts species were dominant. The predominant yeast found during fermentation and drying was Pichia kluyveri. Pichia anomala was found in high numbers during drying of coffee beans. Hanseniaspora uvarum was the predominant yeast during fermentation but decreased during drying. Kluyveromyces marxianus, Candida pseudointermedia, Issatchenkia orientalis, Pichia ohmeri and Torulaspora delbrueckii occurred in concentrations of 10(3) CFU/g or below in coffee samples. Saccharomyces cerevisiae and Candida xestobii were not isolated by cultivation, but by the DGGE technique. A good agreement was found between the sequence analysis of the D1/D2 domain of the 26S rRNA gene and sequencing of the DGGE bands.     

3D brain tumor segmentation in MRI images based on a modified PSO technique

Three-dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional-order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1-C modality.     

Towards adaptive structured Dirichlet smoothing model for digital resource objects

Multimedia Tools and Applications - Digital resource objects (DRO) are among the most valuable resources that store the accumulated knowledge of humankind. Nowadays, many organisations aim to make...     

Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model

Wireless Personal Communications - This paper presents the advantages of using a wideband spectrum adopting multi-carrier to improve targets localization within a simulated indoor...     

Bayesian inference approach for establishing efficient routes in wireless ad-hoc networks

Mobile ad hoc networks (MANETs) are dynamic wireless networks that have no fixed infrastructures and do not require predefined configurations. In this infrastructure-less paradigm, nodes in addition of being hosts, they also act as relays and forward data packets for other nodes in the network. Due to limited resources in MANETs such as bandwidth and power, the performance of the routing protocol plays a significant role. A routing protocol in MATET should not introduce excessive control messages to the network in order to save network bandwidth and nodes power. In this paper, we propose a probabilistic approach based on Bayesian inference to enable efficient routing in MANETs. Nodes in the proposed approach utilize the broadcast nature of the wireless channel to observe the network topology by overhearing wireless transmissions at neighboring nodes in a distributed manner, and learn from these observations when taking packet forwarding decision on the IP network layer. Our simulation results show that our routing approach reduces the number of control message (routing overhead) by a ratio up to 20 % when the network size is 60 nodes, while maintaining similar average route establishment delay as compared to the ad-hoc on demand routing protocol.