A deconvolution method for deriving the transit time spectrum for ultrasound propagation through cancellous bone replica models

The acceptance of broadband ultrasound attenuation for the assessment of osteoporosis suffers from a limited understanding of ultrasound wave propagation through cancellous bone. It has recently been proposed that the ultrasound wave propagation can be described by a concept of parallel sonic rays. This concept approximates the detected transmission signal to be the superposition of all sonic rays that travel directly from transmitting to receiving transducer. The transit time of each ray is defined by the proportion of bone and marrow propagated. An ultrasound transit time spectrum describes the proportion of sonic rays having a particular transit time, effectively describing lateral inhomogeneity of transit times over the surface of the receiving ultrasound transducer. The aim of this study was to provide a proof of concept that a transit time spectrum may be derived from digital deconvolution of input and output ultrasound signals. We have applied the active-set method deconvolution algorithm to determine the ultrasound transit time spectra in the three orthogonal directions of four cancellous bone replica samples and have compared experimental data with the prediction from the computer simulation. The agreement between experimental and predicted ultrasound transit time spectrum analyses derived from Bland–Altman analysis ranged from 92% to 99%, thereby supporting the concept of parallel sonic rays for ultrasound propagation in cancellous bone. In addition to further validation of the parallel sonic ray concept, this technique offers the opportunity to consider quantitative characterisation of the material and structural properties of cancellous bone, not previously available utilising ultrasound.

Active control of car suspension systems using IDA-PBC

This paper considers the design of active control for car suspension systems using a particular form of energy-based control called Interconnection-and-Damping-Assignment Passivity-Based Control (IDA-PBC). This approach allows one to shape the kinetic and potential energy as well as modify the power flow among different components of the system by changing the interconnection and dissipative structure in a meaningful way. Different controller parameterisations are considered to design a class of controllers for active suspension systems.

Stabilization of parametric roll resonance with active u-tanks via Lyapunov control design

Parametric ship roll resonance is a phenomenon where a ship can rapidly develop high roll motion while sailing in longitudinal waves. This effect can be described mathematically by periodic changes of the parameters of the equations of motion, which lead to a bifurcation. In this paper, the control design of an active u-tank stabilizer is carried out using Lyapunov theory. A nonlinear backstepping controller is developed to provide global exponential stability of roll. An extension of commonly used u-tank models is presented to account for large roll angles, and the control design is tested via simulation on a high-fidelity model of a vessel under parametric roll resonance.

Why are early maturing girls less active? Links between pubertal development, psychological well-being, and physical activity among girls at ages 11 and 13

Previous research has shown that early maturing girls at age I I have lower subsequent physical activity at age 13 in comparison to later maturing girls. Possible reasons for this association have not been assessed. This study examines girls' psychological response to puberty and their enjoyment of physical activity as intermediary factors linking pubertal maturation and physical activity. Participants included 178 girls who were assessed at age 11, of whom 168 were reassessed at age 13. All participants were non-Hispanic white and resided in the US. Three measures of pubertal development were obtained at age I I including Tanner breast stage, estradiol levels, and mothers' reports of girls' development on the Pubertal Development Scale (PDS). Measures of psychological well-being at ages I I and 13 included depression, global self-worth, perceived athletic competence, maturation fears, and body esteem. At age 13, girls' enjoyment of physical activity was assessed using the Physical Activity Enjoyment Scale and their daily minutes of moderate-to-vigorous physical activity (MVPA) were assessed using objective monitoring. Structural Equation Modeling was used to assess direct and indirect pathways between pubertal development at age I I and MVPA at age 13. In addition to a direct effect of pubertal development on MVPA, indirect effects were found for depression, global self-worth and maturity fears controlling for covariates. In each instance, more advanced pubertal development at age I I was associated with lower psychological wellbeing at age 13, which predicted lower enjoyment of physical activity at age 13 and in turn lower MVPA. Results from this study suggest that programs designed to increase physical activity among adolescent girls should address the self-consciousness and discontent that girls' experience with their bodies during puberty, particularly if they mature earlier than their peers, and identify activities or settings that make differences in body shape less conspicuous.

Service innovation and business models

Western economies are highly dependent on service innovation for their growth and employment. An important driver for economic growth is, therefore, the development of new, innovative services like electronic services, mobile end-user services, new financial or personalized services. Service innovation joins four trends that currently shape the western economies: the growing importance of services, the need for innovation, changes in consumer and business markets, and the advancements in information and communication technology (ICT).

Self-assembly of uniform carbon nanotip structures in chemically active inductively coupled plasmas

Self-assembly of carbon nanotip (CNTP) structures on Ni-based catalyst in chemically active inductively coupled plasmas of CH 4 + H 2 + Ar gas mixtures is reported. By varying the process conditions, it appears possible to control the shape, size, and density of CNTPs, content of the nanocrystalline phase in the films, as well as to achieve excellent crystallinity, graphitization, uniformity and vertical alignment of the resulting nanostructures at substrate temperatures 300-500°C and low gas pressures (below 13.2 Pa). This study provides a simple and efficient plasma-enhanced chemical vapor deposition (PECVD) technique for the fabrication of vertically aligned CNTP arrays for electron field emitters.

What are pregnant women told about models of maternity care in Australia? A retrospective study of women's reports

Objective To describe women’s reports of the model of care options General Practitioners (GPs) discussed with them at the first pregnancy consultation and women’s self-reported role in decisionmaking about model of care. Methods Women who had recently given birth responded to survey items about the models of care GPs discussed, their role in final decision-making, and socio-demographic, obstetric history, and early pregnancy characteristics. Results The proportion of women with whom each model of care was discussed varied between 8.2% (for private midwifery care with home birth) and 64.4% (GP shared care). Only 7.7% of women reported that all seven models were discussed. Exclusive discussion about private obstetric care and about all public models was common, and women’s health insurance status was the strongest predictor of the presence of discussions about each model. Most women (82.6%) reported active involvement in final decision-making about model of care. Conclusion Although most women report involvement in maternity model of care decisions, they remain largely uninformed about the breadth of available model of care options. Practical implications Strategies that facilitate women’s access to information on the differentiating features and outcomes for all models of care should be prioritized to better ensure equitable and quality decisions.

An evaluation of crowd counting methods, features and regression models

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression

Modeling and control of a CLC resonant dual active bridge

This paper proposes a linear large signal state-space model for a phase controlled CLC (Capacitor Inductor Capacitor) Resonant Dual Active Bridge (RDAB). The proposed model is useful for fast simulation and for the estimation of state variables under large signal variation. The model is also useful for control design because the slow changing dynamics of the dq variables are relatively easy to control. Simulation results of the proposed model are presented and compared to the simulated circuit model to demonstrate the proposed model's accuracy. This proposed model was used for the design of a Proportional-Integral (PI) controller and it has been implemented in the circuit simulation to show the proposed models usefulness in control design.

Negotiating indeterminacy in studio teaching: Using the zones of material and immaterial play to shape pedagogies and practices in art

The processes of studio-based teaching in visual art are often still tied to traditional models of discrete disciplines and largely immersed in skill-based learning. These approaches to training artists are also tied to an individual model of art practice that is clearly defined by the boundaries of those disciplines. This paper will explain how the open studio program at QUT can be broadly understood as an action research model of learning that ‘plays’ with the post-medium, post-studio genealogies and zones of contemporary art. This emphasises developing conceptual, contextual and formal skills as essential for engaging with and practicing in the often-indeterminate spatio-temporal sites of studio teaching. It will explore how this approach looks to Sutton-Smith’s observations on the role of play and Vygotsky’s zone of proximal development in early childhood learning as a way to develop strategies for promoting creative learning environments that are collaborative and self sustainable. Social, cultural, political and philosophical dialogues are examined as they relate to art practice with the aim of forming the shared interests, aims, and ambitions of graduating students into self initiated collectives or ARIs.

Murine, but not human, ephrin-B2 can be efficiently cleaved by the serine protease kallikrein-4: Implications for xenograft models of human prostate cancer

Background Ephrin-B2 is the sole physiologically-relevant ligand of the receptor tyrosine kinase EphB4, which is over-expressed in many epithelial cancers, including 66% of prostate cancers, and contributes to cancer cell survival, invasion and migration. Crucially, however, the cancer-promoting EphB4 signalling pathways are independent of interaction with its ligand ephrin-B2, as activation of ligand-dependent signalling causes tumour suppression. Ephrin-B2, however, is often found on the surface of endothelial cells of the tumour vasculature, where it can regulate angiogenesis to support tumour growth. Proteolytic cleavage of endothelial cell ephrin-B2 has previously been suggested as one mechanism whereby the interaction between tumour cell-expressed EphB4 and endothelial cell ephrin-B2 is regulated to support both cancer promotion and angiogenesis. Methods An in silico approach was used to search accessible surfaces of 3D protein models for cleavage sites for the key prostate cancer serine protease, KLK4, and this identified murine ephrin-B2 as a potential KLK4 substrate. Mouse ephrin-B2 was then confirmed as a KLK4 substrate by in vitro incubation of recombinant mouse ephrin-B2 with active recombinant human KLK4. Cleavage products were visualised by SDS-PAGE, silver staining and Western blot and confirmed by N-terminal sequencing. Results At low molar ratios, KLK4 cleaved murine ephrin-B2 but other prostate-specific KLK family members (KLK2 and KLK3/PSA) were less efficient, suggesting cleavage was KLK4-selective. The primary KLK4 cleavage site in murine ephrin-B2 was verified and shown to correspond to one of the in silico predicted sites between extracellular domain residues arginine 178 and asparagine 179. Surprisingly, the highly homologous human ephrin-B2 was poorly cleaved by KLK4 at these low molar ratios, likely due to the 3 amino acid differences at this primary cleavage site. Conclusion These data suggest that in in vivo mouse xenograft models, endogenous mouse ephrin-B2, but not human tumour ephrin-B2, may be a downstream target of cancer cell secreted human KLK4. This is a critical consideration when interpreting data from murine explants of human EphB4+/KLK4+ cancer cells, such as prostate cancer cells, where differential effects may be seen in mouse models as opposed to human clinical situations.

Active learning: A step towards automating medical concept extraction

Objective This paper presents an automatic active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort, and (2) the robustness of incremental active learning framework across different selection criteria and datasets is determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional Random Fields as the supervised method, and least confidence and information density as two selection criteria for active learning framework were used. The effect of incremental learning vs. standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. Two clinical datasets were used for evaluation: the i2b2/VA 2010 NLP challenge and the ShARe/CLEF 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared to the Random sampling baseline, the saving is at least doubled. Discussion Incremental active learning guarantees robustness across all selection criteria and datasets. The reduction of annotation effort is always above random sampling and longest sequence baselines. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models, while significantly reducing the burden of manual annotation.

External knowledge and query strategies in active learning: A study in clinical information extraction

This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that 1) amongst existing query strategies, the ones based on the classification model’s confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and 2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.

Quantitative genetic modeling of lateral ventricular shape and volume using multi-atlas fluid image alignment in twins

Despite substantial progress in measuring the 3D profile of anatomical variations in the human brain, their genetic and environmental causes remain enigmatic. We developed an automated system to identify and map genetic and environmental effects on brain structure in large brain MRI databases . We applied our multi-template segmentation approach ("Multi-Atlas Fluid Image Alignment") to fluidly propagate hand-labeled parameterized surface meshes into 116 scans of twins (60 identical, 56 fraternal), labeling the lateral ventricles. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps revealed 3D heritability patterns, and their significance, with and without adjustments for global brain scale. These maps visualized detailed profiles of environmental versus genetic influences on the brain, extending genetic models to spatially detailed, automatically computed, 3D maps.

Automated 3D mapping & shape analysis of the lateral ventricles via fluid registration of multiple surface-based atlases

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a manually labeled set of 9 subjects' MRIs (atlases). We fluidly registered each of these atlases and mesh models to a set of MRIs from 12 Alzheimer's disease (AD) patients and 14 matched healthy elderly subjects, and we averaged the resulting meshes for each of these images. Validation experiments on expert segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-related alterations monotonically improved as the number of atlases, N, was increased from 1 to 9. We then combined the segmentations with a radial mapping approach to localize ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer's patients were only detected with the multi-atlas segmentation, which clearly outperformed the standard single-atlas approach.