Prevalence and characteristics of Australian women who use prayer or spiritual healing: A nationally representative cross-sectional study

© 2016 Elsevier Ltd. Objectives: To determine the prevalence and characteristics of users of prayer or spiritual healing among Australian women aged 31-36 years. Design and setting: This cross-sectional study was conducted as a part of the Australian Longitudinal Study on Women's Health (ALSWH). The sample used in the current sub-study were participants from the 'young' cohort (1973-78) (n = 8180) aged between 31 and 36 years. Main outcome measure: Use of prayer or spiritual healing. Results: Prayer or spiritual healing was used on a regular basis by 20% of women aged between 31 and 36 years in 2009. Women who had symptoms of chronic illnesses (p = 0.001), women who had never smoked (p = 0.001) and women who used other forms of CAM (p < 0.001) were significantly more likely to use prayer or spiritual healing. Conclusion: A signifibasis. Further research is required to better understand their rationale for using prayer or spiritual healing and its perceived impact on health related outcomes and general well-being.cant proportion of women use prayer or spiritual healing on a regular basis. Further research is required to better understand their rationale for using prayer or spiritual healing and its perceived impact on health related outcomes and general well-being

Should red cell transfusion be individualized? Yes

SCOPUS: ed.jinfo:eu-repo/semantics/publishe

Bridging Time Scales in Cellular Decision Making with a Stochastic Bistable Switch

Cellular transformations which involve a significant phenotypical change of the cell's state use bistable biochemical switches as underlying decision systems. In this work, we aim at linking cellular decisions taking place on a time scale of years to decades with the biochemical dynamics in signal transduction and gene regulation, occuring on a time scale of minutes to hours. We show that a stochastic bistable switch forms a viable biochemical mechanism to implement decision processes on long time scales. As a case study, the mechanism is applied to model the initiation of follicle growth in mammalian ovaries, where the physiological time scale of follicle pool depletion is on the order of the organism's lifespan. We construct a simple mathematical model for this process based on experimental evidence for the involved genetic mechanisms. Despite the underlying stochasticity, the proposed mechanism turns out to yield reliable behavior in large populations of cells subject to the considered decision process. Our model explains how the physiological time constant may emerge from the intrinsic stochasticity of the underlying gene regulatory network. Apart from ovarian follicles, the proposed mechanism may also be of relevance for other physiological systems where cells take binary decisions over a long time scale.Comment: 14 pages, 4 figure

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

An in vitro method to select malignant cells from surgical biopsies of breast cancer patients

To date, breast cancer (BC) research is mainly studied with cell lines. These cells were passaged multiple times, acquiring phenotypes, additional mutations and epigenetic changes. These changes make the passaged cell lines different from the original malignancy. Thus cell lines, although useful as models could be improved with additional studies with primary BC. It is difficult to obtain malignant cells from breast tissues without contamination from surrounding healthy cells. Selection and expansion of malignant cells from surgical tissues have proved to be daunting tasks. This study describes a reliable and reproducible method for isolating and expanding malignant cells from surgical breast tissues. The method uses co-cultures with BM stroma to select for the cancer cells while the healthy cells undergo rapid cell death. Studies are described to show the cloning efficiencies and sensitivity of the method using surgical samples of varying sizes, different stages of BC, and samples from needle biopsies

Body segment parameters of Paralympic athletes from dual-energy X-ray absorptiometry

The final publication is available at Springer via http://dx.doi.org/10.1007/s12283-016-0200-3This research represents the first documented investigation into the body segment parameters of Paralympic athletes (e.g., individuals with spinal cord injuries and lower extremity amputations). Two-dimensional body segment parameters (i.e., mass, length, position vector of the center of mass, and principal mass moment of inertia about the center of mass) were quantified from dual-energy X-ray absorptiometry (DXA). In addition to establishing a body segment parameter database of Paralympic athletes for prospective biomechanists and engineers, the mass of each body segment as experimentally measured via the DXA imaging was compared with that reported by previous research of able-bodied cadavers. In general, there were significant differences in the body segment masses between the different methods. These findings support the implementation of the proposed database for developing valid multibody biomechanical models of Paralympic athletes with distinct physical disabilities.This research was funded by Dr. John McPhee’s Tier I Canada Research Chair in Biomechatronic System Dynamics

The interplay of intrinsic and extrinsic bounded noises in genetic networks

After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a genetic network. The influence of intrinsic and extrinsic noises on genetic networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: $(i)$ the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, $(ii)$ a model of enzymatic futile cycle and $(iii)$ a genetic toggle switch. In $(ii)$ and $(iii)$ we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possibile functional role of bounded noises