Development of a Blood Brain Barrier (BBB) Mimetic to Study Breast-Brain Metastasis

Cancer metastasis is a highly complex process that causes 90% of all solid tumor deaths. With recent advances in diagnostic modalities and treatment options, the occurrence of brain metastasis has been rising over the last decade. Since many therapeutics are unable to cross the blood brain barrier (BBB), a protective layer separating the vascular system and the brain, brain metastases are notoriously difficult to treat. In certain types of cancer, tumor cells can invade the brain by crossing the BBB from the circulatory system through a process known as extravasation. Brain metastasis in breast cancer leads to poor prognosis with mean survival rate of 2 years. Studying the mechanism of the extravasation of breast cancer into the brain is critical for the elucidation of the pathways driving this metastatic process. Current methods used to study this invasion process cannot fully recapitulate physiological conditions. The gold standard method uses Transwell® inserts that have a non-physiological membrane separating the ‘blood’ and the ‘brain stroma’, which can cause non-physiological behaviors in migration studies. Thus, we developed a three dimensional (3D) 3-layer hydrogel model to study the invasion of breast cancer into the brain. To develop this model, the physical effects of composite Hyaluronic acid (HA) / collagen matrices used as brain stroma mimetics in breast-brain metastasis were investigated. HA was chosen because it is one of the most common glycosaminoglycans found in the brain extracellular matrix (ECM) 5 and collagen was chosen because it is a major component of the basement membrane of the BBB.6 In this study, highly invasive MDA-MB231 breast cancer cells were either encapsulated in or suspended on the surface of the composite hydrogels and the migration velocity was ascertained. It was found that cell proliferation was inhibited by HA concentrations higher than 0.5wt%. Adhesion of cells onto the gel surface and cell migration velocity were decreased with increasing concentration of HA in gel composites. Moreover, cell migration velocity appeared to increasing with time (i.e., it is higher on day 5 than on day 1 of the study), potentially indicating remodeling of the ECM by cancer cells or altered chemical signaling from the composite hydrogel matrix. These results suggest that the HA/collagen composite hydrogel is adequate in modelling the brain stroma and further studies optimizing our proposed BBB mimetic are proposed. If successful, this model could lead to better therapeutics that could help hinder or even prevent brain metastases from occurring.Engineering Dean’s ScholarshipNo embargoAcademic Major: Biomedical EngineeringAcademic Major: Neuroscienc

Adversarial Network Bottleneck Features for Noise Robust Speaker Verification

In this paper, we propose a noise robust bottleneck feature representation which is generated by an adversarial network (AN). The AN includes two cascade connected networks, an encoding network (EN) and a discriminative network (DN). Mel-frequency cepstral coefficients (MFCCs) of clean and noisy speech are used as input to the EN and the output of the EN is used as the noise robust feature. The EN and DN are trained in turn, namely, when training the DN, noise types are selected as the training labels and when training the EN, all labels are set as the same, i.e., the clean speech label, which aims to make the AN features invariant to noise and thus achieve noise robustness. We evaluate the performance of the proposed feature on a Gaussian Mixture Model-Universal Background Model based speaker verification system, and make comparison to MFCC features of speech enhanced by short-time spectral amplitude minimum mean square error (STSA-MMSE) and deep neural network-based speech enhancement (DNN-SE) methods. Experimental results on the RSR2015 database show that the proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE and DNN-SE based MFCCs for different noise types and signal-to-noise ratios. Furthermore, the AN-BN feature is able to improve the speaker verification performance under the clean condition

Adaptive Self-Occlusion Behavior Recognition Based on pLSA

Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods

Neutrophil: A New Player in Metastatic Cancers

The interaction between cancer cells and immune cells is important for the cancer development. However, much attention has been given to T cells and macrophages. Being the most abundant leukocytes in the blood, the functions of neutrophils in cancer have been underdetermined. They have long been considered an “audience” in the development of cancer. However, emerging evidence indicate that neutrophils are a heterogeneous population with plasticity, and subpopulation of neutrophils (such as low density neutrophils, polymorphonuclear-myeloid-derived suppressor cells) are actively involved in cancer growth and metastasis. Here, we review the current understanding of the role of neutrophils in cancer development, with a specific focus on their pro-metastatic functions. We also discuss the potential and challenges of neutrophils as therapeutic targets. A better understanding the role of neutrophils in cancer will discover new mechanisms of metastasis and develop new immunotherapies by targeting neutrophils

DNN Filter Bank Cepstral Coefficients for Spoofing Detection

With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech. The deep neural network filter bank is automatically generated by training a filter bank neural network (FBNN) using natural and synthetic speech. By adding restrictions on the training rules, the learned weight matrix of FBNN is band-limited and sorted by frequency, similar to the normal filter bank. Unlike the manually designed filter bank, the learned filter bank has different filter shapes in different channels, which can capture the differences between natural and synthetic speech more effectively. The experimental results on the ASVspoof {2015} database show that the Gaussian mixture model maximum-likelihood (GMM-ML) classifier trained by the new feature performs better than the state-of-the-art linear frequency cepstral coefficients (LFCC) based classifier, especially on detecting unknown attacks