Representing Program Edits with the Choice Calculus

The problem of supporting more advanced selective undo operations has received a lot of attention. However, selective undo is generally missing in commonly used editors. Moreover, partial selective undo, the ability of undoing just part of some edit so that other edits may be undone, is not supported at all. We observe that a fundamental obstacle is the lack of a more flexible and compositional edit model. This project addresses this issue and proposes the choice edit model, which is based on the representation provided by the choice calculus. The central idea is to represent an edit through a choice that contains the old and the new code as alternatives. Edits inherit properties from choices and can thus be composed, nested, and transformed so that dependent edits may be untangled and undone partially. The choice representation is an internal representation, not meant to be exposed to programmers directly. To communicate the structure and dependencies of edits we introduce program edit graphs as an alternative, more abstract representation. Program edit graphs explicitly represent program variants and their relations. We also discuss the scalability of PEGs

PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models

Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling and the resource-intensive machine-learning models. In this work, we aim to explore HR estimation techniques that are more suitable for lower-power and resource-constrained embedded devices. More specifically, we seek to design techniques that could provide high-accuracy HR estimation with low-frequency PPG sampling, small model size, and fast inference time. First, we show that by combining signal processing and ML, it is possible to reduce the PPG sampling frequency from 125 Hz to only 25 Hz while providing higher HR estimation accuracy. This combination also helps to reduce the ML model feature size, leading to smaller models. Additionally, we present a comprehensive analysis on different ML models and feature sizes to compare their accuracy, model size, and inference time. The models explored include Decision Tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Support vector machines (SVM), and Multi-layer perceptron (MLP). Experiments were conducted using both a widely-utilized dataset and our self-collected dataset. The experimental results show that our method by combining signal processing and ML had only 5% error for HR estimation using low-frequency PPG data. Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time

Social visual preference mediates the effect of cortical thickness on symptom severity in children with autism spectrum disorder

BackgroundEvidence suggests that there is a robust relationship between altered neuroanatomy and autistic symptoms in individuals with autism spectrum disorder (ASD). Social visual preference, which is regulated by specific brain regions, is also related to symptom severity. However, there were a few studies explored the potential relationships among brain structure, symptom severity, and social visual preference.MethodsThe current study investigated relationships among brain structure, social visual preference, and symptom severity in 43 children with ASD and 26 typically developing (TD) children (aged 2–6 years).ResultsSignificant differences were found in social visual preference and cortical morphometry between the two groups. Decreased percentage of fixation time in digital social images (%DSI) was negatively related to not only the thickness of the left fusiform gyrus (FG) and right insula, but also the Calibrated Severity Scores for the Autism Diagnostic Observation Schedule-Social Affect (ADOS-SA-CSS). Mediation analysis showed that %DSI partially mediated the relationship between neuroanatomical alterations (specifically, thickness of the left FG and right insula) and symptom severity.ConclusionThese findings offer initial evidence that atypical neuroanatomical alterations may not only result in direct effects on symptom severity but also lead to indirect effects on symptom severity through social visual preference. This finding enhances our understanding of the multiple neural mechanisms implicated in ASD

Validation of the prognostic performance of Breast Cancer Index (BCI) in hormone receptor-positive (HR+) postmenopausal breast cancer patients in the TEAM trial

Purpose: Early-stage HR+ breast cancer patients face a prolonged risk of recurrence even after adjuvant endocrine therapy. The Breast Cancer Index (BCI) is significantly prognostic for overall (0-10 years) and late (5-10 years) distant recurrence risk (DR) in N0 and N1 patients. Here, BCI prognostic performance was evaluated in HR+ postmenopausal women from the TEAM trial.Experimental Design: 3544 patients were included in the analysis (N=1519 N0, N=2025 N+). BCI risk groups were calculated using pre-specified cut-points. Kaplan-Meier analyses and logranktests were used to assess the prognostic significance of BCI risk groups based on DR. Hazard ratios (HR) and confidence intervals (CI) were calculated using Cox models with and without clinical covariates.Results: For overall 10-year DR, BCI was significantly prognostic in N0 (N=1196) and N1 (N=1234) patients who did not receive prior chemotherapy (p<0.001). In patients who were DRfree for 5 years, 10-year late DR rates for low- and high-risk groups were 5.4% and 9.3% (N0 cohort, N=1285) and 4.8% and 12.2% (N1 cohort, N=1625) with multivariate HRs of 2.25 (95% CI: 1.30-3.88; p=0.004) and 2.67 (95% CI: 1.53-4.63; p=<0.001), respectively. Late DR performance was substantially improved using previously optimized cut-points, identifying BCIlow-risk groups with even lower 10-year late DR rates of 3.8% and 2.7% in N0 and N1 patients, respectively.Conclusions: The TEAM trial represents the largest prognostic validation study for BCI to date and provides a more representative assessment of late DR risk to guide individualized treatment decision-making for HR+ early-stage breast cancer patients

An Improved Approach for Essential Tremor Spirography Processing by Integrating Frequency Domain Information and Spatial Domain Information

Spirography is a standard neurological test that has been commonly used for essential tremor diagnosis and its severity assessment for years. However, current spirography analysis is handled by Fourier Transformation (FT) on acquired data only. Frequency spectrum information through FT, such as frequency distributions, magnitudes of selected frequencies, has been the only information reported in other studies. This thesis provides a brief introduction of Essential Tremor and the application of spirography. In order to validate the algorithms developed in this study, we have created algorithmic simulations for smooth spirals and spirals with tremor oscillation. The developed simulation approach is the first of the two primary contributions of this thesis to the Essential Tremor study. Two chapters for spirography processing in frequency domain and spatial domain describe processing algorithms applied to simulated data sets and tremor patients’ spirography data sets. These two chapters discuss, test and compare two methods in frequency domain analysis to get the dominant frequency related information, along with two methods in spatial domain analysis, one aiming at unwrap the spiral-wired graph, the other designed to quantify the amplitude of tremor oscillation movement. The developed method in spatial domain is the second of the two primary contributions of this thesis to the Essential Tremor study. In the Discussion and Conclusion Chapter, we discuss current obstacles in spirography analysis and further development direction. Suggestions to handle patient spirography data are also provided in this Chapter.</p

Gold Nanoparticles Enhanced Diffuse Optical Imaging and Radiation Therapy

Cancer is a leading cause of morbidity and mortality in the United States and worldwide. Commonly used cancer treatment methods include surgery, chemotherapy, and Radiation therapy (RT), the last of which is used in about half of all treatments. Imaging plays an important role in cancer diagnosis and radiation treatment. Gold nanoparticles (GNP) can strongly absorb optical light and x-ray because of the surface plasmon resonance (SPR) effect of the nanoparticles and the high atomic number of the gold. Therefore, it has a great potential to enhance both optical imaging and radiation dose delivery. This dissertation includes: (1) the development of an optical imaging system using GNP as a contrast agent; (2) the development of a Monte Carlo simulation based treatment planning system (TPS) for a small animal irradiation system to establish a reliable dose calculation platform which is subsequently used in (3) a simulation study of radiation dose enhancement by GNP. In the imaging study, a SPR optical imaging (SPROI) system was developed. Gold nanorods (GNR) of various concentrations were successfully reconstructed from centimeter-scaled volumes in simulation, phantom and animal experiments. SPROI detected GNR at a concentration as low as 18 μg/mL, which is 3 orders more sensitive than regular x-ray imaging. To the best of our knowledge, this is one of the first studies that investigated transmission-based whole body diffuse optical tomography of centimeter- scaled small animals using GNR as contrast agents. In the TPS development, an EGSnrc/BEAMnrc model of our image-guided SMall Animal Arc Radiation Treatment (iSMAART) system was assembled and validated by dosimetric measurement. The results from BEAMnrc modeling and computed tomography (CT) mass density calibration were used in DOZXYZnrc to calculate three- dimensional dose distribution in the irradiated animal. The dose calculation was validated by a dosimeter implanted in the thorax of a mouse. Image guided radiation therapy was demonstrated on a mouse bearing breast tumor and one bearing prostate tumor. In the simulation study, a cylindrical phantom with a sphere inclusion was used to simulate a human breast bearing a tumor in the center. The sphere volume was loaded with 7 and 18 mg/g of GNP, respectively, to simulate the tumor uptake of GNP. Dose distributions were calculated in DOSXYZnrc with a kV and a MV irradiation source respectively, delivered through single or multiple beams. Dose enhancement in the tumor as well as sparing in the normal tissue behind the tumor were observed in the kV irradiations for both nanoparticle concentration groups. Compared to MV irradiation which showed almost no dose enhancement when GNP was used, GNP enhanced kV irradiation significantly increased the tumor-surface ratio while achieving a smaller penumbra and better dose uniformity inside the tumor region.</p

Tissue Regeneration with Gelatine/Polysaccharide Derived Hydrogel Scaffolds: From Formulation to In Vivo Efficacy

Combinations of different biomaterials with certain formulations may lead to improved properties and have significant potential for use in tissue regeneration applications. However, previously reported studies comparing biomaterials often suffered from inconsistent processing methods or inadequate comprehensive application research, hindering a comprehension of their efficacy in tissue engineering. This report explores the significance of screening the combination of gelatine with polysaccharide materials, specifically hyaluronic acid (HA) and carboxymethyl cellulose (CMC), using the same crosslinking method used for tissue regeneration. Hydrogel scaffolds (Gel/HA and Gel/CMC) at various concentrations were developed and characterized to assess their physiochemical properties. The results demonstrated that the hydrogels exhibited desirable mechanical properties, appropriate swelling behaviour, suitable porosity, and excellent cytocompatibility. In particular, the Gel1HA1 and Gel1CMC1 hydrogels showed remarkable cellular proliferation and aggregation. Further, we performed animal studies and explored the tissue regeneration effects of the Gel1HA1 and Gel1CMC1 hydrogels. Both hydrogels exhibited an accelerated wound closure rate and promoted vessel formation in a rodent full-thickness skin excisional model. Additionally, the subcutaneous implantation model demonstrated the induction of angiogenesis and collagen deposition within the implanted hydrogel samples. Overall, the hydrogels developed in this study demonstrated promising potential for use in the regeneration of soft tissue defects and this study emphasizes the significance of screening biomaterial combinations and formulations for tissue regeneration applications

Role of Flaxseed Gum and Whey Protein Microparticles in Formulating Low-Fat Model Mayonnaises

Flaxseed gum (FG) and whey protein microparticles (WPMs) were used to substitute fats in model mayonnaises. WPMs were prepared by grinding the heat-set whey protein gel containing 10 mM CaCl2 into small particles (10&ndash;20 &micro;m). Then, 3 &times; 4 low-fat model mayonnaises were prepared by varying FG (0.3, 0.6, 0.9 wt%) and WPM (0, 8, 16, 24 wt%) concentrations. The effect of the addition of FG and WPMs on rheology, instrumental texture and sensory texture and their correlations were investigated. The results showed that all samples exhibited shear thinning behavior and &lsquo;weak gel&rsquo; properties. Although both FG and WPMs enhanced rheological (e.g., viscosity and storage modulus) and textural properties (e.g., hardness, consistency, adhesiveness, cohesiveness) and kinetic stability, this enhancement was dominated by FG. FG and WPMs affected bulk properties through different mechanisms, (i.e., active filler and entangled polysaccharide networks). Panellists evaluated sensory texture in three stages: extra-oral, intra-oral and after-feel. Likewise, FG dominated sensory texture of model mayonnaises. With increasing FG concentration, sensory scores for creaminess and mouth-coating increased, whereas those of firmness, fluidity and spreadability decreased. Creaminess had a linear negative correlation with firmness, fluidity and spreadability (R2 &gt; 0.985), while it had a linear positive correlation with mouth-coating (R2 &gt; 0.97). A linear positive correlation (R2 &gt; 0.975) was established between creaminess and viscosity at different shear rates/instrumental texture parameters. This study highlights the synergistic role of FG and WPMs in developing low-fat mayonnaises

Quantitative X-ray fluorescence imaging of gold nanoparticles using joint L1 and total variation regularized reconstruction

This work proposed a joint L1 and total variation (TV) regularized reconstruction method for X-ray fluorescence tomography (XFT), and investigated the performance of this method in quantitative imaging of gold nanoparticles (GNPs). We developed a dual-modality XFT/CT imaging system which consisted of a benchtop X-ray source, a translation/rotation stage, a silicon drift detector for X-ray fluorescence (XRF) detection, and a flat panel detector for transmission X-ray detection. A pencil-beam collimator was 3D printed with steel and employed in sample excitation. The sensitivity of the XFT imaging system was determined by imaging water phantoms with multiple inserts containing GNP solutions of various concentrations (0.02-0.16 wt.%). A joint L1 and total variation (TV) regularized algorithm was developed for XFT reconstruction, where the L1 regularization was used to reduce image artifacts and the TV regularization was used to preserve the shape of targets. Nonlinear conjugate gradient (NCG) descent algorithm with backtracking line search was adopted to solve the reconstruction problem. We compared the L1 + TV regularization method with filtered back projection (FBP), maximum likelihood expectation maximization (ML-EM), L1 regularization, and TV regularization methods. Contrast-to-noise ratio (CNR), Dice similarity coefficient (DSC) and localization error (LE) metrics were used to compare the performance of different methods. The CT and XFT imaging doses were also measured using EBT2 radiochromic films. The 3D printed pencil-beam collimator shaped an excitation beam with a 2 mm full width at half maximum at the imaging isocenter. Based on the phantom imaging experiments, the joint L1 and TV regularization method performed better than FBP, ML-EM, L1 regularization and TV regularization methods, with higher localization accuracy (offset <0.6 mm), CNR and DSC values. Compared with CT, XFT with L1 + TV regularized reconstruction demonstrated higher sensitivity in GNP imaging, and could detect GNP at a concentration of 0.02 wt.% or lower. Moreover, there existed a significant linear correlation (R >0.99) between the reconstructed and true GNP concentration. The estimated XFT imaging dose is about 41.22 cGy under current setting. The joint L1 + TV regularized reconstruction algorithm performed better in noise suppression and shape preservation. Using the L1 + TV regularized reconstruction, the XFT system is able to localize GNP targets with submillimeter accuracy and quantify GNP distribution at a concentration of 0.02 wt.% or lower