Central and peripheral circadian clocks and their role in Alzheimer's disease

Molecular and cellular oscillations constitute an internal clock that tracks the time of day and permits organisms to optimize their behaviour and metabolism to suit the daily demands they face. The workings of this internal clock become impaired with age. In this review, we discuss whether such age-related impairments in the circadian clock interact with age-related neurodegenerative disorders, such as Alzheimer's disease. Findings from mouse and fly models of Alzheimer's disease have accelerated our understanding of the interaction between neurodegeneration and circadian biology. These models show that neurodegeneration likely impairs circadian rhythms either by damaging the central clock or by blocking its communication with other brain areas and with peripheral tissues. The consequent sleep and metabolic deficits could enhance the susceptibility of the brain to further degenerative processes. Thus, circadian dysfunction might be both a cause and an effect of neurodegeneration. We also discuss the primary role of light in the entrainment of the central clock and describe important, alternative time signals, such as food, that play a role in entraining central and peripheral circadian clocks. Finally, we propose how these recent insights could inform efforts to develop novel therapeutic approaches to re-entrain arrhythmic individuals with neurodegenerative disease

Combining polynomial chaos expansions and genetic algorithm for the coupling of electrophysiological models

The number of computational models in cardiac research has grown over the last decades. Every year new models with di erent assumptions appear in the literature dealing with di erences in interspecies cardiac properties. Generally, these new models update the physiological knowledge using new equations which reect better the molecular basis of process. New equations require the fi tting of parameters to previously known experimental data or even, in some cases, simulated data. This work studies and proposes a new method of parameter adjustment based on Polynomial Chaos and Genetic Algorithm to nd the best values for the parameters upon changes in the formulation of ionic channels. It minimizes the search space and the computational cost combining it with a Sensitivity Analysis. We use the analysis of di ferent models of L-type calcium channels to see that by reducing the number of parameters, the quality of the Genetic Algorithm dramatically improves. In addition, we test whether the use of the Polynomial Chaos Expansions improves the process of the Genetic Algorithm search. We conclude that it reduces the Genetic Algorithm execution in an order of 103 times in the case studied here, maintaining the quality of the results. We conclude that polynomial chaos expansions can improve and reduce the cost of parameter adjustment in the development of new models.Peer ReviewedPostprint (author's final draft

Dynamics of localization in a waveguide

This is a review of the dynamics of wave propagation through a disordered N-mode waveguide in the localized regime. The basic quantities considered are the Wigner-Smith and single-mode delay times, plus the time-dependent power spectrum of a reflected pulse. The long-time dynamics is dominated by resonant transmission over length scales much larger than the localization length. The corresponding distribution of the Wigner-Smith delay times is the Laguerre ensemble of random-matrix theory. In the power spectrum the resonances show up as a 1/t^2 tail after N^2 scattering times. In the distribution of single-mode delay times the resonances introduce a dynamic coherent backscattering effect, that provides a way to distinguish localization from absorption.Comment: 18 pages including 8 figures; minor correction

Determining the date of diagnosis – is it a simple matter? The impact of different approaches to dating diagnosis on estimates of delayed care for ovarian cancer in UK primary care

Background Studies of cancer incidence and early management will increasingly draw on routine electronic patient records. However, data may be incomplete or inaccurate. We developed a generalisable strategy for investigating presenting symptoms and delays in diagnosis using ovarian cancer as an example. Methods The General Practice Research Database was used to investigate the time between first report of symptom and diagnosis of 344 women diagnosed with ovarian cancer between 01/06/2002 and 31/05/2008. Effects of possible inaccuracies in dating of diagnosis on the frequencies and timing of the most commonly reported symptoms were investigated using four increasingly inclusive definitions of first diagnosis/suspicion: 1. "Definite diagnosis" 2. "Ambiguous diagnosis" 3. "First treatment or complication suggesting pre-existing diagnosis", 4 "First relevant test or referral". Results The most commonly coded symptoms before a definite diagnosis of ovarian cancer, were abdominal pain (41%), urogenital problems(25%), abdominal distension (24%), constipation/change in bowel habits (23%) with 70% of cases reporting at least one of these. The median time between first reporting each of these symptoms and diagnosis was 13, 21, 9.5 and 8.5 weeks respectively. 19% had a code for definitions 2 or 3 prior to definite diagnosis and 73% a code for 4. However, the proportion with symptoms and the delays were similar for all four definitions except 4, where the median delay was 8, 8, 3, 10 and 0 weeks respectively. Conclusion Symptoms recorded in the General Practice Research Database are similar to those reported in the literature, although their frequency is lower than in studies based on self-report. Generalisable strategies for exploring the impact of recording practice on date of diagnosis in electronic patient records are recommended, and studies which date diagnoses in GP records need to present sensitivity analyses based on investigation, referral and diagnosis data. Free text information may be essential in obtaining accurate estimates of incidence, and for accurate dating of diagnoses

Shelf life: Neritic habitat use of a turtle population highly threatened by fisheries

This is the final version. Available on open access from Wiley via the DOI in this recordAim: It is difficult to mitigate threats to marine vertebrates until their habitat use is understood. We report on a decade of satellite tracking loggerhead turtles (Caretta caretta) from an important nesting site to determine priority habitats for their protection in a region where they are known to be heavily impacted by fisheries. Location: Cyprus, Eastern Mediterranean. Method: We tracked 27 adult female loggerheads between 2001 and 2012 from North Cyprus nesting beaches. To eliminate potential biases, we included females nesting on all coasts of our study area, at different periods of the nesting season and from a range of size classes. Results: Foraging sites were distributed over the continental shelf of Cyprus, the Levant and North Africa, up to a maximum distance of 2100 km from nesting sites. Foraging sites were clustered in (1) near-shore waters of Cyprus and Syria, (2) offshore waters of Egypt and (3) offshore and near-shore regions of Libya and Tunisia. The North Cyprus and west Egypt/east Libyan coasts are important areas for loggerhead turtles during migration. Movement patterns within foraging sites strongly suggest benthic feeding in discrete areas. Early nesters visited other rookeries in Turkey, Syria and Israel where they likely laid further clutches. Tracking suggests minimum annual mortality of 11%, comparable to other fishery-impacted loggerhead populations. Main conclusions: This work further highlights the importance of neritic habitats of Libya and Tunisia as areas likely used by loggerhead turtles from many of the Mediterranean rookeries and where the threat of fisheries bycatch is high. Our tracking data also suggest that anthropogenic mortalities may have occurred in North Cyprus, Syria and Egypt; all within near-shore marine areas where small-scale fisheries operate. Protection of this species across many geopolitical units is a major challenge and documenting their distribution is an important first step.Peoples Trust for Endangered SpeciesBritish Chelonia GroupUnited States Agency for International DevelopmentBP EgyptApacheNatural Environment Research Council (NERC)Erwin Warth FoundationKuzey Kıbrıs TurkcellEktam KıbrısSEATURTLE.orgMEDASSETDarwin InitiativeBritish High Commission in CyprusBritish Residents Society of North CyprusMarine Turtle Conservation ProjectMarine Turtle Research GroupSociety for the Protection of Turtles in North Cyprus (SPOT)North Cyprus Department of Environmental Protectio

Collider Phenomenology with Split-UED

We investigate the collider implications of Split Universal Extra Dimensions. The non-vanishing fermion mass in the bulk, which is consistent with the KK-parity, largely modifies the phenomenology of Minimal Universal Exta Dimensions. We scrutinize the behavior of couplings and study the discovery reach of the Tevatron and the LHC for level-2 Kaluza-Klein modes in the dilepton channel, which would indicates the presence of the extra dimensions. Observation of large event rates for dilepton resonances can result from a nontrivial fermion mass profile along the extra dimensions, which, in turn, may corroborate extra dimensional explanation for the observation of the positron excess in cosmic rays.Comment: 23 pages, 15 figure

Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling

We introduce a method for the fast estimation of data-adapted, spatially and temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variation (TV) minimization. The proposed approach is inspired by recent developments in algorithm unrolling using deep neural networks (NNs) and relies on two distinct subnetworks. The first subnetwork estimates the regularization parameter-map from the input data. The second subnetwork unrolls iterations of an iterative algorithm which approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean and corrupted data but crucially without the need for access to labels for the optimal regularization parameter-maps. We first prove consistency of the unrolled scheme by showing that the unrolled minimizing energy functional used for the supervised learning -converges, as tends to infinity, to the corresponding functional that incorporates the exact solution map of the TV-minimization problem. Then, we apply and evaluate the proposed method on a variety of large-scale and dynamic imaging problems with retrospectively simulated measurement data for which the automatic computation of such regularization parameters has been so far challenging using the state-of-the-art methods: a 2D dynamic cardiac magnetic resonance imaging (MRI) reconstruction problem, a quantitative brain MRI reconstruction problem, a low-dose computed tomography problem, and a dynamic image denoising problem. The proposed method consistently improves the TV reconstructions using scalar regularization parameters, and the obtained regularization parameter-maps adapt well to imaging problems and data by leading to the preservation of detailed features. Although the choice of the regularization parameter-maps is data-driven and based on NNs, the subsequent reconstruction algorithm is interpretable since it inherits the properties (e.g., convergence guarantees) of the iterative reconstruction method from which the network is implicitly defined

Rift Valley fever outbreak, Mauritania, 1998: seroepidemiologic, virologic, entomologic, and zoologic investigations.

A Rift Valley fever outbreak occurred in Mauritania in 1998. Seroepidemiologic and virologic investigation showed active circulation of the Rift Valley fever virus, with 13 strains isolated, and 16% (range 1.5%-38%) immunoglobulin (Ig) M-positivity in sera from 90 humans and 343 animals (sheep, goats, camels, cattle, and donkeys). One human case was fatal

Gravitino Dark Matter in Tree Level Gauge Mediation with and without R-parity

We investigate the cosmological aspects of Tree Level Gauge Mediation, a recently proposed mechanism in which the breaking of supersymmetry is communicated to the soft scalar masses by extra gauge interactions at the tree level. Embedding the mechanism in a Grand Unified Theory and requiring the observability of sfermion masses at the Large Hadron Collider, it follows that the Lightest Supersymmetric Particle is a gravitino with a mass of the order of 10 GeV. The analysis in the presence of R-parity shows that a typical Tree Level Gauge Mediation spectrum leads to an overabundance of the Dark Matter relic density and a tension with the constraints from Big Bang Nucleosynthesis. This suggests to relax the exact conservation of the R-parity. The underlying SO(10) Grand Unified Theory together with the bounds from proton decay provide a rationale for considering only bilinear R-parity violating operators. We finally analyze the cosmological implications of this setup by identifying the phenomenologically viable regions of the parameter space.Comment: 28 pages, 5 figures. References added. To appear in JHE