Memory-inspired spiking hyperdimensional network for robust online learning.

Recently, brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths.

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning.

Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their decision. Unfortunately, the existing deep learning algorithms have a weak and nontransparent notion of memorization.

Hierarchical Gaussian Process Modeling and Estimation of State-action Transition Dynamics in Breast Cancer.

Breast cancer is the most prevalent type of cancer in the US. Available treatments, including mastectomy, radiation, and chemotherapy, vary in curability, cost, and mortality probability of patients. This research aims at tracking the result of post-treatment for evidence-based decision making in breast cancer.

Markov Decision Process Modeling for Multi-stage Optimization of Intervention and Treatment Strategies in Breast Cancer.

The breast cancer is a prevalent problem that undermines quality of patients' lives and causes significant impacts on psychosocial wellness. Advanced sensing provides unprecedented opportunities to develop smart cancer care. The available sensing data captured from individuals enable the extraction of information pertinent to the breast cancer conditions to construct efficient and personalized intervention and treatment strategies.

Correction to: Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound.

The authors have noted an omission in the original acknowledgements. The correct acknowledgements are as follows: Acknowledgements: This work was partially supported by Grants from NSERC Discovery to Hagit Shatkay and Parvin Mousavi, NSERC and CIHR CHRP to Parvin Mousavi and NIH R01 LM012527, NIH U54 GM104941, NSF IIS EAGER #1650851 & NSF HDR #1940080 to Hagit Shatkay.

Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound.

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction.

Heterogeneous Recurrence Analysis of Disease-Altered Spatiotemporal Patterns in Multi-Channel Cardiac Signals.

Heart diseases alter the rhythmic behaviors of cardiac electrical activity. Recent advances in sensing technology bring the ease to acquire space-time electrical activity of the heart such as vectorcardiogram (VCG) signals. Recurrence analysis of successive heartbeats is conducive to detect the disease-altered cardiac activities.

Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization.

Objectives: Temporal enhanced ultrasound (TeUS) is a new ultrasound-based imaging technique that provides tissue-specific information. Recent studies have shown the potential of TeUS for improving tissue characterization in prostate cancer diagnosis. We study the temporal properties of TeUS-temporal order and length-and present a new framework to assess their impact on tissue information.

Investigation of Physical Phenomena Underlying Temporal-Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations.

Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration.

Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study.

Purpose: This paper presents the results of a large study involving fusion prostate biopsies to demonstrate that temporal ultrasound can be used to accurately classify tissue labels identified in multi-parametric magnetic resonance imaging (mp-MRI) as suspicious for cancer.

Methods: We use deep learning to analyze temporal ultrasound data obtained from 255 cancer foci identified in mp-MRI. Each target is sampled in axial and sagittal planes.

Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions.

In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions.

Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study.

Unlabelled: This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo.

Methods: We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient.

Augmenting MRI-transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study.

Purpose: In recent years, fusion of multi-parametric MRI (mp-MRI) with transrectal ultrasound (TRUS)-guided biopsy has enabled targeted prostate biopsy with improved cancer yield. Target identification is solely based on information from mp-MRI, which is subsequently transferred to the subject coordinates through an image registration approach. mp-MRI has shown to be highly sensitive to detect higher-grade prostate cancer, but suffers from a high rate of false positives for lower-grade cancer, leading to unnecessary biopsies.

Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis.

Objective: This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer.

Methods: We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient.

Ultrasound-based characterization of prostate cancer: an in vivo clinical feasibility study.

Unlabelled: This paper presents the results of an in vivo clinical study to accurately characterize prostate cancer using new features of ultrasound RF time series.

Methods: The mean central frequency and wavelet features of ultrasound RF time series from seven patients are used along with an elaborate framework of ultrasound to histology registration to identify and verify cancer in prostate tissue regions as small as 1.7 mm x 1.

Ultrasound-guided characterization of interstitial ablated tissue using RF time series: feasibility study.

This paper presents the results of a feasibility study to demonstrate the application of ultrasound RF time series imaging to accurately differentiate ablated and nonablated tissue. For 12 ex vivo and two in situ tissue samples, RF ultrasound signals are acquired prior to, and following, high-intensity ultrasound ablation. Spatial and temporal features of these signals are used to characterize ablated and nonablated tissue in a supervised-learning framework.

Tissue classification using ultrasound-induced variations in acoustic backscattering features.

Ultrasound (US) radio-frequency (RF) time series is an effective tissue classification method that enables accurate cancer diagnosis, but the mechanisms underlying this method are not completely understood. This paper presents a model to describe the variations in tissue temperature and sound speed that take place during the RF time series scanning procedures and relate these variations to US backscattering. The model was used to derive four novel characterization features.

Respiratory syncytial virus matrix protein induces lung epithelial cell cycle arrest through a p53 dependent pathway.

Respiratory syncytial virus (RSV) is the major cause of viral respiratory infections in children. Our previous study showed that the RSV infection induced lung epithelial cell cycle arrest, which enhanced virus replication. To address the mechanism of RSV-induced cell cycle arrest, we examined the contribution of RSV-matrix (RSV-M) protein.

Inosine-containing RNA is a novel innate immune recognition element and reduces RSV infection.

During viral infections, single- and double-stranded RNA (ssRNA and dsRNA) are recognized by the host and induce innate immune responses. The cellular enzyme ADAR-1 (adenosine deaminase acting on RNA-1) activation in virally infected cells leads to presence of inosine-containing RNA (Ino-RNA). Here we report that ss-Ino-RNA is a novel viral recognition element.

Monitoring of tissue ablation using time series of ultrasound RF data.

Purpose: This paper is the first report on the monitoring of tissue ablation using ultrasound RF echo time series.

Methods: We calcuate frequency and time domain features of time series of RF echoes from stationary tissue and transducer, and correlate them with ablated and non-ablated tissue properties.

Results: We combine these features in a nonlinear classification framework and demonstrate up to 99% classification accuracy in distinguishing ablated and non-ablated regions of tissue, in areas as small as 12mm2 in size.

Protein kinase R is a novel mediator of CD40 signaling and plays a critical role in modulating immunoglobulin expression during respiratory syncytial virus infection.

Effective immunoglobulin responses play a vital role in protection against most pathogens. However, the molecular mediators and mechanisms responsible for signaling and selective expression of immunoglobulin types remain to be elucidated. Previous studies in our laboratory have demonstrated that protein kinase R (PKR) plays a crucial role in IgE responses to double-stranded RNA (dsRNA) in vitro.

Double-stranded RNA-activated protein kinase regulates early innate immune responses during respiratory syncytial virus infection.

Respiratory syncytial virus (RSV) is the most common cause of childhood viral bronchiolitis and lung injury. Inflammatory responses significantly contribute to lung pathologies during RSV infections and bronchiolitis but the exact mechanisms have not been completely defined. The double-stranded RNA-activated protein kinase (PKR) functions to inhibit viral replication and participates in several signaling pathways associated with innate inflammatory immune responses.

Cell cycle arrest by transforming growth factor beta1 enhances replication of respiratory syncytial virus in lung epithelial cells.

Respiratory syncytial virus (RSV) is a common respiratory viral infection in children which is associated with immune dysregulation and subsequent induction and exacerbations of asthma. We recently reported that treatment of primary human epithelial cells (PHBE cells) with transforming growth factor beta (TGF-beta) enhanced RSV replication. Here, we report that the enhancement of RSV replication is mediated by induction of cell cycle arrest.

Antiviral activity of Nrf2 in a murine model of respiratory syncytial virus disease.

Rationale: Respiratory syncytial virus (RSV) is the most frequent cause of significant lower respiratory illness in infants and young children, but its pathogenesis is not fully understood. The transcription factor Nrf2 protects lungs from oxidative injury and inflammation via antioxidant response element (ARE)-mediated gene induction.

Objectives: The current study was designed to determine the role of Nrf2-mediated cytoprotective mechanisms in murine airway RSV disease.

Induction of lactoferrin gene expression by innate immune stimuli in mouse mammary epithelial HC-11 cells.

Lactoferrin (LF) is a multifunctional protein. While its functions and mechanism of actions are actively being investigated, the cellular signals that regulate LF expression have not been as explored. We have previously demonstrated that LF is upregulated by estrogen in the reproductive system.