Relationships among auditory representations and overall musicianship of classical and non-classical music students

The focus of this study is on the relationships among three basic auditory representations as well as their interaction with a measure of overall musicianship (sight-singing) among a group of classical and non-classical university music students (N = 112) selected from three different universities. Students were enrolled in level one of an aural skills course at the time. Basic auditory representations included were tonic centrality, measured by Colwell’s (1968) Feeling for Tonal Center, tonal grouping, measured by Colwell’s (1968) Auditory-Visual Discrimination, and harmonic function grouping, measured by a revised version of Holahan, Saunders and Goldberg’s (2000) assessment. I evaluated relationships by correlating scores on each measure and also compared these relationships among classical and non-classical music students. The participants in this study were the most skilled at forming auditory representations of tonic centrality and non-classical musicians significantly (p = .002) outperformed classical musicians in this area. Tonic centrality was also most strongly correlated with overall musicianship (τ = .45, p < .001) within the sample, and this relationship appeared to be stronger among non-classical musicians (τ = .52, p < .001) than among classical musicians (τ = .39, p < .001). This difference may be accounted for by the increased reliance on grounding in a tonal center required by the musical activities of a typical non-classical music student. Given the changing balance of musical endeavors present in tertiary music schools today (Lehmann, Sloboda, & Woody, 2007), educators are encouraged to better understand the particular strengths non-classical musicians may bring to the classroom in terms of ear-based musical abilities. Likewise, music educators on each level are encouraged to incorporate ear-based activities such as improvisation and playing by ear to the benefit of musicians of all genres

Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric

Treatment response assays are often summarized by sigmoidal functions comparing cell survival at a single timepoint to applied drug concentration. This approach has a limited biophysical basis, thereby reducing the biological insight gained from such analysis. In particular, drug pharmacokinetic and pharmacodynamic (PK/PD) properties are overlooked in developing treatment response assays, and the accompanying summary statistics conflate these processes. Here, we utilize mathematical modeling to decouple and quantify PK/PD pathways. We experimentally modulate specific pathways with small molecule inhibitors and filter the results with mechanistic mathematical models to obtain quantitative measures of those pathways. Specifically, we investigate the response of cells to time-varying doxorubicin treatments, modulating doxorubicin pharmacology with small molecules that inhibit doxorubicin efflux from cells and DNA repair pathways. We highlight the practical utility of this approach through proposal of the “equivalent dose metric.” This metric, derived from a mechanistic PK/PD model, provides a biophysically-based measure of drug effect. We define equivalent dose as the functional concentration of drug that is bound to the nucleus following therapy. This metric can be used to quantify drivers of treatment response and potentially guide dosing of combination therapies. We leverage the equivalent dose metric to quantify the specific intracellular effects of these small molecule inhibitors using population-scale measurements, and to compare treatment response in cell lines differing in expression of drug efflux pumps. More generally, this approach can be leveraged to quantify the effects of various pharmaceutical and biologic perturbations on treatment response

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images

Predicting High-Grade Glioma Response to Chemoradiation via MRI-Calibrated

https://openworks.mdanderson.org/sumexp21/1085/thumbnail.jp

Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy

The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) may be able to offer earlier, and more precise, information on treatment response in the neoadjuvant setting than RECIST. We then describe how longitudinal registration of breast images and the incorporation of intelligent bioinformatics approaches with imaging data have the potential to increase the sensitivity of assessing treatment response. We conclude with a discussion of the potential benefits of breast MRI at the higher field strength of 3T. For each of these areas, we provide a review, illustrative examples from clinical trials, and offer insights into future research directions

An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data

The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. The ConvDecoder neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle (VFA) data. Fully-sampled VFA k-space data were retrospectively accelerated by factors of R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the proposed regularization (CD+r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD+r training were evaluated at the \emph{argmin} of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized root-mean square error, the concordance correlation coefficient (CCC), and the structural similarity index (SSIM). The CD+r reconstructions, chosen using the stopping condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs (p=0.95) across R and that were significantly higher than the L1 SSIMs (p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps, respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD+r. We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.Comment: 45 pages, 7 figures, 2 Tables, supplementary material included (10 figures, 4 tables

DCE and DW‐MRI monitoring of vascular disruption following VEGF‐Trap treatment of a rat glioma model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92143/1/nbm1814.pd

Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models