402 resultados para Gaussian probability function


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The quality of conceptual business process models is highly relevant for the design of corresponding information systems. In particular, a precise measurement of model characteristics can be beneficial from a business perspective, helping to save costs thanks to early error detection. This is just as true from a software engineering point of view. In this latter case, models facilitate stakeholder communication and software system design. Research has investigated several proposals as regards measures for business process models, from a rather correlational perspective. This is helpful for understanding, for example size and complexity as general driving forces of error probability. Yet, design decisions usually have to build on thresholds, which can reliably indicate that a certain counter-action has to be taken. This cannot be achieved only by providing measures; it requires a systematic identification of effective and meaningful thresholds. In this paper, we derive thresholds for a set of structural measures for predicting errors in conceptual process models. To this end, we use a collection of 2,000 business process models from practice as a means of determining thresholds, applying an adaptation of the ROC curves method. Furthermore, an extensive validation of the derived thresholds was conducted by using 429 EPC models from an Australian financial institution. Finally, significant thresholds were adapted to refine existing modeling guidelines in a quantitative way.

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Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

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Prostate cancer is a significant health problem faced by aging men. Currently, diagnostic strategies for the detection of prostate cancer are either unreliable, yielding high numbers of false positive results, or too invasive to be used widely as screening tests. Furthermore, the current therapeutic strategies for the treatment of the disease carry considerable side effects. Although organ confined prostate cancer can be curable, most detectable clinical symptoms occur in advanced disease when primary tumour cells have metastasised to distant sites - usually lymph nodes and bone. Many growth factors and steroids assist the continued growth and maintenance of prostatic tumour cells. Of these mitogens, androgens are important in the development of the normal prostate but are also required to sustain the growth of prostate cancer cells in the early stage of the disease. Not only are androgens required in the early stage of disease, but also many other growth factors and hormones interact to cause uncontrolled proliferation of malignant cells. The early, androgen sensitive phase of disease is followed by an androgen insensitive phase, whereby androgens are no longer required to stimulate the growth of the tumour cells. Growth factors such as transforming growth factor  and  (TGF/), epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), insulin-like growth factors (IGFs), Vitamin D and thyroid hormone have been suggested to be important at this stage of disease. Interestingly, some of the kallikrein family of genes, including prostate specific antigen (PSA), the current serum diagnostic marker for prostate cancer, are regulated by androgens and many of the aforementioned growth factors. The kallikrein gene family is a group of serine proteases that are involved in a diverse range of physiological processes: regulation of local blood flow, angiogenesis, tissue invasion and mitogenesis. The earliest members of the kallikrein gene family (KLK1-KLK3) have been strongly associated with general disease states, such as hypertension, inflammation, pancreatitis and renal disease, but are also linked to various cancers. Recently, this family was extended to include 15 genes (KLK1-15). Several newer members of the kallikrein family have been implicated in the carcinogenesis and tumour metastasis of hormone-dependent cancers such as prostate, breast, endometrial and ovarian cancer. The aims of this project were to investigate the expression of the newly identified kallikrein, KLK4, in benign and malignant prostate tissues, and prostate cancer cell lines. This thesis has demonstrated the elevated expression of KLK4 mRNA transcripts in malignant prostate tissue compared to benign prostates. Additionally, expression of the full length KLK4 transcript was detected in the androgen dependent prostate cancer cell line, LNCaP. Based on the above finding, the LNCaP cell line was chosen to assess the potential regulation of full length KLK4 by androgen, thyroid hormone and epidermal growth factor. KLK4 mRNA and protein was found to be up-regulated by androgen and a combination of androgen and thyroid hormone. Thyroid hormone alone produced no significant change in KLK4 mRNA or protein over the control. Epidermal growth factor treatment also resulted in elevated expression levels of KLK4 mRNA and protein. To assess the potential functional role(s) of KLK4/hK4 in processes associated with tumour progression, full length KLK4 was transfected into PC-3 cells - a prostate cancer cell line originally derived from a secondary bone lesion. The KLK4/hK4 over-expressing cells were assessed for their proliferation, migration, invasion and attachment properties. The KLK4 over-expressing clones exhibited a marked change in morphology, indicative of a more aggressive phenotype. The KLK4 clones were irregularly shaped with compromised adhesion to the growth surface. In contrast, the control cell lines (parent PC-3 and empty vector clones) retained a rounded morphology with obvious cell to cell adhesion, as well as significant adhesion to their growth surface. The KLK4 clones exhibited significantly greater attachment to Collagen I and IV than native PC-3s and empty vector controls. Over a 12 hour period, in comparison to the control cells, the KLK4 clones displayed an increase in migration towards PC-3 native conditioned media, a 3 fold increase towards conditioned media from an osteoblastic cell line (Saos-2) and no change in migration towards conditioned media from neonatal foreskin fibroblast cells or 20% foetal bovine serum. Furthermore, the increase in migration exhibited by the KLK4 clones was partially blocked by the serine protease inhibitor, aprotinin. The data presented in this thesis suggests that KLK4/hK4 is important in prostate carcinogenesis due to its over-expression in malignant prostate tissues, its regulation by hormones and growth factors associated with prostate disease and the functional consequences of over-expression of KLK4/hK4 in the PC-3 cell line. These results indicate that KLK4/hK4 may play an important role in tumour invasion and bone metastasis via increased attachment to the bone matrix protein, Collagen I, and enhanced migration due to soluble factors produced by osteoblast cells. This suggestion is further supported by the morphological changes displayed by the KLK4 over-expressing cells. Overall, this data suggests that KLK4/hK4 should be further studied to more fully investigate the potential value of KLK4/hK4 as a diagnostic/prognostic biomarker or in therapeutic applications.

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This paper presents an innovative prognostics model based on health state probability estimation embedded in the closed loop diagnostic and prognostic system. To employ an appropriate classifier for health state probability estimation in the proposed prognostic model, the comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault levels of three faults in HP-LNG pump. Two sets of impeller-rubbing data were employed for the prediction of pump remnant life based on estimation of discrete health state probability using an outstanding capability of SVM and a feature selection technique. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

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PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models (SSM). PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries Numpy and Scipy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimised and parallelised Fortran routines. These Fortran routines heavily utilise Basic Linear Algebra (BLAS) and Linear Algebra Package (LAPACK) functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.

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To date, attempts to regenerate a complete tooth, including the critical periodontal tissues associated with the tooth root, have not been successful. Controversy still exists regarding the origin of the cell source for cellular cementum (epithelial or mesenchymal). This disagreement may be partially due to a lack of understanding of the events leading to the initiation and development of the tooth roots and supportive tissues, such as the cementum. Osterix (OSX) is a transcriptional factor essential for osteogenesis, but its role in cementogenesis has not been addressed. In the present study, we first documented a close relationship between the temporal- and spatial-expression pattern of OSX and the formation of cellular cementum. We then generated 3.6 Col 1-OSX transgenic mice, which displayed accelerated cementum formation vs. WT controls. Importantly, the conditional deletion of OSX in the mesenchymal cells with two different Cre systems (the 2.3 kb Col 1 and an inducible CAG-CreER) led to a sharp reduction in cellular cementum formation (including the cementum mass and mineral deposition rate) and gene expression of dentin matrix protein 1 (DMP1) by cementocytes. However, the deletion of the OSX gene after cellular cementum formed did not alter the properties of the mature cementum as evaluated by backscattered SEM and resin-cast SEM. Transient transfection of Osx in the cementoblasts in vitro significantly inhibited cell proliferation and increased cell differentiation and mineralization. Taken together, these data support 1) the mesenchymal origin of cellular cementum (from PDL progenitor cells); 2) the vital role of OSX in controlling the formation of cellular cementum; and 3) the limited remodeling of cellular cementum in adult mice.

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Background The increasing popularity and use of the internet makes it an attractive option for providing health information and treatment, including alcohol/other drug use. There is limited research examining how people identify and access information about alcohol or other drug (AOD) use online, or how they assess the usefulness of the information presented. This study examined the strategies that individuals used to identify and navigate a range of AOD websites, along with the attitudes concerning presentation and content. Methods Members of the general community in Brisbane and Roma (Queensland, Australia) were invited to participate in a 30-minute search of the internet for sites related to AOD use, followed by a focus group discussion. Fifty one subjects participated in the study across nine focus groups. Results Participants spent a maximum of 6.5 minutes on any one website, and less if the user was under 25 years of age. Time spent was as little as 2 minutes if the website was not the first accessed. Participants recommended that AOD-related websites should have an engaging home or index page, which quickly and accurately portrayed the site’s objectives, and provided clear site navigation options. Website content should clearly match the title and description of the site that is used by internet search engines. Participants supported the development of a portal for AOD websites, suggesting that it would greatly facilitate access and navigation. Treatment programs delivered online were initially viewed with caution. This appeared to be due to limited understanding of what constituted online treatment, including its potential efficacy. Conclusions A range of recommendations arise from this study regarding the design and development of websites, particularly those related to AOD use. These include prudent use of text and information on any one webpage, the use of graphics and colours, and clear, uncluttered navigation options. Implications for future website development are discussed.

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The accuracy of measurement of mechanical properties of a material using instrumented nanoindentation at extremely small penetration depths heavily relies on the determination of the contact area of the indenter. Our experiments have demonstrated that the conventional area function could lead to a significant error when the contact depth was below 40. nm, due to the singularity in the first derivation of the function in this region and thus, the resultant unreasonable sharp peak on the function curve. In this paper, we proposed a new area function that was used to calculate the contact area for the indentations where the contact depths varied from 10 to 40. nm. The experimental results have shown that the new area function has produced better results than the conventional function. © 2011 Elsevier B.V.

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A practical approach for identifying solution robustness is proposed for situations where parameters are uncertain. The approach is based upon the interpretation of a probability density function (pdf) and the definition of three parameters that describe how significant changes in the performance of a solution are deemed to be. The pdf is constructed by interpreting the results of simulations. A minimum number of simulations are achieved by updating the mean, variance, skewness and kurtosis of the sample using computationally efficient recursive equations. When these criterions have converged then no further simulations are needed. A case study involving several no-intermediate storage flow shop scheduling problems demonstrates the effectiveness of the approach.

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Proteasomes are cylindrical particles made up of a stack of four heptameric rings. In animal cells the outer rings are made up of 7 different types of alpha subunits and the inner rings are composed of 7 out of 10 possible different beta subunits. Regulatory complexes can bind to the ends of the cylinder.We have investigated aspects of the assembly, activity and subunit composition of core proteasome particles and 26S proteasomes, the localization of proteasome subpopulations, and the possible role of phosphorylation in determining proteasome localization, activities and association with regulatory components.

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Unmanned Aircraft Systems (UAS) are one of a number of emerging aviation sectors. Such new aviation concepts present a significant challenge to National Aviation Authorities (NAAs) charged with ensuring the safety of their operation within the existing airspace system. There is significant heritage in the existing body of aviation safety regulations for Conventionally Piloted Aircraft (CPA). It can be argued that the promulgation of these regulations has delivered a level of safety tolerable to society, thus justifying the “default position” of applying these same standards, regulations and regulatory structures to emerging aviation concepts such as UAS. An example of this is the proposed “1309” regulation for UAS, which is based on the 1309 regulation for CPA. However, the absence of a pilot on-board an unmanned aircraft creates a fundamentally different risk paradigm to that of CPA. An appreciation of these differences is essential to the justification of the “default position” and in turn, to ensure the development of effective safety standards and regulations for UAS. This paper explores the suitability of the proposed “1309” regulation for UAS. A detailed review of the proposed regulation is provided and a number of key assumptions are identified and discussed. A high-level model characterising the expected number of third party fatalities on the ground is then used to determine the impact of these assumptions. The results clearly show that the “one size fits all” approach to the definition of 1309 regulations for UAS, which mandates equipment design and installation requirements independent of where the UAS is to be operated, will not lead to an effective management of the risks.