Distinct functions of integrin alpha and beta subunit cytoplasmic domains in cell spreading and formation of focal adhesions.

Integrin-mediated cell adhesion often results in cell spreading and the formation of focal adhesions. We exploited the capacity of recombinant human alpha IIb beta 3 integrin to endow heterologous cells with the ability to adhere and spread on fibrinogen to study the role of integrin cytoplasmic domains in initiation of cell spreading and focal adhesions. The same constructs were also used to analyze the role of the cytoplasmic domains in maintenance of the fidelity of the integrin repertoire at focal adhesions. Truncation mutants of the cytoplasmic domain of alpha IIb did not interfere with the ability of alpha IIb beta 3 to initiate cell spreading and form focal adhesions. Nevertheless, deletion of the alpha IIb cytoplasmic domain allowed indiscriminate recruitment of alpha IIb beta 3 to focal adhesions formed by other integrins. Truncation of the beta 3 subunit cytoplasmic domain abolished cell spreading mediated by alpha IIb beta 3 and also abrogated recruitment of alpha IIb beta 3 to focal adhesions. This truncation also dramatically impaired the ability of alpha IIb beta 3 to mediate the contraction of fibrin gels. In contrast, the beta 3 subunit cytoplasmic truncation did not reduce the fibrinogen binding affinity of alpha IIb beta 3. Thus, the integrin beta 3 subunit cytoplasmic domain is necessary and sufficient for initiation of cell spreading and focal adhesion formation. Further, the beta 3 cytoplasmic domain is required for the transmission of intracellular contractile forces to fibrin gels. The alpha subunit cytoplasmic domain maintains the fidelity of recruitment of the integrins to focal adhesions and thus regulates their repertoire of integrins

A Gauge Invariant Theory for Time Dependent Heat Current

published_or_final_versio

Pricing in Social Networks with Negative Externalities

We study the problems of pricing an indivisible product to consumers who are embedded in a given social network. The goal is to maximize the revenue of the seller. We assume impatient consumers who buy the product as soon as the seller posts a price not greater than their values of the product. The product's value for a consumer is determined by two factors: a fixed consumer-specified intrinsic value and a variable externality that is exerted from the consumer's neighbors in a linear way. We study the scenario of negative externalities, which captures many interesting situations, but is much less understood in comparison with its positive externality counterpart. We assume complete information about the network, consumers' intrinsic values, and the negative externalities. The maximum revenue is in general achieved by iterative pricing, which offers impatient consumers a sequence of prices over time. We prove that it is NP-hard to find an optimal iterative pricing, even for unweighted tree networks with uniform intrinsic values. Complementary to the hardness result, we design a 2-approximation algorithm for finding iterative pricing in general weighted networks with (possibly) nonuniform intrinsic values. We show that, as an approximation to optimal iterative pricing, single pricing can work rather well for many interesting cases, but theoretically it can behave arbitrarily bad

Mol-CycleGAN - a generative model for molecular optimization

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results

A new adaptive interpolation algorithm for 3D ultrasound imaging with speckle reduction and edge preservation

Author name used in this publication: Qinghua HuangAuthor name used in this publication: Yongping ZhengAuthor name used in this publication: Minhua Lu2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Platinum binding preferences dominate the binding of novel polyamide amidine anthraquinone platinum(II) complexes to DNA

Complexes incorporating a threading anthraquinone intercalator with pyrrole lexitropsin and platinum(II) moieties attached were developed with the goal of generating novel DNA binding modes, including the targeting of AT-rich regions in order to have high cytotoxicities. The binding of the complexes to DNA has been investigated and profiles surprisingly similar to that for cisplatin were observed; the profiles were different to those for a complex lacking the pyrrole lexitropsin component. The lack of selective binding to AT-rich regions suggests the platinum binding was dominating the sequence selectivity, and is consistent with the pyrrole lexitropsin slowing intercalation. The DNA unwinding profiles following platinum binding were evaluated by gel electrophoresis and suggested that intercalation and platinum binding were both occurring

Hierarchical Learning Algorithms for Multi-scale Expert Problems

In this paper, we study the multi-scale expert problem, where the rewards of different experts vary in different reward ranges. The performance of existing algorithms for the multi-scale expert problem degrades linearly proportional to the maximum reward range of any expert or the best expert and does not capture the non-uniform heterogeneity in the reward ranges among experts. In this work, we propose learning algorithms that construct a hierarchical tree structure based on the heterogeneity of the reward range of experts and then determine differentiated learning rates based on the reward upper bounds and cumulative empirical feedback over time. We then characterize the regret of the proposed algorithms as a function of non-uniform reward ranges and show that their regrets outperform prior algorithms when the rewards of experts exhibit non-uniform heterogeneity in different ranges. Last, our numerical experiments verify our algorithms' efficiency compared to previous algorithms