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

Spatial evolution of the US urban system.

We examine spatial features of the evolution of the US urban system usingUS Census data for 1900 – 1990 with non-parametric kernel estimation techniques that accommodate the complexity of the urban system. We consider spatial features of the location of cities and city outcomes in terms of population and wages. Our results suggest a number of interesting puzzles. In particular, we find that city location is essentially a random process and that interactions between cities do not help determine the size of a city. Both of these findings contradict our theoretical priors about the role of geography (physical and economic) in determining city outcomes. More detailed study suggests some solutions that allow us to restore a role for geography but a number of puzzles remain.

Cross-sectional evolution of the U.S. city size distribution.

We report nonparametrically estimated nonlinear stochastic transition kernels for the evolution of the distribution of populations of metropolitan areas, for the period 1900 to 1990, based on US Census data. Comparison of kernels across successive time periods with the kernel for a pooled sample suggests a fair amount of uniformity in the patterns of mobility during the study period. The distribution of city sizes is predominantely characterised by persistence. Comparison of the kernel for the pooled sample with the kernel for city sizes relative to their own regional average does not reveal any stark differences in intra-region mobility patterns. We then develop measures that allow us to characterise the nature of intra-distribution dynamics for the city size distribution: one is the first-order "serial" (across the ranking) correlation coefficient of the differences in relative sizes of cities with successive rankings; the second is the mean squared variation of the differences in relative sizes of cities with successive rankings. These measures have the major advantages that they do not require discretization of the city size distribution, nor do they obscure subtle changes within the distribution. We employ these measures to study the degree of mobility within the US city size distribution and, separately, within regional and urban subsystems. We find that different regions show different degrees of intra-distribution mobility. In addition, in contrast to received wisdom, second-tier cities show more mobility than top-tier cities.

Spatial Evolution of the US Urban System

We test implications of econom c geography models for location,size and growth of cities with US Census data for 1900 - 1990. Our tests involve non-parametr c estimations of stochastic kernels for the distributions of city sizes and growth rates, conditional on various measures of market potential and on features of neighbors. We show that while these relationships change during the twentieth century, by 1990 they stabilize such that the size distribution of cities conditional on a range of spatial variables are all roughly independent of these conditioning variables. In contrast, similar results suggest that there is a spatial element to the city wage distribution. Our parametric estimations for growth rates against market potential, entry of neighbors, and own lagged population imply a negative effect of market potential on growth rates, unless own lagged population is also ncluded, in which case market potential has a positive effect and own lagged population a negative one. Cities grow faster when they are small relative to their market potential. In total, our results support some theoretical predictions, but also provide a number of interesting puzzles.urban growth, spatial evolution, economics geography

Zipf’s law for cities : an empirical examination.

We use data for metro areas in the United States, from the US Census for 1900 - 1990, to test the validity of Zipf's Law for cities. Previous investigations are restricted to regressions of log size against log rank. In contrast, we use a nonparametric procedure to calculate local Zipf exponents from the mean and variance of city growth rates. This also allows us to test for the validity of Gibrat's Law for city growth processes. Despite variation in growth rates as a function of city size, Gibrat's Law does hold. In addition the local Zipf exponents are broadly consistent with Zipf's Law. Deviations from Zipf's Law are easily explained by deviations from Gibrat's Law.

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

Cyprus' image—a sun and sea destination—as a detrimental factor to seasonal fluctuations. Exploration into motivational factors for holidaying in Cyprus

Cyprus is established as a summer destination. To aid the destination in developing its winter season as well, this research uses a qualitative inductive approach to explore the tourists’ current image of the island and their motivations of visiting it. The research indicates that the current image, which essentially portrays Cyprus as a sun-and-sea destination is thought to dissuade tourists from perceiving the island as a year-round destination. Nonetheless, increasing the pull factors of the destination through the development of unique special interest products can help in extending the tourism season as well as broaden its narrow image