Feasibility investigation of cognitive rehabilitation service after traumatic brain injury in community

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach

Maternal depression and anxiety through pregnancy have lasting societal impacts. It is thus crucial to understand the trajectories of its progression from preconception to postnatal period, and the risk factors associated with it. Within the Bayesian framework, we propose to jointly model seven outcomes, of which two are physiological and five non-physiological indicators of maternal depression and anxiety over time. We model the former two by a Gaussian process and the latter by an autoregressive model, while imposing a multidimensional Dirichlet process prior on the subject-specific random effects to account for subject heterogeneity and induce clustering. The model allows for the inclusion of covariates through a regression term. Our findings reveal four distinct clusters of trajectories of the seven health outcomes, characterising women's mental health progression from before to after pregnancy. Importantly, our results caution against the loose use of hair corticosteroids as a biomarker, or even a causal factor, for pregnancy mental health progression. Additionally, the regression analysis reveals a range of preconception determinants and risk factors for depressive and anxiety symptoms during pregnancy

Segmentation of Fluorescence Microscopy Cell Images Using Unsupervised Mining

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu’s threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu’s threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications

Chronic hepatitis B: whom to treat and for how long? Propositions, challenges, and future directions

Recent guidelines of the American Association for the Study of Liver Diseases, the European Association for the Study of the Liver, and the Asian Pacific Association for the Study of the Liver 2008 update of the “Asian-Pacific consensus statement on the management of chronic hepatitis B” offer comprehensive recommendations for the general management of chronic hepatitis B (CHB). These recommendations highlight preferred approaches to the prevention, diagnosis, and treatment of CHB. Nonetheless, the results of recent studies have led to an improved understanding of the disease and a belief that current recommendations on specific therapeutic considerations, including CHB treatment initiation and cessation criteria, particularly in patient populations with special circumstances, can be improved. Twelve experts from the Asia-Pacific region formed the Asia-Pacific Panel Recommendations for the Optimal Management of Chronic Hepatitis B (APPROACH) Working Group to review, challenge, and assess relevant new data and inform future updates of CHB treatment guidelines. The significance of and controversy about reported findings were discussed and debated in an expert meeting of the Working Group in Beijing, China, in November 2008. This review paper attempts to identify areas requiring improved CHB management and provide suggestions for future guideline updates, with special emphasis on treatment initiation and duration

Covalent Attachment of Proteins to Solid Supports and Surfaces via Sortase-Mediated Ligation

BACKGROUND: There is growing interest in the attachment of proteins to solid supports for the development of supported catalysts, affinity matrices, and micro devices as well as for the development of planar and bead based protein arrays for multiplexed assays of protein concentration, interactions, and activity. A critical requirement for these applications is the generation of a stable linkage between the solid support and the immobilized, but still functional, protein. METHODOLOGY: Solid supports including crosslinked polymer beads, beaded agarose, and planar glass surfaces, were modified to present an oligoglycine motif to solution. A range of proteins were ligated to the various surfaces using the Sortase A enzyme of S. aureus. Reactions were carried out in aqueous buffer conditions at room temperature for times between one and twelve hours. CONCLUSIONS: The Sortase A transpeptidase of S. aureus provides a general, robust, and gentle approach to the selective covalent immobilization of proteins on three very different solid supports. The proteins remain functional and accessible to solution. Sortase mediated ligation is therefore a straightforward methodology for the preparation of solid supported enzymes and bead based assays, as well as the modification of planar surfaces for microanalytical devices and protein arrays

Catalyst preparation for CMOS-compatible silicon nanowire synthesis

Metallic contamination was key to the discovery of semiconductor nanowires, but today it stands in the way of their adoption by the semiconductor industry. This is because many of the metallic catalysts required for nanowire growth are not compatible with standard CMOS (complementary metal oxide semiconductor) fabrication processes. Nanowire synthesis with those metals which are CMOS compatible, such as aluminium and copper, necessitate temperatures higher than 450 C, which is the maximum temperature allowed in CMOS processing. Here, we demonstrate that the synthesis temperature of silicon nanowires using copper based catalysts is limited by catalyst preparation. We show that the appropriate catalyst can be produced by chemical means at temperatures as low as 400 C. This is achieved by oxidizing the catalyst precursor, contradicting the accepted wisdom that oxygen prevents metal-catalyzed nanowire growth. By simultaneously solving material compatibility and temperature issues, this catalyst synthesis could represent an important step towards real-world applications of semiconductor nanowires.Comment: Supplementary video can be downloaded on Nature Nanotechnology websit

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO