Polymeric matrices for DNA sequencing by capillary electrophoresis

We review the wide range of polymeric materials that have been employed for DNA sequencing separations by capillary electrophoresis. Intensive research in the area has converged in showing that highly entangled solutions of hydrophilic, high molar mass polymers are required to achieve high DNA separation efficiency and long read length, system attributes that are particularly important for genomic sequencing. The extent of DNA-polymer interactions, as well as the robustness of the entangled polymer network, greatly influence the performance of a given polymer matrix for DNA separation. Further fundamental research in the field of polymer physics and chemistry is needed to elucidate the specific mechanisms by which DNA is separated in dynamic, uncross-linked polymer networks.     

Non-treeability for product group actions

A product of two lcsc non-compact groups G ₁ × G ₂ acting freely by measure preserving transformation on a standard Borel probability space gives rise to a non-treeable equivalence relation unless both groups are amenable. Partially supported by NSF grant DMS 0140503     

Privacy and pricing personal information

The issues we address here are – How should a firm (e.g. Internet service provider (ISP)) that is capable of collecting personal information (browsing information, purchase history, etc.) about consumers, price its service, given that consumers vary in their valuation for privacy, and also vary in terms of the value of their personal information to a third party (firms that need consumer information)? Should the firm have a blanket policy of never collecting, or a policy of always collecting and revealing information? Surprisingly we find that in some cases the collector of information may be no worse off in the asymmetric information case than in the full information case. The paper provides a justification for the strategy of some firms such as ISP’s which never collect information and also for the strategy of other firms, like grocery stores that do. We also find that it is non-optimal for the firm to design contracts where the consumer can choose an intermediate level of privacy.     

Particle size control and dependence on solution pH of carboxylate–alumoxane nanoparticles

A series of water dispersible carboxylate–alumoxane nanoparticles were prepared by the reaction of various carboxylic acids with the mineral boehmite. The acids used were: acetic (A–H), methoxyacetic (MA–H), methoxyethoxyacetic (MEA–H), methoxyethoxyethoxyacetic (MEEA–H), and lysine (L–H). For each of the derived carboxylate–alumoxanes, particle size measurements were obtained by means of photon correlation spectroscopy (PCS) as a function of solution pH. In all cases, at higher pHs, slight increases in solution basicity corresponded to large increases in particle size. At lower pHs, however, changes in solution basicity yielded only slight to moderate increases in particle size. It was also found during these investigations that methoxyacetic–alumoxane (MA–A) had a large buffering capacity, and that this effect could be explained by correlation with the aluminum:carboxylate group ratio for the various carboxylate–alumoxanes.     

Displacement affinity chromatography of protein phosphatase one (PP1) complexes

Background  

Protein phosphatase one (PP1) is a ubiquitously expressed, highly conserved protein phosphatase that dephosphorylates target protein serine and threonine residues. PP1 is localized to its site of action by interacting with targeting or regulatory proteins, a majority of which contains a primary docking site referred to as the RVXF/W motif.     

A law of large numbers for finite‐range dependent random matrices

We consider random Hermitian matrices in which distant above‐diagonal entries are independent but nearby entries may be correlated. We find the limit of the empirical distribution of eigenvalues by combinatorial methods. We also prove that the limit has an algebraic Stieltjes transform by an argument based on dimension theory of Noetherian local rings. © 2008 Wiley Periodicals, Inc.     

Discovering Higher-Order Interactions Through Neural Information Decomposition

If regularity in data takes the form of higher-order functions among groups of variables, models which are biased towards lower-order functions may easily mistake the data for noise. To distinguish whether this is the case, one must be able to quantify the contribution of different orders of dependence to the total information. Recent work in information theory attempts to do this through measures of multivariate mutual information (MMI) and information decomposition (ID). Despite substantial theoretical progress, practical issues related to tractability and learnability of higher-order functions are still largely unaddressed. In this work, we introduce a new approach to information decomposition—termed Neural Information Decomposition (NID)—which is both theoretically grounded, and can be efficiently estimated in practice using neural networks. We show on synthetic data that NID can learn to distinguish higher-order functions from noise, while many unsupervised probability models cannot. Additionally, we demonstrate the usefulness of this framework as a tool for exploring biological and artificial neural networks.