The effect of varying stimulus rate and duration on brain activity during reading

The effect of the presentation rate and exposure duration of visually presented words on brain activity was investigated using positron emission tomography. Subjects either read aloud or silently mouthed the names of words. In regions associated with early visual analysis, activity increased with both rate and duration; in regions associated with response generation, activity increased with increasing rate but was unaffected by duration; and in regions associated with word recognition, activity decreased with increasing duration. The variable responses of different brain regions illustrate the functional segregation of these regions. Of particular interest was the dissociation between activity in the posterior fusiform gyri and that in the medial lingual gyrus--in the former, activity increased with rate and duration but the latter was unaffected by either variable. This finding suggests that word processing in the lingual gyrus during reading is distinct from that in the posterior fusiform gyri. A further observation was that during reading aloud, when subjects can hear the sound of their own voice, the response in the primary auditory cortices increased with stimulus rate, demonstrating that subjects process the sound of their own voice in a qualitatively similar way to words spoken by another.     

A novel hybridized metaheuristic technique in enhancing the diagnosis of cross-sectional dent damaged offshore platform members

Offshore jacket platforms are widely used for oil and gas extraction as well as transportation in shallow to moderate water depth. Tubular cross-sectional elements are used to construct offshore platforms. Tubular cross sections impart higher resistance against hydrodynamic forces and have high torsional rigidity. During operation, the members can be partially or fully damaged due to lateral impacts. The lateral impacts can be due to ship collisions or through the impact of falling objects. The impact forces can weaken some members that influence the overall performance of the platform. This demonstrates an urgent need to develop a framework that can accurately forecast dent depth as well as dent angle of the affected members. This study investigates the use of an adaptive metaheuristics algorithm to provide automatic detection of denting damage in an offshore structure. The damage information includes dent depth and the dent angle. A model is developed in combination with the percentage of the dent depth of the damaged member and is used to assess the performance of the method. It demonstrates that small changes in stiffness of individual damaged bracing members are detectable from measurements of global structural motion.     

Cloud based intelligent system for delivering health care as a service

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity.     

Artificial bee colony based energy‐aware resource utilization technique for cloud computing

Cloud computing is a form of distributed computing, which promises to deliver reliable services through next‐generation data centers that are built on virtualized compute and storage technologies. It is becoming truly ubiquitous and with cloud infrastructures becoming essential components for providing Internet services, there is an increase in energy‐hungry data centers deployed by cloud providers. As cloud providers often rely on large data centers to offer the resources required by the users, the energy consumed by cloud infrastructures has become a key environmental and economical concern. Much energy is wasted in these data centers because of under‐utilized resources hence contributing to global warming. To conserve energy, these under‐utilized resources need to be efficiently utilized and to achieve this, jobs need to be allocated to the cloud resources in such a way so that the resources are used efficiently and there is a gain in performance and energy efficiency. In this paper, a model for energy‐aware resource utilization technique has been proposed to efficiently manage cloud resources and enhance their utilization. It further helps in reducing the energy consumption of clouds by using server consolidation through virtualization without degrading the performance of users’ applications. An artificial bee colony based energy‐aware resource utilization technique corresponding to the model has been designed to allocate jobs to the resources in a cloud environment. The performance of the proposed algorithm has been evaluated with the existing algorithms through the CloudSim toolkit. The experimental results demonstrate that the proposed technique outperforms the existing techniques by minimizing energy consumption and execution time of applications submitted to the cloud. Copyright © 2014 John Wiley & Sons, Ltd.     

Serotyping of Streptococcus pneumoniae by coagglutination with 12 pooled antisera

We report on the performance of a recently introduced commercial chessboard method using 12 antisera, in comparison with that of the 55-antiserum panel used in determining the serogroups and types (SGTs) of Streptococcus pneumoniae, both of which were carried out by a coagglutination technique. Of a total of 150 strains of S. pneumoniae studied, 135 (90%) belonged to the SGTs represented in the 23-valent pneumococcal vaccine; of these, 130 (96.3%) were identified as the same SGTs by both typing methods. The remaining five strains showed cross-reactivity with more than two pools by the chessboard method, but could be assigned to a single SGT by the Quellung test. The 96.3% concordance of the chessboard method suggests it can be adopted for determination of the SGTs of S. pneumoniae in laboratories.     

Sequence database searches via de novo peptide sequencing by tandem mass spectrometry

A method is described for searching protein sequence databases using tandem mass spectra of tryptic peptides. The approach uses a de novo sequencing algorithm to derive a short list of possible sequence candidates which serve as query sequences in a subsequent homology-based database search routine. The sequencing algorithm employs a graph theory approach similar to previously described sequencing programs. In addition, amino acid composition, peptide sequence tags and incomplete or ambiguous Edman sequence data can be used to aid in the sequence determinations. Although sequencing of peptides from tandem mass spectra is possible, one of the frequently encountered difficulties is that several alternative sequences can be deduced from one spectrum. Most of the alternative sequences, however, are sufficiently similar for a homology-based sequence database search to be possible. Unfortunately, the available protein sequence database search algorithms (e.g. Blast or FASTA) require a single unambiguous sequence as input. Here we describe how the publicly available FASTA computer program was modified in order to search protein databases more effectively in spite of the ambiguities intrinsic in de novo peptide sequencing algorithms.