A Novel Method for the 3-D Reconstruction of Scoliotic Ribs From Frontal and Lateral Radiographs

Among the external manifestations of scoliosis, the rib hump, which is associated with the ribs' deformities and rotations, constitutes the most disturbing aspect of the scoliotic deformity for patients. A personalized 3-D model of the rib cage is important for a better evaluation of the deformity, and hence, a better treatment planning. A novel method for the 3-D reconstruction of the rib cage, based only on two standard radiographs, is proposed in this paper. For each rib, two points are extrapolated from the reconstructed spine, and three points are reconstructed by stereo radiography. The reconstruction is then refined using a surface approximation. The method was evaluated using clinical data of 13 patients with scoliosis. A comparison was conducted between the reconstructions obtained with the proposed method and those obtained by using a previous reconstruction method based on two frontal radiographs. A first comparison criterion was the distances between the reconstructed ribs and the surface topography of the trunk, considered as the reference modality. The correlation between ribs axial rotation and back surface rotation was also evaluated. The proposed method successfully reconstructed the ribs of the 6th-12th thoracic levels. The evaluation results showed that the 3-D configuration of the new rib reconstructions is more consistent with the surface topography and provides more accurate measurements of ribs axial rotation.

Enlarging a training set for genomic selction by imputation of un-genotyped animals in populations of varying genetic architecture

Background: The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped. ----- Methods: Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets. ----- Results: Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams. ----- Conclusions: Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.

Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

Detection of pressed hazelnut oil in virgin olive oil by analysis of polar components: improvement and validation of the method

An improved method for the detection of pressed hazelnut oil in admixtures with virgin olive oil by analysis of polar components is described. The method. which is based on the SPE-based isolation of the polar fraction followed by RP-HPLC analysis with UV detection. is able to detect virgin olive oil adulterated with pressed hazelnut oil at levels as low as 5% with accuracy (90.0 +/- 4.2% recovery of internal standard), good reproducibility (4.7% RSD) and linearity (R-2: 0.9982 over the 5-40% adulteration range). An international ring-test of the developed method highlighted its capability as 80% of the samples were, on average, correctly identified despite the fact that no training samples were provided to the participating laboratories. However, the large variability in marker components among the pressed hazelnut oils examined prevents the use of the method for quantification of the level of adulteration. (C) 2003 Elsevier Ltd. All rights reserved.

Training conditional and cumulative risk judgements: the role of frequencies, problem-structure and Einstellung

Two experiments implement and evaluate a training scheme for learning to apply frequency formats to probability judgements couched in terms of percentages. Results indicate that both conditional and cumulative probability judgements can be improved in this manner, however the scheme is insufficient to promote any deeper understanding of the problem structure. In both experiments, training on one problem type only (either conditional or cumulative risk judgements) resulted in an inappropriate transfer of a learned method at test. The obstacles facing a frequency-based training programme for teaching appropriate use of probability data are discussed. Copyright (c) 2006 John Wiley & Sons, Ltd.

The full-spectrum correlated-k method for longwave atmospheric radiative transfer using an effective Planck function

The correlated k-distribution (CKD) method is widely used in the radiative transfer schemes of atmospheric models and involves dividing the spectrum into a number of bands and then reordering the gaseous absorption coefficients within each one. The fluxes and heating rates for each band may then be computed by discretizing the reordered spectrum into of order 10 quadrature points per major gas and performing a monochromatic radiation calculation for each point. In this presentation it is shown that for clear-sky longwave calculations, sufficient accuracy for most applications can be achieved without the need for bands: reordering may be performed on the entire longwave spectrum. The resulting full-spectrum correlated k (FSCK) method requires significantly fewer monochromatic calculations than standard CKD to achieve a given accuracy. The concept is first demonstrated by comparing with line-by-line calculations for an atmosphere containing only water vapor, in which it is shown that the accuracy of heating-rate calculations improves approximately in proportion to the square of the number of quadrature points. For more than around 20 points, the root-mean-squared error flattens out at around 0.015 K/day due to the imperfect rank correlation of absorption spectra at different pressures in the profile. The spectral overlap of m different gases is treated by considering an m-dimensional hypercube where each axis corresponds to the reordered spectrum of one of the gases. This hypercube is then divided up into a number of volumes, each approximated by a single quadrature point, such that the total number of quadrature points is slightly fewer than the sum of the number that would be required to treat each of the gases separately. The gaseous absorptions for each quadrature point are optimized such that they minimize a cost function expressing the deviation of the heating rates and fluxes calculated by the FSCK method from line-by-line calculations for a number of training profiles. This approach is validated for atmospheres containing water vapor, carbon dioxide, and ozone, in which it is found that in the troposphere and most of the stratosphere, heating-rate errors of less than 0.2 K/day can be achieved using a total of 23 quadrature points, decreasing to less than 0.1 K/day for 32 quadrature points. It would be relatively straightforward to extend the method to include other gases.

The full-spectrum correlated k method for longwave atmospheric radiative transfer: treatment of gaseous overlap

The correlated k-distribution (CKD) method is widely used in the radiative transfer schemes of atmospheric models, and involves dividing the spectrum into a number of bands and then reordering the gaseous absorption coefficients within each one. The fluxes and heating rates for each band may then be computed by discretizing the reordered spectrum into of order 10 quadrature points per major gas, and performing a pseudo-monochromatic radiation calculation for each point. In this paper it is first argued that for clear-sky longwave calculations, sufficient accuracy for most applications can be achieved without the need for bands: reordering may be performed on the entire longwave spectrum. The resulting full-spectrum correlated k (FSCK) method requires significantly fewer pseudo-monochromatic calculations than standard CKD to achieve a given accuracy. The concept is first demonstrated by comparing with line-by-line calculations for an atmosphere containing only water vapor, in which it is shown that the accuracy of heating-rate calculations improves approximately in proportion to the square of the number of quadrature points. For more than around 20 points, the root-mean-squared error flattens out at around 0.015 K d−1 due to the imperfect rank correlation of absorption spectra at different pressures in the profile. The spectral overlap of m different gases is treated by considering an m-dimensional hypercube where each axis corresponds to the reordered spectrum of one of the gases. This hypercube is then divided up into a number of volumes, each approximated by a single quadrature point, such that the total number of quadrature points is slightly fewer than the sum of the number that would be required to treat each of the gases separately. The gaseous absorptions for each quadrature point are optimized such they minimize a cost function expressing the deviation of the heating rates and fluxes calculated by the FSCK method from line-by-line calculations for a number of training profiles. This approach is validated for atmospheres containing water vapor, carbon dioxide and ozone, in which it is found that in the troposphere and most of the stratosphere, heating-rate errors of less than 0.2 K d−1 can be achieved using a total of 23 quadrature points, decreasing to less than 0.1 K d−1 for 32 quadrature points. It would be relatively straightforward to extend the method to include other gases.

Does training improve understanding of core concepts in cognitive behaviour therapy by people with intellectual disabilities? A randomized experiment

Background. People with intellectual disabilities (ID) experience similar or even higher rates of mental health problems than the general population and there is a need to develop appropriate treatments. Cognitive behaviour therapy (CBT) is effective for a wide range of disorders in the general population. However, there is some evidence that people with ID may lack the cognitive skills needed to take part in CBT. Aims. To test if people with ID can learn skills required for CBT, specifically the ability to distinguish between thoughts, feelings, and behaviours and to link thoughts and feelings (cognitive mediation). Method. A randomized independent groups design was used to examine the effect of training in CBT on two tasks measuring CBT skills. Thirty-four adults with ID were randomly allocated to the experimental condition ðN ¼ 18Þ or to the control condition ðN ¼ 16Þ. CBT skills were assessed blind at baseline and after the intervention. Results. The training led to significant improvements in participants’ ability to link thoughts and feelings, and this skill was generalized to new material. There was no effect of training on participants’ ability to distinguish amongst thoughts, feelings, and behaviours. People with ID can, therefore, learn some skills required for CBT. This implies that preparatory training for CBT might be useful for people with ID. The results might be applicable to other groups who find aspects of CBT difficult.

Therapist characteristics and their effect on training outcomes: what counts?

Background: Evidence exists for a relationship between individual characteristics and both job and training performance; however relationships may not be generalizable. Little is known about the impact of therapist characteristics on performance in postgraduate therapist training programmes. Aims: The aim of this study was to investigate associations between the grades of trainee Low-Intensity and High-Intensity cognitive behavioural therapists and individual characteristics. Method: Trainee Low-Intensity (n=81) and High-Intensity (n=59) therapists completed measures of personality and cognitive ability; demographic and course grade data for participants were collected. Results: Degree classification emerged as the only variable to be significantly associated with performance across assessments and courses. Higher undergraduate degree classifications were associated with superior academic and clinical performance. Agreeableness was the only dimension of personality to be associated (positively) with clinical skill. Age was weakly and negatively associated with performance. Conclusions: Relationships between individual characteristics and training outcomes are complex and may be context specific. These results could have important implications for the selection and development of therapists for Low or High-Intensity cognitive behavioural therapy (CBT) training.

Training on the use of a bespoke continuing professional development framework improves the quality of CPD records

BACKGROUND: Using continuing professional development (CPD) as part of the revalidation of pharmacy professionals has been proposed in the UK but not implemented. We developed a CPD Outcomes Framework (‘the framework’) for scoring CPD records, where the score range was -100 to +150 based on demonstrable relevance and impact of the CPD on practice. OBJECTIVE: This exploratory study aimed to test the outcome of training people to use the framework, through distance-learning material (active intervention), by comparing CPD scores before and after training. SETTING: Pharmacy professionals were recruited in the UK in Reading, Banbury, Southampton, Kingston-upon-Thames and Guildford in 2009. METHOD: We conducted a randomised, double-blinded, parallel-group, before and after study. The control group simply received information on new CPD requirements through the post; the active intervention group also received the framework and associated training. Altogether 48 participants (25 control, 23 active) completed the study. All participants submitted CPD records to the research team before and after receiving the posted resources. The records (n=226) were scored blindly by the researchers using the framework. A subgroup of CPD records (n=96) submitted first (before-stage) and rewritten (after-stage) were analysed separately. MAIN OUTCOME MEASURE: Scores for CPD records received before and after distributing group-dependent material through the post. RESULTS: Using a linear-regression model both analyses found an increase in CPD scores in favour of the active intervention group. For the complete set of records, the effect was a mean difference of 9.9 (95% CI = 0.4 to 19.3), p-value = 0.04. For the subgroup of rewritten records, the effect was a mean difference of 17.3 (95% CI = 5.6 to 28.9), p-value = 0.0048. CONCLUSION: The intervention improved participants’ CPD behaviour. Training pharmacy professionals to use the framework resulted in better CPD activities and CPD records, potentially helpful for revalidation of pharmacy professionals. IMPACT: • Using a bespoke Continuing Professional Development outcomes framework improves the value of pharmacy professionals’ CPD activities and CPD records, with the potential to improve patient care. • The CPD outcomes framework could be helpful to pharmacy professionals internationally who want to improve the quality of their CPD activities and CPD records. • Regulators and officials across Europe and beyond can assess the suitability of the CPD outcomes framework for use in pharmacy CPD and revalidation in their own setting.

Can a computerised training paradigm assist people with intellectual disabilities to learn cognitive mediation skills? A randomised experiment

Aims: The aim was to examine whether specific skills required for cognitive behavioural therapy (CBT) could be taught using a computerised training paradigm with people who have intellectual disabilities (IDs). Training aimed to improve: a) ability to link pairs of situations and mediating beliefs to emotions, and b) ability to link pairs of situations and emotions to mediating beliefs. Method: Using a single-blind mixed experimental design, sixty-five participants with IDs were randomised to receive either computerised training or an attention-control condition. Cognitive mediation skills were assessed before and after training. Results: Participants who received training were significantly better at selecting appropriate emotions within situation beliefs pairs, controlling for baseline scores and IQ. Despite significant improvements in the ability of those who received training to correctly select intermediating beliefs for situation-feelings pairings, no between-group differences were observed at post-test. Conclusions: The findings indicated that computerised training led to a significant improvement in some aspects of cognitive mediation for people with IDs, but whether this has a positive effect upon outcome from therapy is yet to be established. (C) 2015 Elsevier Ltd. All rights reserved.

Is cognitive bias modification training truly beneficial for adolescents?

Background Cognitive Bias Modification (CBM) has been shown to change interpretation biases commonly associated with anxiety and depression and may help ameliorate symptoms of these disorders. However, its evidence base for adolescents is scarce. Previous results have been hard to interpret because of methodological issues. In particular, many studies have used negative bias training as the control condition. This would tend to inflate any apparent benefits of CBM compared to a neutral control. Most studies also only examined the effects of a single training session and lacked follow-up assessment or ecologically valid outcome measures. Method Seventy-four adolescents, aged 16–18 years, were randomised to two sessions of CBM training or neutral control. Interpretation bias and mood were assessed three times: at baseline, immediately post-training and 1 week post-training. A controlled experimental stressor was also used, and responses to everyday stressors were recorded for 1 week after training to assess responses to psychological challenges. Feedback for the training programme was collected. Results The CBM group reported a greater reduction in negative affect than control participants. However, other hypothesised advantages of CBM were not demonstrated. Regardless of training group, participants reported increased positive interpretations, decreased negative interpretations, reduced depressive symptoms and no change in trait anxiety. The two groups did not differ in their stress reactivity. After controlling for group differences in training performance, all the mood effects disappeared. Conclusions When tested under stringent experimental conditions the effects of CBM in healthy adolescents appear to be minimal. Future studies should concentrate on participants with elevated cognitive biases and/or mood symptoms who may be more sensitive to CBM.

Validation of the SenseWear armband in circuit resistance training with different loads

The use of the SenseWear (TM) armband (SWA), an objective monitor of physical activity, is a relatively new device used by researchers to measure energy expenditure. These monitors are practical, relatively inexpensive and easy-to-use. The aim of the present study was to assess the validity of SWAs for the measurement of energy expenditure (EE) in circuit resistance training (CRT) at three different intensities in moderately active, healthy subjects. The study subjects (17 females, 12 males) undertook CRT at 30, 50 and 70% of the 15 repetition maximum for each exercise component wearing an SWA as well as an Oxycon Mobile (OM) portable metabolic system (a gold standard method for measuring EE). The EE rose as exercise intensity increased, but was underestimated by the SWAs. For women, Bland-Altman plots showed a bias of 1.13 +/- A 1.48 METs and 32.1 +/- A 34.0 kcal in favour of the OM system, while for men values of 2.33 +/- A 1.82 METs and 75.8 +/- A 50.8 kcal were recorded.