Overexpression of Inosine 5′-Monophosphate Dehydrogenase Type II Mediates Chemoresistance to Human Osteosarcoma Cells

overexpression in osteosarcoma patients with poor response to chemotherapy. The aim of this study was to provide evidence for direct involvement of IMPDH2 in the development of chemoresistance..IMPDH2 is directly involved in the development of chemoresistance in osteosarcoma cells, suggesting that targeting of IMPDH2 by RNAi or more effective pharmacological inhibitors in combination with chemotherapy might be a promising means of overcoming chemoresistance in osteosarcomas with high IMPDH2 expression

Sustainable Construction by Means of Improved Material Selection Process

Whilst sustainable construction relates to both a building's structure and the use of proper life cycle processes, the selection of the most appropriate material/s is deemed a considerable undertaking. Throughout a building's life-cycle that extends from design, construction, operation, maintenance, renovation, until demolition, the selection of sustainable material/s is a particularly crucial task for the development and establishment of such structures. Traditionally, there are three main materials for general construction: (1) Steel, (2) Concrete and (3) Timber. These materials not only influence the function within the structure, but also affect the operation cost and energy usage. Operation cost reduction and energy savings are typically elements of the sustainable construction sphere. However, in developing countries, there is a variety of highly critical factors, which can impact material selection as well as the long-term sustainability of the structure, including: Fire Performance, Environmental Impact, Structural Performance (strength and durability), and Functioning Capabilities. Accordingly, this paper will first compare the sustainability of these three key materials and then converse with appropriate processes for material selection. Attention will be given to the sustainable construction recompense associated with the different material selection factors. Doing so ensures a more sustainable built environment by means of an improved material selection process

Administering Transportation Infrastructure Project Delivery, Using Gateway Systems

The development, operation and maintenance of Transportation Infrastructure (TI) consume sizable monetary resources and have long term impacts on the business environment and society alike. To streamline the TI development and maintenance decision-making process, various optimisation tools can be utilised under Transportation Infrastructure Management Systems (TIMS); yet, their actual practical application has been limited due to different reasons. The paper proposes the use of Infrastructure Gateway Systems (IGS) to enhance the overall TI development and maintenance management process. An IGS establishes a number of control points (or gates) throughout the life cycle of the TI asset to regulate its development and provide a basis for the life cycle management decisions. The methods of Dynamic Programming (DP), Markov chain process, and others, are incorporated to help establish optimised maintenance strategies. Case study TI projects are used to illustrate the fundamental concepts in the proposed approach. The IGS with its constituent optimisation components can provide TI maintenance agencies with proper and well-founded schedule for routine maintenance activities

Composite High-Rise Structures: Structural Health Monitoring (SHM) and Case Studies

High-rise construction typically implies a multi-storey structure approximately between forty to hundred and twenty meters tall (approximately twelve to forty storeys). On the other hand, composite materials are those made from two or more constituents generally with considerably dissimilar physical or chemical compositions. The focal point in this paper is in-particular on high-rise construction and whether or not composite materials' structural integrity, and long-term sustainability, is comparable to that of a traditional building. To assess the composite materials' structural integrity, Structural Health Monitoring (SHM) will also be utilised. While, composites possess different characteristics from those common to traditional materials; the universal purpose of producing such materials is to produce matters, which are stronger, lighter, and commonly less expensive. Generally, in construction, the composite materials typically include geo-polymers, fiber-reinforced concrete and others. For high-rise construction, these composite materials require to bear a variety of demanding conditions, including high winds and seismic conditions, which are important design factors for such structures. Nevertheless, a particular benefit of composite materials for high rise construction is their overall ability to maintain structural integrity despite their lack of conventional composition. The composite materials are traditionally utilised for high-rise buildings in order to reinforce the overall structural integrity. Accordingly, this paper will also include a number of case studies to support the ever-increasing utilization of composite materials for high-rise construction

Prediction of seismic damage spectra using computational intelligence methods

Predicting seismic damage spectra, capturing both structural and earthquake features, is useful in performance-based seismic design and quantifying the potential seismic damage of structures. The objective of this paper is to accurately predict the seismic damage spectra using computational intelligence methods. For this purpose, an inelastic single-degree-of-freedom system subjected to a set of earthquake ground motion records is used to compute the (exact) spectral damage. The Park-Ang damage index is used to quantify the seismic damage. Both structural and earthquake features are involved in the prediction models where multi-gene genetic programming (MGGP) and artificial neural networks (ANNs) are applied. Common performance metrics were used to assess the models developed for seismic damage spectra, and indicated that their accuracy was higher than a corresponding model in the literature. Although the performance metrics revealed that the ANN model is more accurate than the MGGP model, the explicit MGGP-based mathematical model renders it more practical in quantifying the potential seismic damage of structures

Versatility in sustainable building design (SBD) practices: an empirical study

Purpose: Through an empirical study, this research proposes a multi-dimensional assessment method for Sustainable Building Design (SBD). This approach is adopted to investigate and evaluate the current practices of SBD and to provide a basis for refining such processes while reducing existing shortfalls. In doing so, a holistic sustainable framework for rating the sustainable performance of buildings is proposed. Design/methodology/approach: To achieve the aforementioned purpose, this research (1) investigates the most current trends in SBD including the use of Building Information Modelling (BIM); (2) examines the practical issues of SBD; (3) proposes a multi-dimensional assessment method; and (4) compares 18 separate case studies in the three countries of Australia, United Kingdom and USA, as part of the SBD implementation. To compare these case studies, an additional SBD tool, Green Building Rating and Certification System (GBELS) was carefully selected and applied. Five core values of SBD were identified based on functionality, accessibility and productivity, which align with the GBELS outcomes. Findings: This research found that, for the 18 examined buildings, the main issues in SBD in the three countries concerned environmental and ecological issues. These included the impacts of buildings on the environment, as well as issues concerning the buildings' life cycle analysis and assessment. It was also determined that energy usage, pollution reduction and climate change concerns were important inclusions in establishing these buildings, particularly in the USA. Originality/value: To support the research aim, this paper explores the most innovative trends in SBD assessments including their Structural Health Monitoring (SHM), grade (stars) classification, Relative Weight (RW) and particularly GBELS. GBELS was selected for this research, since it is relatively new and there is little available literature discussing its adaptation. Accordingly, this research further evaluates the application of GBELS as a part of versatility in SBD multi-dimensional assessment method. As the basis of these tests, a total of 18 separate case studies are closely evaluated

Melbourne's East West Link: A Missed Opportunity

The aim of this paper is to review the abandoned "East West link road project" in Melbourne, Australia. Increased population growth, increasing life expectations and rates of family formation combine to place significant pressure on Melbournes infrastructure. In addition, the shift from rural to urban living -especially in Melbourne, exacerbates such impacts. Such demands expose the limitations of existing Melbourne transportation networks. As a consequence on-going transportation infrastructure planning is constantly required for greater Melbourne and its authorities, along with some alignment at the national level. Subsequently Melbourne transportation infrastructure planning needs to carefully adopt a long-term approach. While the processes of land acquisition, design and delivery of transportation infrastructure cannot be achieved in the short term, long-standing strategies need to be cautiously established. For Melbourne in particular, the financing of such longterm assets is problematic and thus possess uncertain conditions, especially when dealing with transportation forecasting and future modeling. Going back to mid-1990s such forward transportation planning was essential to ensure a high-level livability for Melbournians. As Melbourne continues to expand both in population and geographically it was to cope with such demand that the East West Link project was proposed. This project was seen as crucial, not only to uphold the livability status, but also to sustain and prolong Melbourne's ageing road transportation infrastructure. However, soon after their election win in late 2014, the Victorian labor government scrapped this project. In doing so, certain transportation outlook was unfortunately neglected. This paper investigates some of the key missed opportunities of the East West Link project

A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

Joint acoustic emission (JAE) sensing has recently proven to be a viable technique for non-invasive quantification indicating knee joint health. In this work, we adapt the acoustic emission sensing method to measure the JAEs of the wrist—another joint commonly affected by injury and degenerative disease. JAEs of seven healthy volunteers were recorded during wrist flexion-extension and rotation with sensitive uniaxial accelerometers placed at eight locations around the wrist. The acoustic data were bandpass filtered (150 Hz–20 kHz). The signal-to-noise ratio (SNR) was used to quantify the strength of the JAE signals in each recording. Then, nine audio features were extracted, and the intraclass correlation coefficient (ICC) (model 3,k), coefficients of variability (CVs), and Jensen–Shannon (JS) divergence were calculated to evaluate the interrater repeatability of the signals. We found that SNR ranged from 4.1 to 9.8 dB, intrasession and intersession ICC values ranged from 0.629 to 0.886, CVs ranged from 0.099 to 0.241, and JS divergence ranged from 0.18 to 0.20, demonstrating high JAE repeatability and signal strength at three locations. The volunteer sample size is not large enough to represent JAE analysis of a larger population, but this work will lay a foundation for future work in using wrist JAEs to aid in diagnosis and treatment tracking of musculoskeletal pathologies and injury in wearable systems

Influence of image noise on crack detection performance of deep convolutional neural networks

Development of deep learning techniques to analyse image data is an expansive and emerging field. The benefits of tracking, identifying, measuring, and sorting features of interest from image data has endless applications for saving cost, time, and improving safety. Much research has been conducted on classifying cracks from image data using deep convolutional neural networks; however, minimal research has been conducted to study the efficacy of network performance when noisy images are used. This paper will address the problem and is dedicated to investigating the influence of image noise on network accuracy. The methods used incorporate a benchmark image data set, which is purposely deteriorated with two types of noise, followed by treatment with image enhancement pre-processing techniques. These images, including their native counterparts, are then used to train and validate two different networks to study the differences in accuracy and performance. Results from this research reveal that noisy images have a moderate to high impact on the network's capability to accurately classify images despite the application of image pre-processing. A new index has been developed for finding the most efficient method for classification in terms of computation timing and accuracy. Consequently, AlexNet was selected as the most efficient model based on the proposed inde