Gamma-tocotrienol as an effective agent in targeting prostate cancer stem cell-like population

Emerging evidence supports that prostate cancer originates from a rare sub-population of cells, namely prostate cancer stem cells (CSCs). Conventional therapies for prostate cancer are believed to mainly target the majority of differentiated tumor cells but spare CSCs, which may account for the subsequent disease relapse after treatment. Therefore, successful elimination of CSCs may be an effective strategy to achieve complete remission from this disease. Gamma-tocotrienols (-T3) is one of the vitamin-E constituents which have been shown to have anticancer effects against a wide-range of human cancers. Recently, we have reported that -T3 treatment not only inhibits prostate cancer cell invasion but also sensitizes the cells to docetaxel-induced apoptosis, suggesting that -T3 may be an effective therapeutic agent against advanced stage prostate cancer. Here, we demonstrate for the first time that -T3 can down-regulate the expression of prostate CSC markers (CD133/CD44) in androgen independent (AI) prostate cancer cell lines (PC-3 & DU145), as evident from western blotting analysis. Meanwhile, the spheroid formation ability of the prostate cancer cells was significantly hampered by -T3 treatment. In addition, pre-treatment of PC-3 cells with -T3 was found to suppress tumor initiation ability of the cells. More importantly, while CD133-enriched PC-3 cells were highly resistant to docetaxel treatment, these cells were as sensitive to -T3 treatment as the CD133-depleted population. Our data suggest that -T3 may be an effective agent in targeting prostate CSCs, which may account for its anticancer and chemosensitizing effects reported in previous studies.

Automatic spoken language identification utilizing acoustic and phonetic speech information

Automatic spoken Language Identi¯cation (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the speaker. The trend of globalization and the pervasive popularity of the Internet will amplify the need for the capabilities spoken language identi¯ca- tion systems provide. A prominent application arises in call centers dealing with speakers speaking di®erent languages. Another important application is to index or search huge speech data archives and corpora that contain multiple languages. The aim of this research is to develop techniques targeted at producing a fast and more accurate automatic spoken LID system compared to the previous National Institute of Standards and Technology (NIST) Language Recognition Evaluation. Acoustic and phonetic speech information are targeted as the most suitable fea- tures for representing the characteristics of a language. To model the acoustic speech features a Gaussian Mixture Model based approach is employed. Pho- netic speech information is extracted using existing speech recognition technol- ogy. Various techniques to improve LID accuracy are also studied. One approach examined is the employment of Vocal Tract Length Normalization to reduce the speech variation caused by di®erent speakers. A linear data fusion technique is adopted to combine the various aspects of information extracted from speech. As a result of this research, a LID system was implemented and presented for evaluation in the 2003 Language Recognition Evaluation conducted by the NIST.

Immigration distress and associated factors among Vietnamese momen in transnational marriages in Taiwan

The purpose of this study was to explore the types and predictors of immigration distress among Vietnamese women in transnational marriages in Taiwan. A cross-sectional survey with face-toface interviews was conducted for data collection. A convenient sample of 203 Vietnamese women in transnational marriages in southern Taiwan was recruited. The Demographic Inventory measured the participants’ age, education, employment status, religion, length of residency and number of children, as well as their spouse’s age, education, employment status and religion. The Demand of Immigration Specific Distress scale measured the level of distress and had six subscales: loss, novelty, occupational adjustment, language accommodation, discrimination and alienation. Among the 203 participants, 6.4% had a high level of immigration distress; 91.1% had moderate distress; and 2.5% had minor distress. Higher mean scores were found for the loss, novelty and language accommodation subscales of the Demand of Immigration specific Distress scale. Participant’s (r = 0.321, p < 0.01) and spouse’s (r = 0.375, p < 0.01) unemployment, and more children (r = 0.129, p < 0.05) led to greater immigration distress. Length of residency in Taiwan (r = 0.576, p < 0.001) was an effective predictor of immigration distress. It indicated that the participants who had stayed fewer years in Taiwan had a higher level of immigrant distress. Health care professionals need to be aware that the female newcomers in transnational marriages are highly susceptible to immigration distress. The study suggests that healthcare professionals need to provide a comprehensive assessment of immigration distress to detect health problems early and administer culturally appropriate healthcare for immigrant women in transnational marriages.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Selection of solar energy for green building using superiority and inferiority multi-criteria ranking (SIR) method

Green energy is one of the key factors, driving down electricity bill and zero carbon emission generating electricity to green building. However, the climate change and environmental policies are accelerating people to use renewable energy instead of coal-fired (convention type) energy for green building that energy is not environmental friendly. Therefore, solar energy is one of the clean energy solving environmental impact and paying less in electricity fee. The method of solar energy is collecting sun from solar array and saves in battery from which provides necessary electricity to whole house with zero carbon emission. However, in the market a lot of solar arrays suppliers, the aims of this paper attempted to use superiority and inferiority multi-criteria ranking (SIR) method with 13 constraints establishing I-flows and S-flows matrices to evaluate four alternatives solar energies and determining which alternative is the best, providing power to sustainable building. Furthermore, SIR is well-known structured approach of multi-criteria decision support tools and gradually used in construction and building. The outcome of this paper significantly gives an indication to user selecting solar energy.

David F. Treagust : congenial soul, science educator, and international research leader

For almost a half century David F. Treafust has been an exemplary science educator who has contributed through his dedication and commitments to students, curriculum development and collaboration with teachers, and cutting edge research in science education that has impacted the field globally, nationally and locally. A hallmark of his outstanding career is his collaborative style that inspires others to produce their best work.

Neuroprotective effects of low-dose lithium in individuals at ultra-high risk for psychosis. A longitudinal MRI/MRS study

Objectives: To investigate if low-dose lithium may counteract the microstructural and metabolic brain changes proposed to occur in individuals at ultra-high risk (UHR) for psychosis. Methods: Hippocampal T2 relaxation time (HT2RT) and proton magnetic resonance spectroscopy (1H-MRS) measurements were performed prior to initiation and following three months of treatment in 11 UHR patients receiving low-dose lithium and 10 UHR patients receiving treatment as usual (TAU). HT2RT and 1H-MRS percentage change scores between scans were compared using one-way ANOVA and correlated with behavioural change scores. Results: Low-dose lithium significantly reduced HT2RT compared to TAU (p=0.018). No significant group by time effects were seen for any brain metabolites as measured with 1H-MRS, although myo-inositol, creatine, choline-containing compounds and NAA increased in the group receiving low-dose lithium and decreased or remained unchanged in subjects receiving TAU. Conclusions: This pilot study suggests that low-dose lithium may protect the microstructure of the hippocampus in UHR states as reflected by significantly decreasing HT2RT. Larger scale replication studies in UHR states using T2 relaxation time as a proxy for emerging brain pathology seem a feasible mean to test neuroprotective strategies such as low-dose lithium as potential treatments to delay or even prevent the progression to full-blown disorder.