Short-range plasma model for intermediate spectral statistics

We propose a plasma model for spectral statistics displaying level repulsion without long-range spectral rigidity, i.e. statistics intermediate between random matrix and Poisson statistics similar to the ones found numerically at the critical point of the Anderson metal-insulator transition in disordered systems and in certain dynamical systems. The model emerges from Dysons one-dimensional gas corresponding to the eigenvalue distribution of the classical random matrix ensembles by restricting the logarithmic pair interaction to a finite number $k$ of nearest neighbors. We calculate analytically the spacing distributions and the two-level statistics. In particular we show that the number variance has the asymptotic form $\Sigma^2(L)\sim\chi L$ for large $L$ and the nearest-neighbor distribution decreases exponentially when $s\to \infty$, $P(s)\sim\exp (-\Lambda s)$ with $\Lambda=1/\chi=k\beta+1$, where $\beta$ is the inverse temperature of the gas ($\beta=$1, 2 and 4 for the orthogonal, unitary and symplectic symmetry class respectively). In the simplest case of $k=\beta=1$, the model leads to the so-called Semi-Poisson statistics characterized by particular simple correlation functions e.g. $P(s)=4s\exp(-2s)$. Furthermore we investigate the spectral statistics of several pseudointegrable quantum billiards numerically and compare them to the Semi-Poisson statistics.Comment: 24 pages, 4 figure

On the origin of irregular structure in Saturn's rings

We suggest that the irregular structure in Saturn's B ring arises from the formation of shear-free ring-particle assemblies of up to ~100 km in radial extent. The characteristic scale of the irregular structure is set by the competition between tidal forces and the yield stress of these assemblies; the required tensile strength of ~10^5 dyn/cm^2 is consistent with the sticking forces observed in laboratory simulations of frosted ice particles. These assemblies could be the nonlinear outcome of a linear instability that occurs in a rotating fluid disk in which the shear stress is a decreasing function of the shear. We show that a simple model of an incompressible, non-Newtonian fluid in shear flow leads to the Cahn-Hilliard equation, which is widely used to model the formation of structure in binary alloys and other systems.Comment: 21 pages, 1 figure, to be published in Astronomical Journa

On the structure and evolution of a polar crown prominence/filament system

Polar crown prominences are made of chromospheric plasma partially circling the Suns poles between 60 and 70 degree latitude. We aim to diagnose the 3D dynamics of a polar crown prominence using high cadence EUV images from the Solar Dynamics Observatory (SDO)/AIA at 304 and 171A and the Ahead spacecraft of the Solar Terrestrial Relations Observatory (STEREO-A)/EUVI at 195A. Using time series across specific structures we compare flows across the disk in 195A with the prominence dynamics seen on the limb. The densest prominence material forms vertical columns which are separated by many tens of Mm and connected by dynamic bridges of plasma that are clearly visible in 304/171A two-color images. We also observe intermittent but repetitious flows with velocity 15 km/s in the prominence that appear to be associated with EUV bright points on the solar disk. The boundary between the prominence and the overlying cavity appears as a sharp edge. We discuss the structure of the coronal cavity seen both above and around the prominence. SDO/HMI and GONG magnetograms are used to infer the underlying magnetic topology. The evolution and structure of the prominence with respect to the magnetic field seems to agree with the filament linkage model.Comment: 24 pages, 14 figures, Accepted for publication in Solar Physics Journal, Movies can be found at http://www2.mps.mpg.de/data/outgoing/panesar

Polarimetric Properties of Flux-Ropes and Sheared Arcades in Coronal Prominence Cavities

The coronal magnetic field is the primary driver of solar dynamic events. Linear and circular polarization signals of certain infrared coronal emission lines contain information about the magnetic field, and to access this information, either a forward or an inversion method must be used. We study three coronal magnetic configurations that are applicable to polar-crown filament cavities by doing forward calculations to produce synthetic polarization data. We analyze these forward data to determine the distinguishing characteristics of each model. We conclude that it is possible to distinguish between cylindrical flux ropes, spheromak flux ropes, and sheared arcades using coronal polarization measurements. If one of these models is found to be consistent with observational measurements, it will mean positive identification of the magnetic morphology that surrounds certain quiescent filaments, which will lead to a greater understanding of how they form and why they erupt.Comment: 22 pages, 8 figures, Solar Physics topical issue: Coronal Magnetis

Rough droplet model for spherical metal clusters

We study the thermally activated oscillations, or capillary waves, of a neutral metal cluster within the liquid drop model. These deformations correspond to a surface roughness which we characterize by a single parameter $\Delta$. We derive a simple analytic approximate expression determining $\Delta$ as a function of temperature and cluster size. We then estimate the induced effects on shell structure by means of a periodic orbit analysis and compare with recent data for shell energy of sodium clusters in the size range $50 < N < 250$. A small surface roughness $\Delta\simeq 0.6$ \AA~ is seen to give a reasonable account of the decrease of amplitude of the shell structure observed in experiment. Moreover -- contrary to usual Jahn-Teller type of deformations -- roughness correctly reproduces the shape of the shell energy in the domain of sizes considered in experiment.Comment: 20 pages, 4 figures, important modifications of the presentation, to appear in Phys. Rev.

Performance and Consistency of Indicator Groups in Two Biodiversity Hotspots

In a world limited by data availability and limited funds for conservation, scientists and practitioners must use indicator groups to define spatial conservation priorities. Several studies have evaluated the effectiveness of indicator groups, but still little is known about the consistency in performance of these groups in different regions, which would allow their a priori selection.We systematically examined the effectiveness and the consistency of nine indicator groups in representing mammal species in two top-ranked Biodiversity Hotspots (BH): the Brazilian Cerrado and the Atlantic Forest. To test for group effectiveness we first found the best sets of sites able to maximize the representation of each indicator group in the BH and then calculated the average representation of different target species by the indicator groups in the BH. We considered consistent indicator groups whose representation of target species was not statistically different between BH. We called effective those groups that outperformed the target-species representation achieved by random sets of species. Effective indicator groups required the selection of less than 2% of the BH area for representing target species. Restricted-range species were the most effective indicators for the representation of all mammal diversity as well as target species. It was also the only group with high consistency.We show that several indicator groups could be applied as shortcuts for representing mammal species in the Cerrado and the Atlantic Forest to develop conservation plans, however, only restricted-range species consistently held as the most effective indicator group for such a task. This group is of particular importance in conservation planning as it captures high diversity of endemic and endangered species

Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10- 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10- 4, replication P = 6.7 × 10- 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases