78 research outputs found
Three-dimensional visualisation of authentic cases in anatomy learning â An educational design study
Many studies have investigated the value of three-dimensional (3D) images in learning anatomy. However, there is a lack of knowledge about students learning processes using technology and 3D images. To understand how to facilitate and support the learning of anatomy, there is a need to know more about the student perspectives on how they can use and benefit from 3D images
Common Genetic Variation And Age at Onset Of Anorexia Nervosa
Background Genetics and biology may influence the age at onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to AN age at onset and to investigate the genetic associations between age at onset of AN and age at menarche. Methods A secondary analysis of the Psychiatric Genomics Consortium genome-wide association study (GWAS) of AN was performed which included 9,335 cases and 31,981 screened controls, all from European ancestries. We conducted GWASs of age at onset, early-onset AN (< 13 years), and typical-onset AN, and genetic correlation, genetic risk score, and Mendelian randomization analyses. Results Two loci were genome-wide significant in the typical-onset AN GWAS. Heritability estimates (SNP-h2) were 0.01-0.04 for age at onset, 0.16-0.25 for early-onset AN, and 0.17-0.25 for typical-onset AN. Early- and typical-onset AN showed distinct genetic correlation patterns with putative risk factors for AN. Specifically, early-onset AN was significantly genetically correlated with younger age at menarche, and typical-onset AN was significantly negatively genetically correlated with anthropometric traits. Genetic risk scores for age at onset and early-onset AN estimated from independent GWASs significantly predicted age at onset. Mendelian randomization analysis suggested a causal link between younger age at menarche and early-onset AN. Conclusions Our results provide evidence consistent with a common variant genetic basis for age at onset and implicate biological pathways regulating menarche and reproduction.Peer reviewe
Preceptoria MĂ©dica em Serviço de EmergĂȘncia e UrgĂȘncia Hospitalar na Perspectiva de MĂ©dicos
Prediction of voice aperiodicity based on spectral representations in HMM speech synthesis
In hidden Markov model-based speech synthesis, speech is typically parameterized using source-filter decomposition. A widely used analysis/synthesis framework, STRAIGHT, decomposes the speech waveform into a framewise spectral envelope and a mixed mode excitation signal. Inclusion of an aperiodicity measure in the model enables synthesis also for signals that are not purely voiced or unvoiced. In the traditional approach employing hidden Markov modeling and decision tree-based clustering, the connection between speech spectrum and aperiodicities is not taken into account. In this paper, we take advantage of this dependency and predict voice aperiodicities afterwards based on synthetic spectral representations. The evaluations carried out for English data confirm that the proposed approach is able to provide prediction accuracy that is comparable to the traditional approach. Copyright © 2011 ISCA
Multi-annual modes in the 20th century temperature variability in reanalyses and CMIP5 models
A performance expectation is that Earth system models
simulate well the climate mean state and the climate variability. To test
this expectation, we decompose two 20th century reanalysis data sets and 12
CMIP5 model simulations for the years 1901â2005 of the monthly mean
near-surface air temperature using randomised multi-channel singular spectrum
analysis (RMSSA). Due to the relatively short time span, we concentrate on
the representation of multi-annual variability which the RMSSA method
effectively captures as separate and mutually orthogonal spatio-temporal
components. This decomposition is a unique way to separate statistically
significant quasi-periodic oscillations from one another in high-dimensional
data sets.The main results are as follows. First, the total spectra for the two
reanalysis data sets are remarkably similar in all timescales, except that
the spectral power in ERA-20C is systematically slightly higher than in 20CR.
Apart from the slow components related to multi-decadal periodicities, ENSO
oscillations with approximately 3.5- and 5-year periods are the most prominent
forms of variability in both reanalyses. In 20CR, these are relatively
slightly more pronounced than in ERA-20C. Since about the 1970s, the
amplitudes of the 3.5- and 5-year oscillations have increased, presumably due
to some combination of forced climate change, intrinsic low-frequency climate
variability, or change in global observing network. Second, none of the 12
coupled climate models closely reproduce all aspects of the reanalysis
spectra, although some models represent many aspects well. For instance, the
GFDL-ESM2M model has two nicely separated ENSO periods although they are
relatively too prominent as compared with the reanalyses. There is an
extensive Supplement and YouTube videos to illustrate the multi-annual
variability of the data sets
COSIMA data analysis using multivariate techniques
We describe how to use multivariate analysis of complex TOF-SIMS (time-of-flight secondary ion mass spectrometry) spectra
by introducing the method of random projections. The technique allows us to do
full clustering and classification of the measured mass spectra. In this
paper we use the tool for classification purposes. The presentation describes
calibration experiments of 19 minerals on Ag and Au substrates using positive
mode ion spectra. The discrimination between individual minerals gives a
cross-validation Cohen Îș for classification of typically about 80%.
We intend to use the method as a fast tool to deduce a qualitative similarity
of measurements
Using robust Viterbi algorithm and HMM-modeling in unit selection TTS to replace units of poor quality
In hidden Markov model-based unit selection synthesis, the benefits of both unit selection and statistical parametric speech synthesis are combined. However, conventional Viterbi algorithm is forced to do a selection also when no suitable units are available. This can drift the search and decrease the overall quality. Consequently, we propose to use robust Viterbi algorithm that can simultaneously detect bad units and select the best sequence. The unsuitable units are replaced using hidden Markov model-based synthesis. Evaluations indicate that the use of robust Viterbi algorithm combined with unit replacement increases the quality compared to the traditional algorithm. © 2010 ISCA
Three phylaâTwo type specimensâOne shell: History of a snail shell revealed by modern imaging technology
Analysis of COSIMA spectra: Bayesian approach
We describe the use of Bayesian analysis methods applied to time-of-flight
secondary ion mass spectrometer (TOF-SIMS)
spectra. The method is applied to the COmetary Secondary Ion Mass Analyzer (COSIMA) TOF-SIMS mass spectra
where the analysis can be broken into subgroups of lines close to
integer mass values. The effects of the instrumental dead time are discussed
in a new way. The method finds the joint probability density functions of
measured line parameters (number of lines, and their widths, peak
amplitudes, integrated amplitudes and positions). In the case of two or
more lines, these distributions can take complex forms. The derived
line parameters can be used to further calibrate the mass scaling
of TOF-SIMS and to feed the results into other analysis methods such
as multivariate analyses of spectra. We intend to use the method,
first as a comprehensive tool to perform quantitative
analysis of spectra, and second as a fast tool for studying
interesting targets for obtaining additional TOF-SIMS measurements
of the sample, a property unique to COSIMA. Finally, we point out
that the Bayesian method can be thought of as a means to solve inverse
problems but with forward calculations, only with no
iterative corrections or other manipulation of the observed data
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