Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefa- - cts is also presented. Cross-sectional and

The State of Self-Organized Criticality of the Sun During the Last 3 Solar Cycles. I. Observations

We analyze the occurrence frequency distributions of peak fluxes $P$, total fluxes $E$, and durations $T$ of solar flares over the last three solar cycles (during 1980--2010) from hard X-ray data of HXRBS/SMM, BATSE/CGRO, and RHESSI. From the synthesized data we find powerlaw slopes with mean values of $\alpha_P=1.72\pm0.08$ for the peak flux, $\alpha_E=1.60\pm0.14$ for the total flux, and $\alpha_T=1.98\pm0.35$ for flare durations. We find a systematic anti-correlation of the powerlaw slope of peak fluxes as a function of the solar cycle, varying with an approximate sinusoidal variation $\alpha_P(t)=\alpha_0+\Delta \alpha \cos{[2\pi (t-t_0)/T_{cycle}]}$, with a mean of $\alpha_0=1.73$, a variation of $\Delta \alpha =0.14$, a solar cycle period $T_{cycle}=12.6$ yrs, and a cycle minimum time $t_0=1984.1$. The powerlaw slope is flattest during the maximum of a solar cycle, which indicates a higher magnetic complexity of the solar corona that leads to an overproportional rate of powerful flares.Comment: subm. to Solar Physic

Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis

Feature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder’s progression and severity. Rather than only using the low-dimensional biomarkers as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the high-dimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks

Active Galactic Nuclei at the Crossroads of Astrophysics

Over the last five decades, AGN studies have produced a number of spectacular examples of synergies and multifaceted approaches in astrophysics. The field of AGN research now spans the entire spectral range and covers more than twelve orders of magnitude in the spatial and temporal domains. The next generation of astrophysical facilities will open up new possibilities for AGN studies, especially in the areas of high-resolution and high-fidelity imaging and spectroscopy of nuclear regions in the X-ray, optical, and radio bands. These studies will address in detail a number of critical issues in AGN research such as processes in the immediate vicinity of supermassive black holes, physical conditions of broad-line and narrow-line regions, formation and evolution of accretion disks and relativistic outflows, and the connection between nuclear activity and galaxy evolution.Comment: 16 pages, 5 figures; review contribution; "Exploring the Cosmic Frontier: Astrophysical Instruments for the 21st Century", ESO Astrophysical Symposia Serie

Chicken lung lectin is a functional C-type lectin and inhibits haemagglutination by influenza A virus

Many proteins of the calcium-dependent (C-type) lectin family have been shown to play an important role in innate immunity. They can bind to a broad range of carbohydrates, which enables them to interact with ligands present on the surface of micro-organisms.We previously reported the finding of a new putative chicken lectin, which was predominantly localized to the respiratory tract, and thus termed chicken lung lectin (cLL). In order to investigate the biochemical and biophysical properties of cLL, the recombinant protein was expressed, affinity purified and characterized. Recombinant cLL was expressed as four differently sized peptides, which is most likely due to post-translational modification. Crosslinking of the protein led to the formation of two high-molecular weight products, indicating that cLL forms trimeric and possibly even multimeric subunits. cLL was shown to have lectin activity, preferentially binding to a-mannose in a calcium-dependent manner. Furthermore, cLL was shown to inhibit the haemagglutination-activity of human isolates of influenza A virus, subtype H3N2 and H1N1. These result show that cLL is a true C-type lectin with a very distinct sugar specificity, and that this chicken lectin could play an important role in innate immunity

Bounded-Collusion IBE from Key Homomorphism

In this work, we show how to construct IBE schemes that are secure against a bounded number of collusions, starting with underlying PKE schemes which possess linear homomorphisms over their keys. In particular, this enables us to exhibit a new (bounded-collusion) IBE construction based on the quadratic residuosity assumption, without any need to assume the existence of random oracles. The new IBE’s public parameters are of size O(tλlogI) where I is the total number of identities which can be supported by the system, t is the number of collusions which the system is secure against, and λ is a security parameter. While the number of collusions is bounded, we note that an exponential number of total identities can be supported. More generally, we give a transformation that takes any PKE satisfying Linear Key Homomorphism, Identity Map Compatibility, and the Linear Hash Proof Property and translates it into an IBE secure against bounded collusions. We demonstrate that these properties are more general than our quadratic residuosity-based scheme by showing how a simple PKE based on the DDH assumption also satisfies these properties.National Science Foundation (U.S.) (NSF CCF-0729011)National Science Foundation (U.S.) (NSF CCF-1018064)United States. Defense Advanced Research Projects Agency (DARPA FA8750-11-2-0225

Probabilistic non-linear registration with spatially adaptive regularisation

This paper introduces a novel method for inferring spatially varying regularisation in non-linear registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on the transformation parameters is parameterised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a traditional globally defined regularisation penalty, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The strength of the prior may be reduced in areas where the data better support deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce unwanted impacts of regularisation on the inferred transformation. This is especially important for applications where the deformation field itself is of interest, such as tensor based morphometry. The proposed approach is demonstrated using synthetic images, and with application to tensor based morphometry analysis of subjects with Alzheimer’s disease and healthy controls. The results indicate that using the proposed spatially adaptive prior leads to sparser deformations, which provide better localisation of regional volume change. Additionally, the proposed regularisation model leads to more data driven and localised maps of registration uncertainty. This paper also demonstrates for the first time the use of Bayesian model comparison for selecting different types of regularisation

Origins of the Ambient Solar Wind: Implications for Space Weather

The Sun's outer atmosphere is heated to temperatures of millions of degrees, and solar plasma flows out into interplanetary space at supersonic speeds. This paper reviews our current understanding of these interrelated problems: coronal heating and the acceleration of the ambient solar wind. We also discuss where the community stands in its ability to forecast how variations in the solar wind (i.e., fast and slow wind streams) impact the Earth. Although the last few decades have seen significant progress in observations and modeling, we still do not have a complete understanding of the relevant physical processes, nor do we have a quantitatively precise census of which coronal structures contribute to specific types of solar wind. Fast streams are known to be connected to the central regions of large coronal holes. Slow streams, however, appear to come from a wide range of sources, including streamers, pseudostreamers, coronal loops, active regions, and coronal hole boundaries. Complicating our understanding even more is the fact that processes such as turbulence, stream-stream interactions, and Coulomb collisions can make it difficult to unambiguously map a parcel measured at 1 AU back down to its coronal source. We also review recent progress -- in theoretical modeling, observational data analysis, and forecasting techniques that sit at the interface between data and theory -- that gives us hope that the above problems are indeed solvable.Comment: Accepted for publication in Space Science Reviews. Special issue connected with a 2016 ISSI workshop on "The Scientific Foundations of Space Weather." 44 pages, 9 figure

Interaction of inflammatory cytokines and erythropoeitin in iron metabolism and erythropoiesis in anaemia of chronic disease

In chronic inflammatory conditions increased endogenous release of specific cytokines (TNFα, IL-1, IL-6, IFNγ and others) is presumed. It has been shown that those of monocyte lineage play a key role in cytokine expression and synthesis. This may be associated with changes in iron metabolism and impaired erythropoiesis and may lead to development of anaemia in patients with rheumatoid arthritis. Firstly, increased synthesis of acute phase proteins, like ferritin, during chronic inflammation is proposed as the way by which the toxic effect of iron and thereby the synthesis of free oxy-radicals causing the damage on the affected joints, may be reduced. This is associated with a shift of iron towards the mononuclear phagocyte system which may participate in the development of anaemia of chronic disease. Secondly, an inhibitory action of inflammatory cytokines (TNFα, IL-1), on proliferation and differentiation of erythroid progenitors as well as on synthesis of erythropoietin has been shown, thereby also contributing to anaemia. Finally, chronic inflammation causes multiple, complex disturbances in the delicate physiologic equilibrium of interaction between cytokines and cells (erythroid progenitors, cells of mononuclear phagocyte system and erythropoietin producing cells) leading to development of anaemia of chronic disease (Fig. 1)