815 resultados para Comparative Genomics, Non-coding RNAs, Conservation, Segmentation, Change-points, Sliding Window Analysis, Markov Chain Monte Carlo, Bayesian modeling


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Fire safety of light gauge cold-formed steel frame (LSF) wall systems is significant to the build-ing design. Gypsum plasterboard is widely used as a fire safety material in the building industry. It contains gypsum (CaSO4.2H2O), Calcium Carbonate (CaCO3) and most importantly free and chemically bound water in its crystal structure. The dehydration of the gypsum and the decomposition of Calcium Carbonate absorb heat, which gives the gypsum plasterboard fire resistant qualities. Recently a new composite panel system was developed, where a thin insulation layer was used externally between two plasterboards to improve the fire performance of LSF walls. In this research, finite element thermal models of both the traditional LSF wall panels with cavity insulation and the new LSF composite wall panels were developed to simulate their thermal behaviour under standard and realistic design fire conditions. Suitable thermal properties of gypsum plaster-board, insulation materials and steel were used. The developed models were then validated by comparing their results with fire test results. This paper presents the details of the developed finite element models of non-load bearing LSF wall panels and the thermal analysis results. It has shown that finite element models can be used to simulate the thermal behaviour of LSF walls with varying configurations of insulations and plasterboards. The results show that the use of cavity insulation was detrimental to the fire rating of LSF walls while the use of external insulation offered superior thermal protection. Effects of real fire conditions are also presented.

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Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence, reducing the frequency of crashes assists in addressing congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a short time window around the time of a crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists. We will compare them with normal traffic trends and show this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding to traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash. Using the K-Means clustering method with Euclidean distance function allowed the crashes to be clustered. Then, normal situation data was extracted based on the time distribution of crashes and were clustered to compare with the “high risk” clusters. Five major trends have been found in the clustering results for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Based on these findings, crash likelihood estimation models can be fine-tuned based on the monitored traffic conditions with a sliding window of 30 minutes to increase accuracy of the results and minimize false alarms.

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Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

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Children with Autism Spectrum Disorder experience difficulty in communication and in understanding the social world which can have negative consequences for their relationships, in managing emotions, and generally dealing with the challenges of everyday life. This thesis examines the effectiveness of the Active and Reflective components of the Get REAL program through the assessment of the detailed coding of video-recorded observations and longitudinal quantitative analysis. The aim of Get REAL is to increase the social, emotional, and cognitive learning of children with High Functioning Autism (HFA). Get REAL is a group program designed specifically for use in inclusive primary school settings. The Get REAL program was designed in response to the mixed success of generalisation of learning to new contexts of existing social skills programs. The theoretical foundation of Get REAL is based upon pedagogical theory and learning theory to facilitate transfer of learning, combined with experiential, individualised, evaluative and organisational approaches. This thesis is by publication and consists of four refereed journal papers; 1 accepted for publication and 3 that are under review. Paper 1 describes the development and theoretical basis of the Get REAL program and provides detail of the program structure and learning cycle. The focus of Paper 1 reflects the first question of interest in the thesis which is about the extent to which learning derived from participation in the program can be generalised to other contexts. Participants are 16 children with HFA ranging in age from 8-13 years. Results provided support for the generalisability of learning from Get REAL to home and school evidenced by parent and teacher data collected pre and post participation in Get REAL. Following establishment of the generalisation of learning from Get REAL, Papers 2 and 3 focus on the Active and Reflective components of the program in order to examine how individual and group learning takes place. Participants (N = 12) in the program are video-taped during the Active and Reflective Sessions. Using identical coding protocols of video data, improvements in prosocial behaviour and diminishing of inappropriate behaviours were apparent with the exception of perspective taking. Data also revealed that 2 of the participants had atypical trajectories. An in-depth case study analysis was then conducted with these 2 participants in Paper 4. Data included reports from health care and education professionals within the school and externally (e.g., paediatrician) and identified the multi-faceted nature of care needed for children with comorbid diagnoses and extremely challenging family circumstances as a complex task to effect change. Results of this research support the effectiveness of the Get REAL program in promoting pro social behaviours such as improvements in engaging with others and emotional regulation, and in diminishing unwanted behaviours such as conduct problems. Further, the gains made by the participating children were found to be generalisable beyond Get REAL to home and other school settings. The research contained in the thesis adds to current knowledge about how learning can take place for children with HFA. Results show that an experiential learning framework with a focus on social cognition, together with explicit teaching, scaffolded with video feedback, are key ingredients for the generalisation of social learning to broader contexts.

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Objective To describe the trend of overall mortality and major causes of death in Shandong population from 1970 to 2005,and to quantitatively estimate the influential factors. Methods Trends of overall mortality and major causes of death were described using indicators such as mortality rates and age-adjusted death rates by comparing three large-scale mortality surveys in Shandong province. Difference decomposing method was applied to estimate the contribution of demographic and non-demographic factors for the change of mortality. Results The total mortality had had a slight change since 1970s,but had increased since 1990s.However,both the mortality rates of age-adjusted and age-specific decreased significantly. The mortality of Group Ⅰ diseases including infectious diseases as well maternal and perinatal diseases decreased drastically. By contrast, the mortality of non-communicable chronic diseases (NCDs)including cardiovascular diseases(CVDs),cancer and injuries increased. The sustentation of recent overall mortality was caused by the interaction of demographic and non-demographic factors which worked oppositely. Non-demographic factors were responsible for the decrease of Group Ⅰ disease and the increase of injuries. With respect to the increase of NCDs as a whole. Demographic factors might take the full responsibility and the non-demographic factors were the opposite force to reduce the mortality. Nevertheless, for the increase of some leading NCD diseases as CVDs and cancer, the increase was mainly due to non-demographic rather than demographic factors. Conclusion Through the interaction of the aggravation of ageing population and the enhancement of non-demographic effect, the overall mortality in Shandong would maintain a balance or slightly rise in the coming years. Group Ⅰ diseases in Shandong had been effectively under control. Strategies focusing on disease control and prevention should be transferred to chronic diseases, especially leading NCDs, such as CVDs and cancer.

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Background: This open-label, randomised phase III study was designed to further investigate the clinical activity and safety of SRL172 (killed Mycobacterium vaccae suspension) with chemotherapy in the treatment of non-small-cell lung cancer (NSCLC). Patients and methods: Patients were randomised to receive platinum-based chemotherapy, consisting of up to six cycles of MVP (mitomycin, vinblastine and cisplatin or carboplatin) with (210 patients) or without (209 patients) monthly SRL172. Results: There was no statistical difference between the two groups in overall survival (primary efficacy end point) over the course of the study (median overall survival of 223 days versus 225 days; P = 0.65). However, a higher proportion of patients were alive at the end of the 15-week treatment phase in the chemotherapy plus SRL172 group (90%), than in the chemotherapy alone group (83%) (P = 0.061). At the end of the treatment phase, the response rate was 37% in the combined group and 33% in the chemotherapy alone group. Patients in the chemotherapy alone group had greater deterioration in their Global Health Status score (-14.3) than patients in the chemotherapy plus SRL172 group (-6.6) (P = 0.02). Conclusion: In this non-placebo controlled trial, SRL172 when added to standard cancer chemotherapy significantly improved patient quality of life without affecting overall survival times. © 2004 European Society for Medical Oncology.

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Akt, a Serine/Threonine protein kinase, mediates growth factor-associated cell survival. Constitutive activation of Akt (phosphorylated Akt, P-Akt) has been observed in several human cancers, including lung cancer and may be associated with poor prognosis and chemotherapy and radiotherapy resistance. The clinical relevance of P-Akt in non-small cell lung cancer (NSCLC) is not well described. In the present study, we examined 82 surgically resected snap-frozen and paraffin-embedded stage I to IIIA NSCLC samples for P-Akt and Akt by Western blotting and for P-Akt by immunohistochemistry. P-Akt protein levels above the median, measured using reproducible semiquantitative band densitometry, correlated with a favorable outcome (P = 0.007). Multivariate analysis identified P-Akt as a significant independent favorable prognostic factor (P = 0.004). Although associated with a favorable prognosis, high P-Akt levels correlated with high tumor grade (P = 0.02). Adenocarcinomas were associated with low P-Akt levels (P = 0.039). Akt was not associated with either outcome or clinicopathologic variables. Cytoplasmic (CP-Akt) and nuclear (NP-Akt) P-Akt tumor cell staining was detected in 96% and 42% of cases, respectively. Both CP-Akt and NP-Akt correlated with well-differentiated tumors (P = 0.008 and 0.017, respectively). NP-Akt also correlated with nodal metastases (P = 0.022) and squamous histology (P = 0.037). These results suggest P-Akt expression is a favorable prognostic factor in NSCLC. Immunolocalization of P-Akt, however, may be relevant as NP-Akt was associated with nodal metastases, a known poor prognostic feature in this disease. P-Akt may be a potential novel therapeutic target for the management of NSCLC. © 2005 American Association for Cancer Research.

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The Interleukin-23 (IL-23)/IL-23R signaling axis is an important inflammatory pathway, involved in the stimulation and regulation of the T helper (Th) 17 lymphocytes, resulting in the production of IL-17. Aside from auto-immunity, this cytokine has also been linked to carcinogenesis and polymorphisms in the IL-23R gene are associated with an increased risk for the development of a number of different cancers. Activation of the IL-23 pathway results in the up-regulation of STAT3 and it is thought that the pathological consequences associated with this are in part due to the production of IL-17. We have previously identified IL-23A as pro-proliferative and epigenetically regulated in non-small cell lung cancer (NSCLC). The current study aims to evaluate IL-23R in greater detail in NSCLC. We demonstrate that IL-23R is expressed and epigenetically regulated in NSCLC through histone post-translation modifications and CpG island methylation. In addition, Gemcitabine treatment, a chemotherapy drug used in the treatment of NSCLC, resulted in the up-regulation of the IL-23R. Furthermore, Apilimod (STA 5326), a small molecule which blocks the expression of IL-23 and IL-12, reduced the proliferative capacity of NSCLC cells, particularly in the adenocarcinoma (A549) sub-type. Apilimod is currently undergoing investigation in a number of clinical trials for the treatment of auto-immune conditions such as Crohn's disease and Rheumatoid Arthritis. Our results may have implications for treating NSCLC patients with Gemcitabine or epigenetic targeted therapies. However, Apilimod may possibly provide a new treatment avenue for NSCLC patients. Work is currently ongoing to further delineate the IL-23/IL-23R axis in this disease.

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MapReduce frameworks such as Hadoop are well suited to handling large sets of data which can be processed separately and independently, with canonical applications in information retrieval and sales record analysis. Rapid advances in sequencing technology have ensured an explosion in the availability of genomic data, with a consequent rise in the importance of large scale comparative genomics, often involving operations and data relationships which deviate from the classical Map Reduce structure. This work examines the application of Hadoop to patterns of this nature, using as our focus a wellestablished workflow for identifying promoters - binding sites for regulatory proteins - Across multiple gene regions and organisms, coupled with the unifying step of assembling these results into a consensus sequence. Our approach demonstrates the utility of Hadoop for problems of this nature, showing how the tyranny of the "dominant decomposition" can be at least partially overcome. It also demonstrates how load balance and the granularity of parallelism can be optimized by pre-processing that splits and reorganizes input files, allowing a wide range of related problems to be brought under the same computational umbrella.

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Bats account for one-fifth of mammalian species, are the only mammals with powered flight, and are among the few animals that echolocate. The insect-eating Brandt’s bat (Myotis brandtii) is the longest-lived bat species known to date (lifespan exceeds 40 years) and, at 4–8 g adult body weight, is the most extreme mammal with regard to disparity between body mass and longevity. Here we report sequencing and analysis of the Brandt’s bat genome and transcriptome, which suggest adaptations consistent with echolocation and hibernation, as well as altered metabolism, reproduction and visual function. Unique sequence changes in growth hormone and insulin-like growth factor 1 receptors are also observed. The data suggest that an altered growth hormone/insulin-like growth factor 1 axis, which may be common to other long-lived bat species, together with adaptations such as hibernation and low reproductive rate, contribute to the exceptional lifespan of the Brandt’s bat.

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Established Monte Carlo user codes BEAMnrc and DOSXYZnrc permit the accurate and straightforward simulation of radiotherapy experiments and treatments delivered from multiple beam angles. However, when an electronic portal imaging detector (EPID) is included in these simulations, treatment delivery from non-zero beam angles becomes problematic. This study introduces CTCombine, a purpose-built code for rotating selected CT data volumes, converting CT numbers to mass densities, combining the results with model EPIDs and writing output in a form which can easily be read and used by the dose calculation code DOSXYZnrc...

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Aim A new method of penumbral analysis is implemented which allows an unambiguous determination of field size and penumbra size and quality for small fields and other non-standard fields. Both source occlusion and lateral electronic disequilibrium will affect the size and shape of cross-axis profile penumbrae; each is examined in detail. Method A new method of penumbral analysis is implemented where the square of the derivative of the cross-axis profile is plotted. The resultant graph displays two peaks in the place of the two penumbrae. This allows a strong visualisation of the quality of a field penumbra, as well as a mathematically consistent method of determining field size (distance between the two peak’s maxima), and penumbra (full-widthtenth-maximum of peak). Cross-axis profiles were simulated in a water phantom at a depth of 5 cm using Monte Carlo modelling, for field sizes between 5 and 30 mm. The field size and penumbra size of each field was calculated using the method above, as well as traditional definitions set out in IEC976. The effect of source occlusion and lateral electronic disequilibrium on the penumbrae was isolated by repeating the simulations removing electron transport and using an electron spot size of 0 mm, respectively. Results All field sizes calculated using the traditional and proposed methods agreed within 0.2 mm. The penumbra size measured using the proposed method was systematically 1.8 mm larger than the traditional method at all field sizes. The size of the source had a larger effect on the size of the penumbra than did lateral electronic disequilibrium, particularly at very small field sizes. Conclusion Traditional methods of calculating field size and penumbra are proved to be mathematically adequate for small fields. However, the field size definition proposed in this study would be more robust amongst other nonstandard fields, such as flattening filter free. Source occlusion plays a bigger role than lateral electronic disequilibrium in small field penumbra size.

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This thesis addressed issues that have prevented qualitative researchers from using thematic discovery algorithms. The central hypothesis evaluated whether allowing qualitative researchers to interact with thematic discovery algorithms and incorporate domain knowledge improved their ability to address research questions and trust the derived themes. Non-negative Matrix Factorisation and Latent Dirichlet Allocation find latent themes within document collections but these algorithms are rarely used, because qualitative researchers do not trust and cannot interact with the themes that are automatically generated. The research determined the types of interactivity that qualitative researchers require and then evaluated interactive algorithms that matched these requirements. Theoretical contributions included the articulation of design guidelines for interactive thematic discovery algorithms, the development of an Evaluation Model and a Conceptual Framework for Interactive Content Analysis.