Performance analysis of interferometric noise due to unequally powered interferers in optical networks

Interferometric crosstalk has been identified as the cause of performance limits in future transparent all-optical networks. A large number of studies have been conducted on this phenomenon using a vast array of evaluation techniques. However, most major studies have considered that although the interfering terms may differ in number, the power contribution that they all make will be identical for all interfering terms. Although this situation is easy to analyze, it does not necessarily represent the situation that is likely to occur in a real network, which will be constructed of nodes with different degrees of connectivity, quite possibly from different vendors, and therefore with differing crosstalk characteristics. This paper describes a study on the impact of unequally powered interfering terms using a rigorous analysis technique. To validate the use of the chosen technique, the paper begins by bench-marking a number of common evaluation techniques against empirically derived, experimentally verified noise performance formulas

Virtuality in human supervisory control: Assessing the effects of psychological and social remoteness

Virtuality would seem to offer certain advantages for human supervisory control. First, it could provide a physical analogue of the 'real world' environment. Second, it does not require control room engineers to be in the same place as each other. In order to investigate these issues, a low-fidelity simulation of an energy distribution network was developed. The main aims of the research were to assess some of the psychological concerns associated with virtual environments. First, it may result in the social isolation of the people, and it may have dramatic effects upon the nature of the work. Second, a direct physical correspondence with the 'real world' may not best support human supervisory control activities. Experimental teams were asked to control an energy distribution network. Measures of team performance, group identity and core job characteristics were taken. In general terms, the results showed that teams working in the same location performed better than team who were remote from one another

Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β2-adrenergic receptor (β2AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β2AR. We show that the select ligands bind preferentially to different predicted conformers of β2AR, and identify a role of β2AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance. © 2012 Isin et al

Severity of self-reported diseases and symptoms in Denmark

OBJECTIVE: To estimate and rank the relative severity of self-reported diseases and symptoms in Denmark. METHOD: The 1994 Danish Health and Morbidity Survey collected data from 5,472 Danes older than 16 years of age. Interviews (response frequency: 79%) gave information on diseases and symptoms; a self-administered SF-36 questionnaire (response frequency: 64%) provided information on health-related quality of life. The severity of diseases and symptoms was represented by the health-related quality of life scores that individuals suffering from particular diseases and symptoms obtained on the single dimensions of the SF-36 and on a combined sum of all dimensions. We applied logistic regression to control for the influence of sex, age and socio-economic status on the SF-36 score. We also analysed the interaction between socio-economic status and diseases on the SF-36 score. RESULTS: Females, more frequently than males, reported on all symptoms and all disease groups except injuries. People with relatively low levels of education reported most diseases, especially musculoskeletal and cardiovascular diseases, more frequently than people with higher education. Age-adjusted mean SF-36 scores for all dimensions combined showed that the symptoms of melancholy/depression and breathing difficulties, psychiatric disorders and respiratory diseases scored lowest (i.e. were most often associated with worse health). Females had lower SF-36 combined scores (worse health) than males on all symptoms. We found interaction between socio-economic status and respiratory diseases and musculoskeletal diseases on the SF-36 score. SF-36 scores also indicated significantly worse health among Danes with low education and income levels compared to those with higher education and income. CONCLUSION: In 1994 the Danes most frequently reported musculoskeletal symptoms and diseases. Psychiatric disorders and respiratory diseases were identified as the most severe reported diseases. Due to the interaction between socio-economic status and some diseases, severity estimates should be interpreted with caution or stratified by socio-economic groups

Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordLOD 2019: Fifth International Conference on Machine Learning, Optimization, and Data Science, 10-13 September 2019, Siena, ItalyGaussian processes (GPs) belong to a class of probabilistic techniques that have been successfully used in different domains of machine learning and optimization. They are popular because they provide uncertainties in predictions, which sets them apart from other modelling methods providing only point predictions. The uncertainty is particularly useful for decision making as we can gauge how reliable a prediction is. One of the fundamental challenges in using GPs is that the efficacy of a model is conferred by selecting an appropriate kernel and the associated hyperparameter values for a given problem. Furthermore, the training of GPs, that is optimizing the hyperparameters using a data set is traditionally performed using a cost function that is a weighted sum of data fit and model complexity, and the underlying trade-off is completely ignored. Addressing these challenges and shortcomings, in this article, we propose the following automated training scheme. Firstly, we use a weighted product of multiple kernels with a view to relieve the users from choosing an appropriate kernel for the problem at hand without any domain specific knowledge. Secondly, for the first time, we modify GP training by using a multi-objective optimizer to tune the hyperparameters and weights of multiple kernels and extract an approximation of the complete trade-off front between data-fit and model complexity. We then propose to use a novel solution selection strategy based on mean standardized log loss (MSLL) to select a solution from the estimated trade-off front and finalise training of a GP model. The results on three data sets and comparison with the standard approach clearly show the potential benefit of the proposed approach of using multi-objective optimization with multiple kernels.Natural Environment Research Council (NERC

X Chromosome Inactivation and Differentiation Occur Readily in ES Cells Doubly-Deficient for MacroH2A1 and MacroH2A2

Macrohistones (mH2As) are unusual histone variants found exclusively in vertebrate chromatin. In mice, the H2afy gene encodes two splice variants, mH2A1.1 and mH2A1.2 and a second gene, H2afy2, encodes an additional mH2A2 protein. Both mH2A isoforms have been found enriched on the inactive X chromosome (Xi) in differentiated mammalian female cells, and are incorporated into the chromatin of developmentally-regulated genes. To investigate the functional significance of mH2A isoforms for X chromosome inactivation (XCI), we produced male and female embryonic stem cell (ESC) lines with stably-integrated shRNA constructs that simultaneously target both mH2A1 and mH2A2. Surprisingly, we find that female ESCs deficient for both mH2A1 and mH2A2 readily execute and maintain XCI upon differentiation. Furthermore, male and female mH2A-deficient ESCs proliferate normally under pluripotency culture conditions, and respond to several standard differentiation procedures efficiently. Our results show that XCI can readily proceed with substantially reduced total mH2A content

Multiple reassortment events in the evolutionary history of H1N1 influenza A virus since 1918

The H1N1 subtype of influenza A virus has caused substantial morbidity and mortality in humans, first documented in the global pandemic of 1918 and continuing to the present day. Despite this disease burden, the evolutionary history of the A/H1N1 virus is not well understood, particularly whether there is a virological basis for several notable epidemics of unusual severity in the 1940s and 1950s. Using a data set of 71 representative complete genome sequences sampled between 1918 and 2006, we show that segmental reassortment has played an important role in the genomic evolution of A/H1N1 since 1918. Specifically, we demonstrate that an A/H1N1 isolate from the 1947 epidemic acquired novel PB2 and HA genes through intra-subtype reassortment, which may explain the abrupt antigenic evolution of this virus. Similarly, the 1951 influenza epidemic may also have been associated with reassortant A/H1N1 viruses. Intra-subtype reassortment therefore appears to be a more important process in the evolution and epidemiology of H1N1 influenza A virus than previously realized