Wearable-based seizure detection devices hold promise in reducing seizure-related adverse events and relieving the daily stress experienced by people with epilepsy. In this work, we present the latest evidence regarding the performance of three seizure detection wearables (eight studies) commercially available in Canada to provide guidance to clinicians. Overall, their ability to detect focal-to-bilateral and/or generalized tonic-clonic seizures ranges between 21.0% and 98.15% in sensitivity, with the 24h false alarm rates ranging from 0 to 1.28. While performance in epilepsy monitoring units show promise, the lack of evidence in outpatient settings precludes strong recommendations for their use in daily life.

We evaluated the effectiveness and tolerability of brivaracetam (BRV), an adjunctive antiseizure medication, as a treatment for focal epilepsy in adults. In this prospective study, we enrolled 51 participants from 3 sites across Canada. At 6 months, 68% (26/38) of participants were still taking BRV, among whom 35% (8/23) attained seizure freedom and 48% (11/23) saw their seizure frequency reduced by over 50%. We did not measure any significant change in irritability, quality of life, depression, and anxiety while treated with BRV. Our findings suggest BRV is effective in reducing seizure frequency among adults with focal epilepsy.

The dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of accurate reduced-order models challenging. The overarching objective of this paper is to propose a nonlinear decomposition of the turbulent state to predict the flow based on a reduced-order representation of the dynamics. We divide the turbulent flow into a spatial problem and a temporal problem. First, we compute the latent space, which is the manifold onto which the turbulent dynamics live. The latent space is found by a series of nonlinear filtering operations, which are performed by a convolutional autoencoder (CAE). The CAE provides the decomposition in space. Second, we predict the time evolution of the turbulent state in the latent space, which is performed by an echo state network (ESN). The ESN provides the evolution in time. Third, by combining the CAE and the ESN, we obtain an autonomous dynamical system: the CAE-ESN. This is the reduced-order model of the turbulent flow. We test the CAE-ESN on the two-dimensional Kolmogorov flow and the three-dimensional minimal flow unit. We show that the CAE-ESN: (i) finds a latent-space representation of the turbulent flow that has ${\lesssim }1\,\%$ of the degrees of freedom than the physical space; (ii) time-accurately and statistically predicts the flow at different Reynolds numbers; and (iii) takes ${\lesssim }1\,\%$ computational time to predict the flow with respect to solving the governing equations. This work opens possibilities for nonlinear decomposition and reduced-order modelling of turbulent flows from data.

Modeling complex dynamical systems with only partial knowledge of their physical mechanisms is a crucial problem across all scientific and engineering disciplines. Purely data-driven approaches, which only make use of an artificial neural network and data, often fail to accurately simulate the evolution of the system dynamics over a sufficiently long time and in a physically consistent manner. Therefore, we propose a hybrid approach that uses a neural network model in combination with an incomplete partial differential equations (PDEs) solver that provides known, but incomplete physical information. In this study, we demonstrate that the results obtained from the incomplete PDEs can be efficiently corrected at every time step by the proposed hybrid neural network—PDE solver model, so that the effect of the unknown physics present in the system is correctly accounted for. For validation purposes, the obtained simulations of the hybrid model are successfully compared against results coming from the complete set of PDEs describing the full physics of the considered system. We demonstrate the validity of the proposed approach on a reactive flow, an archetypal multi-physics system that combines fluid mechanics and chemistry, the latter being the physics considered unknown. Experiments are made on planar and Bunsen-type flames at various operating conditions. The hybrid neural network—PDE approach correctly models the flame evolution of the cases under study for significantly long time windows, yields improved generalization and allows for larger simulation time steps.

OBJECTIVES/GOALS: In triple negative breast cancer (TNBC), obesity is associated with poor outcomes. Adipose stem cells (ASCs) from obese patients (obASCs) secrete higher levels of adipokines compared to ASCs from lean individuals. Leptin, one of these adipokines, has been implicated in many cancers. This study seeks to examine the role of leptin signaling in TNBC. METHODS/STUDY POPULATION: Previous work in conjunction with a collaborating lab has shown that leptin signaling promotes metastasis and increased expression of epithelial-mesenchymal transition (EMT) markers in triple negative breast cancer cell lines. This project expands upon this work through using both patient-derived cell lines and and patient-derived xenografts (PDX), and examines the role of leptin signaling both in vitro and in vivo. To determine the effects of obesity upon a PDX model of TNBC, a high fat diet was used to induce obesity in vivo. A pharmacological inhibitor of the leptin receptor was used to test the requirement for leptin signaling both in vivo and in vitro. RESULTS/ANTICIPATED RESULTS: Exposure to conditioned media harvested from obASCs increased the percentage of TNBC cells that expressed cancer stem cell markers, whereas exposure to an inhibitor of the leptin receptor decreased the percentage of cells with cancer stem cell markers. PDX tumors implanted into mice with diet-induced obesity had an increased volume compared to tumors implanted into lean controls. Further analysis will be conducted on metastasis, circulating tumor cells, and survival in both lean and obese mice. DISCUSSION/SIGNIFICANCE: Understanding the complex signaling events in the obese tumor microenvironment is essential, as these molecular differences may contribute to different outcomes for obese and lean individuals with triple negative breast cancer. Therefore, study of the crosstalk between obASCs and TNBC cells is critical.

OBJECTIVES/GOALS: Current approaches to drug development for the aggressive triple negative breast cancer rely on current 2D and 3D in vitro models which have limited capabilities. We have developed a translational microphysiological system that can maintain the human breast microenvironment to capture the complex interaction with the tumor microenvironment. METHODS/STUDY POPULATION: Three different TNBC cell lines were seeded in BC-MPS: MDA-MB-231 parental cell line, MDA-MB-231wiht the gene, LKB1 overexpressed, which is a tumor suppressor, and MDA-MB-231 with the enzyme, ERK5, an enzyme associated with increased metastasis and drug resistance, knocked out. These three TNBC cell lines were cultured in a standard 2D 96-well plate and in BC-MPS. Time-lapse videos were taken to track cellular mobility. RNA-sequencing was performed to compare different expression levels of various cancer related genes of the cell lines cultured in standard 2D and BC-MPS. RESULTS/ANTICIPATED RESULTS: The LKB1 overexpressed MDA-MB-231 and the ERK5-ko MDA-MB-231 cell lines are expected to have decreased mobility compared to the parental cells. The cell lines are expected to have increased expression of cancer related genes when cultured in BC-MPS than when cultured in standard 2D due to the presence of human breast tissue. DISCUSSION/SIGNIFICANCE: BC-MPS is a promising new translational MPS that facilitates studying long term interactions between real human breast tissue and cancer cells. The BC-MPS systems ability to support the growth of established cell lines has been demonstrated. Future studies will focus on developing the model for personalized medicine.

Let $N\geq 1$ be squarefree with $(N,6)=1$ . Let $c\phi _N(n)$ denote the number of N-colored generalized Frobenius partitions of n introduced by Andrews in 1984, and $P(n)$ denote the number of partitions of n. We prove

$$ \begin{align*}c\phi_N(n)= \sum_{d \mid N} N/d \cdot P\left( \frac{ N}{d^2}n - \frac{N^2-d^2}{24d^2} \right) + b(n),\end{align*} $$

$C(z) := (q;q)^N_\infty \sum _{n=1}^{\infty } b(n) q^n$

$S_{(N-1)/2} (\Gamma _0(N),\chi _N)$

$c\phi _N(n)$

$P(n)$

We explore the possibility of combining a knowledge-based reduced order model (ROM) with a reservoir computing approach to learn and predict the dynamics of chaotic systems. The ROM is based on proper orthogonal decomposition (POD) with Galerkin projection to capture the essential dynamics of the chaotic system while the reservoir computing approach used is based on echo state networks (ESNs). Two different hybrid approaches are explored: one where the ESN corrects the modal coefficients of the ROM (hybrid-ESN-A) and one where the ESN uses and corrects the ROM prediction in full state space (hybrid-ESN-B). These approaches are applied on two chaotic systems: the Charney–DeVore system and the Kuramoto–Sivashinsky equation and are compared to the ROM obtained using POD/Galerkin projection and to the data-only approach based uniquely on the ESN. The hybrid-ESN-B approach is seen to provide the best prediction accuracy, outperforming the other hybrid approach, the POD/Galerkin projection ROM, and the data-only ESN, especially when using ESNs with a small number of neurons. In addition, the influence of the accuracy of the ROM on the overall prediction accuracy of the hybrid-ESN-B is assessed rigorously by considering ROMs composed of different numbers of POD modes. Further analysis on how hybrid-ESN-B blends the prediction from the ROM and the ESN to predict the evolution of the system is also provided.

ABSTRACT IMPACT: Identifying an important pathway in treatment resistant TNBC will allow for the future development of clinical therapeutics specific for this disease. OBJECTIVES/GOALS: Triple Negative Breast Cancer (TNBC) is a subtype of breast cancer characterized by negative expression of estrogen receptor, progesterone receptor, and HER2/neu amplification. It resists therapies and has a high recurrence rate after resection. The goal of my research is to identify & characterize a TNBC pathway for future development of therapies. METHODS/STUDY POPULATION: The project uses a combination of cell lines, patient derived xenograft (PDX) models, as well as patient databases. Standard cellular and molecular biology techniques will be used including: Cell culture, qPCR, western blotting, and flow cytometry. RESULTS/ANTICIPATED RESULTS: LKB1 is a master kinase that activates 14 possible downstream kinases. The signaling pathway has been demonstrated to play a role in energy homeostasis and metabolism. Mutation of LKB1 signaling results in Peutz-Jeghers Syndrome and is associated with neoplasias of the lung, pancreas, and breast. Based on preliminary analysis, overexpression of LKB1 by shRNA in TNBC cell lines results in suppression of EMT and reduction of the cancer stem cell population. Additional studies show that LKB1 overexpression has no effect on growth rate in 2D culture while significant reduction in 3D mammosphere formations can be seen. Downstream studies using commercially available SIK1 inhibitor HG-9-91-01 is able to induce a larger fraction of CSC from reduced LKB1 overexpression as well as from baseline levels. DISCUSSION/SIGNIFICANCE OF FINDINGS: Overall, our results suggest that LKB1 acts through SIK1 to suppress EMT and the generation of cancer stem cells. This results in reduced cancer functionality, as evidenced by inhibition of mammosphere formation. These results establishes a foundation for future mechanistic studies on the LKB1 axis and its mechanisms in TNBC.