2,752 research outputs found
Turning to art as a positive way of living with cancer: A qualitative study of personal motives and contextual influences
Why do some women turn to creative art-making after a diagnosis of cancer? Eleven women provided qualitative accounts that were analyzed following guidelines for interpretative phenomenological analysis (IPA). Some described taking up artistic leisure activities initially in order to manage emotional distress. Others emphasized their need for positive well-being, taking up art to experience achievement and satisfaction, to regain a positive identity, and to normalize family dynamics in the context of living with cancer. Participants’ turn to art-making was facilitated by biographical and contextual factors, including pre-existing craft skills, long-standing personal values and coping philosophies, family role models for managing adversity, and the supportive encouragement of family and friends. Other research has acknowledged that positive lifestyle change and post-traumatic growth can occur after a cancer diagnosis, and this study reveals a multi-faceted process. The findings suggest a need for further research into the experiences that facilitate positive lifestyle change and subjective well-being among people who are living with cancer
Quantum fluctuations in high field magnetization of 2D square lattice J1-J2 antiferromagnets
The J1-J2 square lattice Heisenberg model with spin S=1/2 has three phases
with long-range magnetic order and two unconventionally ordered phases
depending on the ratio of exchange constants. It describes a number of recently
found layered vanadium oxide compounds. A simple means of investigating the
ground state is the study of the magnetization curve and high-field
susceptibility. We discuss these quantities by using the spin-wave theory and
the exact diagonalization in the whole J1-J2 plane. We compare both results and
find good overall agreement in the sectors of the phase diagram with magnetic
order. Close to the disordered regions the magnetization curve shows strong
deviations from the classical linear behaviour caused by large quantum
fluctuations and spin-wave approximation breaks down. On the FM side (J1<0)
where one approaches the quantum gapless spin nematic ground state this region
is surprisingly large. We find that inclusion of second order spin-wave
corrections does not lead to fundamental improvement. Quantum corrections to
the tilting angle of the ordered moments are also calculated. They may have
both signs, contrary to the always negative first order quantum corrections to
the magnetization. Finally we investigate the effect of the interlayer coupling
and find that the quasi-2D picture remains valid up to |J_\perp/J1| ~ 0.3.Comment: 13 pages, 6figure
Tuning of Human Modulation Filters Is Carrier-Frequency Dependent
Licensed under the Creative Commons Attribution License
Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle
<p>Abstract</p> <p>Background</p> <p>In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior.</p> <p>Results</p> <p>We introduce <it>PathwayOracle</it>, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates <it>PathwayOracle </it>from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, <it>PathwayOracle </it>includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model.</p> <p>Conclusion</p> <p><it>PathwayOracle </it>provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models. The application has been developed in Python and is designed to be easily extensible by groups interested in adding new or extending existing features. <it>PathwayOracle </it>is freely available for download and use.</p
Fast automatic quantitative cell replication with fluorescent live cell imaging
Hoffmann N, Keck M, Neuweger H, et al. Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets. BMC Bioinformatics. 2012;13(1): 21.Background
Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features.
Results
In this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CEMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CEMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum).
Conclusions
We have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CEMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available from http://maltcms.sf.net webcite. The evaluation scripts of the present study are available from the same source
Consequences of converting graded to action potentials upon neural information coding and energy efficiency
Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation
Robust estimation of microbial diversity in theory and in practice
Quantifying diversity is of central importance for the study of structure,
function and evolution of microbial communities. The estimation of microbial
diversity has received renewed attention with the advent of large-scale
metagenomic studies. Here, we consider what the diversity observed in a sample
tells us about the diversity of the community being sampled. First, we argue
that one cannot reliably estimate the absolute and relative number of microbial
species present in a community without making unsupported assumptions about
species abundance distributions. The reason for this is that sample data do not
contain information about the number of rare species in the tail of species
abundance distributions. We illustrate the difficulty in comparing species
richness estimates by applying Chao's estimator of species richness to a set of
in silico communities: they are ranked incorrectly in the presence of large
numbers of rare species. Next, we extend our analysis to a general family of
diversity metrics ("Hill diversities"), and construct lower and upper estimates
of diversity values consistent with the sample data. The theory generalizes
Chao's estimator, which we retrieve as the lower estimate of species richness.
We show that Shannon and Simpson diversity can be robustly estimated for the in
silico communities. We analyze nine metagenomic data sets from a wide range of
environments, and show that our findings are relevant for empirically-sampled
communities. Hence, we recommend the use of Shannon and Simpson diversity
rather than species richness in efforts to quantify and compare microbial
diversity.Comment: To be published in The ISME Journal. Main text: 16 pages, 5 figures.
Supplement: 16 pages, 4 figure
Effect of Biodiversity Changes in Disease Risk: Exploring Disease Emergence in a Plant-Virus System
The effect of biodiversity on the ability of parasites to infect their host and cause disease (i.e. disease risk) is a major question in pathology, which is central to understand the emergence of infectious diseases, and to develop strategies for their management. Two hypotheses, which can be considered as extremes of a continuum, relate biodiversity to disease risk: One states that biodiversity is positively correlated with disease risk (Amplification Effect), and the second predicts a negative correlation between biodiversity and disease risk (Dilution Effect). Which of them applies better to different host-parasite systems is still a source of debate, due to limited experimental or empirical data. This is especially the case for viral diseases of plants. To address this subject, we have monitored for three years the prevalence of several viruses, and virus-associated symptoms, in populations of wild pepper (chiltepin) under different levels of human management. For each population, we also measured the habitat species diversity, host plant genetic diversity and host plant density. Results indicate that disease and infection risk increased with the level of human management, which was associated with decreased species diversity and host genetic diversity, and with increased host plant density. Importantly, species diversity of the habitat was the primary predictor of disease risk for wild chiltepin populations. This changed in managed populations where host genetic diversity was the primary predictor. Host density was generally a poorer predictor of disease and infection risk. These results support the dilution effect hypothesis, and underline the relevance of different ecological factors in determining disease/infection risk in host plant populations under different levels of anthropic influence. These results are relevant for managing plant diseases and for establishing conservation policies for endangered plant species
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