Fast Predictive Image Registration

We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D

Determining Absorption, Emissivity Reduction, and Local Suppression Coefficients inside Sunspots

The power of solar acoustic waves is reduced inside sunspots mainly due to absorption, emissivity reduction, and local suppression. The coefficients of these power-reduction mechanisms can be determined by comparing time-distance cross-covariances obtained from sunspots and from the quiet Sun. By analyzing 47 active regions observed by SOHO/MDI without using signal filters, we have determined the coefficients of surface absorption, deep absorption, emissivity reduction, and local suppression. The dissipation in the quiet Sun is derived as well. All of the cross-covariances are width corrected to offset the effect of dispersion. We find that absorption is the dominant mechanism of the power deficit in sunspots for short travel distances, but gradually drops to zero at travel distances longer than about 6 degrees. The absorption in sunspot interiors is also significant. The emissivity-reduction coefficient ranges from about 0.44 to 1.00 within the umbra and 0.29 to 0.72 in the sunspot, and accounts for only about 21.5% of the umbra's and 16.5% of the sunspot's total power reduction. Local suppression is nearly constant as a function of travel distance with values of 0.80 and 0.665 for umbrae and whole sunspots respectively, and is the major cause of the power deficit at large travel distances.Comment: 14 pages, 21 Figure

Mathematical models for vulnerable plaques

A plaque is an accumulation and swelling in the artery walls and typically consists of cells, cell debris, lipids, calcium deposits and fibrous connective tissue. A person is likely to have many plaques inside his/her body even if they are healthy. However plaques may become "vulnerable", "high-risk" or "thrombosis-prone" if the person engages in a high-fat diet and does not exercise regularly. In this study group, we proposed two mathematical models to describe plaque growth and rupture. The first model is a mechanical one that approximately treats the plaque as an inflating elastic balloon. In this model, the pressure inside the core increases and then decreases suggesting that plaque stabilization and prevention of rupture is possible. The second model is a biochemical one that focuses on the role of MMPs in degrading the fibrous plaque cap. The cap stress, MMP concentration, plaque volume and cap thickness are coupled together in a system of phenomenological equations. The equations always predict an eventual rupture since the volume, stresses and MMP concentrations generally grow without bound. The main weakness of the model is that many of the important parameters that control the behavior of the plaque are unknown. The two simple models suggested by this group could serve as a springboard for more realistic theoretical studies. But most importantly, we hope they will motivate more experimental work to quantify some of the important mechanical and biochemical properties of vulnerable plaques

The battle of the SNPs

This month’s Genome Watch highlights new perspectives on polygenic adaptation and its consequences for fitness in microbial populations

Validation of satellite retrievals of cloud microsphysics and liquid water path using observations from FIRE

Cloud effective radii (r(sub e)) and cloud liquid water path (LWP) are derived from ISCCP spatially sampled satellite data and validated with ground-based pyranometer and microwave radiometer measurements taken on San Nicolas Island during the 1987 FIRE IFO. Values of r(sub e) derived from the ISCCP data are also compared to values retrieved by a hybrid method that uses the combination of LWP derived from microwave measurement and optical thickness derived from GOES data. The results show that there is significant variability in cloud properties over a 100 km x 80 km area and that the values at San Nicolas Island are not necessarily representative of the surrounding cloud field. On the other hand, even though there were large spatial variations in optical depth, the r(sub e) values remained relatively constant (with sigma less than or equal to 2-3 microns in most cases) in the marine stratocumulus. Furthermore, values of r(sub e) derived from the upper portion of the cloud generally are representative of the entire stratiform cloud. When LWP values are less than 100 g m(exp -2), then LWP values derived from ISCCP data agree well with those values estimated from ground-based microwave measurements. In most cases LWP differences were less than 20 g m(exp -2). However, when LWP values become large (e.g., greater than or equal to 200 g m(exp -2)), then relative differences may be as large as 50%- 100%. There are two reasons for this discrepancy in the large LWP clouds: (1) larger vertical inhomogeneities in precipitating clouds and (2) sampling errors on days of high spatial variability of cloud optical thicknesses. Variations of r(sub e) in stratiform clouds may indicate drizzle: clouds with droplet sizes larger than 15 microns appear to be associated with drizzling, while those less than 10 microns are indicative of nonprecipitating clouds. Differences in r(sub e) values between the GOES and ISCCP datasets are found to be 0.16 +/- 0.98 micron

Multiple magnetic transitions in multiferroic BiMnO3

The magnetic phase variations under hydrostatic pressure on multiferroic BiMnO3 have been examined by the dc magnetization [Mg(T)], magnetic hysteresis [Ueff(H)], and ac susceptibility [X'g(T)]. Three magnetic transitions, manifested as kinks I, II, and III on the Mg(T)], curves, were identified at 8.7 and 9.4 kbar. With increasing pressure, transition temperatures of kink I and kink II TkI and TkII tend to decrease, but the temperature of kink III TkIII showed more complex variation. Under increasing magnetic field, TkI and TkII increase; however, TkIII decreases. Combining [Mg(T)] curves with Ueff(H) and X'g(T), more detailed properties of these three kinks would be shown as follows. Kink I is a long-range soft ferromagnetic transition which occurs at TkI 100 K under ambient pressure but is suppressed completely at 11.9 kbar. Kink II emerges at 8.7 kbar along with TkII 93 K which is also long-range soft ferromagnetic but canted in nature. Kink III, a canted antiferromagnetic transition, appears at TkIII 72.5 K along with kink II also at 8.7 kbar. The proposed phase diagrams at ambient pressure, 9.4 and 11.9 kbar show the different magnetic features of BiMnO3. These findings are believed to result from the variations in crystal structure influenced by the external pressure. These results also indicate the common complicatedComment: 6 paages, 5 gifures, published in Physical Review B 80, 184426/200

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels