Analysis of expressed sequence tags from Actinidia : Applications of a cross species EST database for gene discovery in the areas of flavor, health, color and ripening

Background Kiwifruit (Actinidia spp.) are a relatively new, but economically important crop grown in many different parts of the world. Commercial success is driven by the development of new cultivars with novel consumer traits including flavor, appearance, healthful components and convenience. To increase our understanding of the genetic diversity and gene-based control of these key traits in Actinidia, we have produced a collection of 132,577 expressed sequence tags (ESTs). Results The ESTs were derived mainly from four Actinidia species (A. chinensis, A. deliciosa, A. arguta and A. eriantha) and fell into 41,858 non redundant clusters (18,070 tentative consensus sequences and 23,788 EST singletons). Analysis of flavor and fragrance-related gene families (acyltransferases and carboxylesterases) and pathways (terpenoid biosynthesis) is presented in comparison with a chemical analysis of the compounds present in Actinidia including esters, acids, alcohols and terpenes. ESTs are identified for most genes in color pathways controlling chlorophyll degradation and carotenoid biosynthesis. In the health area, data are presented on the ESTs involved in ascorbic acid and quinic acid biosynthesis showing not only that genes for many of the steps in these pathways are represented in the database, but that genes encoding some critical steps are absent. In the convenience area, genes related to different stages of fruit softening are identified. Conclusion This large EST resource will allow researchers to undertake the tremendous challenge of understanding the molecular basis of genetic diversity in the Actinidia genus as well as provide an EST resource for comparative fruit genomics. The various bioinformatics analyses we have undertaken demonstrates the extent of coverage of ESTs for genes encoding different biochemical pathways in Actinidia.

An instrument to assess the obesogenic environment of child care centers

OBJECTIVES To describe protocol and interobserver agreements of an instrument to evaluate nutrition and physical activity environments at child care. METHODS Interobserver data were collected from 9 child care centers, through direct observation and document review (17 observer pairs). RESULTS Mean agreement between observer pairs was 87.26% and 79.29% for the observation and document review, respectively. Items with lower agreement were primarily staff behavior, counting across the day/week, and policy classifications. CONCLUSIONS Although some revisions are required, the interobserver agreement for the environment and policy assessment and observation (EPAO instrument) appears to be quite good for assessing the nutrition and physical activity environment of child care centers.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Profiling : an application assignment approach for green data centers

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

Community environment education centers : Facilitating community created ecoscapes

This study focuses on designing a community environment education center (CEEC) for Chillingham, as a hub for community transition to sustainability, redressing social fragmentation, youth unemployment, a high eco-footprint and economic rural decline due to globalisation. The ecologically sustainable development framework was delivered by integrating environment education and community development through project-based experiential learning. The development of Chillingham Community Centre involved case study research and incorporated participatory design charrettes, transformative learning, eco-positive development and community-public-private partnerships. This process evolved from community strategic planning in a small rural village buffering world heritage rainforests impacted by a rapidly expanding urban conurbation on Australia’s east coast. This community space encompasses socio-environmental flows connecting people to each other and the ecoscape to grow natural capital, community cohesion and empower eco-governance. Modelling passive solar design, on-site renewable energy/water/nutrient cycling, community garden/market and environment education programs sowed the seeds for a green local economy, demonstrating community capacity to participate in transition to sustainability. A small rural community can demonstrate to other communities that a CEEC enables people to meet their socio-environmental and economic needs locally and sustainably. The ecologically sustainable solution is holistic, all settlements need to be richly biodiverse, locally specific and globally wise.

Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

A Strength Pareto Evolutionary Algorithm for live migration of multiple interdependent virtual machines in data centers

Although live VM migration has been intensively studied, the problem of live migration of multiple interdependent VMs has hardly been investigated. The most important problem in the live migration of multiple interdependent VMs is how to schedule VM migrations as the schedule will directly affect the total migration time and the total downtime of those VMs. Aiming at minimizing both the total migration time and the total downtime simultaneously, this paper presents a Strength Pareto Evolutionary Algorithm 2 (SPEA2) for the multi-VM migration scheduling problem. The SPEA2 has been evaluated by experiments, and the experimental results show that the SPEA2 can generate a set of VM migration schedules with a shorter total migration time and a shorter total downtime than an existing genetic algorithm, namely Random Key Genetic Algorithm (RKGA). This paper also studies the scalability of the SPEA2.

Functional localization of the human color center by decreased water displacement using diffusion-weighted fMRI

Introduction Decreased water displacement following increased neural activity has been observed using diffusion-weighted functional MRI (DfMRI) at high b-values. The physiological mechanisms underlying the diffusion signal change may be unique from the standard blood oxygenation level-dependent (BOLD) contrast and closer to the source of neural activity. Whether DfMRI reflects neural activity more directly than BOLD outside the primary cerebral regions remains unclear. Methods Colored and achromatic Mondrian visual stimuli were statistically contrasted to functionally localize the human color center Area V4 in neurologically intact adults. Spatial and temporal properties of DfMRI and BOLD activation were examined across regions of the visual cortex. Results At the individual level, DfMRI activation patterns showed greater spatial specificity to V4 than BOLD. The BOLD activation patterns were more prominent in the primary visual cortex than DfMRI, where activation was localized to the ventral temporal lobe. Temporally, the diffusion signal change in V4 and V1 both preceded the corresponding hemodynamic response, however the early diffusion signal change was more evident in V1. Conclusions DfMRI may be of use in imaging applications implementing cognitive subtraction paradigms, and where highly precise individual functional localization is required.

Art museums and the global tourist: Experience centers in experiencescapes

Art museums are playing an important role is attracting cultural tourists to global cities and regions. Traditionally, art museums were primarily known for their didactic role. In a post-avant-garde era however museums are more focused on appealing to a broader clientele that want art to be novel and entertaining. Art museums have also come to play a greater role in gentrification projects and cultural precincts. This is because they are ideally suited for tourist-centric environments that offer a variety of immersive sensory experiences, and combine museums (often designed by star-architects), international hotels, restaurants, high-end shopping zones, and other leisure platforms. These "experiencescapes" include Port Maravilha urban waterfront development in Rio de Janiero, the Shanghai Bund, and the Broad project in Los Angeles. The Museum of Old and New Art in Hobart Australia is a boutique player in the global market for experiencescapes. It is smaller than many of its competitors and is situated in a remote part of the world, yet it has made an important contribution to Tasmania’s tourism industry.

In response: UV–vis light transmittance through tinted contact lenses and the effect of color on values

In the study, we used the Agilent 8453 spectrophotometer (which is equipped with a limiting aperture that restricts the light beam to the central 5 mm of the contact lens), to measure the transmittance of various coloured contact lenses including the one Day Acuvue define manufactured by Johnson and Johnson which the authors represent. We measured the instrument baseline before the transmittance spectra of lenses were tested. The values of lens transmittances were thus the difference between baseline and lens measurement at each time. The transmittance measurements were obtained at 0.5 nm intervals, from 200 to 700 nm after a soak in saline to remove the influence of any surface active agents within the packaging products. The technique used in our study was not very different from how other research studies [2], [3], [4], [5] and [6] have measured the spectra transmittances of contact lenses...

Cost-efficient virtual machine management in data centers

Virtual Machine (VM) management is an obvious need in today's data centers for various management activities and is accomplished in two phases— finding an optimal VM placement plan and implementing that placement through live VM migrations. These phases result in two research problems— VM placement problem (VMPP) and VM migration scheduling problem (VMMSP). This research proposes and develops several evolutionary algorithms and heuristic algorithms to address the VMPP and VMMSP. Experimental results show the effectiveness and scalability of the proposed algorithms. Finally, a VM management framework has been proposed and developed to automate the VM management activity in cost-efficient way.