The Role of Modeling in Monarch Butterfly Research and Conservation

Models are an integral part of the scientific endeavor, whether they be conceptual, mathematical, statistical, or simulation models. Models of appropriate complexity facilitate comprehension and improve understanding of the variables driving system processes. In the context of conservation planning decision-making or research efforts, a useful model can aid interpretation and avoid overfitting by including only essential elements. Models can serve two related, but different purposes: understanding and prediction of future system behavior. Predictive models can require several iterations of refinement and empirical data gathering to be useful for conservation planning. Models with less predictive ability can be used to enhance understanding of system function and generate hypotheses for empirical evaluation. Modeling monarch butterfly systems, whether it be landscape-scale movement in breeding habitats, migratory behavior, or population dynamics at monthly or yearly timeframes, is challenging because the systems encompass complex spatial and temporal interactions across nested scales that are difficult, if not impossible, to empirically observe or comprehend without simplification. We review mathematical, statistical, and simulation models that have provided insights into monarch butterfly systems. Mathematical models have provided understanding of underlying processes that may be driving monarch systems. Statistical models have provided understanding of patterns in empirical data, which may represent underlying mechanisms. Simulations models have provided understanding of mechanisms driving systems and provide the potential to link mechanisms with data to build more predictive models. As an example, recently published agent-based models of non-migratory eastern North American monarch butterfly movement and egg-laying may provide the means to explore how different spatial patterns of habitat, habitat quality, and the interaction of stressors can influence future adult recruitment. The migratory process, however, has not been addressed with agent-based modeling. Using western monarch migration as an example, we describe how modeling could be used to provide insights into migratory dynamics. Future integration of migratory models with non-migratory and population dynamics models may provide better understanding and ultimately prediction of monarch butterfly movement and population dynamics at a continental scale

Teachers\u27 Perceptions on Implementation of RTI

Responsiveness to Intervention (RTI) is progressing its way through school districts and could be used as a new model for identifying learning disabilities, thus replacing the discrepancy model of learning disability identification. Although RTI was established within IDEA 2005 and the No Child Left Behind Act, school districts are slowly transitioning to thisinnovative, three-tiered process. The purpose of this study was to explore the implementation of RTI and introduce its use to elementary school teachers. Further, another goal of the study was to ascertain its connection to reading instruction and intervention already present in one elementary school and their views of RTI

An Efficient, Fast Converging Adaptive Filter for Network Echo Cancellation

This paper discusses a fast efficient adaptive filtering algorithm for network echo cancellers PNLMS++ (proportionate normalized least mean squares ++). Compared to the conventional normalized least mean squares (NLMS) algorithm, PNLMSI++ converges much more quickly when the echo path is sparse. When the echo path is dispersive, the convergence rate is the same as NLMS. In addition, the new algorithm diverges at the same rate and to the same misalignment level as NLMS during periods of undetected double-talk. PNLMS++ is only 50% more computationally complex than NLMS and requires no additional memor

A Multiple Principal Components Based Adaptive Filter

Proportionate normalized least mean squares (PNLMS) is an adaptive filter that has been shown to provide exceptionally fast convergence and tracking when the underlying system parameters are sparse. A good example of such a system is a network echo canceller. Principal components based PNLMS (PCP) extends this fast convergence property to certain nonsparse systems by applying PNLMS while using the principal components of the underlying system as basis vectors. An acoustic echo canceller is a possible example of this type of nonsparse system. Simulations of acoustic echo paths and cancellers indicate that PCP converges and tracks much faster than the classical normalized least mean squares (NLMS) and fast recursive least squares (FRLS) adaptive filters. However, when a basic parameter, like room temperature, changes, the underlying acoustic structure of the room changes as well and principal components of the room responses at one temperature are very different from those at another. This paper addresses this problem by using multiple sets of principle components as basis vectors and performing PNLMS in each basis set. Each set of principle components are derived from the room at a different temperature. The new algorithm, multiple principal components PNLMS (MPCP) is a generalization of PNLMS++. Simulations show the potential effectiveness of the approach

A Fast Converging, Low Complexity Adaptive Filtering Algorithm

This paper introduces a new adaptive filtering algorithm called fast affine projections (FAP). Its main attributes include RLS (recursive least squares) like convergence and tracking with NLMS (normalized least mean squares) like complexity. This mix of complexity and performance is similar to the recently introduced fast Newton transversal filter (FNTF) algorithm. While FAP shares some similar properties with FNTF it is derived from a different perspective, namely the generalization of the affine projection interpretation of NLMS. FAP relies on a sliding windowed fast RLS (FRLS) algorithm to generate forward and backward prediction vectors and expected prediction error energies. Since sliding windowed FRLS algorithms easily incorporate regularization of the implicit inverse of the covariance matrix, FAP is regularized as well

ROLE OF INTRACELLULAR GROWTH DURING THE GASTROINTESTINAL STAGE OF \u3cem\u3eLISTERIA MONOCYTOGENES\u3c/em\u3e INFECTION

Listeria monocytogenes is a facultative intracellular bacterium that causes foodborne disease in humans. L. monocytogenes invade the gut mucosa and then disseminate, causing systemic infections associated with high mortality rates in immunocompromised individuals. It is unknown how L. monocytogenes traffic to the mesenteric lymph nodes, which represent an important bottleneck for systemic spread. In addition, little is known about the gastrointestinal stage of infection due to the general resistance of mice to oral infection with L. monocytogenes. Our laboratory developed a novel foodborne mouse model of listeriosis utilizing a murinized strain of L. monocytogenes to investigate the gastrointestinal stage of infection. First, we found that the majority of L. monocytogenes isolated from the intestinal tissue and MLN were extracellular; however, the minimal fraction of intracellular L. monocytogenes was vital for persistence in the gut and spread to the MLN. The vast majority of cell-associated L. monocytogenes in the MLN were adhered to inflammatory monocytes, but these cells did not support the intracellular growth of L. monocytogenes. A minor proportion of L. monocytogenes were associated with migratory dendritic cells in the intestinal lamina propria and MLN, but like monocytes, these cells did not appear to serve as an intracellular growth niche for L. monocytogenes. Lastly, extracellular L. monocytogenes were observed migrating in mesenteric lymphatic vessels that drain from the intestine to the MLN, suggesting that L. monocytogenes can spread beyond the intestinal mucosa independent of migratory immune cells. Overall, these studies are the first to characterize the interaction of L. monocytogenes with immune cells in the intestine and MLN following foodborne infection and suggest that extracellular, and not cytosolic L. monocytogenes, primarily drive innate immune responses in the gut

Dynamically Regularized Fast RLS with Application to Echo Cancellation

This paper introduces a dynamically regularized fast recursive least squares (DR-FRLS) adaptive filtering algorithm. Numerically stabilized FRLS algorithms exhibit reliable and fast convergence with low complexity even when the excitation signal is highly self-correlated. FRLS still suffers from instability, however, when the condition number of the implicit excitation sample covariance matrix is very high. DR-FRLS, overcomes this problem with a regularization process which only increases the computational complexity by 50%. The benefits of regularization include: (1) the ability to use small forgetting factors resulting in improved tracking ability and (2) better convergence over the standard regularization technique of noise injection. Also, DR-FRLS allows the degree of regularization to be modified quickly without restarting the algorithm. The application of DR-FRLS to stabilizing the fast affine projection (FAR) algorithm is also discussed