Methods and compositions for stimulating T-lymphocytes

Disclosed are methods, compositions, antibodies, and therapeutic kits for use in stimulating cytotoxic T-lymphocytes and generating immune responses against epitopes of protooncogenes. Novel peptides are described which have been shown to stimulate cytotoxic T-lymphocytes, and act as antigens in generation of oncogenic epitope-recognizing antibodies. Methods are disclosed for use in treating various proliferative disorders, and diagnosing HER-2/neu-containing cells; also disclosed are therapeutic kits useful in the treatment of cancer and production of potential anti-cancer vaccines.Board of Regents, University of Texas Syste

A coupled finite-volume CFD solver for two-dimensional elasto-hydrodynamic lubrication problems with particular application to rolling element bearings

This paper describes a new computational fluid dynamics methodology for modelling elastohydrodynamic contacts. A finite-volume technique is implemented in the ‘OpenFOAM’ package to solve the Navier-Stokes equations and resolve all gradients in a lubricated rolling-sliding contact. The method fully accounts for fluid-solid interactions and is stable over a wide range of contact conditions, including pressures representative of practical rolling bearing and gear applications. The elastic deformation of the solid, fluid cavitation and compressibility, as well as thermal effects are accounted for. Results are presented for rolling-sliding line contacts of an elastic cylinder on a rigid flat to validate the model predictions, illustrate its capabilities, and identify some example conditions under which the traditional Reynolds-based predictions deviate from the full CFD solution

Specific components of face perception in the human fusiform gyrus studied by tomographic estimates of magnetoencephalographic signals: a tool for the evaluation of non-verbal communication in psychosomatic paradigms)

<p>Abstract</p> <p>Aims</p> <p>The aim of this study was to determine the specific spatiotemporal activation patterns of face perception in the fusiform gyrus (FG). The FG is a key area in the specialized brain system that makes possible the recognition of face with ease and speed in our daily life. Characterization of FG response provides a quantitative method for evaluating the fundamental functions that contribute to non-verbal communication in various psychosomatic paradigms.</p> <p>Methods</p> <p>The MEG signal was recorded during passive visual stimulus presentation with three stimulus types – Faces, Hands and Shoes. The stimuli were presented separately to the central and peripheral visual fields. We performed statistical parametric mapping (SPM) analysis of tomographic estimates of activity to compare activity between a pre- and post-stimulus period in the same object (baseline test), and activity between objects (active test). The time course of regional activation curves was analyzed for each stimulus condition.</p> <p>Results</p> <p>The SPM baseline test revealed a response to each stimulus type, which was very compact at the initial segment of main M_FG170. For hands and shoes the area of significant change remains compact. For faces the area expanded widely within a few milliseconds and its boundaries engulfed the other object areas. The active test demonstrated that activity for faces was significantly larger than the activity for hands. The same face specific compact area as in the baseline test was identified, and then again expanded widely. For each stimulus type and presentation in each one of the visual fields locations, the analysis of the time course of FG activity identified three components in the FG: M_FG100, M_FG170, and M_FG200 – all showed preference for faces.</p> <p>Conclusion</p> <p>Early compact face-specific activity in the FG expands widely along the occipito-ventral brain within a few milliseconds. The significant difference between faces and the other object stimuli in M_FG100 shows that processing of faces is already differentiated from processing of other objects within 100 ms. Standardization of the three face-specific MEG components could have diagnostic value for the integrity of the initial process of non-verbal communication in various psychosomatic paradigms.</p

Regression quantiles with errors-in-variables

In a lot of situations, variables are measured with errors. While this problem has been previously studied in the kontext of kernel regression, no work has been done in quantile regression. To estimate this function we use deconvoluting kernel estimators. The asymptotic behaviour of these estimators depends on the smoothness of the noise distribution

Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals

Methods on modelling the human brain as a Complex System have increased remarkably in the literature as researchers seek to understand the underlying foundations behind cognition, behaviour, and perception. Computational methods, especially Graph Theory-based methods, have recently contributed significantly in understanding the wiring connectivity of the brain, modelling it as a set of nodes connected by edges. Therefore, the brain's spatiotemporal dynamics can be holistically studied by considering a network, which consists of many neurons, represented by nodes. Various models have been proposed for modelling such neurons. A recently proposed method in training such networks, called full-Force, produces networks that perform tasks with fewer neurons and greater noise robustness than previous least-squares approaches (i.e. FORCE method). In this paper, the first direct applicability of a variant of the full-Force method to biologically-motivated Spiking RNNs (SRNNs) is demonstrated. The SRNN is a graph consisting of modules. Each module is modelled as a Small-World Network (SWN), which is a specific type of a biologically-plausible graph. So, the first direct applicability of a variant of the full-Force method to modular SWNs is demonstrated, evaluated through regression and information theoretic metrics. For the first time, the aforementioned method is applied to spiking neuron models and trained on various real-life Electroencephalography (EEG) signals. To the best of the authors' knowledge, all the contributions of this paper are novel. Results show that trained SRNNs match EEG signals almost perfectly, while network dynamics can mimic the target dynamics. This demonstrates that the holistic setup of the network model and the neuron model which are both more biologically plausible than previous work, can be tuned into real biological signal dynamics

Wages and employment in a random social network with arbitrary degree distribution

Empirical studies of labor markets show that social contacts are an important source of job-related information [Ioannides and Loury (2004)]. At the same time, wage differences among workers may be explained only in part by differences in individual background characteristics. Such findings motivate our model in which differences in social connectedness among otherwise identical workers result in wage inequality and differences in unemployment rates. The paper is related to theoretical contributions by Calvo- Armengol and Jackson (2004) and Calvo-Armengol and Zenou (2005) and builds on the Pissarides (2000) model. Workers may hear about job openings directly from employers or through their social contacts. We go further by introducing heterogeneity in the number of contacts each worker has with others, i.e. in the workers' degree. We utilize results from the technical literature on random graphs with arbitrary degree distributions [Newman, (2003a)] to account for a consequence of workers' receiving information about job openings from their social contacts: they compete with their social contacts' other contacts. For social networks with arbitrary degree distributions we show that people who are better connected receive a higher wage on average and face a lower unemployment rate. Numerical computations for the specific case in which connections follow a Poisson distribution show that variability in connections can result in substantial variation in the above labor market outcomes