Zone Determinant Expansions for Nuclear Lattice Simulations

We introduce a new approximation to nucleon matrix determinants that is physically motivated by chiral effective theory. The method involves breaking the lattice into spatial zones and expanding the determinant in powers of the boundary hopping parameter.Comment: 20 pages, 6 figures, revtex4 (version to appear in PRC

Monte Carlo simulations of fluid vesicles with in plane orientational ordering

We present a method for simulating fluid vesicles with in-plane orientational ordering. The method involves computation of local curvature tensor and parallel transport of the orientational field on a randomly triangulated surface. It is shown that the model reproduces the known equilibrium conformation of fluid membranes and work well for a large range of bending rigidities. Introduction of nematic ordering leads to stiffening of the membrane. Nematic ordering can also result in anisotropic rigidity on the surface leading to formation of membrane tubes.Comment: 11 Pages, 12 Figures, To appear in Phys. Rev.

A machine learning pipeline for discriminant pathways identification

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix

BDDC and FETI-DP under Minimalist Assumptions

The FETI-DP, BDDC and P-FETI-DP preconditioners are derived in a particulary simple abstract form. It is shown that their properties can be obtained from only on a very small set of algebraic assumptions. The presentation is purely algebraic and it does not use any particular definition of method components, such as substructures and coarse degrees of freedom. It is then shown that P-FETI-DP and BDDC are in fact the same. The FETI-DP and the BDDC preconditioned operators are of the same algebraic form, and the standard condition number bound carries over to arbitrary abstract operators of this form. The equality of eigenvalues of BDDC and FETI-DP also holds in the minimalist abstract setting. The abstract framework is explained on a standard substructuring example.Comment: 11 pages, 1 figure, also available at http://www-math.cudenver.edu/ccm/reports

C24 Sphingolipids Govern the Transbilayer Asymmetry of Cholesterol and Lateral Organization of Model and Live-Cell Plasma Membranes

Mammalian sphingolipids, primarily with C24 or C16 acyl chains, reside in the outer leaflet of the plasma membrane. Curiously, little is known how C24 sphingolipids impact cholesterol and membrane microdomains. Here, we present evidence that C24 sphingomyelin, when placed in the outer leaflet, suppresses microdomains in giant unilamellar vesicles and also suppresses submicron domains in the plasma membrane of HeLa cells. Free energy calculations suggested that cholesterol has a preference for the inner leaflet if C24 sphingomyelin is in the outer leaflet. We indeed observe that cholesterol enriches in the inner leaflet (80%) if C24 sphingomyelin is in the outer leaflet. Similarly, cholesterol primarily resides in the cytoplasmic leaflet (80%) in the plasma membrane of human erythrocytes where C24 sphingolipids are naturally abundant in the outer leaflet. We conclude that C24 sphingomyelin uniquely interacts with cholesterol and regulates the lateral organization in asymmetric membranes, potentially by generating cholesterol asymmetry

Secure multiparty PageRank algorithm for collaborative fraud detection

Collaboration between financial institutions helps to improve detection of fraud. However, exchange of relevant data between these institutions is often not possible due to privacy constraints and data confidentiality. An important example of relevant data for fraud detection is given by a transaction graph, where the nodes represent bank accounts and the links consist of the transactions between these accounts. Previous works show that features derived from such graphs, like PageRank, can be used to improve fraud detection. However, each institution can only see a part of the whole transaction graph, corresponding to the accounts of its own customers. In this research a new method is described, making use of secure multiparty computation (MPC) techniques, allowing multiple parties to jointly compute the PageRank values of their combined transaction graphs securely, while guaranteeing that each party only learns the PageRank values of its own accounts and nothing about the other transaction graphs. In our experiments this method is applied to graphs containing up to tens of thousands of nodes. The execution time scales linearly with the number of nodes, and the method is highly parallelizable. Secure multiparty PageRank is feasible in a realistic setting with millions of nodes per party by extrapolating the results from our experiments

Prospects for a Statistical Theory of LC/TOFMS Data

The critical importance of employing sound statistical arguments when seeking to draw inferences from inexact measurements is well-established throughout the sciences. Yet fundamental statistical methods such as hypothesis testing can currently be applied to only a small subset of the data analytical problems encountered in LC/MS experiments. The means of inference that are more generally employed are based on a variety of heuristic techniques and a largely qualitative understanding of their behavior. In this article, we attempt to move towards a more formalized approach to the analysis of LC/TOFMS data by establishing some of the core concepts required for a detailed mathematical description of the data. Using arguments that are based on the fundamental workings of the instrument, we derive and validate a probability distribution that approximates that of the empirically obtained data and on the basis of which formal statistical tests can be constructed. Unlike many existing statistical models for MS data, the one presented here aims for rigor rather than generality. Consequently, the model is closely tailored to a particular type of TOF mass spectrometer although the general approach carries over to other instrument designs. Looking ahead, we argue that further improvements in our ability to characterize the data mathematically could enable us to address a wide range of data analytical problems in a statistically rigorous manner