Extending feature usage: a study of the post-adoption of electronic medical records

Technology Acceptance Model (TAM) posits that Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) influence the ‘intention to use’. The Post-Acceptance Model (PAM) posits that continued use is influenced by prior experience. In order to study the factors that influence how professionals use complex systems, we create a tentative research model that builds on PAM and TAM. Specifically we include PEOU and the construct ‘Professional Association Guidance’. We postulate that feature usage is enhanced when professional associations influence PU by highlighting additional benefits. We explore the theory in the context of post-adoption use of Electronic Medical Records (EMRs) by primary care physicians in Ontario. The methodology can be extended to other professional environments and we suggest directions for future research.

Parkinsonian tremor identification with multiple local field potential feature classification

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

Direct evidence for attention-dependent influences of the frontal eye-fields on feature-responsive visual cortex

Voluntary selective attention can prioritize different features in a visual scene. The frontal eye-fields (FEF) are one potential source of such feature-specific top-down signals, but causal evidence for influences on visual cortex (as was shown for "spatial" attention) has remained elusive. Here, we show that transcranial magnetic stimulation (TMS) applied to right FEF increased the blood oxygen level-dependent (BOLD) signals in visual areas processing "target feature" but not in "distracter feature"-processing regions. TMS-induced BOLD signals increase in motion-responsive visual cortex (MT+) when motion was attended in a display with moving dots superimposed on face stimuli, but in face-responsive fusiform area (FFA) when faces were attended to. These TMS effects on BOLD signal in both regions were negatively related to performance (on the motion task), supporting the behavioral relevance of this pathway. Our findings provide new causal evidence for the human FEF in the control of nonspatial "feature"-based attention, mediated by dynamic influences on feature-specific visual cortex that vary with the currently attended property.

Content validation for level of use of feature rich systems: a Delphi study of electronic medical records systems

Introduction. Feature usage is a pre-requisite to realising the benefits of investments in feature rich systems. We propose that conceptualising the dependent variable 'system use' as 'level of use' and specifying it as a formative construct has greater value for measuring the post-adoption use of feature rich systems. We then validate the content of the construct as a first step in developing a research instrument to measure it. The context of our study is the post-adoption use of electronic medical records (EMR) by primary care physicians. Method. Initially, a literature review of the empirical context defines the scope based on prior studies. Having identified core features from the literature, they are further refined with the help of experts in a consensus seeking process that follows the Delphi technique. Results.The methodology was successfully applied to EMRs, which were selected as an example of feature rich systems. A review of EMR usage and regulatory standards provided the feature input for the first round of the Delphi process. A panel of experts then reached consensus after four rounds, identifying ten task-based features that would be indicators of level of use. Conclusions. To study why some users deploy more advanced features than others, theories of post-adoption require a rich formative dependent variable that measures level of use. We have demonstrated that a context sensitive literature review followed by refinement through a consensus seeking process is a suitable methodology to validate the content of this dependent variable. This is the first step of instrument development prior to statistical confirmation with a larger sample.

Objective determination of feature resolution in two sea surface temperature analyses

Considerable effort is presently being devoted to producing high-resolution sea surface temperature (SST) analyses with a goal of spatial grid resolutions as low as 1 km. Because grid resolution is not the same as feature resolution, a method is needed to objectively determine the resolution capability and accuracy of SST analysis products. Ocean model SST fields are used in this study as simulated “true” SST data and subsampled based on actual infrared and microwave satellite data coverage. The subsampled data are used to simulate sampling errors due to missing data. Two different SST analyses are considered and run using both the full and the subsampled model SST fields, with and without additional noise. The results are compared as a function of spatial scales of variability using wavenumber auto- and cross-spectral analysis. The spectral variance at high wavenumbers (smallest wavelengths) is shown to be attenuated relative to the true SST because of smoothing that is inherent to both analysis procedures. Comparisons of the two analyses (both having grid sizes of roughly ) show important differences. One analysis tends to reproduce small-scale features more accurately when the high-resolution data coverage is good but produces more spurious small-scale noise when the high-resolution data coverage is poor. Analysis procedures can thus generate small-scale features with and without data, but the small-scale features in an SST analysis may be just noise when high-resolution data are sparse. Users must therefore be skeptical of high-resolution SST products, especially in regions where high-resolution (~5 km) infrared satellite data are limited because of cloud cover.

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Fractal-to-Euclidean crossover of the isotropy restoration feature in a family of fractal resistor networks

Fractal with microscopic anisotropy shows a unique type of macroscopic isotropy restoration phenomenon that is absent in Euclidean space [M. T. Barlow et al., Phys. Rev. Lett. 75, 3042]. In this paper the isotropy restoration feature is considered for a family of two-dimensional Sierpinski gasket type fractal resistor networks. A parameter xi is introduced to describe this phenomenon. Our numerical results show that xi satisfies the scaling law xi similar to l(-alpha), where l is the system size and alpha is an exponent independent of the degree of microscopic anisotropy, characterizing the isotropy restoration feature of the fractal systems. By changing the underlying fractal structure towards the Euclidean triangular lattice through increasing the side length b of the gasket generators, the fractal-to-Euclidean crossover behavior of the isotropy restoration feature is discussed.

Meta-morphs: – being a mediator of the ‘thin’ place and ‘thin’ experience in the HE classroom. A paper on the experience of creating transformations in HE teaching and learning

The research which underpins this paper began as a doctoral project exploring archaic beliefs concerning Otherworlds and Thin Places in two particular landscapes - the West Coast of Wales and the West Coast of Ireland. A Thin Place is an ancient Celtic Christian term used to describe a marginal, liminal realm, beyond everyday human experience and perception, where mortals could pass into the Otherworld more readily, or make contact with those in the Otherworld more willingly. To encounter a Thin Place in ancient folklore was significant because it engendered a state of alertness, an awakening to what the theologian John O’ Donohue (2004: 49) called “the primal affection.” These complex notions and terms will be further explored in this paper in relation to Education. Thin Teaching is a pedagogical approach which offers students the space to ruminate on the possibility that their existence can be more and can mean more than the categories they believed they belonged to or felt they should inhabit. Central to the argument then, is that certain places and their inhabitants can become revitalised by sensitively considered teaching methodologies. This raises interesting questions about the role spirituality plays in teaching practice as a tool for healing in the twenty first century.

Training and the commitment of outsourced information technologies' workers: psychological contract fulfillment as a mediator

Outsourced workers in information technologies (IT) generally have high skills and a high value on the job market. Their IT outsourcing organizations are likely to provide them with training, in the first place for skill development, but perhaps also as a way to bind the workers to them. This can be understood along the role of the psychological contract. Outsourced IT workers may see training as a fulfillment of their psychological contract. Accordingly, we hypothesize that psychological contract fulfillment mediates the relationship between training and affective commitment to the IT outsourcer. This was tested in a sample of 158 Portuguese outsourced IT workers. The results showed that employees who considered that they were receiving good training opportunities felt a greater affective commitment to their IT outsourcers. This relationship was mediated by the fulfillment of the relational psychological contract.

Cognitive emotion regulation as a mediator between posttraumatic stress symptoms and hypomanic personality within a non-clinical population

Background: There is a high prevalence of traumatic life events within individuals diagnosed with bipolar disorder. However, currently there is limited theoretical understanding of this relationship. Aims: To explore whether non-clinical symptoms of posttraumatic stress have a direct effect on the non-clinical symptoms of bipolar disorder, or whether this relationship is mediated by cognitive emotion regulation strategies. Method: A cross-sectional design within non-clinical participants completing an online survey including the Impact of Events Scale, Cognitive Emotion Regulation Questionnaire and the Hypomanic Personality Scale. Results: Posttraumatic stress symptoms were associated with hypomanic personality. Intrusive memories contributed a small but significant proportion of the variance between these two measures. Rumination of negative emotions mediated the relationship between posttraumatic stress and hypomanic personality. Conclusions: The relationship between traumatic events and an increased prevalence of bipolar disorder remains poorly understood. Further research should explore rumination as a potential target for treatment within those suffering from both posttraumatic stress and bipolar disorder.

Myostatin is a key mediator between energy metabolism and endurance capacity of skeletal muscle

Myostatin (Mstn) participates in the regulation of skeletal muscle size and has emerged as a regulator of muscle metabolism. Here, we hypothesized that lack of myostatin profoundly depresses oxidative phosphorylation-dependent muscle function. Toward this end, we explored Mstn/ mice as a model for the constitutive absence of myostatin and AAV-mediated overexpression of myostatin propeptide as a model of myostatin blockade in adult wild-type mice. We show that muscles from Mstn/ mice, although larger and stronger, fatigue extremely rapidly. Myostatin deficiency shifts muscle from aerobic toward anaerobic energy metabolism, as evidenced by decreased mitochondrial respiration, reduced expression of PPAR transcriptional regulators, increased enolase activity, and exercise-induced lactic acidosis. As a consequence, constitutively reduced myostatin signaling diminishes exercise capacity, while the hypermuscular state of Mstn/ mice increases oxygen consumption and the energy cost of running. We wondered whether these results are the mere consequence of the congenital fiber-type switch toward a glycolytic phenotype of constitutive Mstn/ mice. Hence, we overexpressed myostatin propeptide in adult mice, which did not affect fiber-type distribution, while nonetheless causing increased muscle fatigability, diminished exercise capacity, and decreased Pparb/d and Pgc1a expression. In conclusion, our results suggest that myostatin endows skeletal muscle with high oxidative capacity and low fatigability, thus regulating the delicate balance between muscle mass, muscle force, energy metabolism, and endurance capacity.