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- Creators: Harrington Bioengineering Program
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We carried out secondary analyses on a subsample of sedentary, overweight/obese adults who participated in a 4-month, 2x2, randomized-controlled walking intervention examining the effects of goal setting (static v. adaptive goals) and rewards (immediate v. delayed) on steps/day (N=96). Fasting blood samples (n=58) were collected from participants before and after the intervention. Premenopausal females were in the follicular phase of their menstrual cycles. Lipid and glucose levels were measured using an automated chemistry analyzer, while insulin was measured using radio-immunoassay. Homeostatic model of insulin resistance (HOMA-IR) was calculated using the following formula (HOMA-IR=glucose x insulin / 405). We examined associations [partial correlations (adjusted for age)] between changes in blood biomarkers and VO2peak and cfPWV, irrespective of group, and we used linear mixed models to examine between-group differences in levels of and change in biomarker outcomes.
Groups did not differ in overall levels of, or degree of change in, biomarker outcomes (all p>0.05). Mean changes, irrespective of group, in biomarkers were as follows: glucose Δ= 0.74± 4.5mg/dl; insulin Δ= 0.09 ± 4.1 µU/ml; total cholesterol Δ= 0.24 ± 20.6 mg/dl; HDL-C Δ= 0.27 ± 5.1 mg/dl; LDL-C Δ= 1.3 ± 19.9 mg/dl; triglycerides Δ= 1.7 ± 27.2 mg/dl; HOMA-IR Δ = -.0548 ± 1.05). We found no significant associations between change in biomarker levels and change in VO2peak or change in cfPWV (all correlation coefficients < 0.15; p > 0.05).
A 4-month, behavioral economics-based mHealth intervention focused on increasing steps/day did not bring about favorable changes on markers of glycemia, insulin resistance and blood lipids.
This thesis is a tutorial for a MATLAB user-interface, known as EEGLAB. Cognitive and neural correlates of analytical and insight processes were evaluated and analyzed in the CRAT using EEG. It was hypothesized that different EEG signals will be measured for analytical versus insight problem solving, primarily observed in the gamma wave production. The data was interpreted using EEGLAB, which allows psychological processes to be quantified based on physiological response. I have written a tutorial showing how to process the EEG signal through filtering, extracting epochs, artifact detection, independent component analysis, and the production of a time – frequency plot. This project has combined my interest in psychology with my knowledge of engineering and expand my knowledge of bioinstrumentation.
X-ray phase contrast imaging (XPCI) is a novel imaging method that utilizes phase information of X-rays in order to produce images. XPCI allows for highly resolved features that traditional X-ray imaging modalities cannot discern. The objective of this experiment was to model initial simulations predicting the output signal of the future compact x-ray free electron laser (CXFEL) XPCI source. The signal was reported in tonal values (“counts”), where MATLAB and MATLAB App Designer were the computing environments used to develop the simulations. The experimental setup’s components included a yttrium aluminum garnet (YAG) scintillating screen, mirror, and Mako G-507C camera with a Sony IMX264 sensor. The main function of the setup was to aim the X-rays at the YAG screen, then measure its scintillation through the photons emitted that hit the camera sensor. The resulting quantity used to assess the signal strength was tonal values (“counts”) per pixel on the sensor. Data for X-ray transmission through water, air, and polyimide was sourced from The Center for X-ray Optics’s simulations website, after which the data was interpolated and referenced in MATLAB. Matrices were an integral part of the saturation calculations; field-of-view (FOV), magnification and photon energies were also necessary. All the calculations were compiled into a graphical user interface (GUI) using App Designer. The code used to build this GUI can be used as a template for later, more complex GUIs and is a great starting point for future work in XPCI research at CXFEL.
X-ray phase contrast imaging (XPCI) is a novel imaging method that utilizes phase information of X-rays in order to produce images. XPCI allows for highly resolved features that traditional X-ray imaging modalities cannot discern. The objective of this experiment was to model initial simulations predicting the output signal of the future compact x-ray free electron laser (CXFEL) XPCI source. The signal was reported in tonal values (“counts”), where MATLAB and MATLAB App Designer were the computing environments used to develop the simulations. The experimental setup’s components included a yttrium aluminum garnet (YAG) scintillating screen, mirror, and Mako G-507C camera with a Sony IMX264 sensor. The main function of the setup was to aim the X-rays at the YAG screen, then measure its scintillation through the photons emitted that hit the camera sensor. The resulting quantity used to assess the signal strength was tonal values (“counts”) per pixel on the sensor. Data for X-ray transmission through water, air, and polyimide was sourced from The Center for X-ray Optics’s simulations website, after which the data was interpolated and referenced in MATLAB. Matrices were an integral part of the saturation calculations; field-of-view (FOV), magnification and photon energies were also necessary. All the calculations were compiled into a graphical user interface (GUI) using App Designer. The code used to build this GUI can be used as a template for later, more complex GUIs and is a great starting point for future work in XPCI research at CXFEL.