PARADISE: A Framework for Evaluating Spoken Dialogue Agents

This paper presents PARADISE (PARAdigm for DIalogue System Evaluation), a general framework for evaluating spoken dialogue agents. The framework decouples task requirements from an agent's dialogue behaviors, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different tasks by normalizing for task complexity.Comment: 10 pages, uses aclap, psfig, lingmacros, time

Two-Locus Likelihoods under Variable Population Size and Fine-Scale Recombination Rate Estimation

Two-locus sampling probabilities have played a central role in devising an efficient composite likelihood method for estimating fine-scale recombination rates. Due to mathematical and computational challenges, these sampling probabilities are typically computed under the unrealistic assumption of a constant population size, and simulation studies have shown that resulting recombination rate estimates can be severely biased in certain cases of historical population size changes. To alleviate this problem, we develop here new methods to compute the sampling probability for variable population size functions that are piecewise constant. Our main theoretical result, implemented in a new software package called LDpop, is a novel formula for the sampling probability that can be evaluated by numerically exponentiating a large but sparse matrix. This formula can handle moderate sample sizes ($n \leq 50$) and demographic size histories with a large number of epochs ($\mathcal{D} \geq 64$). In addition, LDpop implements an approximate formula for the sampling probability that is reasonably accurate and scales to hundreds in sample size ($n \geq 256$). Finally, LDpop includes an importance sampler for the posterior distribution of two-locus genealogies, based on a new result for the optimal proposal distribution in the variable-size setting. Using our methods, we study how a sharp population bottleneck followed by rapid growth affects the correlation between partially linked sites. Then, through an extensive simulation study, we show that accounting for population size changes under such a demographic model leads to substantial improvements in fine-scale recombination rate estimation. LDpop is freely available for download at https://github.com/popgenmethods/ldpopComment: 32 pages, 13 figure

In Vitro Modeling of Mechanics in Cancer Metastasis

In addition to a multitude of genetic and biochemical alterations, abnormal morphological, structural, and mechanical changes in cells and their extracellular environment are key features of tumor invasion and metastasis. Furthermore, it is now evident that mechanical cues alongside biochemical signals contribute to critical steps of cancer initiation, progression, and spread. Despite its importance, it is very challenging to study mechanics of different steps of metastasis in the clinic or even in animal models. While considerable progress has been made in developing advanced in vitro models for studying genetic and biological aspects of cancer, less attention has been paid to models that can capture both biological and mechanical factors realistically. This is mainly due to lack of appropriate models and measurement tools. After introducing the central role of mechanics in cancer metastasis, we provide an outlook on the emergence of novel in vitro assays and their combination with advanced measurement technologies to probe and recapitulate mechanics in conditions more relevant to the metastatic disease

Numerical solution of kinetic SPDEs via stochastic Magnus expansion

In this paper, we show how the Itô-stochastic Magnus expansion can be used to efficiently solve stochastic partial differential equations (SPDE) with two space variables numerically. To this end, we will first discretize the SPDE in space only by utilizing finite difference methods and vectorize the resulting equation exploiting its sparsity. As a benchmark, we will apply it to the case of the stochastic Langevin equation with constant coefficients, where an explicit solution is available, and compare the Magnus scheme with the Euler–Maruyama scheme. We will see that the Magnus expansion is superior in terms of both accuracy and especially computational time by using a single GPU and verify it in a variable coefficient case. Notably, we will see speed-ups of order ranging form 20 to 200 compared to the Euler–Maruyama scheme, depending on the accuracy target and the spatial resolution

Endothelial cell phenotypic behaviors cluster into dynamic state transition programs modulated by angiogenic and angiostatic cytokines

Angiogenesis requires coordinated dynamic regulation of multiple phenotypic behaviors of endothelial cells in response to environmental cues. Multi-scale computational models of angiogenesis can be useful for analyzing effects of cell behaviors on the tissue level outcome, but these models require more intensive experimental studies dedicated to determining the required quantitative “rules” for cell-level phenotypic responses across a landscape of pro- and anti-angiogenic stimuli in order to ascertain how changes in these single cell responses lead to emerging multi-cellular behavior such as sprout formation. Here we employ single-cell microscopy to ascertain phenotypic behaviors of more than 800 human microvascular endothelial cells under various combinational angiogenic (VEGF) and angiostatic (PF4) cytokine treatments, analyzing their dynamic behavioral transitions among sessile, migratory, proliferative, and apoptotic states. We find that an endothelial cell population clusters into an identifiable set of a few distinct phenotypic state transition patterns (clusters) that is consistent across all cytokine conditions. Varying the cytokine conditions, such as VEGF and PF4 combinations here, modulates the proportion of the population following a particular pattern (referred to as phenotypic cluster weights) without altering the transition dynamics within the patterns. We then map the phenotypic cluster weights to quantified population level sprout densities using a multi-variate regression approach, and identify linear combinations of the phenotypic cluster weights that associate with greater or lesser sprout density across the various treatment conditions. VEGF-dominant cytokine combinations yielding high sprout densities are characterized by high proliferative and low apoptotic cluster weights, whereas PF4-dominant conditions yielding low sprout densities are characterized by low proliferative and high apoptotic cluster weights. Migratory cluster weights show only mild association with sprout density outcomes under the VEGF/PF4 conditions and the sprout formation characteristics explored here.National Science Foundation (U.S.) (NSF grant EFRI-0735007)National Institutes of Health (U.S.) (NIH grant R01-GM081336)National Institutes of Health (U.S.) (NIH grant R01-EB010246

Engineering a 3D microfluidic culture platform for tumor-treating field application.

The limitations of current cancer therapies highlight the urgent need for a more effective therapeutic strategy. One promising approach uses an alternating electric field; however, the mechanisms involved in the disruption of the cancer cell cycle as well as the potential adverse effects on non-cancerous cells must be clarified. In this study, we present a novel microfluidic device with embedded electrodes that enables the application of an alternating electric field therapy to cancer cells in a 3D extracellular matrix. To demonstrate the potential of our system to aid in designing and testing new therapeutic approaches, cancer cells and cancer cell aggregates were cultured individually or co-cultured with endothelial cells. The metastatic potential of the cancer cells was reduced after electric field treatment. Moreover, the proliferation rate of the treated cancer cells was lower compared with that of the untreated cells, whereas the morphologies and proliferative capacities of the endothelial cells were not significantly affected. These results demonstrate that our novel system can be used to rapidly screen the effect of an alternating electric field on cancer and normal cells within an in vivo-like microenvironment with the potential to optimize treatment protocols and evaluate synergies between tumor-treating field treatment and chemotherapy