Multipolarity of quasicontinuum γ-rays from collective high-spin states in 152Dy

Measured internal conversion coefficients for quasicontinuum transitions in 152Dy in the spin range of 30–50 establish their predominantly stretched E2 character. Those transitions are attributed to triaxial bands near the yrast line as calculated in terms of the cranking approximation using the Woods-Saxon potential

The significance of the sense of coherence for various coping resources in stress situations used by police officers in on-the-beat service

Background: Police officers meet many stressors as part of their occupation. The psychological resource "sense of coherence" (SOC) protects against ill-health, but its impact on coping resources for stress situations has not been studied in the population of police officers. Different approaches to investigate the significance of SOC for different outcomes have been identified in literature, leading to some difficulties in the interpretation and generalization of results. The aim was therefore to explore SOC and the coping resources, and to examine the significance of SOC for various coping resources for stress using different models in a sample of Swedish police officers providing on-the-beat service. Materials and Methods: One hundred and one police officers (age: mean = 33 years, SD = 8; 29 females) were included, and the Orientation to Life Questionnaire (SOC-29) and the Coping Resources Inventory (CRI) were used. The dependent variable in each regression analysis was one of the coping resources: cognitive, social, emotional, spiritual/philosophical, physical, and a global resource. Global SOC-29 and/or its components (comprehensibility, manageability, and meaningfulness) were investigated as independent variables. Results: All CRI and SOC-29 scores except for that of spiritual/philosophical resources were higher than those of reference groups. Manageability was the most important component of SOC for various coping resources in stress situations used by police officers. Conclusion: A deeper study of manageability will give useful information, because this component of SOC is particularly significant in the variation in resources used by police officers to cope with stress. Salutogenesis, the origin of well-being, should be more in focus of future research on workplaces with a high level of occupational stress

Grammatical evolution decision trees for detecting gene-gene interactions

<p>Abstract</p> <p>Background</p> <p>A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing.</p> <p>Methods</p> <p>Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interacting effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. In the current study, we introduce the Grammatical Evolution Decision Trees (GEDT) method and software and evaluate this approach on simulated data representing gene-gene interaction models of a range of effect sizes. We compare the performance of the method to a traditional decision tree algorithm and a random search approach and demonstrate the improved performance of the method to detect purely epistatic interactions.</p> <p>Results</p> <p>The results of our simulations demonstrate that GEDT has high power to detect even very moderate genetic risk models. GEDT has high power to detect interactions with and without main effects.</p> <p>Conclusions</p> <p>GEDT, while still in its initial stages of development, is a promising new approach for identifying gene-gene interactions in genetic association studies.</p

Phase I study of bortezomib and cetuximab in patients with solid tumours expressing epidermal growth factor receptor

Bortezomib inhibits nuclear factor-κB (NF-κB). Cetuximab is a chimeric mouse–human antibody targeted against epidermal growth factor receptor (EGFR). We hypothesised that concomitant blockade of NF-κB and EGFR signalling would overcome EGFR-mediated resistance to single-agent bortezomib and induce apoptosis through two molecular pathways. The aim of this phase I trial was to establish the maximum tolerated dose (MTD) for bortezomib plus cetuximab in patients with EGFR-expressing epithelial tumours. The 21-day treatment cycle consisted of bortezomib administered on days 1 and 8 through dose escalation (1.3–2 mg m−2). Cetuximab was delivered at a dose of 250 mg m−2 on days 1, 8 and 15 (400 mg m−2 day 1 cycle 1). A total of 37 patients were enroled and given a total 91 cycles. No grade ⩾3 haematological toxicity was noted. Non-hematological grade ⩾3 toxicities included fatigue (22% of patients), dyspnoea (16%) and infection (11%). The MTD was not reached at the highest tested bortezomib dose (2.0 mg m−2). Efficacy outcomes included disease progression in 21 patients (56.7%) and stable disease (SD) at 6 weeks in 16 patients (43.3%). Five of the six patients with SD at 12 weeks were diagnosed with cancers of the lungs or head and neck. This combination therapy was moderately effective in extensively pretreated patients with non-small cell lung or head and neck cancers and warrants further investigation

The Impact of Phenocopy on the Genetic Analysis of Complex Traits

A consistent debate is ongoing on genome-wide association studies (GWAs). A key point is the capability to identify low-penetrance variations across the human genome. Among the phenomena reducing the power of these analyses, phenocopy level (PE) hampers very seriously the investigation of complex diseases, as well known in neurological disorders, cancer, and likely of primary importance in human ageing. PE seems to be the norm, rather than the exception, especially when considering the role of epigenetics and environmental factors towards phenotype. Despite some attempts, no recognized solution has been proposed, particularly to estimate the effects of phenocopies on the study planning or its analysis design. We present a simulation, where we attempt to define more precisely how phenocopy impacts on different analytical methods under different scenarios. With our approach the critical role of phenocopy emerges, and the more the PE level increases the more the initial difficulty in detecting gene-gene interactions is amplified. In particular, our results show that strong main effects are not hampered by the presence of an increasing amount of phenocopy in the study sample, despite progressively reducing the significance of the association, if the study is sufficiently powered. On the opposite, when purely epistatic effects are simulated, the capability of identifying the association depends on several parameters, such as the strength of the interaction between the polymorphic variants, the penetrance of the polymorphism and the alleles (minor or major) which produce the combined effect and their frequency in the population. We conclude that the neglect of the possible presence of phenocopies in complex traits heavily affects the analysis of their genetic data

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

<p>Abstract</p> <p>Background</p> <p>Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability.</p> <p>Methods</p> <p>Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications <it>in silico </it>using simulated datasets.</p> <p>Results</p> <p>We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage.</p> <p>Conclusions</p> <p>We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait.</p

Brf1 loss and not overexpression disrupts tissues homeostasis in the intestine, liver and pancreas

RNA polymerase III (Pol-III) transcribes tRNAs and other small RNAs essential for protein synthesis and cell growth. Pol-III is deregulated during carcinogenesis; however, its role in vivo has not been studied. To address this issue, we manipulated levels of Brf1, a Pol-III transcription factor that is essential for recruitment of Pol-III holoenzyme at tRNA genes in vivo. Knockout of Brf1 led to embryonic lethality at blastocyst stage. In contrast, heterozygous Brf1 mice were viable, fertile and of a normal size. Conditional deletion of Brf1 in gastrointestinal epithelial tissues, intestine, liver and pancreas, was incompatible with organ homeostasis. Deletion of Brf1 in adult intestine and liver induced apoptosis. However, Brf1 heterozygosity neither had gross effects in these epithelia nor did it modify tumorigenesis in the intestine or pancreas. Overexpression of BRF1 rescued the phenotypes of Brf1 deletion in intestine and liver but was unable to initiate tumorigenesis. Thus, Brf1 and Pol-III activity are absolutely essential for normal homeostasis during development and in adult epithelia. However, Brf1 overexpression or heterozygosity are unable to modify tumorigenesis, suggesting a permissive, but not driving role for Brf1 in the development of epithelial cancers of the pancreas and gut

Calcium control of triphasic hippocampal STDP

Bush D, Jin Y. Calcium control of triphasic hippocampal STDP. Journal of Computational Neuroscience. 2012;33(3):495-514.Synaptic plasticity is believed to represent the neural correlate of mammalian learning and memory function. It has been demonstrated that changes in synaptic conductance can be induced by approximately synchronous pairings of pre- and post- synaptic action potentials delivered at low frequencies. It has also been established that NMDAr-dependent calcium influx into dendritic spines represents a critical signal for plasticity induction, and can account for this spike-timing dependent plasticity (STDP) as well as experimental data obtained using other stimulation protocols. However, subsequent empirical studies have delineated a more complex relationship between spike-timing, firing rate, stimulus duration and post-synaptic bursting in dictating changes in the conductance of hippocampal excitatory synapses. Here, we present a detailed biophysical model of single dendritic spines on a CA1 pyramidal neuron, describe the NMDAr-dependent calcium influx generated by different stimulation protocols, and construct a parsimonious model of calcium driven kinase and phosphatase dynamics that dictate the probability of stochastic transitions between binary synaptic weight states in a Markov model. We subsequently demonstrate that this approach can account for a range of empirical observations regarding the dynamics of synaptic plasticity induced by different stimulation protocols, under regimes of pharmacological blockade and metaplasticity. Finally, we highlight the strengths and weaknesses of this parsimonious, unified computational synaptic plasticity model, discuss differences between the properties of cortical and hippocampal plasticity highlighted by the experimental literature, and the manner in which further empirical and theoretical research might elucidate the cellular basis of mammalian learning and memory function