Reductions in movement-associated fear are dependent upon graded exposure in chronic low back pain: An exploratory analysis of a modified 3-item fear hierarchy

Objective: To explore the effectiveness of a modified fear hierarchy on measuring improvements in movement-associated fear in chronic low back pain. Methods: A modified 3-item fear hierarchy was created and implemented based on principles of graded exposure. This study was an exploratory analysis of the modified 3-item fear hierarchy from a larger clinical trial data set. Both groups received pain education and exercise, either bodyweight or strength training. Both groups performed item one on the hierarchy, the squat. Only the strength training group performed item 2, the deadlift. Neither group performed item 3, the overhead press. Analysis of Covariance and stepwise linear regression were used to explore results. Results: Improvement in movement-associated fear was conditional upon graded exposure. Both groups improved in the squat movement (p ≤ 0.05), which both performed. Only the strength training group improved in the deadlift (p ≤ 0.01), and neither improved in the overhead press (p ≥ 0.05). Conclusion: Reductions in movement-associated fear are conditional upon graded exposure, based on the use of a novel modified 3-item fear hierarchy. Further research is needed to understand the utility of this tool in a patient-led approach to co-designing a graded exposure-based intervention

Visualizing Natural Image Statistics

Natural image statistics is an important area of research in cognitive sciences and computer vision. Visualization of statistical results can help identify clusters and anomalies as well as analyze deviation, distribution and correlation. Furthermore, they can provide visual abstractions and symbolism for categorized data. In this paper, we begin our study of visualization of image statistics by considering visual representations of power spectra, which are commonly used to visualize different categories of images. We show that they convey a limited amount of statistical information about image categories and their support for analytical tasks is ineffective. We then introduce several new visual representations, which convey different or more information about image statistics. We apply ANOVA to the image statistics to help select statistically more meaningful measurements in our design process. A task-based user evaluation was carried out to compare the new visual representations with the conventional power spectra plots. Based on the results of the evaluation, we made further improvement of visualizations by introducing composite visual representations of image statistics

Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae

Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.Comment: 10 pages, SBP-BRiMS 201

Quantum simulation of the wavefunction to probe frustrated Heisenberg spin systems

Quantum simulators are controllable quantum systems that can reproduce the dynamics of the system of interest, which are unfeasible for classical computers. Recent developments in quantum technology enable the precise control of individual quantum particles as required for studying complex quantum systems. Particularly, quantum simulators capable of simulating frustrated Heisenberg spin systems provide platforms for understanding exotic matter such as high-temperature superconductors. Here we report the analog quantum simulation of the ground-state wavefunction to probe arbitrary Heisenberg-type interactions among four spin-1/2 particles . Depending on the interaction strength, frustration within the system emerges such that the ground state evolves from a localized to a resonating valence-bond state. This spin-1/2 tetramer is created using the polarization states of four photons. The single-particle addressability and tunable measurement-induced interactions provide us insights into entanglement dynamics among individual particles. We directly extract ground-state energies and pair-wise quantum correlations to observe the monogamy of entanglement

Could rebel child soldiers prolong civil wars?

While we know why rebels may recruit children for their cause, our understanding of the consequences of child soldiering by non-state armed groups remains limited. The following research contributes to addressing this by examining how rebels? child recruitment practice affects the duration of internal armed conflicts. We advance the argument that child soldiering increases the strength of rebel organizations vis-�-vis the government. This, in turn, lowers the capability asymmetry between these nonstate actors and the incumbent, allowing the former to sustain in dispute. Ultimately, the duration of armed conflicts is likely to be prolonged. We analyze this relationship with quantitative data on child soldier recruitment by rebel groups in the post-1989 period. The results confirm our main hypothesis: disputes are substantially longer when rebels recruit children. This work has important implications for the study of armed conflicts, conflict duration, and our understanding of child soldiering

Stretching the Rules: Monocentric Chromosomes with Multiple Centromere Domains

The centromere is a functional chromosome domain that is essential for faithful chromosome segregation during cell division and that can be reliably identified by the presence of the centromere-specific histone H3 variant CenH3. In monocentric chromosomes, the centromere is characterized by a single CenH3-containing region within a morphologically distinct primary constriction. This region usually spans up to a few Mbp composed mainly of centromere-specific satellite DNA common to all chromosomes of a given species. In holocentric chromosomes, there is no primary constriction; the centromere is composed of many CenH3 loci distributed along the entire length of a chromosome. Using correlative fluorescence light microscopy and high-resolution electron microscopy, we show that pea (Pisum sativum) chromosomes exhibit remarkably long primary constrictions that contain 3-5 explicit CenH3-containing regions, a novelty in centromere organization. In addition, we estimate that the size of the chromosome segment delimited by two outermost domains varies between 69 Mbp and 107 Mbp, several factors larger than any known centromere length. These domains are almost entirely composed of repetitive DNA sequences belonging to 13 distinct families of satellite DNA and one family of centromeric retrotransposons, all of which are unevenly distributed among pea chromosomes. We present the centromeres of Pisum as novel ``meta-polycentric'' functional domains. Our results demonstrate that the organization and DNA composition of functional centromere domains can be far more complex than previously thought, do not require single repetitive elements, and do not require single centromere domains in order to segregate properly. Based on these findings, we propose Pisum as a useful model for investigation of centromere architecture and the still poorly understood role of repetitive DNA in centromere evolution, determination, and function

The pseudogap: friend or foe of high Tc?

Although nineteen years have passed since the discovery of high temperature superconductivity, there is still no consensus on its physical origin. This is in large part because of a lack of understanding of the state of matter out of which the superconductivity arises. In optimally and underdoped materials, this state exhibits a pseudogap at temperatures large compared to the superconducting transition temperature. Although discovered only three years after the pioneering work of Bednorz and Muller, the physical origin of this pseudogap behavior and whether it constitutes a distinct phase of matter is still shrouded in mystery. In the summer of 2004, a band of physicists gathered for five weeks at the Aspen Center for Physics to discuss the pseudogap. In this perspective, we would like to summarize some of the results presented there and discuss its importance in the context of strongly correlated electron systems.Comment: expanded version, 20 pages, 11 figures, to be published, Advances in Physic

Predicting protein functions by relaxation labelling protein interaction network

<p>Abstract</p> <p>Background</p> <p>One of key issues in the post-genomic era is to assign functions to uncharacterized proteins. Since proteins seldom act alone; rather, they must interact with other biomolecular units to execute their functions. Thus, the functions of unknown proteins may be discovered through studying their interactions with proteins having known functions. Although many approaches have been developed for this purpose, one of main limitations in most of these methods is that the dependence among functional terms has not been taken into account.</p> <p>Results</p> <p>We developed a new network-based protein function prediction method which combines the likelihood scores of local classifiers with a relaxation labelling technique. The framework can incorporate the inter-relationship among functional labels into the function prediction procedure and allow us to efficiently discover relevant non-local dependence. We evaluated the performance of the new method with one other representative network-based function prediction method using E. coli protein functional association networks.</p> <p>Conclusion</p> <p>Our results showed that the new method has better prediction performance than the previous method. The better predictive power of our method gives new insights about the importance of the dependence between functional terms in protein functional prediction.</p